diff --git a/README.md b/README.md index bd031b4b..520a1e0e 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,17 @@ 简体中文

+

Important things should be said three times.

+ +# [2024-05-17:Latest Installation and Usage Documentation](https://github.com/netease-youdao/QAnything/blob/master/QAnything%E4%BD%BF%E7%94%A8%E8%AF%B4%E6%98%8E.md) +# [2024-05-17:Latest Installation and Usage Documentation](https://github.com/netease-youdao/QAnything/blob/master/QAnything%E4%BD%BF%E7%94%A8%E8%AF%B4%E6%98%8E.md) +# [2024-05-17:Latest Installation and Usage Documentation](https://github.com/netease-youdao/QAnything/blob/master/QAnything%E4%BD%BF%E7%94%A8%E8%AF%B4%E6%98%8E.md) + +## Business contact information： +### 010-82558901 +![](docs/images/business.jpeg) + + ## Installation Requirements: diff --git a/README_zh.md b/README_zh.md index 00059432..d95c63cb 100644 --- a/README_zh.md +++ b/README_zh.md @@ -3,6 +3,19 @@ English | 简体中文

+ + +

重要的事情说三遍！

+ +# [2024-05-17:最新的安装和使用文档](https://github.com/netease-youdao/QAnything/blob/master/QAnything%E4%BD%BF%E7%94%A8%E8%AF%B4%E6%98%8E.md) +# [2024-05-17:最新的安装和使用文档](https://github.com/netease-youdao/QAnything/blob/master/QAnything%E4%BD%BF%E7%94%A8%E8%AF%B4%E6%98%8E.md) +# [2024-05-17:最新的安装和使用文档](https://github.com/netease-youdao/QAnything/blob/master/QAnything%E4%BD%BF%E7%94%A8%E8%AF%B4%E6%98%8E.md) + +## 商务问题联系方式： +### 010-82558901 +![](docs/images/business.jpeg) + + ## 安装 要求: diff --git a/docs/images/business.jpeg b/docs/images/business.jpeg new file mode 100644 index 00000000..177958a4 Binary files /dev/null and b/docs/images/business.jpeg differ diff --git a/qanything_kernel/configs/model_config.py b/qanything_kernel/configs/model_config.py index 22fa50f7..8c2d0fe5 100644 --- a/qanything_kernel/configs/model_config.py +++ b/qanything_kernel/configs/model_config.py @@ -6,8 +6,20 @@ os_system = platform.system() +#### 用户配置区 #### # 默认的CUDA设备 CUDA_DEVICE = '0' +# 设置是否使用快速PDF解析器，设置为False时，使用优化后的PDF解析器，但速度下降 +USE_FAST_PDF_PARSER = True +# 设置rerank的batch大小，16GB内存建议设置为8，32GB内存建议设置为16 +LOCAL_RERANK_BATCH = 8 +# 设置rerank的多线程worker数量，默认设置为4，根据机器性能调整 +LOCAL_RERANK_WORKERS = 4 +# 设置embed的batch大小，16GB内存建议设置为8，32GB内存建议设置为16 +LOCAL_EMBED_BATCH = 8 +# 设置embed的多线程worker数量，默认设置为4，根据机器性能调整 +LOCAL_EMBED_WORKERS = 4 +#### 用户配置区 #### # 获取项目根目录 # 获取当前脚本的绝对路径 @@ -18,6 +30,7 @@ # 如果不存在则创建 if not os.path.exists(UPLOAD_ROOT_PATH): os.makedirs(UPLOAD_ROOT_PATH) +PDF_MODEL_PATH = os.path.join(root_path, "qanything_kernel/utils/loader/pdf_to_markdown") nltk_data_path = os.path.join(root_path, 'qanything_kernel/nltk_data') @@ -99,7 +112,6 @@ print('LOCAL_RERANK_REPO:', LOCAL_RERANK_REPO) LOCAL_RERANK_MODEL_NAME = 'rerank' LOCAL_RERANK_MAX_LENGTH = 512 -LOCAL_RERANK_BATCH = 8 LOCAL_EMBED_PATH = os.path.join(root_path, 'qanything_kernel/connector/embedding', 'embedding_model_configs_v0.0.1') if os_system == 'Darwin': @@ -111,7 +123,6 @@ print('LOCAL_EMBED_REPO:', LOCAL_EMBED_REPO) LOCAL_EMBED_MODEL_NAME = 'embed' LOCAL_EMBED_MAX_LENGTH = 512 -LOCAL_EMBED_BATCH = 8 # VLLM PARAMS model_path = os.path.join(root_path, "assets", "custom_models") diff --git a/qanything_kernel/connector/embedding/embedding_backend.py b/qanything_kernel/connector/embedding/embedding_backend.py index f352cf55..b6456a7b 100644 --- a/qanything_kernel/connector/embedding/embedding_backend.py +++ b/qanything_kernel/connector/embedding/embedding_backend.py @@ -1,7 +1,7 @@ """Wrapper around YouDao embedding models.""" from typing import List from qanything_kernel.configs.model_config import LOCAL_EMBED_MODEL_PATH, LOCAL_EMBED_MAX_LENGTH, LOCAL_EMBED_BATCH, \ - LOCAL_EMBED_PATH, LOCAL_EMBED_REPO + LOCAL_EMBED_PATH, LOCAL_EMBED_REPO, LOCAL_EMBED_WORKERS from qanything_kernel.utils.general_utils import get_time from qanything_kernel.utils.custom_log import debug_logger from langchain_core.embeddings import Embeddings @@ -31,6 +31,7 @@ class EmbeddingBackend(Embeddings): def __init__(self, use_cpu): self.use_cpu = use_cpu self._tokenizer = AutoTokenizer.from_pretrained(LOCAL_EMBED_PATH) + self.workers = LOCAL_EMBED_WORKERS @abstractmethod def get_embedding(self, sentences, max_length) -> List: @@ -41,7 +42,7 @@ def get_len_safe_embeddings(self, texts: List[str]) -> List[List[float]]: all_embeddings = [] batch_size = LOCAL_EMBED_BATCH - with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: + with concurrent.futures.ThreadPoolExecutor(max_workers=self.workers) as executor: futures = [] for i in range(0, len(texts), batch_size): batch = texts[i:i + batch_size] diff --git a/qanything_kernel/connector/rerank/rerank_backend.py b/qanything_kernel/connector/rerank/rerank_backend.py index 7cc1a59e..50374d94 100644 --- a/qanything_kernel/connector/rerank/rerank_backend.py +++ b/qanything_kernel/connector/rerank/rerank_backend.py @@ -3,7 +3,7 @@ from typing import List from qanything_kernel.configs.model_config import LOCAL_RERANK_MODEL_PATH, LOCAL_RERANK_MAX_LENGTH, \ LOCAL_RERANK_MODEL_NAME, \ - LOCAL_RERANK_BATCH, LOCAL_RERANK_PATH, LOCAL_RERANK_REPO + LOCAL_RERANK_BATCH, LOCAL_RERANK_PATH, LOCAL_RERANK_REPO, LOCAL_RERANK_WORKERS from qanything_kernel.utils.custom_log import debug_logger from qanything_kernel.utils.general_utils import get_time from modelscope import snapshot_download @@ -32,6 +32,7 @@ def __init__(self, use_cpu): self.batch_size = LOCAL_RERANK_BATCH self.max_length = LOCAL_RERANK_MAX_LENGTH self.return_tensors = None + self.workers = LOCAL_RERANK_WORKERS @abstractmethod def inference(self, batch) -> List: @@ -90,7 +91,7 @@ def predict(self, tot_batches, merge_inputs_idxs_sort = self.tokenize_preproc(query, passages) tot_scores = [] - with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: + with concurrent.futures.ThreadPoolExecutor(max_workers=self.workers) as executor: futures = [] for k in range(0, len(tot_batches), self.batch_size): batch = self._tokenizer.pad( diff --git a/qanything_kernel/core/local_file.py b/qanything_kernel/core/local_file.py index cd85af49..61a74468 100644 --- a/qanything_kernel/core/local_file.py +++ b/qanything_kernel/core/local_file.py @@ -1,13 +1,12 @@ from qanything_kernel.utils.general_utils import * from typing import List, Union, Callable -from qanything_kernel.configs.model_config import UPLOAD_ROOT_PATH, SENTENCE_SIZE, ZH_TITLE_ENHANCE +from qanything_kernel.configs.model_config import UPLOAD_ROOT_PATH, SENTENCE_SIZE, ZH_TITLE_ENHANCE, USE_FAST_PDF_PARSER from langchain.docstore.document import Document from qanything_kernel.utils.loader.my_recursive_url_loader import MyRecursiveUrlLoader from langchain_community.document_loaders import UnstructuredFileLoader, TextLoader from langchain_community.document_loaders import UnstructuredWordDocumentLoader from langchain_community.document_loaders import UnstructuredExcelLoader from langchain_community.document_loaders import UnstructuredPDFLoader -import langchain_community.document_loaders.pdf from langchain_community.document_loaders import UnstructuredEmailLoader from langchain_community.document_loaders import UnstructuredPowerPointLoader from qanything_kernel.utils.loader.csv_loader import CSVLoader @@ -16,6 +15,8 @@ from qanything_kernel.utils.splitter import ChineseTextSplitter from qanything_kernel.utils.loader import UnstructuredPaddleImageLoader, UnstructuredPaddlePDFLoader, UnstructuredPaddleAudioLoader from qanything_kernel.utils.splitter import zh_title_enhance +from qanything_kernel.utils.loader.self_pdf_loader import PdfLoader +from qanything_kernel.utils.loader.markdown_parser import convert_markdown_to_langchaindoc from sanic.request import File import pandas as pd import os @@ -65,6 +66,27 @@ def __init__(self, user_id, kb_id, file: Union[File, str, dict], file_id, file_n f.write(self.file_content) debug_logger.info(f'success init localfile {self.file_name}') + @staticmethod + def pdf_process(dos: List[Document]): + new_docs = [] + for doc in dos: + # metadata={'title_lst': ['#樊昊天个人简历', '##教育经历'], 'has_table': False} + title_lst = doc.metadata['title_lst'] + # 删除所有仅有多个#的title + title_lst = [t for t in title_lst if t.replace('#', '') != ''] + has_table = doc.metadata['has_table'] + if has_table: + doc.page_content = '\n'.join(title_lst) + '\n本段为表格，内容如下：' + doc.page_content + new_docs.append(doc) + continue + # doc.page_content = '\n'.join(title_lst) + '\n' + doc.page_content + slices = pdf_text_splitter.split_documents([doc]) + for idx, slice in enumerate(slices): + slice.page_content = '\n'.join(title_lst) + f'\n######第{idx+1}段内容如下：\n' + slice.page_content + new_docs.extend(slices) + return new_docs + + @get_time def split_file_to_docs(self, ocr_engine: Callable, sentence_size=SENTENCE_SIZE, using_zh_title_enhance=ZH_TITLE_ENHANCE): if self.url: @@ -82,10 +104,16 @@ def split_file_to_docs(self, ocr_engine: Callable, sentence_size=SENTENCE_SIZE, texts_splitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size) docs = loader.load_and_split(texts_splitter) elif self.file_path.lower().endswith(".pdf"): - loader = UnstructuredPaddlePDFLoader(self.file_path, ocr_engine, self.use_cpu) - # texts_splitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size) - # docs = loader.load_and_split(texts_splitter) - docs = loader.load() + if USE_FAST_PDF_PARSER: + loader = UnstructuredPaddlePDFLoader(self.file_path, ocr_engine, self.use_cpu) + texts_splitter = ChineseTextSplitter(pdf=True, sentence_size=sentence_size) + docs = loader.load_and_split(texts_splitter) + else: + loader = PdfLoader(filename=self.file_path, root_dir=os.path.dirname(self.file_path)) + markdown_dir = loader.load_to_markdown() + docs = convert_markdown_to_langchaindoc(markdown_dir) + docs = self.pdf_process(docs) + # print(docs) elif self.file_path.lower().endswith(".jpg") or self.file_path.lower().endswith( ".png") or self.file_path.lower().endswith(".jpeg"): loader = UnstructuredPaddleImageLoader(self.file_path, ocr_engine, self.use_cpu) @@ -121,6 +149,7 @@ def split_file_to_docs(self, ocr_engine: Callable, sentence_size=SENTENCE_SIZE, debug_logger.info("using_zh_title_enhance %s", using_zh_title_enhance) docs = zh_title_enhance(docs) print('docs number:', len(docs)) + # print(docs) # 不是csv，xlsx和FAQ的文件，需要再次分割 if not self.file_path.lower().endswith(".csv") and not self.file_path.lower().endswith(".xlsx") and not self.file_path == 'FAQ': new_docs = [] @@ -136,7 +165,8 @@ def split_file_to_docs(self, ocr_engine: Callable, sentence_size=SENTENCE_SIZE, new_docs.append(doc) debug_logger.info(f"before 2nd split doc lens: {len(new_docs)}") if self.file_path.lower().endswith(".pdf"): - docs = pdf_text_splitter.split_documents(new_docs) + if USE_FAST_PDF_PARSER: + docs = pdf_text_splitter.split_documents(new_docs) else: docs = text_splitter.split_documents(new_docs) debug_logger.info(f"after 2nd split doc lens: {len(docs)}") diff --git a/qanything_kernel/qanything_server/sanic_api.py b/qanything_kernel/qanything_server/sanic_api.py index 8bd40add..b1a115e9 100644 --- a/qanything_kernel/qanything_server/sanic_api.py +++ b/qanything_kernel/qanything_server/sanic_api.py @@ -18,7 +18,7 @@ sys.path.append(root_dir) from qanything_kernel.configs.model_config import DT_7B_MODEL_PATH, \ - DT_7B_DOWNLOAD_PARAMS, DT_3B_MODEL_PATH, DT_3B_DOWNLOAD_PARAMS + DT_7B_DOWNLOAD_PARAMS, DT_3B_MODEL_PATH, DT_3B_DOWNLOAD_PARAMS, PDF_MODEL_PATH import qanything_kernel.configs.model_config as model_config from qanything_kernel.utils.custom_log import debug_logger from qanything_kernel.utils.general_utils import download_file, get_gpu_memory_utilization, check_package_version @@ -81,18 +81,27 @@ if os_system != "Linux": raise ValueError(f"Unsupported system: {os_system}") system_name = 'manylinux_2_28_x86_64' - # 官方发布的1.17.1不支持cuda12以上的系统，需要根据官方文档:https://onnxruntime.ai/docs/install/里提到的地址手动下载whl - if not check_package_version("onnxruntime-gpu", "1.17.1"): - download_url = f"https://aiinfra.pkgs.visualstudio.com/PublicPackages/_apis/packaging/feeds/9387c3aa-d9ad-4513-968c-383f6f7f53b8/pypi/packages/onnxruntime-gpu/versions/1.17.1/onnxruntime_gpu-1.17.1-cp3{python3_version}-cp3{python3_version}-{system_name}.whl/content" - debug_logger.info(f'开始从{download_url}下载onnxruntime，也可以手动下载并通过pip install *.whl安装') - whl_name = f'onnxruntime_gpu-1.17.1-cp3{python3_version}-cp3{python3_version}-{system_name}.whl' - download_file(download_url, whl_name) - exit_status = os.system(f"pip install {whl_name}") - if exit_status != 0: - # raise ValueError(f"安装onnxruntime失败，请手动安装{whl_name}") - debug_logger.warning(f"安装onnxruntime-gpu失败，将安装onnxruntime来代替") - print(f"安装onnxruntime-gpu失败，将安装onnxruntime来代替", flush=True) + glibc_info = platform.libc_ver() + if glibc_info[0] != 'glibc': + raise ValueError(f"Unsupported libc: {glibc_info[0]}, 请确认系统是否为Linux系统。") + glibc_version = float(glibc_info[1]) + if glibc_version < 2.28: + if not check_package_version("onnxruntime", "1.16.3"): + print(f"当前系统glibc版本为{glibc_version}<2.28，无法使用onnxruntime-gpu(cuda12.x)，将安装onnxruntime来代替", flush=True) os.system("pip install onnxruntime") + else: + # 官方发布的1.17.1不支持cuda12以上的系统，需要根据官方文档:https://onnxruntime.ai/docs/install/里提到的地址手动下载whl + if not check_package_version("onnxruntime-gpu", "1.17.1"): + download_url = f"https://aiinfra.pkgs.visualstudio.com/PublicPackages/_apis/packaging/feeds/9387c3aa-d9ad-4513-968c-383f6f7f53b8/pypi/packages/onnxruntime-gpu/versions/1.17.1/onnxruntime_gpu-1.17.1-cp3{python3_version}-cp3{python3_version}-{system_name}.whl/content" + debug_logger.info(f'开始从{download_url}下载onnxruntime，也可以手动下载并通过pip install *.whl安装') + whl_name = f'onnxruntime_gpu-1.17.1-cp3{python3_version}-cp3{python3_version}-{system_name}.whl' + download_file(download_url, whl_name) + exit_status = os.system(f"pip install {whl_name}") + if exit_status != 0: + # raise ValueError(f"安装onnxruntime失败，请手动安装{whl_name}") + debug_logger.warning(f"安装onnxruntime-gpu失败，将安装onnxruntime来代替") + print(f"安装onnxruntime-gpu失败，将安装onnxruntime来代替", flush=True) + os.system("pip install onnxruntime") if not args.use_openai_api: if not check_package_version("vllm", "0.2.7"): os.system(f"pip install vllm==0.2.7 -i https://pypi.mirrors.ustc.edu.cn/simple/ --trusted-host pypi.mirrors.ustc.edu.cn") @@ -145,6 +154,15 @@ debug_logger.info(f'{args.model}路径已存在，不再重复下载大模型（如果下载出错可手动删除此目录）') debug_logger.info(f"CUDA_DEVICE: {model_config.CUDA_DEVICE}") +# 下载pdf解析相关的模型 +pdf_models_path = os.path.join(PDF_MODEL_PATH, 'checkpoints') +if not os.path.exists(pdf_models_path): + debug_logger.info(f'开始下载大模型：{model_download_params}') + model_dir = snapshot_download('netease-youdao/QAnything-pdf-parser') + subprocess.check_output(['ln', '-s', model_dir, pdf_models_path], text=True) + debug_logger.info(f'PDF解析相关模型下载完毕！cache地址：{model_dir}, 软链接地址：{pdf_models_path}') + + WorkerManager.THRESHOLD = 6000 app = Sanic("QAnything") diff --git a/qanything_kernel/utils/loader/markdown_parser.py b/qanything_kernel/utils/loader/markdown_parser.py new file mode 100644 index 00000000..943a3fe7 --- /dev/null +++ b/qanything_kernel/utils/loader/markdown_parser.py @@ -0,0 +1,253 @@ +import random +from langchain.schema.document import Document + +RANDOM_NUMBER_SET = set() + +def contains_table(text): + lines = text.split('\n') + if len(lines) < 2: + return False + has_separator = False + for i in range(len(lines) - 1): + if '|' in lines[i] and '|' in lines[i + 1]: + separator_line = lines[i + 1].strip() + if separator_line.startswith('|') and separator_line.endswith('|'): + separator_parts = separator_line[1:-1].split('|') + if all(part.strip().startswith('-') and len(part.strip()) >= 3 for part in separator_parts): + has_separator = True + break + return has_separator + +def _get_heading_level_offset(document): + + total_levels = [] + for block in document: + if not isinstance(block, list): continue + total_levels += [item['attrs']['level'] for item in block if item['type'] == 'heading'] + + offset = min(total_levels) - 1 if len(total_levels) > 0 else 0 + max_depth = max(total_levels) - min(total_levels) + 1 if len(total_levels) > 0 else 0 + + for i, block in enumerate(document): + if not isinstance(block, list): continue + for j, item in enumerate(block): + if item['type'] != 'heading': continue + document[i][j]['attrs']['level'] -= offset + + return document, offset, max_depth + + +def _init_node(node_type, title, id_len=4): + + while True: + random_number = random.randint(0, 16 ** id_len - 1) + if random_number in RANDOM_NUMBER_SET: continue + RANDOM_NUMBER_SET.add(random_number) + node_id = format(random_number, 'x').zfill(id_len) + break + + return { + 'node_id' : node_id, + 'node_type' : node_type, + 'title' : title, + 'blocks' : [] + } + + +def _get_content_dfs(item): + + def dfs_child(child, lines): + if 'children' in child: + for c in child['children']: + dfs_child(c, lines) + else: + if 'raw' in child: + lines.append(child['raw']) + return lines + + text_lines = dfs_child(item, []) + content = '\n'.join(text_lines) + '\n' + + return content + + +def _add_content_to_block(content, block): + + while content.endswith('\n'): + content = content[:-1] + + if len(content) > 0: + content_node = _init_node('ContentNode', '文字内容') + content_node['content'] = content + del content_node['blocks'] + block['blocks'].append(content_node) + + return block + + +def _update_heading_recursive(hierarchy_blocks, heading_depth, content): + + upper_level = max([heading_depth - i for i in range(1, heading_depth + 1) \ + if hierarchy_blocks[heading_depth - i] is not None]) + + # add content + if heading_depth == len(hierarchy_blocks) or hierarchy_blocks[heading_depth] is None: + hierarchy_blocks[upper_level] = _add_content_to_block( + content, hierarchy_blocks[upper_level]) + # update heading tree + else: + _update_heading_recursive(hierarchy_blocks, heading_depth + 1, content) + hierarchy_blocks[upper_level]['blocks'].append(hierarchy_blocks[heading_depth]) + hierarchy_blocks[heading_depth] = None + + return hierarchy_blocks + + +def _process_heading(item, hierarchy_blocks, content): + + heading_depth = item['attrs']['level'] + heading_text = item['children'][0]['raw'] + + if heading_depth < len(hierarchy_blocks): + hierarchy_blocks = _update_heading_recursive(hierarchy_blocks, heading_depth, content) + hierarchy_blocks[heading_depth] = _init_node('Level#%dHeadingNode' %heading_depth, heading_text) + content = '' + else: + content += '#' * heading_depth + ' ' + heading_text + '\n' + + return hierarchy_blocks, content + + +def _process_block(block, doc_json, max_heading_depth): + + content = '' + hierarchy_blocks = [doc_json] + [None] * max_heading_depth + + for item in block: + if item['type'] == 'heading': + hierarchy_blocks, content = _process_heading(item, hierarchy_blocks, content) + elif item['type'] in ['blank_line', 'thematic_break']: + continue + elif item['type'] in ['paragraph', 'list', 'block_quote']: + content += _get_content_dfs(item) + elif item['type'] in ['block_code', 'block_html']: + content += item['raw'] + '\n' + else: + raise ValueError('Unknown Type %s !!!' %item['type']) + + hierarchy_blocks = _update_heading_recursive(hierarchy_blocks, 1, content) + + return hierarchy_blocks[0] + + +def _update_node_id_title_dfs(doc_json): + + def dfs_recursive(node, node_id_list=[], title_list=[]): + + node_id_list.append(node['node_id']) + node['node_id'] = '-'.join(node_id_list) + + title_list.append(node['title']) + # node['title'] = '-'.join(title_list) + node['title'] = title_list.copy() + + if 'blocks' in node: + for block in node['blocks']: + node_id_list, title_list = dfs_recursive( + block, node_id_list, title_list) + + node_id_list = node_id_list[:-1] + title_list = title_list[:-1] + + return node_id_list, title_list + + dfs_recursive(doc_json) + + +def parse_markdown_mistune(file_path, doc_title=None, max_heading_depth=2): + + import mistune + + if not file_path.endswith('.md'): + raise ValueError('Not a markdown file !!!') + + with open(file_path, 'r', encoding='utf-8') as file: + markdown_content = file.read() + + mistune_parser = mistune.Markdown() + document = mistune_parser.parse(markdown_content) + print('Markdown parsing done.') + document, level_offset, max_depth = _get_heading_level_offset(document) + if max_heading_depth is None or max_heading_depth <= 0: + max_heading_depth = max_depth + + file_tile = file_path.split('/')[-1].replace('.md', '') + doc_title = file_tile if doc_title is None else '-'.join([doc_title, file_tile]) + doc_json = _init_node('DocumentNode', doc_title, id_len=8) + + for block in document: + if not isinstance(block, list): continue + doc_json = _process_block(block, doc_json, max_heading_depth) + # print(doc_json) + _update_node_id_title_dfs(doc_json) + # print(doc_json) + # with open('markdown_test.json','w',encoding='utf-8') as f_w: + # json.dump(doc_json,f_w,ensure_ascii=False) + print('Tree building done.') + return doc_json + + +def _convert_to_node_lists_dfs(parsing_json): + + node_lists = {} + + def traverse_and_group_by_depth(node, depth, group): + node_info = node.copy() + if 'blocks' in node_info: + node_info['child_id_list'] = [block['node_id'] for block in node_info['blocks']] + del node_info['blocks'] + if depth not in node_lists.keys(): + node_lists[depth] = [node_info] + else: + node_lists[depth].append(node_info) + if 'blocks' not in node.keys(): + return + for child in node['blocks']: + traverse_and_group_by_depth(child, depth + 1, node_lists) + + for doc_json in parsing_json: + traverse_and_group_by_depth(doc_json, 0, node_lists) + + return node_lists + +def convert_node_to_document(node_lists): + doc_lst = [ ] + for k,v in node_lists.items(): + for item in v: + if item['node_type'].startswith('Level'): + if len(item['child_id_list']) == 0: #是一个单独的标题，并且没有子节点，可能是markdown解析出现了问题 + title_lst = [] + for index,title in enumerate(item['title']): + title_lst.append('#'*(index+1)+title) + doc = Document(page_content='',metadata={'title_lst':title_lst,'has_table':False}) + doc_lst.append(doc) + if item['node_type'] == 'ContentNode': + title_lst = [] + for index,title in enumerate(item['title'][:-1]): + title_lst.append('#'*(index+1)+title) + has_table = contains_table(item['content']) + doc = Document(page_content=item['content'],metadata={'title_lst':title_lst,'has_table':has_table}) + doc_lst.append(doc) + return doc_lst + + +def convert_markdown_to_langchaindoc(md_file): + doc_json = parse_markdown_mistune(md_file) + node_lists = _convert_to_node_lists_dfs([doc_json]) + doc_lst = convert_node_to_document(node_lists) + return doc_lst + + +if __name__ =='__main__': + doc_lst = convert_markdown_to_langchaindoc('/ssd8/exec/qinhaibo/code/RAG/release/git/document-layout-parser/results/樊昊天个人简历_1715841225/樊昊天个人简历_md/樊昊天个人简历.md') + print(doc_lst) \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/convert2markdown.py b/qanything_kernel/utils/loader/pdf_to_markdown/convert2markdown.py new file mode 100644 index 00000000..b2d88b29 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/convert2markdown.py @@ -0,0 +1,50 @@ +import json +import re + + +def json2markdown(json_dir, markdown_dir): + pdf = [] + para_before = '' + before_patt = ['[a-z]', ',', '，', '-', '\(', '\)', '（', '）'] + after_patt = ['[a-z]', '·', '\(', '\)', '（', '）'] + js = json_dir + f = json.load(open(js)) + md = markdown_dir + for i, section in f.items(): + text = section['text'] + type = section['type'] + if isinstance(text, dict): + table = text['table_markdown'] + caption = text['table_caption'] + pdf.append(table) + pdf.append(caption + '\n\n') + elif type.startswith('title'): + pdf.append('## '+text.split('@@')[0]) + elif 'figure' in type: + pdf.append('![figure]'+'({}.jpg "{}")'.format(type, text) + '\n') + elif 'equation' in type: + pdf.append('![equation]'+'({}.jpg)'.format(type) + '\n') + else: + para = text.split('@@')[0] + '\n' + pdf.append(para) + if para_before: + if any([re.match(p, para_before[-2]) for p in before_patt]) and any([re.match(p, para[0]) for p in after_patt]): + pdf.pop(pdf.index(para)) + pdf.pop(pdf.index(para_before)) + pdf.append(para_before[:-1] + ' ' + para) + para_before = para_before[:-1] + ' ' + para + else: + para_before = para + else: + para_before = para + # print(pdf, flush=True) + for p in pdf: + print(p, file=open(md, 'a')) + return '\n'.join(pdf) + + + + +if __name__ =='__main__': + json_dir = '/ssd8/exec/qinhaibo/code/RAG/release/git/document-layout-parser/table_test.json' + json2markdown(json_dir) \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/__init__.py new file mode 100644 index 00000000..8af58ff0 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/__init__.py @@ -0,0 +1 @@ +from .table_parser_e2e import TableRecognizer \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/layout_model.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/layout_model.py new file mode 100644 index 00000000..ceea9d73 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/layout_model.py @@ -0,0 +1,274 @@ +import cv2 +import os +import numpy as np +import onnxruntime +from tqdm import tqdm +import torch + + +class YOLOV8: + + def __init__(self, path, conf_thres=0.4, iou_thres=0.5, imgsz=(800, 800), device=None): + self.conf_threshold = conf_thres + self.iou_threshold = iou_thres + self.imgsz = imgsz + + # Initialize model + self.initialize_model(path,device) + + def __call__(self, image): + return self.box_objects(image) + + def initialize_model(self, path, device): + if device == torch.device('cuda'): + self.session = onnxruntime.InferenceSession(path,providers=['CUDAExecutionProvider']) + else: + self.session = onnxruntime.InferenceSession(path,providers=['CPUExecutionProvider']) + # Get model info + self.get_input_details() + self.get_output_details() + + def box_objects(self, image): + input_tensor,h,w = self.prepare_input(image) + + # Perform inference on the image + outputs = self.inference(input_tensor) + self.boxes, self.scores, self.class_ids = self.process_box_output(outputs[0],h,w) + + return self.boxes, self.scores, self.class_ids + + # def prepare_input(self, image): + # self.img_height, self.img_width = image.shape[:2] + + # input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + + # # Resize input image + # scaled_img = cv2.resize(input_img, (self.imgsz[1], self.imgsz[0])) + # input_img = scaled_img + + # h,w = scaled_img.shape[:2] + + # # Scale input pixel values to 0 to 1 + # input_img = input_img / 255.0 + # input_img = input_img.transpose(2, 0, 1) + # input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32) + + # return input_tensor,h,w + + def prepare_input(self, image): + self.img_height, self.img_width = image.shape[:2] + + input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + + # Resize input image + scale = min(self.imgsz[0] / self.img_height, self.imgsz[1] / self.img_width) + scaled_img = cv2.resize(input_img, (int(round(scale * self.img_width)), int(round(scale * self.img_height)))) + dw, dh = self.imgsz[1] - scaled_img.shape[1], self.imgsz[0] - scaled_img.shape[0] + dw /= 2 # divide padding into 2 sides + dh /= 2 + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + scaled_img = cv2.copyMakeBorder(scaled_img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) + input_img = scaled_img + + h,w = scaled_img.shape[:2] + + # Scale input pixel values to 0 to 1 + input_img = input_img / 255.0 + input_img = input_img.transpose(2, 0, 1) + input_tensor = input_img[np.newaxis, :, :, :].astype(np.float32) + + return input_tensor,h,w + + def scale_boxes(self, img1_shape, boxes, img0_shape, ratio_pad=None): + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + self.clip_boxes(boxes, img0_shape) + return boxes + + def clip_boxes(self, boxes, shape): + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + + + def inference(self, input_tensor): + outputs = self.session.run(self.output_names, {self.input_names[0]: input_tensor}) + + return outputs + + def process_box_output(self, box_output,h,w): + predictions = np.squeeze(box_output).T + num_classes = box_output.shape[1] - 4 + # Filter out object confidence scores below threshold + scores = np.max(predictions[:, 4:4+num_classes], axis=1) + predictions = predictions[scores > self.conf_threshold, :] + scores = scores[scores > self.conf_threshold] + if len(scores) == 0: + return [], [], [] + + box_predictions = predictions[..., :num_classes+4] + + # Get the class with the highest confidence + class_ids = np.argmax(box_predictions[:, 4:], axis=1) + + # Get bounding boxes for each object + boxes = self.extract_boxes(box_predictions,h,w) + + # Apply non-maxima suppression to suppress weak, overlapping bounding boxes + #indices = nms_rc(boxes, scores, self.iou_threshold) + indices = nms(boxes, scores, self.iou_threshold) + + return boxes[indices], scores[indices], class_ids[indices] + + + def extract_boxes(self, box_predictions,h,w): + # Extract boxes from predictions + boxes = box_predictions[:, :4] + + # Scale boxes to original image dimensions + # boxes = self.rescale_boxes(boxes, + # (h, w), + # (self.img_height, self.img_width)) + + # Convert boxes to xyxy format + boxes = xywh2xyxy(boxes) + # Scale boxes to original image dimensions + boxes = self.scale_boxes((h, w), + boxes, + (self.img_height, self.img_width)) + + # Check the boxes are within the image + # boxes[:, 0] = np.clip(boxes[:, 0], 0, self.img_width) + # boxes[:, 1] = np.clip(boxes[:, 1], 0, self.img_height) + # boxes[:, 2] = np.clip(boxes[:, 2], 0, self.img_width) + # boxes[:, 3] = np.clip(boxes[:, 3], 0, self.img_height) + + return boxes + + def get_input_details(self): + model_inputs = self.session.get_inputs() + self.input_names = [model_inputs[i].name for i in range(len(model_inputs))] + + # self.input_shape = model_inputs[0].shape + # self.input_height = self.input_shape[2] + # self.input_width = self.input_shape[3] + + def get_output_details(self): + model_outputs = self.session.get_outputs() + self.output_names = [model_outputs[i].name for i in range(len(model_outputs))] + + @staticmethod + def rescale_boxes(boxes, input_shape, image_shape): + # Rescale boxes to original image dimensions + input_shape = np.array([input_shape[1], input_shape[0], input_shape[1], input_shape[0]]) + boxes = np.divide(boxes, input_shape, dtype=np.float32) + boxes *= np.array([image_shape[1], image_shape[0], image_shape[1], image_shape[0]]) + + return boxes + +def nms(boxes, scores, iou_threshold): + # Sort by score + sorted_indices = np.argsort(scores)[::-1] + # print(boxes.shape) + # print(scores.shape) + keep_boxes = [] + while sorted_indices.size > 0: + # Pick the last box + box_id = sorted_indices[0] + keep_boxes.append(box_id) + + # Compute IoU of the picked box with the rest + ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :]) + + # Remove boxes with IoU over the threshold + keep_indices = np.where(ious < iou_threshold)[0] + + # print(keep_indices.shape, sorted_indices.shape) + sorted_indices = sorted_indices[keep_indices + 1] + + return keep_boxes + +def compute_iou(box, boxes): + # Compute xmin, ymin, xmax, ymax for both boxes + xmin = np.maximum(box[0], boxes[:, 0]) + ymin = np.maximum(box[1], boxes[:, 1]) + xmax = np.minimum(box[2], boxes[:, 2]) + ymax = np.minimum(box[3], boxes[:, 3]) + + # Compute intersection area + intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin) + + # Compute union area + box_area = (box[2] - box[0]) * (box[3] - box[1]) + boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + union_area = box_area + boxes_area - intersection_area + + # Compute IoU + iou = intersection_area / union_area + + return iou + +def xywh2xyxy(x): + # Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2) + y = np.copy(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 + y[..., 1] = x[..., 1] - x[..., 3] / 2 + y[..., 2] = x[..., 0] + x[..., 2] / 2 + y[..., 3] = x[..., 1] + x[..., 3] / 2 + return y + + +def sigmoid(x): + return 1 / (1 + np.exp(-x)) + + + + # if self.class_names[class_ids[idx]] == 'Caption': + + + + + + + + + + +# model_path = '/ssd7/exec/huangjy/ultralytics/runs/detect/train81/weights//best.onnx' +# yolov8 = YOLOV8(model_path, conf_thres=0.4, iou_thres=0.65,imgsz=(800, 800)) + +# test_dir = '/ssd8/exec/huangjy/ultralytics/' +# files = [item for item in os.listdir(test_dir) if item.endswith('.jpg')] + +# class_names = ( +# 'Text', 'Title', 'Figure', 'Equation', 'Table', +# 'Caption', 'Header', 'Footer', 'BibInfo', 'Reference', +# 'Content', 'Code', 'Other', 'Item', 'Author', +# ) +# for file in tqdm(files): +# img_path = os.path.join(test_dir,file) +# img = cv2.imread(file) +# h, w = img.shape[:2] +# # Detect Objects +# boxes, scores, class_ids = yolov8(img) +# for idx, box in enumerate(boxes): +# color = np.random.rand(3) * 160 +# cv2.rectangle(img, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), color) +# cv2.putText(img, '%.3f %s' % (scores[idx], class_names[class_ids[idx]]), +# (int(box[0]), int(box[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2) +# cv2.imwrite('{}_res.jpg'.format(os.path.basename(file)[:-4]), img) +# # 输出的结果 +# """ +# boxes: 输出的bounding box，正矩形。N*4 +# scored: 每个框的置信度。N +# class_ids: 每个框的类别 +# """ + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/layout_parser.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/layout_parser.py new file mode 100644 index 00000000..4ef333d5 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/layout_parser.py @@ -0,0 +1,108 @@ +import numpy as np +import cv2 +import math +from layout.layout_model import YOLOV8 +from layout.table_cls.infer_onnx import TableCls +from layout.table_rec.pipeline import TableParser +import torch + + + + +def distance(x1,y1,x2,y2): + return math.sqrt((x2-x1)**2 + (y2-y1)**2) + + +class LayoutParser(object): + def __init__(self,device=torch.device("cuda")): + model_path = 'layout/best.onnx' + self.model = YOLOV8(model_path, conf_thres=0.4, iou_thres=0.65,imgsz=(800, 800),device=device) + self.class_names = ('Text', 'Title', 'Figure', 'Equation', 'Table','Caption', 'Header', 'Footer', 'BibInfo', 'Reference','Content', 'Code', 'Other', 'Item', 'Author',) + + self.table_cls = TableCls(device=device) + self.table_parse = TableParser(device=device) + + + def process_image(self,image): + boxes, scores, class_ids = self.model(image) + for idx, box in enumerate(boxes): + # print(box) + color = np.random.rand(3) * 160 + image = np.int32(image) + image = cv2.rectangle(image, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), color) + image = cv2.putText(image, '%.3f %s' % (scores[idx], self.class_names[class_ids[idx]]), + (int(box[0]), int(box[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2) + # cv2.imwrite('tmp_res.jpg',image) + + def get_min_dis_box(self,caption_boxes,box): + dis = 1e6 + min_box = None + for c_box in caption_boxes: + # print(c_box) + c_x, c_y = (c_box[0] + c_box[2]) / 2, (c_box[1] + c_box[3]) / 2 + x, y = (box[0] + box[2]) / 2, (box[1] + box[3]) / 2 + c_dis = distance(c_x, c_y, x, y) + # print('*********') + # print(box) + # print(c_box) + # print(c_dis) + if c_dis < dis: + min_box = c_box + dis = c_dis + return min_box + + def extract_table(self,image,ocr_result): + boxes, scores, class_ids = self.model(image.copy()) + table_dict = {} + index = 0 + caption_boxes = [] + for idx,(box,class_id) in enumerate(zip(boxes,class_ids)): + if self.class_names[class_ids[idx]] == 'Caption': + caption_boxes.append(box) + # print(len(caption_boxes)) + # print(caption_boxes) + # vis_img = image.copy() + # for idx, box in enumerate(boxes): + # color = np.random.rand(3) * 160 + # cv2.rectangle(vis_img, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), color) + # cv2.putText(vis_img, '%.3f %s' % (scores[idx], self.class_names[class_ids[idx]]), + # (int(box[0]), int(box[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2) + # cv2.imwrite('layout_vis.jpg', vis_img) + # print(caption_boxes) + for idx, box in enumerate(boxes): + if self.class_names[class_ids[idx]] == 'Table': + # print(box) + x1,y1,x2,y2 = int(box[0]),int(box[1]),int(box[2]),int(box[3]) + # print(x1,y1,x2,y2) + table_image = image[y1:y2,x1:x2,:] + + # cv2.imwrite('/ssd8/exec/qinhaibo/code/RAG/QAnything-master/tools/TreeIndex/paddle_test/table_tmp3.jpg',table_image) + table_type = self.table_cls.process(table_image.copy()) + # print(table_type) + # cv2.imwrite('/ssd8/exec/qinhaibo/code/RAG/QAnything-master/tools/TreeIndex/paddle_test/table_tmp4.jpg',table_image.copy()) + # ti = cv2.imread('/ssd8/exec/qinhaibo/code/RAG/QAnything-master/tools/TreeIndex/paddle_test/table_tmp3.jpg') + # table_html,table_markdown = self.table_parse.process(ti,table_type,convert2markdown=True) + table_html,table_markdown = self.table_parse.process(table_image.copy(),table_type,ocr_result=ocr_result,convert2markdown=True) + # print(table_str) + if len(caption_boxes) == 0: + related_caption_box = box + has_caption = False + else: + related_caption_box = self.get_min_dis_box(caption_boxes,box) + has_caption = True + c_x1,c_y1,c_x2,c_y2 = int(related_caption_box[0]),int(related_caption_box[1]),int(related_caption_box[2]),int(related_caption_box[3]) + caption_image = image[c_y1:c_y2, c_x1:c_x2] + table_dict[index] = { + 'has_caption': has_caption, + 'table_html': table_html, + 'table_markdown': table_markdown, + 'table_box': box, + 'caption_box': related_caption_box, + 'caption_image': caption_image, + } + index +=1 + # print(table_dict[0]['table_content']) + # print(table_dict[0]['caption_box']) + # print(len(table_dict.keys())) + #对table dict按照box的左上角顶点进行排序 + return table_dict \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_cls/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_cls/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_cls/infer_onnx.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_cls/infer_onnx.py new file mode 100644 index 00000000..f50d066e --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_cls/infer_onnx.py @@ -0,0 +1,33 @@ +import onnxruntime +from PIL import Image +import numpy as np +import torch +from torchvision import transforms +from qanything_kernel.configs.model_config import PDF_MODEL_PATH +import cv2 +import os + +data_transform = transforms.Compose( + [transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) +cls = {0: 'wired', 1: 'wireless'} + + +class TableCls(): + def __init__(self, device=torch.device('cpu')): + # 输出当前文件绝对路径 + cls_model = os.path.join(PDF_MODEL_PATH, 'checkpoints/table/table_cls_l.onnx') + if device == torch.device('cuda'): + self.table_cls = onnxruntime.InferenceSession(cls_model, None, providers=['CUDAExecutionProvider']) + else: + self.table_cls = onnxruntime.InferenceSession(cls_model, None, providers=['CPUExecutionProvider']) + + def process(self, image): + img = Image.fromarray(np.uint8(image)) + img = data_transform(img) + img = torch.unsqueeze(img, dim=0) + output = self.table_cls.run(None, {'input': img.numpy()}) + predict = torch.softmax(torch.from_numpy(output[0]), dim=1) + predict_cla = torch.argmax(predict, dim=1).numpy()[0] + return cls[predict_cla] diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_parser_e2e.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_parser_e2e.py new file mode 100644 index 00000000..52570d0a --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_parser_e2e.py @@ -0,0 +1,16 @@ +from .table_rec.pipeline import TableParser +from .table_cls.infer_onnx import TableCls + + +class TableRecognizer(): + def __init__(self, device): + print('table model initing...') + self.table_cls = TableCls(device) + self.table_parse = TableParser(device) + print('table model inited...') + + def extract_table(self, table_image, ocr_result): + table_type = self.table_cls.process(table_image.copy()) + table_html, table_markdown = self.table_parse.process(table_image.copy(), table_type, ocr_result=ocr_result, + convert2markdown=True) + return table_html, table_markdown diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table.py new file mode 100644 index 00000000..c58caeb4 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table.py @@ -0,0 +1,106 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import pycocotools.coco as coco +from pycocotools.cocoeval import COCOeval +import numpy as np +import json +import random +import os + +import torch.utils.data as data + +class Table(data.Dataset): + num_classes = 2 + table_size = 1024 + default_resolution = [1024, 1024] + mean = np.array([0.40789654, 0.44719302, 0.47026115], + dtype=np.float32).reshape(1, 1, 3) + std = np.array([0.28863828, 0.27408164, 0.27809835], + dtype=np.float32).reshape(1, 1, 3) + + def __init__(self, opt, split): + super(Table, self).__init__() + self.split = split + self.data_dir = os.path.join(opt.data_dir, opt.dataset_name) + print(self.data_dir) + self.img_dir = opt.image_dir + _ann_name = {'train':'train', 'val':'test','test':'test'} + self.annot_path = os.path.join(self.data_dir, 'json', '{}.json').format(_ann_name[split]) + print(self.annot_path) + + self.max_objs = 300 + self.max_pairs = 900 + self.max_cors = 1200 #800 #1200 #800 + + self.class_name = [ + '__background__', 'center','corner'] + self._valid_ids = [1,2] + self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)} + self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \ + for v in range(1, self.num_classes + 1)] + self._data_rng = np.random.RandomState(123) + self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571], + dtype=np.float32) + self._eig_vec = np.array([ + [-0.58752847, -0.69563484, 0.41340352], + [-0.5832747, 0.00994535, -0.81221408], + [-0.56089297, 0.71832671, 0.41158938] + ], dtype=np.float32) + # self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3) + # self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3) + + self.split = split + self.opt = opt + + print('==> initializing table {} data.'.format(split)) + self.coco = coco.COCO(self.annot_path) + self.images = self.coco.getImgIds() + self.num_samples = len(self.images) + + print('Loaded {} {} samples'.format(split, self.num_samples)) + + def _to_float(self, x): + return float("{:.2f}".format(x)) + + def convert_eval_format(self, all_bboxes, thresh): + # import pdb; pdb.set_trace() + detections = [] + for image_id in all_bboxes: + for cls_ind in all_bboxes[image_id]: + category_id = self._valid_ids[cls_ind - 1] + for bbox in all_bboxes[image_id][cls_ind]: + if bbox[4] > float(thresh): + bbox[2] -= bbox[0] + bbox[3] -= bbox[1] + score = bbox[4] + bbox_out = list(map(self._to_float, bbox[0:4])) + + detection = { + "image_id": int(image_id), + "category_id": int(category_id), + "bbox": bbox_out, + "score": float("{:.2f}".format(score)) + } + if len(bbox) > 5: + extreme_points = list(map(self._to_float, bbox[5:13])) + detection["extreme_points"] = extreme_points + detections.append(detection) + print('total:',len(detections)) + return detections + + def __len__(self): + return self.num_samples + + def save_results(self, results, save_dir, thresh): + json.dump(self.convert_eval_format(results, thresh), + open('{}/results.json'.format(save_dir), 'w')) + + def run_eval(self, results, save_dir, thresh): + self.save_results(results, save_dir, thresh) + coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir)) + coco_eval = COCOeval(self.coco, coco_dets, "bbox") + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table_mid.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table_mid.py new file mode 100644 index 00000000..5696c7fe --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table_mid.py @@ -0,0 +1,106 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import pycocotools.coco as coco +from pycocotools.cocoeval import COCOeval +import numpy as np +import json +import random +import os + +import torch.utils.data as data + +class Table(data.Dataset): + num_classes = 2 + table_size = 768 + default_resolution = [768, 768] + mean = np.array([0.40789654, 0.44719302, 0.47026115], + dtype=np.float32).reshape(1, 1, 3) + std = np.array([0.28863828, 0.27408164, 0.27809835], + dtype=np.float32).reshape(1, 1, 3) + + def __init__(self, opt, split): + super(Table, self).__init__() + self.split = split + self.data_dir = os.path.join(opt.data_dir, opt.dataset_name) + print(self.data_dir) + self.img_dir = opt.image_dir + _ann_name = {'train':'train', 'val':'test','test':'test'} + self.annot_path = os.path.join(self.data_dir, 'json', '{}.json').format(_ann_name[split]) + print(self.annot_path) + + self.max_objs = 300 + self.max_pairs = 900 + self.max_cors = 1200 #800 #1200 #800 + + self.class_name = [ + '__background__', 'center','corner'] + self._valid_ids = [1,2] + self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)} + self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \ + for v in range(1, self.num_classes + 1)] + self._data_rng = np.random.RandomState(123) + self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571], + dtype=np.float32) + self._eig_vec = np.array([ + [-0.58752847, -0.69563484, 0.41340352], + [-0.5832747, 0.00994535, -0.81221408], + [-0.56089297, 0.71832671, 0.41158938] + ], dtype=np.float32) + # self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3) + # self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3) + + self.split = split + self.opt = opt + + print('==> initializing table {} data.'.format(split)) + self.coco = coco.COCO(self.annot_path) + self.images = self.coco.getImgIds() + self.num_samples = len(self.images) + + print('Loaded {} {} samples'.format(split, self.num_samples)) + + def _to_float(self, x): + return float("{:.2f}".format(x)) + + def convert_eval_format(self, all_bboxes, thresh): + # import pdb; pdb.set_trace() + detections = [] + for image_id in all_bboxes: + for cls_ind in all_bboxes[image_id]: + category_id = self._valid_ids[cls_ind - 1] + for bbox in all_bboxes[image_id][cls_ind]: + if bbox[4] > float(thresh): + bbox[2] -= bbox[0] + bbox[3] -= bbox[1] + score = bbox[4] + bbox_out = list(map(self._to_float, bbox[0:4])) + + detection = { + "image_id": int(image_id), + "category_id": int(category_id), + "bbox": bbox_out, + "score": float("{:.2f}".format(score)) + } + if len(bbox) > 5: + extreme_points = list(map(self._to_float, bbox[5:13])) + detection["extreme_points"] = extreme_points + detections.append(detection) + print('total:',len(detections)) + return detections + + def __len__(self): + return self.num_samples + + def save_results(self, results, save_dir, thresh): + json.dump(self.convert_eval_format(results, thresh), + open('{}/results.json'.format(save_dir), 'w')) + + def run_eval(self, results, save_dir, thresh): + self.save_results(results, save_dir, thresh) + coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir)) + coco_eval = COCOeval(self.coco, coco_dets, "bbox") + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table_small.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table_small.py new file mode 100644 index 00000000..eeab2265 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset/table_small.py @@ -0,0 +1,106 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import pycocotools.coco as coco +from pycocotools.cocoeval import COCOeval +import numpy as np +import json +import random +import os + +import torch.utils.data as data + +class Table(data.Dataset): + num_classes = 2 + table_size = 512 + default_resolution = [512, 512] + mean = np.array([0.40789654, 0.44719302, 0.47026115], + dtype=np.float32).reshape(1, 1, 3) + std = np.array([0.28863828, 0.27408164, 0.27809835], + dtype=np.float32).reshape(1, 1, 3) + + def __init__(self, opt, split): + super(Table, self).__init__() + self.split = split + self.data_dir = os.path.join(opt.data_dir, opt.dataset_name) + print(self.data_dir) + self.img_dir = opt.image_dir + _ann_name = {'train':'train', 'val':'test','test':'test'} + self.annot_path = os.path.join(self.data_dir, 'json', '{}.json').format(_ann_name[split]) + print(self.annot_path) + + self.max_objs = 300 + self.max_pairs = 900 + self.max_cors = 1200 #800 #1200 #800 + + self.class_name = [ + '__background__', 'center','corner'] + self._valid_ids = [1,2] + self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)} + self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \ + for v in range(1, self.num_classes + 1)] + self._data_rng = np.random.RandomState(123) + self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571], + dtype=np.float32) + self._eig_vec = np.array([ + [-0.58752847, -0.69563484, 0.41340352], + [-0.5832747, 0.00994535, -0.81221408], + [-0.56089297, 0.71832671, 0.41158938] + ], dtype=np.float32) + # self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3) + # self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3) + + self.split = split + self.opt = opt + + print('==> initializing table {} data.'.format(split)) + self.coco = coco.COCO(self.annot_path) + self.images = self.coco.getImgIds() + self.num_samples = len(self.images) + + print('Loaded {} {} samples'.format(split, self.num_samples)) + + def _to_float(self, x): + return float("{:.2f}".format(x)) + + def convert_eval_format(self, all_bboxes, thresh): + # import pdb; pdb.set_trace() + detections = [] + for image_id in all_bboxes: + for cls_ind in all_bboxes[image_id]: + category_id = self._valid_ids[cls_ind - 1] + for bbox in all_bboxes[image_id][cls_ind]: + if bbox[4] > float(thresh): + bbox[2] -= bbox[0] + bbox[3] -= bbox[1] + score = bbox[4] + bbox_out = list(map(self._to_float, bbox[0:4])) + + detection = { + "image_id": int(image_id), + "category_id": int(category_id), + "bbox": bbox_out, + "score": float("{:.2f}".format(score)) + } + if len(bbox) > 5: + extreme_points = list(map(self._to_float, bbox[5:13])) + detection["extreme_points"] = extreme_points + detections.append(detection) + print('total:',len(detections)) + return detections + + def __len__(self): + return self.num_samples + + def save_results(self, results, save_dir, thresh): + json.dump(self.convert_eval_format(results, thresh), + open('{}/results.json'.format(save_dir), 'w')) + + def run_eval(self, results, save_dir, thresh): + self.save_results(results, save_dir, thresh) + coco_dets = self.coco.loadRes('{}/results.json'.format(save_dir)) + coco_eval = COCOeval(self.coco, coco_dets, "bbox") + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset_factory.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset_factory.py new file mode 100644 index 00000000..bd00b272 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/dataset_factory.py @@ -0,0 +1,28 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from .sample.ctdet import CTDetDataset + +from .dataset.table import Table +from .dataset.table_small import Table as Table_small +from .dataset.table_mid import Table as Table_mid + + +dataset_factory = { + 'table':Table, + 'table_mid':Table_mid, + 'table_small':Table_small +} + +_sample_factory = { + 'ctdet': CTDetDataset, + 'ctdet_mid': CTDetDataset, + 'ctdet_small': CTDetDataset +} + +def get_dataset(dataset, task): + class Dataset(dataset_factory[dataset], _sample_factory[task]): + pass + return Dataset + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/sample/ctdet.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/sample/ctdet.py new file mode 100644 index 00000000..ef43ac0e --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/datasets/sample/ctdet.py @@ -0,0 +1,380 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torch.utils.data as data +import numpy as np +import torch +import json +import cv2 +import os +from utils.image import flip, color_aug +from utils.image import get_affine_transform, affine_transform, get_affine_transform_upper_left +from utils.post_process import ctdet_4ps_post_process +from utils.image import gaussian_radius, draw_umich_gaussian, draw_umich_gaussian_wh, draw_msra_gaussian +from utils.image import draw_dense_reg +from utils.adjacency import adjacency, h_adjacency, v_adjacency, same_col, same_row +import math +import time +import random +import imgaug.augmenters as iaa +import time + +class CTDetDataset(data.Dataset): + def _coco_box_to_bbox(self, box): + bbox = np.array([box[0], box[1], box[0] + box[2], box[1] + box[3]], + dtype=np.float32) + return bbox + + def _get_border(self, border, size): + i = 1 + while size - border // i <= border // i: + i *= 2 + return border // i + + def _get_border_upper_left(self, border, size): + i = 1 + while size/2 - border // i <= border // i: + i *= 2 + return border // i + + def _get_radius(self,r,w,h): + if w > h: + k = float(w)/float(h) + else: + k = float(h)/float(w) + ratio = k**0.5 + if w>h: + r_w = r*ratio + r_h = r + else: + r_h = r*ratio + r_w = r + return int(r_w),int(r_h) + + def color(self,image,p,magnitude): + if np.random.randint(0,10) > p*10: + return image + gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + bgr_img = cv2.cvtColor(gray_img, cv2.COLOR_GRAY2BGR) + img_float,bgr_img_float = img.astype(float), bgr_img.astype(float) + diff = img_float - bgr_img_float + diff = diff*magnitude + diff_img_ = diff + bgr_img_float + diff_img_ = diff_img_.astype(np.uint8) + diff_img_ = np.array(diff_img_) + diff_img_ = np.clip(diff_img_,0,255) + diff_img_ = cv2.cvtColor(diff_img_,cv2.COLOR_BGR2RGB) + diff_img_ = cv2.cvtColor(diff_img_,cv2.COLOR_RGB2BGR) + return diff_img_ + + def rotate(self,p,magnitude): + if np.random.randint(0,10) > p*10: + return 0 + rot = np.random.randint(magnitude[0],magnitude[1]) + return rot + + def hisEqulColor(self,img): + (b, g, r) = cv2.split(img) + bH = cv2.equalizeHist(b) + gH = cv2.equalizeHist(g) + rH = cv2.equalizeHist(r) + result = cv2.merge((bH, gH, rH)) + return img + + def _judge(self,box): + countx = len(list(set([box[0],box[2],box[4],box[6]]))) + county = len(list(set([box[1],box[3],box[5],box[7]]))) + if countx<2 or county<2: + return False + + return True + + def _get_Center(self, point): + x1 = point[0] + y1 = point[1] + x3 = point[2] + y3 = point[3] + x2 = point[4] + y2 = point[5] + x4 = point[6] + y4 = point[7] + w1 = math.sqrt((x1-x3)*(x1-x3)+(y1-y3)*(y1-y3)) + w2 = math.sqrt((x2-x4)*(x2-x4)+(y2-y4)*(y2-y4)) + h1 = math.sqrt((x1-x4)*(x1-x4)+(y1-y4)*(y1-y4)) + h2 = math.sqrt((x2-x3)*(x2-x3)+(y2-y3)*(y2-y3)) + nw = min(w1,w2) + nh = min(h1,h2) + x_dev = x4*y2-x4*y1-x3*y2+x3*y1-x2*y4+x2*y3+x1*y4-x1*y3 + y_dev = y4*x2-y4*x1-y3*x2+x1*y3-y2*x4+y2*x3+y1*x4-y1*x3 + c_x = 0 + c_y = 0 + if x_dev != 0: + c_x = (y3*x4*x2-y4*x3*x2-y3*x4*x1+y4*x3*x1-y1*x2*x4+y2*x1*x4+y1*x2*x3-y2*x1*x3)/x_dev + if y_dev != 0: + c_y = (-y3*x4*y2+y4*x3*y2+y3*x4*y1-y4*x3*y1+y1*x2*y4-y1*x2*y3-y2*x1*y4+y2*x1*y3)/y_dev + return nw,nh,c_x,c_y + + def _rank(self,bbox,cter,file_name): + init_bbox = bbox + #bbox = list(map(float,bbox)) + continue_sign = False + bbox = [bbox[0:2],bbox[2:4],bbox[4:6],bbox[6:8]] + bbox_= np.array(bbox) - np.array(cter) + i,box_y,sign= 0,[],'LT' + choice = [] + for box in bbox_: + if box[0]<0 and box[1]<0: + box_y.append(box) + choice.append(i) + i = i + 1 + if len(choice)==0: + i,box_y,sign = 0,[],'RT' + for box in bbox_: + if box[0]>0 and box[1]<0: + box_y.append(box) + choice.append(i) + i = i + 1 + if sign=='LT': + ylist = np.array(box_y)[:,1] + #index = list(ylist).index(max(ylist)) + index = list(ylist).index(min(ylist)) + elif sign=='RT': + try: + xlist = np.array(box_y)[:,0] + except Exception as e: + print("center:",cter,"box:",init_bbox,"box_y:",box_y) + return True,bbox + index = list(xlist).index(min(xlist)) + + index = choice[index] + p = [] + for i in range(4): + if i + index < 4: + p.append(bbox[index+i]) + else: + p.append(bbox[index+i-4]) + return continue_sign,[p[0][0],p[0][1],p[1][0],p[1][1],p[2][0],p[2][1],p[3][0],p[3][1]] + + def __getitem__(self, index): + img_id = self.images[index] + file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name'] + if self.opt.dataset_name == 'ICDAR19': + if self.split == 'train': + img_path = os.path.join(self.img_dir, 'train_images' ,file_name) + else: + img_path = os.path.join(self.img_dir, 'test_images' ,file_name) + else: + img_path = os.path.join(self.img_dir, file_name) + ann_ids = self.coco.getAnnIds(imgIds=[img_id]) + anns = self.coco.loadAnns(ids=ann_ids) + num_objs = min(len(anns), self.max_objs) + num_cors = self.max_cors + if self.opt.dataset_name == 'TG24K': + img_path = img_path.replace('.jpg', '_org.png') + elif self.opt.dataset_name == 'SciTSR': + img_path = img_path.replace('.jpg', '.png') + elif self.opt.dataset_name == 'PTN': + img_path = img_path.replace('.jpg', '.png') + elif self.opt.dataset_name == 'bankdata_june': + img_path = img_path[:-4] + + img = cv2.imread(img_path) + img_size = img.shape + + height, width = img.shape[0], img.shape[1] + + if self.opt.upper_left: + c = np.array([0, 0], dtype=np.float32) + else: + c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32) + + if self.opt.keep_res: + input_h = (height | self.opt.pad)# + 1 + input_w = (width | self.opt.pad)# + 1 + s = np.array([input_w, input_h], dtype=np.float32) + else: + s = max(img.shape[0], img.shape[1]) * 1.0 + input_h, input_w = self.opt.input_h, self.opt.input_w + + + flipped = False + if self.split == 'train': + if not self.opt.not_rand_crop: + if self.opt.upper_left: + c = np.array([0, 0], dtype=np.float32) + else: + s = s * np.random.choice(np.arange(0.6, 1.4, 0.1)) + w_border = self._get_border(128, img.shape[1]) + h_border = self._get_border(128, img.shape[0]) + c[0] = np.random.randint(low=w_border, high=img.shape[1] - w_border) + c[1] = np.random.randint(low=h_border, high=img.shape[0] - h_border) + + else: + sf = self.opt.scale + cf = self.opt.shift + c[0] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf) + c[1] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf) + s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf) + + rot = 0 + if self.opt.rotate==1: + print('----rotate----') + rot = np.random.randint(-15,15) + + output_h = input_h // self.opt.down_ratio + output_w = input_w // self.opt.down_ratio + + if self.opt.upper_left: + trans_input = get_affine_transform_upper_left(c, s, rot, [input_w, input_h]) + trans_output = get_affine_transform_upper_left(c, s, rot, [output_w, output_h]) + trans_output_mk = get_affine_transform_upper_left(c, s, rot, [output_w, output_h]) + else: + + trans_input = get_affine_transform(c, s, rot, [input_w, input_h]) + trans_output = get_affine_transform(c, s, rot, [output_w, output_h]) + trans_output_mk = get_affine_transform(c, s, rot, [output_w, output_h]) + + num_classes = self.num_classes + + hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32) + wh = np.zeros((self.max_objs, 8), dtype=np.float32) + reg = np.zeros((self.max_objs*5, 2), dtype=np.float32) + st = np.zeros((self.max_cors, 8), dtype=np.float32) + hm_ctxy = np.zeros((self.max_objs, 2), dtype=np.float32) + hm_ind = np.zeros((self.max_objs), dtype=np.int64) + hm_mask = np.zeros((self.max_objs), dtype=np.uint8) + mk_ind = np.zeros((self.max_cors), dtype=np.int64) + mk_mask = np.zeros((self.max_cors), dtype=np.uint8) + reg_ind = np.zeros((self.max_objs*5), dtype=np.int64) + reg_mask = np.zeros((self.max_objs*5), dtype=np.uint8) + ctr_cro_ind = np.zeros((self.max_objs*4), dtype=np.int64) + log_ax = np.zeros((self.max_objs, 4), dtype=np.float32) + cc_match = np.zeros((self.max_objs, 4), dtype=np.int64) + h_pair_ind = np.zeros((self.max_pairs), dtype=np.int64) + v_pair_ind = np.zeros((self.max_pairs), dtype=np.int64) + draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \ + draw_umich_gaussian + gt_det = [] + corList = [] + point = [] + pair_mark = 0 + inp = cv2.warpAffine(img, trans_input, (input_w, input_h),flags=cv2.INTER_LINEAR) + + for k in range(num_objs): + ann = anns[k] + + seg_mask = ann['segmentation'][0] #[[351.0, 73.0, 172.0, 70.0, 174.0, 127.0, 351.0, 129.0, 351.0, 73.0]] + x1,y1 = seg_mask[0],seg_mask[1] + x2,y2 = seg_mask[2],seg_mask[3] + x3,y3 = seg_mask[4],seg_mask[5] + x4,y4 = seg_mask[6],seg_mask[7] + + CorNer = np.array([x1,y1,x2,y2,x3,y3,x4,y4]) + boxes = [[CorNer[0],CorNer[1]],[CorNer[2],CorNer[3]],\ + [CorNer[4],CorNer[5]],[CorNer[6],CorNer[7]]] + cls_id = int(self.cat_ids[ann['category_id']]) + + if flipped: + + CorNer[[0,2,4,6]] = width - CorNer[[2,0,6,4]] - 1 + + CorNer[0:2] = affine_transform(CorNer[0:2], trans_output_mk) + CorNer[2:4] = affine_transform(CorNer[2:4], trans_output_mk) + CorNer[4:6] = affine_transform(CorNer[4:6], trans_output_mk) + CorNer[6:8] = affine_transform(CorNer[6:8], trans_output_mk) + CorNer[[0,2,4,6]] = np.clip(CorNer[[0,2,4,6]], 0, output_w - 1) + CorNer[[1,3,5,7]] = np.clip(CorNer[[1,3,5,7]], 0, output_h - 1) + if not self._judge(CorNer): + continue + + maxx = max([CorNer[2*I] for I in range(0,4)]) + minx = min([CorNer[2*I] for I in range(0,4)]) + maxy = max([CorNer[2*I+1] for I in range(0,4)]) + miny = min([CorNer[2*I+1] for I in range(0,4)]) + h, w = maxy-miny, maxx-minx #bbox[3] - bbox[1], bbox[2] - bbox[0] + if h > 0 and w > 0: + + radius = gaussian_radius((math.ceil(h), math.ceil(w))) + radius = max(0, int(radius)) + radius = self.opt.hm_gauss if self.opt.mse_loss else radius + + ct = np.array([(maxx+minx)/2.0,(maxy+miny)/2.0], dtype=np.float32) + ct_int = ct.astype(np.int32) + + draw_gaussian(hm[cls_id], ct_int, radius) + + for i in range(4): + Cor = np.array([CorNer[2*i],CorNer[2*i+1]], dtype=np.float32) + Cor_int = Cor.astype(np.int32) + Cor_key = str(Cor_int[0])+"_"+str(Cor_int[1]) + if Cor_key not in corList: + + corNum = len(corList) + + corList.append(Cor_key) + reg[self.max_objs+corNum] = np.array([abs(Cor[0]-Cor_int[0]),abs(Cor[1]-Cor_int[1])]) + mk_ind[corNum] = Cor_int[1]*output_w + Cor_int[0] + cc_match[k][i] = mk_ind[corNum] + reg_ind[self.max_objs+corNum] = Cor_int[1]*output_w + Cor_int[0] + mk_mask[corNum] = 1 + reg_mask[self.max_objs+corNum] = 1 + draw_gaussian(hm[num_classes-1], Cor_int, 2) + st[corNum][i*2:(i+1)*2] = np.array([Cor[0]-ct[0],Cor[1]-ct[1]]) + ctr_cro_ind[4*k+i] = corNum*4 + i + + else: + index_of_key = corList.index(Cor_key) + cc_match[k][i] = mk_ind[index_of_key] + st[index_of_key][i*2:(i+1)*2] = np.array([Cor[0]-ct[0],Cor[1]-ct[1]]) + ctr_cro_ind[4*k+i] = index_of_key*4 + i + + wh[k] = ct[0] - 1. * CorNer[0], ct[1] - 1. * CorNer[1], \ + ct[0] - 1. * CorNer[2], ct[1] - 1. * CorNer[3], \ + ct[0] - 1. * CorNer[4], ct[1] - 1. * CorNer[5], \ + ct[0] - 1. * CorNer[6], ct[1] - 1. * CorNer[7] + + hm_ind[k] = ct_int[1] * output_w + ct_int[0] + hm_mask[k] = 1 + reg_ind[k] = ct_int[1] * output_w + ct_int[0] + reg_mask[k] = 1 + reg[k] = ct - ct_int + hm_ctxy[k] = ct[0],ct[1] + + log_ax[k] = ann['logic_axis'][0][0], ann['logic_axis'][0][1], ann['logic_axis'][0][2], ann['logic_axis'][0][3] + + + gt_det.append([ct[0] - 1. * CorNer[0], ct[1] - 1. * CorNer[1], + ct[0] - 1. * CorNer[2], ct[1] - 1. * CorNer[3], + ct[0] - 1. * CorNer[4], ct[1] - 1. * CorNer[5], + ct[0] - 1. * CorNer[6], ct[1] - 1. * CorNer[7], 1, cls_id]) + + hm_mask_v = hm_mask.reshape(1, hm_mask.shape[0]) + + inp = (inp.astype(np.float32) / 255.) + if self.split == 'train' and not self.opt.no_color_aug: + color_aug(self._data_rng, inp, self._eig_val, self._eig_vec) + + + inp = (inp - self.mean) / self.std + inp = inp.transpose(2, 0, 1) + + ret = {'input': inp, 'hm': hm, 'hm_ind':hm_ind, 'hm_mask':hm_mask, 'mk_ind':mk_ind, 'mk_mask':mk_mask, 'reg':reg,'reg_ind':reg_ind,'reg_mask': reg_mask, \ + 'wh': wh,'st':st, 'ctr_cro_ind':ctr_cro_ind, 'cc_match': cc_match, 'hm_ctxy':hm_ctxy, 'logic': log_ax, 'h_pair_ind': h_pair_ind, 'v_pair_ind': v_pair_ind} + + if self.opt.dense_wh: + hm_a = hm.max(axis=0, keepdims=True) + dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0) + ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask}) + del ret['wh'] + elif self.opt.cat_spec_wh: + ret.update({'cat_spec_wh': cat_spec_wh, 'cat_spec_mask': cat_spec_mask}) + del ret['wh'] + if self.opt.reg_offset: + ret.update({'reg': reg}) + if self.opt.debug > 0 or not self.split == 'train': + gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \ + np.zeros((1, 10), dtype=np.float32) + meta = {'c': c, 's': s, 'rot':rot, 'gt_det': gt_det, 'img_id': img_id} + ret['meta'] = meta + return ret diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/base_detector.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/base_detector.py new file mode 100644 index 00000000..7e0d6248 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/base_detector.py @@ -0,0 +1,264 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import cv2 +import numpy as np +from progress.bar import Bar +import torch + +from models.model import create_model, load_model +from models.classifier import Processor, load_processor +from utils.utils import make_batch +from utils.image import get_affine_transform, get_affine_transform_upper_left + +from utils.debugger import Debugger + +class BaseDetector(object): + def __init__(self, opt): + if opt.gpus[0] >= 0: + opt.device = torch.device('cuda') + else: + opt.device = torch.device('cpu') + + self.model = create_model(opt.arch, opt.heads, opt.head_conv) + self.model = load_model(self.model, opt.load_model) + self.model = self.model.to(opt.device) + self.model.eval() + + self.processor = Processor(opt) + self.processor = load_model(self.processor, opt.load_processor) + self.processor.cuda() + + self.mean = np.array(opt.mean, dtype=np.float32).reshape(1, 1, 3) + self.std = np.array(opt.std, dtype=np.float32).reshape(1, 1, 3) + self.max_per_image = opt.K + self.num_classes = opt.num_classes + self.scales = opt.test_scales + self.opt = opt + self.pause = True + + def pre_process(self, image, scale, meta=None): + height, width = image.shape[0:2] + new_height = int(height * scale) + new_width = int(width * scale) + if self.opt.fix_res: + inp_height, inp_width = self.opt.input_h, self.opt.input_w + c = np.array([new_width / 2., new_height / 2.], dtype=np.float32) + s = max(height, width) * 1.0 + else: + inp_height = (new_height | self.opt.pad) #+ 1 + inp_width = (new_width | self.opt.pad) #+ 1 + c = np.array([new_width // 2, new_height // 2], dtype=np.float32) + s = np.array([inp_width, inp_height], dtype=np.float32) + if self.opt.upper_left: + c = np.array([0, 0], dtype=np.float32) + s = max(height, width) * 1.0 + trans_input = get_affine_transform_upper_left(c, s, 0, [inp_width, inp_height]) + else: + trans_input = get_affine_transform(c, s, 0, [inp_width, inp_height]) + resized_image = cv2.resize(image, (new_width, new_height)) + inp_image = cv2.warpAffine( + resized_image, trans_input, (inp_width, inp_height), + flags=cv2.INTER_LINEAR) + + inp_image = ((inp_image / 255. - self.mean) / self.std).astype(np.float32) + + images = inp_image.transpose(2, 0, 1).reshape(1, 3, inp_height, inp_width) + if self.opt.flip_test: + images = np.concatenate((images, images[:, :, :, ::-1]), axis=0) + images = torch.from_numpy(images) + meta = {'c': c, 's': s, + 'input_height':inp_height, + 'input_width':inp_width, + 'out_height': inp_height // self.opt.down_ratio, + 'out_width': inp_width // self.opt.down_ratio} + return images, meta + + def save_img_txt(self,img): + shape = list(img.shape) + f1 = open('/home/rujiao.lrj/CenterNet_cell_Coord/src/img.txt','w') + for i in range(shape[0]): + for j in range(shape[1]): + for k in range(shape[2]): + data = img[i][j][k].item() + f1.write(str(data)+'\n') + f1.close() + + def Duplicate_removal(self, results, corners): + bbox = [] + for j in range(len(results)): + box = results[j] + if box[-1] > self.opt.scores_thresh: + for i in range(8): + if box[i]<0: + box[i]=0 + if box[i]>1024: + box[i]=1024 + def dist(p1,p2): + return ((p1[0]-p2[0])*(p1[0]-p2[0])+(p1[1]-p2[1])*(p1[1]-p2[1]))**0.5 + p1,p2,p3,p4 = [box[0],box[1]],[box[2],box[3]],[box[4],box[5]],[box[6],box[7]] + if dist(p1,p2)>3 and dist(p2,p3)>3 and dist(p3,p4)>3 and dist(p4,p1)>3: + bbox.append(box) + else: + continue + + corner = [] + for i in range(len(corners)): + if corners[i][-1] > self.opt.vis_thresh_corner: + corner.append(corners[i]) + return np.array(bbox),np.array(corner) + + def filter(self, image_name, results, logi, ps): + # this function select boxes + batch_size, feat_dim = logi.shape[0], logi.shape[2] + num_valid = sum(results[1][:,8] >= self.opt.vis_thresh) + + #if num_valid <= 900 : #opt.max_objs + slct_logi = np.zeros((batch_size, num_valid, feat_dim), dtype=np.float32) + slct_dets = np.zeros((batch_size, num_valid, 8), dtype=np.int32) + for i in range(batch_size): + for j in range(num_valid): + slct_logi[i,j,:] = logi[i,j,:].cpu() + slct_dets[i,j,:] = ps[i,j,:].cpu() + #else: + #print('Error: Number of Detected Boxes Exceed the Model Defaults.') + #quit() + + return torch.Tensor(slct_logi).cuda(), torch.Tensor(slct_dets).cuda() + + def process_logi(self, logi): + logi_floor = logi.floor() + dev = logi - logi_floor + logi = torch.where(dev>0.5, logi_floor+1, logi_floor) + + return logi + + def _normalized_ps(self, ps, vocab_size): + ps = torch.round(ps).to(torch.int64) + ps = torch.where(ps < vocab_size, ps, (vocab_size-1) * torch.ones(ps.shape).to(torch.int64).cuda()) + ps = torch.where(ps >= 0, ps, torch.zeros(ps.shape).to(torch.int64).cuda()) + return ps + + def resize(self,image): + h,w,_ = image.shape + scale = 1024/(max(w,h)+1e-4) + image = cv2.resize(image,(int(w*scale),int(h*scale))) + image = cv2.copyMakeBorder(image,0,1024 - int(h*scale), 0, 1024 - int(w*scale),cv2.BORDER_CONSTANT, value=[0,0,0]) + return image,scale + + def process(self, images, return_time=False): + raise NotImplementedError + + def post_process(self, dets, meta, scale=1): + raise NotImplementedError + + def merge_outputs(self, detections): + raise NotImplementedError + + def debug(self, debugger, images, dets, output, scale=1): + raise NotImplementedError + + def show_results(self, debugger, image, results): + raise NotImplementedError + + def ps_convert_minmax(self,results): + detection = {} + for j in range(1,self.num_classes+1): + detection[j]=[] + for j in range(1,self.num_classes+1): + for bbox in results[j]: + minx = min(bbox[0],bbox[2],bbox[4],bbox[6]) + miny = min(bbox[1],bbox[3],bbox[5],bbox[7]) + maxx = max(bbox[0],bbox[2],bbox[4],bbox[6]) + maxy = max(bbox[1],bbox[3],bbox[5],bbox[7]) + detection[j].append([minx,miny,maxx,maxy,bbox[-1]]) + for j in range(1,self.num_classes+1): + detection[j] = np.array(detection[j]) + return detection + + def run(self, opt, image_or_path_or_tensor, image_anno=None, meta=None): + + debugger = Debugger(dataset=self.opt.dataset, ipynb=(self.opt.debug==3), + theme=self.opt.debugger_theme) + + pre_processed = False + if isinstance(image_or_path_or_tensor, np.ndarray): + image = image_or_path_or_tensor + elif type(image_or_path_or_tensor) == type (''): + image = cv2.imread(image_or_path_or_tensor) + else: + image = image_or_path_or_tensor['image'][0].numpy() + pre_processed_images = image_or_path_or_tensor + pre_processed = True + + + if not opt.wiz_detect: + batch = make_batch(opt, image_or_path_or_tensor, image_anno) + + detections = [] + hm = [] + corner_st = [] + if self.opt.demo!='': + image_name = image_or_path_or_tensor.split('/')[-1] + + for scale in self.scales: + + if not pre_processed: + images, meta = self.pre_process(image, scale, meta) + + else: + images = pre_processed_images['images'][scale][0] + meta = pre_processed_images['meta'][scale] + meta = {k: v.numpy()[0] for k, v in meta.items()} + + images = images.to(self.opt.device) + + torch.cuda.synchronize() + + if self.opt.wiz_detect: + outputs, output, dets, corner_st_reg, forward_time, logi, cr, keep = self.process(images, image, return_time=True) + else: + outputs, output, dets, corner_st_reg, forward_time, logi, cr, keep = self.process(images, image, return_time=True, batch=batch) + + raw_dets = dets + + torch.cuda.synchronize() + + if self.opt.debug >= 2: + self.debug(debugger, images, dets, output, scale) + + dets,corner_st_reg = self.post_process(dets, meta, corner_st_reg, scale) + torch.cuda.synchronize() + + detections.append(dets) + hm.append(keep) + + if self.opt.wiz_4ps or self.opt.wiz_2dpe: + logi = logi + cr + + results = self.merge_outputs(detections) + torch.cuda.synchronize() + + slct_logi, slct_dets = self.filter(image_or_path_or_tensor, results, logi, raw_dets[:,:,:8]) + slct_dets = self._normalized_ps(slct_dets, 256) + + if self.opt.wiz_2dpe: + if self.opt.wiz_stacking: + _, slct_logi = self.processor(slct_logi, dets = slct_dets) + else: + slct_logi = self.processor(slct_logi, dets = slct_dets) + else: + if self.opt.wiz_stacking: + _, slct_logi = self.processor(slct_logi) + else: + slct_logi = self.processor(slct_logi) + + slct_logi = self.process_logi(slct_logi) + + + if self.opt.debug >= 1: + self.show_results(debugger, image, results, corner_st_reg, image_name, slct_logi.squeeze()) + + Results = self.ps_convert_minmax(results) + return {'results': Results,'4ps':results,'corner_st_reg':corner_st_reg, 'hm': hm} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/ctdet.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/ctdet.py new file mode 100644 index 00000000..0f73fc06 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/ctdet.py @@ -0,0 +1,196 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import cv2 +import numpy as np +from progress.bar import Bar +import time +import torch + +from external.shapelyNMS import pnms +from models.decode import ctdet_decode,corner_decode,ctdet_4ps_decode,ctdet_st_decode +from models.utils import flip_tensor +from utils.image import get_affine_transform +from utils.post_process import ctdet_post_process,ctdet_4ps_post_process,ctdet_4ps_post_process_upper_left +from utils.post_process import ctdet_corner_post_process +from utils.debugger import Debugger + +from .base_detector import BaseDetector +from PIL import Image + +from matplotlib import cm + + +class CtdetDetector(BaseDetector): + def __init__(self, opt): + super(CtdetDetector, self).__init__(opt) + + def process_logi(self, logi): + logi_floor = logi.floor() + dev = logi - logi_floor + logi = torch.where(dev>0.5, logi_floor+1, logi_floor) + + logi0 = logi[:,:,0].unsqueeze(2) + logi2 = logi[:,:,2].unsqueeze(2) + + logi_st = torch.cat((logi0, logi0, logi2, logi2), dim=2) + logi = torch.where(logi 1 or self.opt.nms: + #soft_nms(results[j], Nt=0.5, method=2) + results[j] = pnms(results[j],self.opt.thresh_min,self.opt.thresh_conf) + scores = np.hstack( + [results[j][:, 8] for j in range(1, self.num_classes + 1)]) + if len(scores) > self.max_per_image: + kth = len(scores) - self.max_per_image + thresh = np.partition(scores, kth)[kth] + for j in range(1, self.num_classes + 1): + keep_inds = (results[j][:, 8] >= thresh) + results[j] = results[j][keep_inds] + return results + + def debug(self, debugger, images, dets, output, scale=1): + detection = dets.detach().cpu().numpy().copy() + detection[:, :, :8] *= self.opt.down_ratio + for i in range(1): + img = images[i].detach().cpu().numpy().transpose(1, 2, 0) + img = ((img * self.std + self.mean) * 255).astype(np.uint8) + pred = debugger.gen_colormap(output['hm'][i].detach().cpu().numpy()) + debugger.add_blend_img(img, pred, 'pred_hm_{:.1f}'.format(scale)) + debugger.add_img(img, img_id='out_pred_{:.1f}'.format(scale)) + for k in range(len(dets[i])): + if detection[i, k, 8] > self.opt.center_thresh: + debugger.add_4ps_coco_bbox(detection[i, k, :8], detection[i, k, -1], + detection[i, k, 8], + img_id='out_pred_{:.1f}'.format(scale)) + + def show_results(self, debugger, image, results, corner, image_name, logi=None): + debugger.add_img(image, img_id='ctdet') + m,n = corner.shape + + count = 0 + + fc = open(self.opt.output_dir + self.opt.demo_name +'/center/'+image_name+'.txt','w') #bounding boxes saved + fv = open(self.opt.output_dir + self.opt.demo_name +'/corner/'+image_name+'.txt','w') + fl = open(self.opt.output_dir + self.opt.demo_name +'/logi/'+image_name+'.txt','w') #logic axis saved + for j in range(1, self.num_classes + 1): + k = 0 + for m in range(len(results[j])): + bbox = results[j][m] + k = k + 1 + if bbox[8] > self.opt.vis_thresh: + + if len(logi.shape) == 1: + debugger.add_4ps_coco_bbox(bbox[:8], j-1, bbox[8], logi, show_txt=True, img_id='ctdet') + else: + debugger.add_4ps_coco_bbox(bbox[:8], j-1, bbox[8], logi[m,:], show_txt=True, img_id='ctdet') + for i in range(0,3): + position_holder = 1 + fc.write(str(bbox[2*i])+','+str(bbox[2*i+1])+';') + if not logi is None: + if len(logi.shape) == 1: + fl.write(str(int(logi[i]))+',') + else: + fl.write(str(int(logi[m,:][i]))+',') + fc.write(str(bbox[6])+','+str(bbox[7])+'\n') + + if not logi is None: + if len(logi.shape) == 1: + fl.write(str(int(logi[3]))+'\n') + else: + fl.write(str(int(logi[m,:][3]))+'\n') + + if self.opt.vis_corner==1: + for i in range(m): + if corner[i,10] > self.opt.vis_thresh_corner: + for w in range(0,4): + position_holder = 1 + fv.write(str(corner[i,2*w])+','+str(corner[i,2*w+1])+';') + fv.write(str(corner[i,8])+','+str(corner[i,9])+'\n') + count+=1 + + fc.close() + fv.close() + debugger.save_all_imgs(image_name, self.opt.demo_dir) + + \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/detector_factory.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/detector_factory.py new file mode 100644 index 00000000..e51aeef8 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/detectors/detector_factory.py @@ -0,0 +1,11 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from .ctdet import CtdetDetector + +detector_factory = { + 'ctdet': CtdetDetector, + 'ctdet_mid': CtdetDetector, + 'ctdet_small': CtdetDetector +} diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/Makefile b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/Makefile new file mode 100644 index 00000000..a4823985 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/Makefile @@ -0,0 +1,3 @@ +all: + python setup.py build_ext --inplace + rm -rf build diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/nms.pyx b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/nms.pyx new file mode 100644 index 00000000..64991023 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/nms.pyx @@ -0,0 +1,391 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +# ---------------------------------------------------------- +# Soft-NMS: Improving Object Detection With One Line of Code +# Copyright (c) University of Maryland, College Park +# Licensed under The MIT License [see LICENSE for details] +# Written by Navaneeth Bodla and Bharat Singh +# ---------------------------------------------------------- + +import numpy as np +cimport numpy as np + +cdef inline np.float32_t max(np.float32_t a, np.float32_t b): + return a if a >= b else b + +cdef inline np.float32_t min(np.float32_t a, np.float32_t b): + return a if a <= b else b + +def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): + cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] + cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] + cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] + + cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) + cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] + + cdef int ndets = dets.shape[0] + cdef np.ndarray[np.int_t, ndim=1] suppressed = \ + np.zeros((ndets), dtype=np.int) + + # nominal indices + cdef int _i, _j + # sorted indices + cdef int i, j + # temp variables for box i's (the box currently under consideration) + cdef np.float32_t ix1, iy1, ix2, iy2, iarea + # variables for computing overlap with box j (lower scoring box) + cdef np.float32_t xx1, yy1, xx2, yy2 + cdef np.float32_t w, h + cdef np.float32_t inter, ovr + + keep = [] + for _i in range(ndets): + i = order[_i] + if suppressed[i] == 1: + continue + keep.append(i) + ix1 = x1[i] + iy1 = y1[i] + ix2 = x2[i] + iy2 = y2[i] + iarea = areas[i] + for _j in range(_i + 1, ndets): + j = order[_j] + if suppressed[j] == 1: + continue + xx1 = max(ix1, x1[j]) + yy1 = max(iy1, y1[j]) + xx2 = min(ix2, x2[j]) + yy2 = min(iy2, y2[j]) + w = max(0.0, xx2 - xx1 + 1) + h = max(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (iarea + areas[j] - inter) + if ovr >= thresh: + suppressed[j] = 1 + + return keep + +def soft_nms(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0): + cdef unsigned int N = boxes.shape[0] + cdef float iw, ih, box_area + cdef float ua + cdef int pos = 0 + cdef float maxscore = 0 + cdef int maxpos = 0 + cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov + + for i in range(N): + maxscore = boxes[i, 4] + maxpos = i + + tx1 = boxes[i,0] + ty1 = boxes[i,1] + tx2 = boxes[i,2] + ty2 = boxes[i,3] + ts = boxes[i,4] + + pos = i + 1 + # get max box + while pos < N: + if maxscore < boxes[pos, 4]: + maxscore = boxes[pos, 4] + maxpos = pos + pos = pos + 1 + + # add max box as a detection + boxes[i,0] = boxes[maxpos,0] + boxes[i,1] = boxes[maxpos,1] + boxes[i,2] = boxes[maxpos,2] + boxes[i,3] = boxes[maxpos,3] + boxes[i,4] = boxes[maxpos,4] + + # swap ith box with position of max box + boxes[maxpos,0] = tx1 + boxes[maxpos,1] = ty1 + boxes[maxpos,2] = tx2 + boxes[maxpos,3] = ty2 + boxes[maxpos,4] = ts + + tx1 = boxes[i,0] + ty1 = boxes[i,1] + tx2 = boxes[i,2] + ty2 = boxes[i,3] + ts = boxes[i,4] + + pos = i + 1 + # NMS iterations, note that N changes if detection boxes fall below threshold + while pos < N: + x1 = boxes[pos, 0] + y1 = boxes[pos, 1] + x2 = boxes[pos, 2] + y2 = boxes[pos, 3] + s = boxes[pos, 4] + + area = (x2 - x1 + 1) * (y2 - y1 + 1) + iw = (min(tx2, x2) - max(tx1, x1) + 1) + if iw > 0: + ih = (min(ty2, y2) - max(ty1, y1) + 1) + if ih > 0: + ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih) + ov = iw * ih / ua #iou between max box and detection box + + if method == 1: # linear + if ov > Nt: + weight = 1 - ov + else: + weight = 1 + elif method == 2: # gaussian + weight = np.exp(-(ov * ov)/sigma) + else: # original NMS + if ov > Nt: + weight = 0 + else: + weight = 1 + + boxes[pos, 4] = weight*boxes[pos, 4] + + # if box score falls below threshold, discard the box by swapping with last box + # update N + if boxes[pos, 4] < threshold: + boxes[pos,0] = boxes[N-1, 0] + boxes[pos,1] = boxes[N-1, 1] + boxes[pos,2] = boxes[N-1, 2] + boxes[pos,3] = boxes[N-1, 3] + boxes[pos,4] = boxes[N-1, 4] + N = N - 1 + pos = pos - 1 + + pos = pos + 1 + + keep = [i for i in range(N)] + return keep + +def soft_nms_39(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0): + cdef unsigned int N = boxes.shape[0] + cdef float iw, ih, box_area + cdef float ua + cdef int pos = 0 + cdef float maxscore = 0 + cdef int maxpos = 0 + cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov + cdef float tmp + + for i in range(N): + maxscore = boxes[i, 4] + maxpos = i + + tx1 = boxes[i,0] + ty1 = boxes[i,1] + tx2 = boxes[i,2] + ty2 = boxes[i,3] + ts = boxes[i,4] + + pos = i + 1 + # get max box + while pos < N: + if maxscore < boxes[pos, 4]: + maxscore = boxes[pos, 4] + maxpos = pos + pos = pos + 1 + + # add max box as a detection + boxes[i,0] = boxes[maxpos,0] + boxes[i,1] = boxes[maxpos,1] + boxes[i,2] = boxes[maxpos,2] + boxes[i,3] = boxes[maxpos,3] + boxes[i,4] = boxes[maxpos,4] + + # swap ith box with position of max box + boxes[maxpos,0] = tx1 + boxes[maxpos,1] = ty1 + boxes[maxpos,2] = tx2 + boxes[maxpos,3] = ty2 + boxes[maxpos,4] = ts + + for j in range(5, 39): + tmp = boxes[i, j] + boxes[i, j] = boxes[maxpos, j] + boxes[maxpos, j] = tmp + + tx1 = boxes[i,0] + ty1 = boxes[i,1] + tx2 = boxes[i,2] + ty2 = boxes[i,3] + ts = boxes[i,4] + + pos = i + 1 + # NMS iterations, note that N changes if detection boxes fall below threshold + while pos < N: + x1 = boxes[pos, 0] + y1 = boxes[pos, 1] + x2 = boxes[pos, 2] + y2 = boxes[pos, 3] + s = boxes[pos, 4] + + area = (x2 - x1 + 1) * (y2 - y1 + 1) + iw = (min(tx2, x2) - max(tx1, x1) + 1) + if iw > 0: + ih = (min(ty2, y2) - max(ty1, y1) + 1) + if ih > 0: + ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih) + ov = iw * ih / ua #iou between max box and detection box + + if method == 1: # linear + if ov > Nt: + weight = 1 - ov + else: + weight = 1 + elif method == 2: # gaussian + weight = np.exp(-(ov * ov)/sigma) + else: # original NMS + if ov > Nt: + weight = 0 + else: + weight = 1 + + boxes[pos, 4] = weight*boxes[pos, 4] + + # if box score falls below threshold, discard the box by swapping with last box + # update N + if boxes[pos, 4] < threshold: + boxes[pos,0] = boxes[N-1, 0] + boxes[pos,1] = boxes[N-1, 1] + boxes[pos,2] = boxes[N-1, 2] + boxes[pos,3] = boxes[N-1, 3] + boxes[pos,4] = boxes[N-1, 4] + for j in range(5, 39): + tmp = boxes[pos, j] + boxes[pos, j] = boxes[N - 1, j] + boxes[N - 1, j] = tmp + N = N - 1 + pos = pos - 1 + + pos = pos + 1 + + keep = [i for i in range(N)] + return keep + +def soft_nms_merge(np.ndarray[float, ndim=2] boxes, float sigma=0.5, float Nt=0.3, float threshold=0.001, unsigned int method=0, float weight_exp=6): + cdef unsigned int N = boxes.shape[0] + cdef float iw, ih, box_area + cdef float ua + cdef int pos = 0 + cdef float maxscore = 0 + cdef int maxpos = 0 + cdef float x1,x2,y1,y2,tx1,tx2,ty1,ty2,ts,area,weight,ov + cdef float mx1,mx2,my1,my2,mts,mbs,mw + + for i in range(N): + maxscore = boxes[i, 4] + maxpos = i + + tx1 = boxes[i,0] + ty1 = boxes[i,1] + tx2 = boxes[i,2] + ty2 = boxes[i,3] + ts = boxes[i,4] + + pos = i + 1 + # get max box + while pos < N: + if maxscore < boxes[pos, 4]: + maxscore = boxes[pos, 4] + maxpos = pos + pos = pos + 1 + + # add max box as a detection + boxes[i,0] = boxes[maxpos,0] + boxes[i,1] = boxes[maxpos,1] + boxes[i,2] = boxes[maxpos,2] + boxes[i,3] = boxes[maxpos,3] + boxes[i,4] = boxes[maxpos,4] + + mx1 = boxes[i, 0] * boxes[i, 5] + my1 = boxes[i, 1] * boxes[i, 5] + mx2 = boxes[i, 2] * boxes[i, 6] + my2 = boxes[i, 3] * boxes[i, 6] + mts = boxes[i, 5] + mbs = boxes[i, 6] + + # swap ith box with position of max box + boxes[maxpos,0] = tx1 + boxes[maxpos,1] = ty1 + boxes[maxpos,2] = tx2 + boxes[maxpos,3] = ty2 + boxes[maxpos,4] = ts + + tx1 = boxes[i,0] + ty1 = boxes[i,1] + tx2 = boxes[i,2] + ty2 = boxes[i,3] + ts = boxes[i,4] + + pos = i + 1 + # NMS iterations, note that N changes if detection boxes fall below threshold + while pos < N: + x1 = boxes[pos, 0] + y1 = boxes[pos, 1] + x2 = boxes[pos, 2] + y2 = boxes[pos, 3] + s = boxes[pos, 4] + + area = (x2 - x1 + 1) * (y2 - y1 + 1) + iw = (min(tx2, x2) - max(tx1, x1) + 1) + if iw > 0: + ih = (min(ty2, y2) - max(ty1, y1) + 1) + if ih > 0: + ua = float((tx2 - tx1 + 1) * (ty2 - ty1 + 1) + area - iw * ih) + ov = iw * ih / ua #iou between max box and detection box + + if method == 1: # linear + if ov > Nt: + weight = 1 - ov + else: + weight = 1 + elif method == 2: # gaussian + weight = np.exp(-(ov * ov)/sigma) + else: # original NMS + if ov > Nt: + weight = 0 + else: + weight = 1 + + mw = (1 - weight) ** weight_exp + mx1 = mx1 + boxes[pos, 0] * boxes[pos, 5] * mw + my1 = my1 + boxes[pos, 1] * boxes[pos, 5] * mw + mx2 = mx2 + boxes[pos, 2] * boxes[pos, 6] * mw + my2 = my2 + boxes[pos, 3] * boxes[pos, 6] * mw + mts = mts + boxes[pos, 5] * mw + mbs = mbs + boxes[pos, 6] * mw + + boxes[pos, 4] = weight*boxes[pos, 4] + + # if box score falls below threshold, discard the box by swapping with last box + # update N + if boxes[pos, 4] < threshold: + boxes[pos,0] = boxes[N-1, 0] + boxes[pos,1] = boxes[N-1, 1] + boxes[pos,2] = boxes[N-1, 2] + boxes[pos,3] = boxes[N-1, 3] + boxes[pos,4] = boxes[N-1, 4] + N = N - 1 + pos = pos - 1 + + pos = pos + 1 + + boxes[i, 0] = mx1 / mts + boxes[i, 1] = my1 / mts + boxes[i, 2] = mx2 / mbs + boxes[i, 3] = my2 / mbs + + keep = [i for i in range(N)] + return keep diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/setup.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/setup.py new file mode 100644 index 00000000..c4d25719 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/setup.py @@ -0,0 +1,18 @@ +import numpy +from distutils.core import setup +from distutils.extension import Extension +from Cython.Build import cythonize + +extensions = [ + Extension( + "nms", + ["nms.pyx"], + extra_compile_args=["-Wno-cpp", "-Wno-unused-function"] + ) +] + +setup( + name="coco", + ext_modules=cythonize(extensions), + include_dirs=[numpy.get_include()] +) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/shapelyNMS.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/shapelyNMS.py new file mode 100644 index 00000000..77c088c4 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/external/shapelyNMS.py @@ -0,0 +1,158 @@ +import numpy as np +from shapely.geometry import * + +def delet_min_first(dets,pts,areas,inter_areas,min_areas,scores,thresh_min,thresh_conf): + choose_dict = {} + for i in range(0,len(pts)): + areai = areas[i] + for j in range(0, len(pts)): + if i==j: + continue + areaj = areas[j] + ovr = inter_areas[i][j] / min_areas[i][j] + if ovr > thresh_min and areai != min_areas[i][j]: + if str(i) not in choose_dict.keys(): + choose_dict[str(i)] = [j] + else: + choose_dict[str(i)].append(j) + delet_list = [] + for i in choose_dict.keys(): + ovr_list = choose_dict[i] + if len(ovr_list) == 1: + j = choose_dict[i][0] + #ovr_max = inter_areas[int(i)][j] / max(areas[int(i)],areas[j]) + #ovr_min = inter_areas[int(i)][j] / min(areas[int(i)],areas[j]) + #if ovr_min > thresh_min: + # conf_i,conf_j = scores[int(i)],scores[j] + # #if abs(conf_i - conf_j) > thresh_conf: + # # index = int(i) if conf_i < conf_j else j + # # delet_list.append(index) + # #else: + delet_list.append(j) + elif len(ovr_list) == 2: + j,k = choose_dict[i][0],choose_dict[i][1] + area_sum = areas[j] + areas[k] - inter_areas[j][k] + ovr = area_sum / areas[int(i)] + if ovr > thresh_min and ovr < 1.1: + delet_list.append(j) + delet_list.append(k) + else: + for index in choose_dict[i]: + delet_list.append(index) + dets = list(dets) + keep = [] + for i in range(len(dets)): + if i not in delet_list: + keep.append(dets[i]) + return np.array(keep)#dets + +def delet_min(dets,pts,areas,inter_areas,min_areas,scores,thresh_min,thresh_conf): + choose_dict = {} + for i in range(0,len(pts)): + areai = areas[i] + for j in range(0, len(pts)): + if i==j: + continue + areaj = areas[j] + ovr = inter_areas[i][j] / (areas[i] + areas[j] - inter_areas[i][j]) + if ovr > thresh_min and areai != min_areas[i][j]: + if str(i) not in choose_dict.keys(): + choose_dict[str(i)] = [j] + else: + choose_dict[str(i)].append(j) + delet_list = [] + for i in choose_dict.keys(): + ovr_list = choose_dict[i] + if len(ovr_list) == 1: + j = choose_dict[i][0] + delet_list.append(j) + elif len(ovr_list) == 2: + j,k = choose_dict[i][0],choose_dict[i][1] + area_sum = areas[j] + areas[k] - inter_areas[j][k] + ovr = area_sum / areas[int(i)] + if ovr > thresh_min and ovr < 1.1: + delet_list.append(j) + delet_list.append(k) + ''' + else: + for index in choose_dict[i]: + delet_list.append(index) + ''' + dets = list(dets) + keep = [] + for i in range(len(dets)): + if i not in delet_list: + keep.append(dets[i]) + return np.array(keep)#dets + + +def cross_union(dets,pts,areas,inter_areas,min_areas,scores,thresh_min,thresh_conf): + choose_dict = {} + for i in range(0,len(pts)): + choose_dict[i] = 0 + + for i in range(0,len(pts)): + areai = areas[i] + for j in range(0, len(pts)): + if i==j or scores[i] < scores[j]: + continue + areaj = areas[j] + ovr_union = inter_areas[i][j] / (areai+areaj-inter_areas[i][j]) + ovr_min = inter_areas[i][j] / min(areai,areaj) + if ovr_union > thresh_min: + #if scores[i]-scores[j]>0.2: + # choose_dict[j]=1 + #elif scores[j]-scores[i]>0.2: + # choose_dict[i]=1 + if areai > areaj: + choose_dict[j]=1 + else: + choose_dict[i]=1 + dets = list(dets) + keep = [] + for i in range(len(dets)): + if choose_dict[i]==0: + keep.append(dets[i]) + return np.array(keep)#dets + + +def pnms(dets,thresh_min,thresh_conf): + scores = dets[:,-1] + + pts = [] + for i in range(dets.shape[0]): + pts.append([dets[i][0:2],dets[i][2:4],dets[i][4:6],dets[i][6:8]]) + + areas = np.zeros(scores.shape) + order = scores.argsort()[::-1] + inter_areas = np.zeros((scores.shape[0],scores.shape[0])) + min_areas = np.zeros((scores.shape[0],scores.shape[0])) + + for i in range(0,len(pts)): + polyi = Polygon(pts[i]) + areas[i] = polyi.area + + for j in range(i, len(pts)): + polyj = Polygon(pts[j]) + try: + inS = polyi.intersection(polyj) + except: + print(pts[i]) + print(pts[j]) + inter_areas[i][j] = inS.area + inter_areas[j][i] = inS.area + min_areas[i][j] = min(areas[i],polyj.area) + min_areas[j][i] = min_areas[i][j] + + keep = [] + ''' + while order.size > 0: + i = order[0] + keep.append(dets[i]) + #ovr = inter_areas[i][order[1:]] / min_areas[i][order[1:]] + ovr = inter_areas[i][order[1:]] / (areas[i] + areas[order[1:]] - inter_areas[i][order[1:]]) + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + ''' + #return delet_min(dets,pts,areas,inter_areas,min_areas,scores,thresh_min,thresh_conf) + return cross_union(dets,pts,areas,inter_areas,min_areas,scores,thresh_min,thresh_conf) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/logger.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/logger.py new file mode 100644 index 00000000..eac9a146 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/logger.py @@ -0,0 +1,72 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514 +import os +import time +import sys +import torch +USE_TENSORBOARD = True +try: + import tensorboardX + print('Using tensorboardX') +except: + USE_TENSORBOARD = False + +class Logger(object): + def __init__(self, opt): + """Create a summary writer logging to log_dir.""" + if not os.path.exists(opt.save_dir): + os.makedirs(opt.save_dir) + if not os.path.exists(opt.debug_dir): + os.makedirs(opt.debug_dir) + + time_str = time.strftime('%Y-%m-%d-%H-%M') + + args = dict((name, getattr(opt, name)) for name in dir(opt) + if not name.startswith('_')) + file_name = os.path.join(opt.save_dir, 'opt.txt') + with open(file_name, 'wt') as opt_file: + opt_file.write('==> torch version: {}\n'.format(torch.__version__)) + opt_file.write('==> cudnn version: {}\n'.format( + torch.backends.cudnn.version())) + opt_file.write('==> Cmd:\n') + opt_file.write(str(sys.argv)) + opt_file.write('\n==> Opt:\n') + for k, v in sorted(args.items()): + opt_file.write(' %s: %s\n' % (str(k), str(v))) + + log_dir = opt.save_dir + '/logs_{}'.format(time_str) + if USE_TENSORBOARD: + self.writer = tensorboardX.SummaryWriter(log_dir=log_dir) + else: + if not os.path.exists(os.path.dirname(log_dir)): + os.mkdir(os.path.dirname(log_dir)) + if not os.path.exists(log_dir): + os.mkdir(log_dir) + self.log = open(log_dir + '/log.txt', 'w') + try: + os.system('cp {}/opt.txt {}/'.format(opt.save_dir, log_dir)) + except: + pass + self.start_line = True + + def write(self, txt): + if self.start_line: + time_str = time.strftime('%Y-%m-%d-%H-%M') + self.log.write('{}: {}'.format(time_str, txt)) + else: + self.log.write(txt) + self.start_line = False + if '\n' in txt: + self.start_line = True + self.log.flush() + + def close(self): + self.log.close() + + def scalar_summary(self, tag, value, step): + """Log a scalar variable.""" + if USE_TENSORBOARD: + self.writer.add_scalar(tag, value, step) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/opts.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/opts.py new file mode 100644 index 00000000..ee24136c --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/opts.py @@ -0,0 +1,469 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import os +import sys + +class opts(object): + def __init__(self): + self.parser = argparse.ArgumentParser() + # basic experiment setting + self.parser.add_argument('task', default='ctdet', + help='ctdet | ddd | multi_pose | exdet') + self.parser.add_argument('--dataset', default='table', + help='coco | kitti | coco_hp | pascal') + self.parser.add_argument('--dataset_name', default='WTW', + help='WTW | TG24K | PTN | ICDAR | SciTSR') + self.parser.add_argument('--exp_id', default='default', + help='name of this experiment') + self.parser.add_argument('--test', action='store_true') + self.parser.add_argument('--hold_det', action='store_true') + self.parser.add_argument('--vis_corner', type=int, default=0, + help='vis corner or not' + '0: do not vis corner' + '1: vis corner') + self.parser.add_argument('--convert_onnx', type=int, default=0, + help='0: donot convert' + '1: convert pytorch model to onnx') + self.parser.add_argument('--debug', type=int, default=0, + help='level of visualization.' + '1: only show the final detection results' + '2: show the network output features' + '3: use matplot to display' # useful when lunching training with ipython notebook + '4: save all visualizations to disk') + self.parser.add_argument('--demo', default='./demo_wtw/', + help='path to image/ image folders/ video. ' + 'or "webcam"') + self.parser.add_argument('--demo_name', default='demo_results', + help='path to image/ image folders/ video. ' + 'or "webcam"') + self.parser.add_argument('--output_dir', default='../data/table/', + help='path to image/ image folders/ video. ' + 'or "webcam"') + self.parser.add_argument('--demo_dir', default='../demo/', + help='path to demo output. ' + 'or "webcam"') + self.parser.add_argument('--demo_output', default='', + help='path to image/ image folders/ video. ' + 'or "webcam"') + self.parser.add_argument('--image_dir', default='../data/WTW/full_images', + help='path to images of training set') + self.parser.add_argument('--load_model', default='', + help='path to pretrained model') + self.parser.add_argument('--load_processor', default='', + help='path to pretrained processor') + self.parser.add_argument('--resume', action='store_true', + help='resume an experiment. ' + 'Reloaded the optimizer parameter and ' + 'set load_model to model_last.pth ' + 'in the exp dir if load_model is empty.') + + # system + self.parser.add_argument('--gpus', default='0', + help='-1 for CPU, use comma for multiple gpus') + self.parser.add_argument('--num_workers', type=int, default=4, + help='dataloader threads. 0 for single-thread.') + self.parser.add_argument('--not_cuda_benchmark', action='store_true', + help='disable when the input size is not fixed.') + self.parser.add_argument('--seed', type=int, default=317, + help='random seed') # from CornerNet + + # log + self.parser.add_argument('--print_iter', type=int, default=0, + help='disable progress bar and print to screen.') + self.parser.add_argument('--hide_data_time', action='store_true', + help='not display time during training.') + self.parser.add_argument('--save_all', action='store_true', + help='save model to disk every 5 epochs.') + self.parser.add_argument('--metric', default='loss', + help='main metric to save best model') + self.parser.add_argument('--anno_path', default='', + help='path to annotations of coco data') + self.parser.add_argument('--vis_thresh', type=float, default=0.3, + help='visualization threshold.') + self.parser.add_argument('--vis_thresh_corner', type=float, default=0.1, + help='visualization threshold for corner.') + self.parser.add_argument('--debugger_theme', default='white', + choices=['white', 'black']) + + # model + self.parser.add_argument('--arch', default='dla_34', + help='model architecture. Currently tested' + 'res_18 | res_101 | resdcn_18 | resdcn_101 |' + 'dlav0_34 | dla_34 | hourglass') + self.parser.add_argument('--head_conv', type=int, default=-1, + help='conv layer channels for output head' + '0 for no conv layer' + '-1 for default setting: ' + '64 for resnets and 256 for dla.') + self.parser.add_argument('--down_ratio', type=int, default=4, + help='output stride. Currently only supports 4.') + self.parser.add_argument('--load_mode', type=str, default='single', + help='single or multiple') + self.parser.add_argument('--wiz_2dpe', action='store_true', default=False, + help='whether to use the 2-dimensional position embeddings. ') + self.parser.add_argument('--wiz_4ps', action='store_true', default=False, + help='whether to use the feature of the four corner points. ') + self.parser.add_argument('--wiz_stacking', action='store_true', default=False, + help='whether to use the cascading regressor or the vinilla regressor. ') + + # model logical prediction head + self.parser.add_argument('--input_size', type=int, default=256, + help='size of inputs of the logical location head') + self.parser.add_argument('--hidden_size', type=int, default=256, + help='size of hidden states of the logical location head') + self.parser.add_argument('--output_size', type=int, default=4, + help='size of output, i.e., the four logical indices') + self.parser.add_argument('--tsfm_layers', type=int, default=6, + help='layers of base transformers') + self.parser.add_argument('--stacking_layers', type=int, default=3, + help='single or multiple') + self.parser.add_argument('--num_heads', type=int, default=8, + help='number of attention heads') + self.parser.add_argument('--att_dropout', type=float, default=0.1, + help='dropout used in attention') + self.parser.add_argument('--max_fmp_size', type=float, default=256, + help='max size of feature map, which corresponding to image input of 1024*1024') + + # input + self.parser.add_argument('--input_res', type=int, default=-1, + help='input height and width. -1 for default from ' + 'dataset. Will be overriden by input_h | input_w') + self.parser.add_argument('--input_h', type=int, default=-1, + help='input height. -1 for default from dataset.') + self.parser.add_argument('--input_w', type=int, default=-1, + help='input width. -1 for default from dataset.') + + # train + self.parser.add_argument('--lr', type=float, default=1.25e-4, + help='learning rate for batch size 32.') + self.parser.add_argument('--lr_step', type=str, default='80', + help='drop learning rate by 10.') + self.parser.add_argument('--num_epochs', type=int, default=90, + help='total training epochs.') + self.parser.add_argument('--batch_size', type=int, default=32, + help='batch size') + self.parser.add_argument('--master_batch_size', type=int, default=-1, + help='batch size on the master gpu.') + self.parser.add_argument('--num_iters', type=int, default=-1, + help='default: #samples / batch_size.') + self.parser.add_argument('--val_intervals', type=int, default=5, + help='number of epochs to run validation.') + self.parser.add_argument('--trainval', action='store_true', + help='include validation in training and ' + 'test on test set') + self.parser.add_argument('--full_loss', default=False) + self.parser.add_argument('--upper_left', action='store_true', + help='changing the padding pattern for on-line cloud service. ') + self.parser.add_argument('--wiz_pairloss', action='store_true', default=False, + help='using pairloss for wired table only to apply the parsing-grouping process. ') + self.parser.add_argument('--wiz_dsloss', action='store_true', default=False, + help='using the inter-cell loss. ') + + # inference + self.parser.add_argument('--flip_test', action='store_true', + help='flip data augmentation.') + self.parser.add_argument('--save_path', default='../data/dingding_shiyu/', + help='path for save test res.') + self.parser.add_argument('--test_scales', type=str, default='1', + help='multi scale test augmentation.') + self.parser.add_argument('--nms', action='store_false', + help='run nms in testing.') + self.parser.add_argument('--K', type=int, default=100, + help='max number of output objects.') + self.parser.add_argument('--not_prefetch_test', action='store_true', + help='not use parallal data pre-processing.') + self.parser.add_argument('--thresh_min', type=float, default=0.5, + help='max number of output objects.') + self.parser.add_argument('--thresh_max', type=float, default=0.7, + help='max number of output objects.') + self.parser.add_argument('--thresh_conf', type=float, default=0.1, + help='max number of output objects.') + self.parser.add_argument('--fix_res', action='store_true', + help='fix testing resolution or keep ' + 'the original resolution') + self.parser.add_argument('--keep_res', action='store_true', + help='keep the original resolution' + ' during validation.') + self.parser.add_argument('--wiz_rev', action='store_true', + help='using the pasing-grouping mechanism for wired table only. ') + self.parser.add_argument('--wiz_detect', action='store_true', default=False, + help='using the ground-truth bounding boxes and only infering the logical locations. ') + + # dataset + self.parser.add_argument('--not_rand_crop', action='store_true', + help='not use the random crop data augmentation' + 'from CornerNet.') + self.parser.add_argument('--shift', type=float, default=0.1, + help='when not using random crop' + 'apply shift augmentation.') + self.parser.add_argument('--scale', type=float, default=0.4, + help='when not using random crop' + 'apply scale augmentation.') + self.parser.add_argument('--rotate', type=float, default=0, + help='when not using random crop' + 'apply rotation augmentation.') + self.parser.add_argument('--flip', type = float, default=0.5, + help='probability of applying flip augmentation.') + self.parser.add_argument('--no_color_aug', action='store_true', + help='not use the color augmenation ' + 'from CornerNet') + self.parser.add_argument('--MK', default=700, + help='max corner number') + # multi_pose + self.parser.add_argument('--aug_rot', type=float, default=0, + help='probability of applying ' + 'rotation augmentation.') + # ddd + self.parser.add_argument('--aug_ddd', type=float, default=0.5, + help='probability of applying crop augmentation.') + self.parser.add_argument('--rect_mask', action='store_true', + help='for ignored object, apply mask on the ' + 'rectangular region or just center point.') + self.parser.add_argument('--kitti_split', default='3dop', + help='different validation split for kitti: ' + '3dop | subcnn') + + # loss + self.parser.add_argument('--mse_loss', action='store_true', + help='use mse loss or focal loss to train ' + 'keypoint heatmaps.') + # ctdet + self.parser.add_argument('--reg_loss', default='l1', + help='regression loss: sl1 | l1 | l2') + self.parser.add_argument('--hm_weight', type=float, default=1, + help='loss weight for keypoint heatmaps.') + self.parser.add_argument('--mk_weight', type=float, default=1, + help='loss weight for corner keypoint heatmaps.') + self.parser.add_argument('--off_weight', type=float, default=1, + help='loss weight for keypoint local offsets.') + self.parser.add_argument('--wh_weight', type=float, default=1, + help='loss weight for bounding box size.') + self.parser.add_argument('--st_weight', type=float, default=1, + help='loss weight for cell coor reg.') + # multi_pose + self.parser.add_argument('--hp_weight', type=float, default=1, + help='loss weight for human pose offset.') + self.parser.add_argument('--hm_hp_weight', type=float, default=1, + help='loss weight for human keypoint heatmap.') + # ddd + self.parser.add_argument('--dep_weight', type=float, default=1, + help='loss weight for depth.') + self.parser.add_argument('--dim_weight', type=float, default=1, + help='loss weight for 3d bounding box size.') + self.parser.add_argument('--rot_weight', type=float, default=1, + help='loss weight for orientation.') + self.parser.add_argument('--peak_thresh', type=float, default=0.1) + + # task + # ctdet + self.parser.add_argument('--norm_wh', action='store_true', + help='L1(\hat(y) / y, 1) or L1(\hat(y), y)') + self.parser.add_argument('--dense_wh', action='store_true', + help='apply weighted regression near center or ' + 'just apply regression on center point.') + self.parser.add_argument('--cat_spec_wh', action='store_true', + help='category specific bounding box size.') + self.parser.add_argument('--not_reg_offset', action='store_true', + help='not regress local offset.') + # exdet + self.parser.add_argument('--agnostic_ex', action='store_true', + help='use category agnostic extreme points.') + self.parser.add_argument('--scores_thresh', type=float, default=0.1, + help='threshold for extreme point heatmap.') + self.parser.add_argument('--center_thresh', type=float, default=0.1, + help='threshold for centermap.') + self.parser.add_argument('--aggr_weight', type=float, default=0.0, + help='edge aggregation weight.') + # multi_pose + self.parser.add_argument('--dense_hp', action='store_true', + help='apply weighted pose regression near center ' + 'or just apply regression on center point.') + self.parser.add_argument('--not_hm_hp', action='store_true', + help='not estimate human joint heatmap, ' + 'directly use the joint offset from center.') + self.parser.add_argument('--not_reg_hp_offset', action='store_true', + help='not regress local offset for ' + 'human joint heatmaps.') + self.parser.add_argument('--not_reg_bbox', action='store_true', + help='not regression bounding box size.') + + # ground truth validation + self.parser.add_argument('--eval_oracle_hm', action='store_true', + help='use ground center heatmap.') + self.parser.add_argument('--eval_oracle_mk', action='store_true', + help='use ground corner heatmap.') + self.parser.add_argument('--eval_oracle_wh', action='store_true', + help='use ground truth bounding box size.') + self.parser.add_argument('--eval_oracle_offset', action='store_true', + help='use ground truth local heatmap offset.') + self.parser.add_argument('--eval_oracle_kps', action='store_true', + help='use ground truth human pose offset.') + self.parser.add_argument('--eval_oracle_hmhp', action='store_true', + help='use ground truth human joint heatmaps.') + self.parser.add_argument('--eval_oracle_hp_offset', action='store_true', + help='use ground truth human joint local offset.') + self.parser.add_argument('--eval_oracle_dep', action='store_true', + help='use ground truth depth.') + + def parse(self, args=''): + if args == '': + opt = self.parser.parse_args() + else: + opt = self.parser.parse_args(args) + + opt.gpus_str = opt.gpus + opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')] + opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1] + opt.lr_step = [int(i) for i in opt.lr_step.split(',')] + opt.test_scales = [float(i) for i in opt.test_scales.split(',')] + + opt.fix_res = not opt.keep_res + print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.') + opt.reg_offset = not opt.not_reg_offset + opt.reg_bbox = not opt.not_reg_bbox + opt.hm_hp = not opt.not_hm_hp + opt.reg_hp_offset = (not opt.not_reg_hp_offset) and opt.hm_hp + + if opt.head_conv == -1: # init default head_conv + opt.head_conv = 256 if 'dla' in opt.arch else 64 + opt.pad = 127 if 'hourglass' in opt.arch else 31 + if 'resfpn' in opt.arch: + opt.pad = 0 + opt.num_stacks = 2 if opt.arch == 'hourglass' else 1 + + if opt.trainval: + opt.val_intervals = 100000000 + + if opt.debug > 0: + opt.num_workers = 0 + opt.batch_size = 1 + opt.gpus = [opt.gpus[0]] + opt.master_batch_size = -1 + + if opt.master_batch_size == -1: + opt.master_batch_size = opt.batch_size // len(opt.gpus) + rest_batch_size = (opt.batch_size - opt.master_batch_size) + opt.chunk_sizes = [opt.master_batch_size] + for i in range(len(opt.gpus) - 1): + slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1) + if i < rest_batch_size % (len(opt.gpus) - 1): + slave_chunk_size += 1 + opt.chunk_sizes.append(slave_chunk_size) + print('training chunk_sizes:', opt.chunk_sizes) + + opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..') + opt.data_dir = os.path.join(opt.root_dir, 'data') + opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task) + opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id) + opt.debug_dir = os.path.join(opt.save_dir, 'debug') + print('The output will be saved to ', opt.save_dir) + + if opt.resume and opt.load_model == '': + model_path = opt.save_dir[:-4] if opt.save_dir.endswith('TEST') \ + else opt.save_dir + opt.load_model = os.path.join(model_path, 'model_last.pth') + return opt + + def update_dataset_info_and_set_heads(self, opt, dataset): + input_h, input_w = dataset.default_resolution + opt.mean, opt.std = dataset.mean, dataset.std + opt.num_classes = dataset.num_classes + + # input_h(w): opt.input_h overrides opt.input_res overrides dataset default + input_h = opt.input_res if opt.input_res > 0 else input_h + input_w = opt.input_res if opt.input_res > 0 else input_w + opt.input_h = opt.input_h if opt.input_h > 0 else input_h + opt.input_w = opt.input_w if opt.input_w > 0 else input_w + opt.output_h = opt.input_h // opt.down_ratio + opt.output_w = opt.input_w // opt.down_ratio + opt.input_res = max(opt.input_h, opt.input_w) + opt.output_res = max(opt.output_h, opt.output_w) + + if opt.task == 'exdet': + # assert opt.dataset in ['coco'] + num_hm = 1 if opt.agnostic_ex else opt.num_classes + opt.heads = {'hm_t': num_hm, 'hm_l': num_hm, + 'hm_b': num_hm, 'hm_r': num_hm, + 'hm_c': opt.num_classes} + if opt.reg_offset: + opt.heads.update({'reg_t': 2, 'reg_l': 2, 'reg_b': 2, 'reg_r': 2}) + elif opt.task == 'ddd': + # assert opt.dataset in ['gta', 'kitti', 'viper'] + opt.heads = {'hm': opt.num_classes, 'dep': 1, 'rot': 8, 'dim': 3} + if opt.reg_bbox: + opt.heads.update( + {'wh': 2}) + if opt.reg_offset: + opt.heads.update({'reg': 2}) + elif opt.task == 'ctdet': + # assert opt.dataset in ['pascal', 'coco'] + opt.heads = {'hm': opt.num_classes,'st':8, + 'wh': 8 if not opt.cat_spec_wh else 8 * opt.num_classes, + 'ax': 256, 'cr': 256} + if opt.reg_offset: + opt.heads.update({'reg': 2}) + elif opt.task == 'ctdet_mid': + # assert opt.dataset in ['pascal', 'coco'] + opt.heads = {'hm': opt.num_classes,'st':8, + 'wh': 8 if not opt.cat_spec_wh else 8 * opt.num_classes, + 'ax': 256, 'cr': 256} + if opt.reg_offset: + opt.heads.update({'reg': 2}) + elif opt.task == 'ctdet_small': + # assert opt.dataset in ['pascal', 'coco'] + opt.heads = {'hm': opt.num_classes,'st':8, + 'wh': 8 if not opt.cat_spec_wh else 8 * opt.num_classes, + 'ax': 256, 'cr': 256} + if opt.reg_offset: + opt.heads.update({'reg': 2}) + elif opt.task == 'multi_pose': + # assert opt.dataset in ['coco_hp'] + opt.flip_idx = dataset.flip_idx + opt.heads = {'hm': opt.num_classes, 'wh': 2, 'hps': 34} + if opt.reg_offset: + opt.heads.update({'reg': 2}) + if opt.hm_hp: + opt.heads.update({'hm_hp': 17}) + if opt.reg_hp_offset: + opt.heads.update({'hp_offset': 2}) + else: + assert 0, 'task not defined!' + print('heads', opt.heads) + return opt + + def init(self, args=''): + default_dataset_info = { + 'ctdet': {'default_resolution': [1024, 1024], 'num_classes': 2, + 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], + 'dataset': 'table'}, + 'ctdet_mid': {'default_resolution': [768, 768], 'num_classes': 2, + 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], + 'dataset': 'table'}, + 'ctdet_small': {'default_resolution': [512, 512], 'num_classes': 2, + 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], + 'dataset': 'table'}, + 'exdet': {'default_resolution': [512, 512], 'num_classes': 80, + 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], + 'dataset': 'coco'}, + 'multi_pose': { + 'default_resolution': [512, 512], 'num_classes': 1, + 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], + 'dataset': 'coco_hp', 'num_joints': 17, + 'flip_idx': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], + [11, 12], [13, 14], [15, 16]]}, + 'ddd': {'default_resolution': [384, 1280], 'num_classes': 3, + 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], + 'dataset': 'kitti'}, + } + class Struct: + def __init__(self, entries): + for k, v in entries.items(): + self.__setattr__(k, v) + opt = self.parse(args) + dataset = Struct(default_dataset_info[opt.task]) + opt.dataset = dataset.dataset + opt = self.update_dataset_info_and_set_heads(opt, dataset) + return opt diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/base_trainer.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/base_trainer.py new file mode 100644 index 00000000..efb22fec --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/base_trainer.py @@ -0,0 +1,143 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time +import torch +from progress.bar import Bar +from models.data_parallel import DataParallel +from utils.utils import AverageMeter +from models.classifier import Processor +from utils.adjacency import loss_mask + + +class ModleWithLoss(torch.nn.Module): + def __init__(self, opt, model, loss, processor=None): + super(ModleWithLoss, self).__init__() + self.opt = opt + + self.model = model + + if self.opt.hold_det: + for param in self.model.named_parameters(): + #if param[0] in need_frozen_list: + param[1].requires_grad = False + + self.processor = processor + self.loss = loss + + def forward(self, epoch, batch): + + outputs = self.model(batch['input']) + + if self.opt.wiz_stacking: + logic_axis, stacked_axis = self.processor(outputs, batch) + loss, loss_stats = self.loss(epoch, outputs, batch, logic_axis, stacked_axis) + else: + logic_axis = self.processor(outputs, batch) + loss, loss_stats = self.loss(epoch, outputs, batch, logic_axis) + + return outputs[-1], loss, loss_stats + +class BaseTrainer(object): + def __init__(self, opt, model, optimizer=None, processor=None): + self.opt = opt + self.optimizer = optimizer + self.loss_stats, self.loss = self._get_losses(opt) + self.model_with_loss = ModleWithLoss(self.opt, model, self.loss, processor) + + + def set_device(self, gpus, chunk_sizes, device): + if len(gpus) > 1: + self.model_with_loss = DataParallel( + self.model_with_loss, device_ids=gpus, + chunk_sizes=chunk_sizes).to(device) + else: + self.model_with_loss = self.model_with_loss.to(device) + + for state in self.optimizer.state.values(): + for k, v in state.items(): + if isinstance(v, torch.Tensor): + state[k] = v.to(device=device, non_blocking=True) + + def run_epoch(self, phase, epoch, data_loader): + model_with_loss = self.model_with_loss + + if phase == 'train': + model_with_loss.train() + else: + if len(self.opt.gpus) > 1: + model_with_loss = self.model_with_loss.module + model_with_loss.eval() + torch.cuda.empty_cache() + + opt = self.opt + results = {} + data_time, batch_time = AverageMeter(), AverageMeter() + avg_loss_stats = {l: AverageMeter() for l in self.loss_stats} + num_iters = len(data_loader) if opt.num_iters < 0 else opt.num_iters + bar = Bar('{}/{}'.format(opt.task, opt.exp_id), max=num_iters) + end = time.time() + + for iter_id, batch in enumerate(data_loader): + if iter_id >= num_iters: + break + data_time.update(time.time() - end) + + for k in batch: + if k != 'meta': + batch[k] = batch[k].to(device=opt.device, non_blocking=True) + + output, loss, loss_stats = model_with_loss(epoch, batch) + loss = loss.mean() + if phase == 'train': + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + batch_time.update(time.time() - end) + end = time.time() + + Bar.suffix = '{phase}: [{0}][{1}/{2}]|Tot: {total:} |ETA: {eta:} '.format( + epoch, iter_id, num_iters, phase=phase, + total=bar.elapsed_td, eta=bar.eta_td) + for l in avg_loss_stats: + avg_loss_stats[l].update( + loss_stats[l].mean().item(), batch['input'].size(0)) + if l == 'loss_mj' or l == 'loss_mn' or l == 'smo_loss': + Bar.suffix = Bar.suffix + '|{} {:.2f} '.format(l, avg_loss_stats[l].avg) + else: + Bar.suffix = Bar.suffix + '|{} {:.4f} '.format(l, avg_loss_stats[l].avg) + + if opt.print_iter > 0: + if iter_id % opt.print_iter == 0: + print('{}/{}| {}'.format(opt.task, opt.exp_id, Bar.suffix)) + else: + bar.next() + + if opt.debug > 0: + self.debug(batch, output, iter_id) + + if opt.test: + self.save_result(output, batch, results) + del output, loss, loss_stats + + bar.finish() + ret = {k: v.avg for k, v in avg_loss_stats.items()} + ret['time'] = bar.elapsed_td.total_seconds() / 60. + + return ret, results + + def debug(self, batch, output, iter_id): + raise NotImplementedError + + def save_result(self, output, batch, results): + raise NotImplementedError + + def _get_losses(self, opt): + raise NotImplementedError + + def val(self, epoch, data_loader): + return self.run_epoch('val', epoch, data_loader) + + def train(self, epoch, data_loader): + return self.run_epoch('train', epoch, data_loader) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/ctdet.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/ctdet.py new file mode 100644 index 00000000..03bc05a3 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/ctdet.py @@ -0,0 +1,168 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torch +import numpy as np + +from models.losses import FocalLoss +from models.losses import RegL1Loss, RegLoss, NormRegL1Loss, RegWeightedL1Loss, PairLoss, AxisLoss, _axis_eval +from models.decode import ctdet_decode +from models.decode import ctdet_decode +from models.utils import _sigmoid +from utils.debugger import Debugger +from utils.post_process import ctdet_post_process +from utils.oracle_utils import gen_oracle_map +from .base_trainer import BaseTrainer + +class CtdetLoss(torch.nn.Module): + def __init__(self, opt): + super(CtdetLoss, self).__init__() + self.crit = torch.nn.MSELoss() if opt.mse_loss else FocalLoss() + self.crit_mk = torch.nn.MSELoss() if opt.mse_loss else FocalLoss() + self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \ + RegLoss() if opt.reg_loss == 'sl1' else None + self.crit_wh = torch.nn.L1Loss(reduction='sum') if opt.dense_wh else \ + NormRegL1Loss() if opt.norm_wh else \ + RegWeightedL1Loss() if opt.cat_spec_wh else self.crit_reg + self.crit_st = self.crit_reg + self.crit_ax = AxisLoss() + self.pair_loss = PairLoss() + self.opt = opt + + def forward(self, epoch, outputs, batch, logi=None, slogi=None): + + opt = self.opt + """hm, re, off, wh losses are original losses of CenterNet detector, and the st loss is the loss for parsing-grouping in Cycle-CenterNet.""" + hm_loss, st_loss, re_loss, off_loss, wh_loss, lo_loss, ax_loss, sax_loss, sm_loss = 0, 0, 0, 0, 0, 0, 0, 0, 0 + + for s in range(opt.num_stacks): + output = outputs[s] + if not opt.mse_loss: + output['hm'] = _sigmoid(output['hm']) + + if opt.eval_oracle_hm: + output['hm'] = batch['hm'] + if opt.eval_oracle_wh: + output['wh'] = torch.from_numpy(gen_oracle_map( + batch['wh'].detach().cpu().numpy(), + batch['hm_ind'].detach().cpu().numpy(), + output['wh'].shape[3], output['wh'].shape[2])).to(opt.device) + if opt.eval_oracle_offset: + output['reg'] = torch.from_numpy(gen_oracle_map(batch['reg'].detach().cpu().numpy(), \ + batch['reg_ind'].detach().cpu().numpy(), output['reg'].shape[3], output['reg'].shape[2])).to(opt.device) + + """LOSS FOR DETECTION MODULE""" + + if self.opt.wiz_pairloss: + hm_loss += self.crit(output['hm'] , batch['hm'] ) / opt.num_stacks + + loss1, loss2 = \ + self.pair_loss(output['wh'],batch['hm_ind'],output['st'],batch['mk_ind'],batch['hm_mask'], \ + batch['mk_mask'],batch['ctr_cro_ind'],batch['wh'],batch['st'],batch['hm_ctxy']) + + wh_loss += loss1 / opt.num_stacks + st_loss += loss2 / opt.num_stacks + else: + hm_loss += self.crit(output['hm'][:,0,:,:], batch['hm'][:,0,:,:]) / opt.num_stacks # only supervision on centers + wh_loss += self.crit_wh(output['wh'], batch['hm_mask'], batch['hm_ind'], batch['wh']) + + if opt.reg_offset and opt.off_weight > 0: + off_loss += self.crit_reg(output['reg'], batch['reg_mask'], batch['reg_ind'], batch['reg']) / opt.num_stacks + + """LOSS FOR RECONSTRUCTION MODULE""" + + ax_loss = self.crit_ax(output['ax'], batch['hm_mask'], batch['hm_ind'], batch['logic'], logi) + + '''COMBINING LOSSES''' + + loss = opt.hm_weight * hm_loss + opt.wh_weight * wh_loss + \ + opt.off_weight * off_loss + 2 * ax_loss + + if self.opt.wiz_pairloss: + loss = loss + st_loss + + if self.opt.wiz_stacking: + sax_loss = self.crit_ax(output['ax'], batch['hm_mask'], batch['hm_ind'], batch['logic'], slogi) + loss = loss + 2 * sax_loss + sacc = _axis_eval(output['ax'], batch['hm_mask'], batch['hm_ind'], batch['logic'], slogi) + '''CONSTRUCTING LOSS STATUS''' + + #weather asking for grouping + if self.opt.wiz_pairloss : + loss_stats = {'loss': loss, 'hm_l': hm_loss, 'wh_l': wh_loss, "st_l": st_loss, "ax_l": ax_loss} + else: + loss_stats = {'loss': loss, 'hm_l': hm_loss, 'wh_l': wh_loss, "ax_l": ax_loss} + + #weather asking for stacking + if self.opt.wiz_stacking: + loss_stats['sax_l'] = sax_loss + + return loss, loss_stats + +class CtdetTrainer(BaseTrainer): + def __init__(self, opt, model, optimizer=None, processor=None): + super(CtdetTrainer, self).__init__(opt, model, optimizer, processor) + + def _get_losses(self, opt): + + if opt.wiz_pairloss: + loss_stats = ['loss', 'hm_l', 'wh_l', 'st_l', 'ax_l'] + else: + loss_stats = ['loss', 'hm_l', 'wh_l', 'ax_l'] + + if opt.wiz_stacking: + loss_stats.append('sax_l') + + loss = CtdetLoss(opt) + return loss_stats, loss + + def debug(self, batch, output, iter_id): + opt = self.opt + reg = output['reg'] if opt.reg_offset else None + mk_reg = output['mk_reg'] + dets = ctdet_decode( + output['hm'], output['wh'], reg=reg, + cat_spec_wh=opt.cat_spec_wh, K=opt.K) + dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2]) + dets[:, :, :8] *= opt.down_ratio + dets_gt = batch['meta']['gt_det'].numpy().reshape(1, -1, dets.shape[2]) + dets_gt[:, :, :8] *= opt.down_ratio + for i in range(1): + debugger = Debugger( + dataset=opt.dataset, ipynb=(opt.debug==3), theme=opt.debugger_theme) + img = batch['input'][i].detach().cpu().numpy().transpose(1, 2, 0) + img = np.clip((( + img * opt.std + opt.mean) * 255.), 0, 255).astype(np.uint8) + pred = debugger.gen_colormap(output['hm'][i].detach().cpu().numpy()) + gt = debugger.gen_colormap(batch['hm'][i].detach().cpu().numpy()) + debugger.add_blend_img(img, pred, 'pred_hm') + debugger.add_blend_img(img, gt, 'gt_hm') + debugger.add_img(img, img_id='out_pred') + for k in range(len(dets[i])): + if dets[i, k, 8] > opt.center_thresh: + debugger.add_coco_bbox(dets[i, k, :8], dets[i, k, -1], + dets[i, k, 8], img_id='out_pred') + + debugger.add_img(img, img_id='out_gt') + for k in range(len(dets_gt[i])): + if dets_gt[i, k, 8] > opt.center_thresh: + debugger.add_coco_bbox(dets_gt[i, k, :8], dets_gt[i, k, -1], + dets_gt[i, k, 8], img_id='out_gt') + + if opt.debug == 4: + debugger.save_all_imgs(opt.debug_dir, prefix='{}'.format(iter_id)) + else: + debugger.show_all_imgs(pause=True) + + def save_result(self, output, batch, results): + reg = output['reg'] if self.opt.reg_offset else None + dets = ctdet_decode( + output['hm'], output['wh'], reg=reg, + cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) + dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2]) + dets_out = ctdet_post_process( + dets.copy(), batch['meta']['c'].cpu().numpy(), + batch['meta']['s'].cpu().numpy(), + output['hm'].shape[2], output['hm'].shape[3], output['hm'].shape[1]) + results[batch['meta']['img_id'].cpu().numpy()[0]] = dets_out[0] \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/train_factory.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/train_factory.py new file mode 100644 index 00000000..6da63d38 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/trains/train_factory.py @@ -0,0 +1,11 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from .ctdet import CtdetTrainer + +train_factory = { + 'ctdet': CtdetTrainer, + 'ctdet_mid': CtdetTrainer, + 'ctdet_small': CtdetTrainer +} diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/adjacency.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/adjacency.py new file mode 100644 index 00000000..6c9e5b33 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/adjacency.py @@ -0,0 +1,130 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +def adjacency (box1, box2): + sr1 = box1[0] + er1 = box1[1] + sc1 = box1[2] + ec1 = box1[3] + + sr2 = box2[0] + er2 = box2[1] + sc2 = box2[2] + ec2 = box2[3] + + if sr2 - er1 == 1 or sr1 - er2 == 1: + #l-r adjacency + if (sc1<=sc2 and ec1>=ec2) or (sc1>=sc2 and ec1<=ec2): + return True + else: + return False + elif sc2 - ec1 == 1 or sc1 - ec2 == 1: + #u-d adjacency + if (sr1<=sr2 and er1>=er2) or (sr1>=sr2 and er1<=er2): + return True + else: + return False + else: + #no adjacency + return False + +def same_row(box1, box2): + sc1 = box1[0] + ec1 = box1[1] + sr1 = box1[2] + er1 = box1[3] + + sc2 = box2[0] + ec2 = box2[1] + sr2 = box2[2] + er2 = box2[3] + + #unit1.axis[2] sr1 + #unit1.axis[3] er1 + #unit2.axis[2] sr2 + #unit2.axis[3] er2 + + if sr1 <= sr2 <= er1: + return True + + elif sr2 <= sr1 <= er2: + return True + + else: + return False + +def same_col(box1, box2): + sc1 = box1[0] + ec1 = box1[1] + sr1 = box1[2] + er1 = box1[3] + + sc2 = box2[0] + ec2 = box2[1] + sr2 = box2[2] + er2 = box2[3] + + #unit1.axis[0] sc1 + #unit2.axis[0] sc2 + #unit1.axis[1] ec1 + #unit2.axis[1] ec2 + + if sc1 <= sc2 <= ec1: + return True + elif sc2 <= sc1 <= ec2: + return True + else: + return False + + +def loss_mask(num_obj): + + mask = torch.zeros(num_obj, num_obj) + for i in range(num_obj): + for j in range(i): + mask[i][j] = 1.0 + + return mask + +def v_adjacency (box1, box2): + sr1 = box1[0] + er1 = box1[1] + sc1 = box1[2] + ec1 = box1[3] + + sr2 = box2[0] + er2 = box2[1] + sc2 = box2[2] + ec2 = box2[3] + + if sc2 - ec1 == 1 or sc1 - ec2 == 1: + #u-d adjacency + if (sr1<=sr2 and er1>=er2) or (sr1>=sr2 and er1<=er2): + return True + else: + return False + else: + #no adjacency + return False + +def h_adjacency (box1, box2): + sr1 = box1[0] + er1 = box1[1] + sc1 = box1[2] + ec1 = box1[3] + + sr2 = box2[0] + er2 = box2[1] + sc2 = box2[2] + ec2 = box2[3] + + if sr2 - er1 == 1 or sr1 - er2 == 1: + #l-r adjacency + if (sc1<=sc2 and ec1>=ec2) or (sc1>=sc2 and ec1<=ec2): + return True + else: + return False + else: + #no adjacency + return False \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/debugger.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/debugger.py new file mode 100644 index 00000000..500c9d1e --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/debugger.py @@ -0,0 +1,488 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 + +class Debugger(object): + def __init__(self, ipynb=False, theme='black', + num_classes=-1, dataset=None, down_ratio=4): + self.ipynb = ipynb + if not self.ipynb: + import matplotlib.pyplot as plt + self.plt = plt + self.imgs = {} + self.theme = theme + colors = [(color_list[_]).astype(np.uint8) \ + for _ in range(len(color_list))] + self.colors = np.array(colors, dtype=np.uint8).reshape(len(colors), 1, 1, 3) + if self.theme == 'white': + self.colors = self.colors.reshape(-1)[::-1].reshape(len(colors), 1, 1, 3) + self.colors = np.clip(self.colors, 0., 0.6 * 255).astype(np.uint8) + self.dim_scale = 1 + if dataset == 'coco_hp': + self.names = ['p'] + self.num_class = 1 + self.num_joints = 17 + self.edges = [[0, 1], [0, 2], [1, 3], [2, 4], + [3, 5], [4, 6], [5, 6], + [5, 7], [7, 9], [6, 8], [8, 10], + [5, 11], [6, 12], [11, 12], + [11, 13], [13, 15], [12, 14], [14, 16]] + self.ec = [(255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), + (255, 0, 0), (0, 0, 255), (255, 0, 255), + (255, 0, 0), (255, 0, 0), (0, 0, 255), (0, 0, 255), + (255, 0, 0), (0, 0, 255), (255, 0, 255), + (255, 0, 0), (255, 0, 0), (0, 0, 255), (0, 0, 255)] + self.colors_hp = [(255, 0, 255), (255, 0, 0), (0, 0, 255), + (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), + (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), + (255, 0, 0), (0, 0, 255), (255, 0, 0), (0, 0, 255), + (255, 0, 0), (0, 0, 255)] + elif num_classes == 80 or dataset == 'coco': + self.names = coco_class_name + elif num_classes == 20 or dataset == 'pascal': + self.names = pascal_class_name + elif num_classes == 1 or dataset == 'table': + self.names = table_class_name + + num_classes = len(self.names) + self.down_ratio=down_ratio + # for bird view + self.world_size = 64 + self.out_size = 384 + + def add_img(self, img, img_id='default', revert_color=False): + if revert_color: + img = 255 - img + self.imgs[img_id] = img.copy() + + def add_mask(self, mask, bg, imgId = 'default', trans = 0.8): + self.imgs[imgId] = (mask.reshape( + mask.shape[0], mask.shape[1], 1) * 255 * trans + \ + bg * (1 - trans)).astype(np.uint8) + + def show_img(self, pause = False, imgId = 'default'): + cv2.imshow('{}'.format(imgId), self.imgs[imgId]) + if pause: + cv2.waitKey() + + def add_blend_img(self, back, fore, img_id='blend', trans=0.7): + if self.theme == 'white': + fore = 255 - fore + if fore.shape[0] != back.shape[0] or fore.shape[0] != back.shape[1]: + fore = cv2.resize(fore, (back.shape[1], back.shape[0])) + if len(fore.shape) == 2: + fore = fore.reshape(fore.shape[0], fore.shape[1], 1) + self.imgs[img_id] = (back * (1. - trans) + fore * trans) + self.imgs[img_id][self.imgs[img_id] > 255] = 255 + self.imgs[img_id][self.imgs[img_id] < 0] = 0 + self.imgs[img_id] = self.imgs[img_id].astype(np.uint8).copy() + + + def gen_colormap(self, img, output_res=None): + img = img.copy() + c, h, w = img.shape[0], img.shape[1], img.shape[2] + if output_res is None: + output_res = (h * self.down_ratio, w * self.down_ratio) + img = img.transpose(1, 2, 0).reshape(h, w, c, 1).astype(np.float32) + colors = np.array( + self.colors, dtype=np.float32).reshape(-1, 3)[:c].reshape(1, 1, c, 3) + if self.theme == 'white': + colors = 255 - colors + color_map = (img * colors).max(axis=2).astype(np.uint8) + color_map = cv2.resize(color_map, (output_res[0], output_res[1])) + return color_map + + def gen_colormap_hp(self, img, output_res=None): + c, h, w = img.shape[0], img.shape[1], img.shape[2] + if output_res is None: + output_res = (h * self.down_ratio, w * self.down_ratio) + img = img.transpose(1, 2, 0).reshape(h, w, c, 1).astype(np.float32) + colors = np.array( + self.colors_hp, dtype=np.float32).reshape(-1, 3)[:c].reshape(1, 1, c, 3) + if self.theme == 'white': + colors = 255 - colors + color_map = (img * colors).max(axis=2).astype(np.uint8) + color_map = cv2.resize(color_map, (output_res[0], output_res[1])) + return color_map + + + def add_rect(self, rect1, rect2, c, conf=1, img_id='default'): + cv2.rectangle( + self.imgs[img_id], (rect1[0], rect1[1]), (rect2[0], rect2[1]), c, 2) + if conf < 1: + cv2.circle(self.imgs[img_id], (rect1[0], rect1[1]), int(10 * conf), c, 1) + cv2.circle(self.imgs[img_id], (rect2[0], rect2[1]), int(10 * conf), c, 1) + cv2.circle(self.imgs[img_id], (rect1[0], rect2[1]), int(10 * conf), c, 1) + cv2.circle(self.imgs[img_id], (rect2[0], rect1[1]), int(10 * conf), c, 1) + + def add_coco_bbox(self, bbox, cat, conf=1, show_txt=False, img_id='default'): + bbox = np.array(bbox, dtype=np.int32) + # cat = (int(cat) + 1) % 80 + cat = int(cat) + # print('cat', cat, self.names[cat]) + c = self.colors[cat][0][0].tolist() + if self.theme == 'white': + c = (255 - np.array(c )).tolist() + txt = '{}{:.1f}'.format(self.names[cat], conf) + font = cv2.FONT_HERSHEY_SIMPLEX + cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0] + cv2.rectangle( + self.imgs[img_id], (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0,0,255), 10) # -2 + if show_txt: + cv2.rectangle(self.imgs[img_id], + (bbox[0], bbox[1] - cat_size[1] - 2), + (bbox[0] + cat_size[0], bbox[1] - 2), c, -1) + #0.5-2 + cv2.putText(self.imgs[img_id], txt, (bbox[0], bbox[1] - 2), + font, 10, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA) + + def add_4ps_coco_bbox(self, bbox, cat, conf, logi=None, show_txt=False, img_id='default'): + bbox = np.array(bbox, dtype=np.int32) + # cat = (int(cat) + 1) % 80 + cat = int(cat) + # print('cat', cat, self.names[cat]) + c = self.colors[cat][0][0].tolist() + if self.theme == 'white': + c = (255 - np.array(c )).tolist() + + if not logi is None: + txt = '{:.0f},{:.0f},{:.0f},{:.0f}'.format(logi[0], logi[1], logi[2], logi[3]) + font = cv2.FONT_HERSHEY_SIMPLEX + cat_size = cv2.getTextSize(txt, font, 0.3, 2)[0] + r = np.random.randint(0,255) + g = np.random.randint(0,255) + b = np.random.randint(0,255) + cv2.line(self.imgs[img_id],(bbox[0],bbox[1]),(bbox[2],bbox[3]),(0,0,255),2) + cv2.line(self.imgs[img_id],(bbox[2],bbox[3]),(bbox[4],bbox[5]),(0,0,255),2) + cv2.line(self.imgs[img_id],(bbox[4],bbox[5]),(bbox[6],bbox[7]),(0,0,255),2) + cv2.line(self.imgs[img_id],(bbox[6],bbox[7]),(bbox[0],bbox[1]),(0,0,255),2) # 1 - 5 + + if not logi is None: + cv2.rectangle(self.imgs[img_id], + (bbox[0], bbox[1] - cat_size[1] - 2), + (bbox[0] + cat_size[0], bbox[1] - 2), c, -1) + cv2.putText(self.imgs[img_id], txt, (bbox[0], bbox[1] - 2), + font, 0.30, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA) #1 - 5 # 0.20 _ 0.60 + + + def add_coco_hp(self, points, img_id='default'): + points = np.array(points, dtype=np.int32).reshape(self.num_joints, 2) + for j in range(self.num_joints): + cv2.circle(self.imgs[img_id], + (points[j, 0], points[j, 1]), 3, self.colors_hp[j], -1) + for j, e in enumerate(self.edges): + if points[e].min() > 0: + cv2.line(self.imgs[img_id], (points[e[0], 0], points[e[0], 1]), + (points[e[1], 0], points[e[1], 1]), self.ec[j], 2, + lineType=cv2.LINE_AA) + + def add_points(self, points, img_id='default'): + num_classes = len(points) + # assert num_classes == len(self.colors) + for i in range(num_classes): + for j in range(len(points[i])): + c = self.colors[i, 0, 0] + cv2.circle(self.imgs[img_id], (points[i][j][0] * self.down_ratio, + points[i][j][1] * self.down_ratio), + 5, (255, 255, 255), -1) + cv2.circle(self.imgs[img_id], (points[i][j][0] * self.down_ratio, + points[i][j][1] * self.down_ratio), + 3, (int(c[0]), int(c[1]), int(c[2])), -1) + + def add_corner(self, corner, img_id='default'): + if abs(int(corner[2])+int(corner[3])) > 10: + cv2.arrowedLine(self.imgs[img_id], (int(corner[0]),int(corner[1])),(int(corner[2]),int(corner[3])),(0,0,255),thickness=1, line_type=cv2.LINE_4, shift=0, tipLength=0.02) + if abs(int(corner[4])+int(corner[5])) > 10: + cv2.arrowedLine(self.imgs[img_id], (int(corner[0]),int(corner[1])),(int(corner[4]),int(corner[5])),(0,255,0),thickness=1, line_type=cv2.LINE_4, shift=0, tipLength=0.02) + if abs(int(corner[6])+int(corner[7])) > 10: + cv2.arrowedLine(self.imgs[img_id], (int(corner[0]),int(corner[1])),(int(corner[6]),int(corner[7])),(255,0,0),thickness=1, line_type=cv2.LINE_4, shift=0, tipLength=0.02) + if abs(int(corner[8])+int(corner[9])) > 10: + cv2.arrowedLine(self.imgs[img_id], (int(corner[0]),int(corner[1])),(int(corner[8]),int(corner[9])),(0,0, 0),thickness=1, line_type=cv2.LINE_4, shift=0, tipLength=0.02) + cv2.circle(self.imgs[img_id], (int(corner[0]), int(corner[1])), 3, (255, 0, 0), -1) + + def show_all_imgs(self, pause=False, time=0): + if not self.ipynb: + for i, v in self.imgs.items(): + cv2.imshow('{}'.format(i), v) + if cv2.waitKey(0 if pause else 1) == 27: + import sys + sys.exit(0) + else: + self.ax = None + nImgs = len(self.imgs) + fig=self.plt.figure(figsize=(nImgs * 10,10)) + nCols = nImgs + nRows = nImgs // nCols + for i, (k, v) in enumerate(self.imgs.items()): + fig.add_subplot(1, nImgs, i + 1) + if len(v.shape) == 3: + self.plt.imshow(cv2.cvtColor(v, cv2.COLOR_BGR2RGB)) + else: + self.plt.imshow(v) + self.plt.show() + + def save_img(self, imgId='default', path='./cache/debug/'): + cv2.imwrite(path + '{}.png'.format(imgId), self.imgs[imgId]) + + def save_all_imgs(self, image_name, path='./cache/debug/', prefix='', genID=True): + if genID: + try: + idx = int(np.loadtxt(path + '/id.txt')) + except: + idx = 0 + prefix=idx + np.savetxt(path + '/id.txt', np.ones(1) * (idx + 1), fmt='%d') + for i, v in self.imgs.items(): + #cv2.imwrite(path + '/{}{}.png'.format(prefix,i), v) + cv2.imwrite(path + '/%s'%image_name, v) + #print(path+'/%s'%image_name) + + def remove_side(self, img_id, img): + if not (img_id in self.imgs): + return + ws = img.sum(axis=2).sum(axis=0) + l = 0 + while ws[l] == 0 and l < len(ws): + l+= 1 + r = ws.shape[0] - 1 + while ws[r] == 0 and r > 0: + r -= 1 + hs = img.sum(axis=2).sum(axis=1) + t = 0 + while hs[t] == 0 and t < len(hs): + t += 1 + b = hs.shape[0] - 1 + while hs[b] == 0 and b > 0: + b -= 1 + self.imgs[img_id] = self.imgs[img_id][t:b+1, l:r+1].copy() + + def project_3d_to_bird(self, pt): + pt[0] += self.world_size / 2 + pt[1] = self.world_size - pt[1] + pt = pt * self.out_size / self.world_size + return pt.astype(np.int32) + + def add_ct_detection( + self, img, dets, show_box=False, show_txt=True, + center_thresh=0.5, img_id='det'): + # dets: max_preds x 5 + self.imgs[img_id] = img.copy() + if type(dets) == type({}): + for cat in dets: + for i in range(len(dets[cat])): + if dets[cat][i, 2] > center_thresh: + cl = (self.colors[cat, 0, 0]).tolist() + ct = dets[cat][i, :2].astype(np.int32) + if show_box: + w, h = dets[cat][i, -2], dets[cat][i, -1] + x, y = dets[cat][i, 0], dets[cat][i, 1] + bbox = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2], + dtype=np.float32) + self.add_coco_bbox( + bbox, cat - 1, dets[cat][i, 2], + show_txt=show_txt, img_id=img_id) + else: + for i in range(len(dets)): + if dets[i, 2] > center_thresh: + # print('dets', dets[i]) + cat = int(dets[i, -1]) + cl = (self.colors[cat, 0, 0] if self.theme == 'black' else \ + 255 - self.colors[cat, 0, 0]).tolist() + ct = dets[i, :2].astype(np.int32) * self.down_ratio + cv2.circle(self.imgs[img_id], (ct[0], ct[1]), 3, cl, -1) + if show_box: + w, h = dets[i, -3] * self.down_ratio, dets[i, -2] * self.down_ratio + x, y = dets[i, 0] * self.down_ratio, dets[i, 1] * self.down_ratio + bbox = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2], + dtype=np.float32) + self.add_coco_bbox(bbox, dets[i, -1], dets[i, 2], img_id=img_id) + + def add_2d_detection( + self, img, dets, show_box=False, show_txt=True, + center_thresh=0.5, img_id='det'): + self.imgs[img_id] = img + for cat in dets: + for i in range(len(dets[cat])): + cl = (self.colors[cat - 1, 0, 0]).tolist() + if dets[cat][i, -1] > center_thresh: + bbox = dets[cat][i, 1:5] + self.add_coco_bbox( + bbox, cat - 1, dets[cat][i, -1], + show_txt=show_txt, img_id=img_id) + + def add_bird_view(self, dets, center_thresh=0.3, img_id='bird'): + bird_view = np.ones((self.out_size, self.out_size, 3), dtype=np.uint8) * 230 + for cat in dets: + cl = (self.colors[cat - 1, 0, 0]).tolist() + lc = (250, 152, 12) + for i in range(len(dets[cat])): + if dets[cat][i, -1] > center_thresh: + dim = dets[cat][i, 5:8] + loc = dets[cat][i, 8:11] + rot_y = dets[cat][i, 11] + rect = compute_box_3d(dim, loc, rot_y)[:4, [0, 2]] + for k in range(4): + rect[k] = self.project_3d_to_bird(rect[k]) + # cv2.circle(bird_view, (rect[k][0], rect[k][1]), 2, lc, -1) + cv2.polylines( + bird_view,[rect.reshape(-1, 1, 2).astype(np.int32)], + True,lc,2,lineType=cv2.LINE_AA) + for e in [[0, 1]]: + t = 4 if e == [0, 1] else 1 + cv2.line(bird_view, (rect[e[0]][0], rect[e[0]][1]), + (rect[e[1]][0], rect[e[1]][1]), lc, t, + lineType=cv2.LINE_AA) + self.imgs[img_id] = bird_view + + def add_bird_views(self, dets_dt, dets_gt, center_thresh=0.3, img_id='bird'): + alpha = 0.5 + bird_view = np.ones((self.out_size, self.out_size, 3), dtype=np.uint8) * 230 + for ii, (dets, lc, cc) in enumerate( + [(dets_gt, (12, 49, 250), (0, 0, 255)), + (dets_dt, (250, 152, 12), (255, 0, 0))]): + # cc = np.array(lc, dtype=np.uint8).reshape(1, 1, 3) + for cat in dets: + cl = (self.colors[cat - 1, 0, 0]).tolist() + for i in range(len(dets[cat])): + if dets[cat][i, -1] > center_thresh: + dim = dets[cat][i, 5:8] + loc = dets[cat][i, 8:11] + rot_y = dets[cat][i, 11] + rect = compute_box_3d(dim, loc, rot_y)[:4, [0, 2]] + for k in range(4): + rect[k] = self.project_3d_to_bird(rect[k]) + if ii == 0: + cv2.fillPoly( + bird_view,[rect.reshape(-1, 1, 2).astype(np.int32)], + lc,lineType=cv2.LINE_AA) + else: + cv2.polylines( + bird_view,[rect.reshape(-1, 1, 2).astype(np.int32)], + True,lc,2,lineType=cv2.LINE_AA) + # for e in [[0, 1], [1, 2], [2, 3], [3, 0]]: + for e in [[0, 1]]: + t = 4 if e == [0, 1] else 1 + cv2.line(bird_view, (rect[e[0]][0], rect[e[0]][1]), + (rect[e[1]][0], rect[e[1]][1]), lc, t, + lineType=cv2.LINE_AA) + self.imgs[img_id] = bird_view + + +kitti_class_name = [ + 'p', 'v', 'b' +] + +gta_class_name = [ + 'p', 'v' +] + +pascal_class_name = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", + "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", + "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] + +coco_class_name = [ + 'person', 'bicycle', 'car', 'motorcycle', 'airplane', + 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', + 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', + 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', + 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', + 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', + 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', + 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', + 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', + 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', + 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', + 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', + 'scissors', 'teddy bear', 'hair drier', 'toothbrush' +] + +table_class_name = ["box"] + +color_list = np.array( + [ + 1.000, 1.000, 1.000, + 0.850, 0.325, 0.098, + 0.929, 0.694, 0.125, + 0.494, 0.184, 0.556, + 0.466, 0.674, 0.188, + 0.301, 0.745, 0.933, + 0.635, 0.078, 0.184, + 0.300, 0.300, 0.300, + 0.600, 0.600, 0.600, + 1.000, 0.000, 0.000, + 1.000, 0.500, 0.000, + 0.749, 0.749, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 1.000, + 0.667, 0.000, 1.000, + 0.333, 0.333, 0.000, + 0.333, 0.667, 0.000, + 0.333, 1.000, 0.000, + 0.667, 0.333, 0.000, + 0.667, 0.667, 0.000, + 0.667, 1.000, 0.000, + 1.000, 0.333, 0.000, + 1.000, 0.667, 0.000, + 1.000, 1.000, 0.000, + 0.000, 0.333, 0.500, + 0.000, 0.667, 0.500, + 0.000, 1.000, 0.500, + 0.333, 0.000, 0.500, + 0.333, 0.333, 0.500, + 0.333, 0.667, 0.500, + 0.333, 1.000, 0.500, + 0.667, 0.000, 0.500, + 0.667, 0.333, 0.500, + 0.667, 0.667, 0.500, + 0.667, 1.000, 0.500, + 1.000, 0.000, 0.500, + 1.000, 0.333, 0.500, + 1.000, 0.667, 0.500, + 1.000, 1.000, 0.500, + 0.000, 0.333, 1.000, + 0.000, 0.667, 1.000, + 0.000, 1.000, 1.000, + 0.333, 0.000, 1.000, + 0.333, 0.333, 1.000, + 0.333, 0.667, 1.000, + 0.333, 1.000, 1.000, + 0.667, 0.000, 1.000, + 0.667, 0.333, 1.000, + 0.667, 0.667, 1.000, + 0.667, 1.000, 1.000, + 1.000, 0.000, 1.000, + 1.000, 0.333, 1.000, + 1.000, 0.667, 1.000, + 0.167, 0.000, 0.000, + 0.333, 0.000, 0.000, + 0.500, 0.000, 0.000, + 0.667, 0.000, 0.000, + 0.833, 0.000, 0.000, + 1.000, 0.000, 0.000, + 0.000, 0.167, 0.000, + 0.000, 0.333, 0.000, + 0.000, 0.500, 0.000, + 0.000, 0.667, 0.000, + 0.000, 0.833, 0.000, + 0.000, 1.000, 0.000, + 0.000, 0.000, 0.167, + 0.000, 0.000, 0.333, + 0.000, 0.000, 0.500, + 0.000, 0.000, 0.667, + 0.000, 0.000, 0.833, + 0.000, 0.000, 1.000, + 0.000, 0.000, 0.000, + 0.143, 0.143, 0.143, + 0.286, 0.286, 0.286, + 0.429, 0.429, 0.429, + 0.571, 0.571, 0.571, + 0.714, 0.714, 0.714, + 0.857, 0.857, 0.857, + 0.000, 0.447, 0.741, + 0.50, 0.5, 0 + ] + ).astype(np.float32) +color_list = color_list.reshape((-1, 3)) * 255 diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/eval_utils.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/eval_utils.py new file mode 100644 index 00000000..9c92b916 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/eval_utils.py @@ -0,0 +1,244 @@ +import re +import os +import numpy as np +import scipy.spatial +from tqdm import tqdm + +import pycocotools.coco as coco +from glob import glob + +def coco_into_labels(annot_path, label_path): + #annot_path = 'rev_scitsr_st_full/test_full.json' + coco_data = coco.COCO(annot_path) + images = coco_data.getImgIds() + + gt_center_dir = '{}/gt_center/'.format(label_path) + gt_logi_dir = '{}/gt_logi/'.format(label_path) + + if not os.path.exists(gt_center_dir): + os.mkdir(gt_center_dir) + else: return 0 + + if not os.path.exists(gt_logi_dir): + os.mkdir(gt_logi_dir) + + print('Changing COCO Labels into TXT Labels...') + for i in tqdm(range(len(images))): + img_id = images[i] + file_name = coco_data.loadImgs(ids=[img_id])[0]['file_name'] + #file_names.append(file_name) + + # using this for your dataset + center_file = '{}/gt_center/'.format(label_path) + file_name +'.txt' + logi_file = '{}/gt_logi/'.format(label_path) + file_name +'.txt' + + #TODO: revise the file names in the annotation of PubTabNet + # center_file = gt_center_dir + file_name.replace('.jpg', '.png') +'.txt' + # logi_file = gt_logi_dir + file_name.replace('.jpg', '.png') +'.txt' + + + ann_ids = coco_data.getAnnIds(imgIds=[img_id]) + anns = coco_data.loadAnns(ids=ann_ids) + + fc = open(center_file, 'w') + fl = open(logi_file, 'w') + for j in range(len(anns)): + ann = anns[j] + bbox = ann['segmentation'][0] + logi = ann['logic_axis'][0] + for i in range(0,3): + fc.write(str(bbox[2*i])+','+str(bbox[2*i+1])+';') + fl.write(str(int(logi[i]))+',') + + fc.write(str(bbox[6])+','+str(bbox[7])+'\n') + fl.write(str(int(logi[3]))+'\n') + + print('Finished: Changing COCO Labels into TXT Labels!') + +class pairTab(): + def __init__(self, pred_table, gt_table): + self.gt_list = gt_table.ulist + self.pred_list = pred_table.ulist + + self.match_list = [] + self.matching() + + def matching(self): + for tunit in self.gt_list: + if_find = 0 + for sunit in self.pred_list: + #TODO: Adding Parameters for IOU threshold + #Using IOU=0.5 as Default + if self.compute_IOU(tunit.bbox, sunit.bbox) >= 0.5: + self.match_list.append(sunit) + if_find = 1 + break + if if_find == 0: + self.match_list.append('empty') + + def evalBbox(self, eval_type): + tp = 0 + for u in self.match_list: + if u != 'empty': + tp = tp + 1.0 + + ap = len(self.pred_list) + at = len(self.gt_list) + if eval_type == 'recall': + if at == 0: + return 'null' + else: + return tp/at + elif eval_type == 'precision': + if ap == 0: + return 'null' + else: + return tp/ap + + def compute_IOU(self, bbox1,bbox2): + rec1 = (bbox1.point1[0][0], bbox1.point1[0][1], bbox1.point3[0][0], bbox1.point3[0][1]) + rec2 = (bbox2.point1[0][0], bbox2.point1[0][1], bbox2.point3[0][0], bbox2.point3[0][1]) + + left_column_max = max(rec1[0],rec2[0]) + right_column_min = min(rec1[2],rec2[2]) + up_row_max = max(rec1[1],rec2[1]) + down_row_min = min(rec1[3],rec2[3]) + + if left_column_max>=right_column_min or down_row_min<=up_row_max: + return 0 + else: + S1 = (rec1[2]-rec1[0])*(rec1[3]-rec1[1]) + S2 = (rec2[2]-rec2[0])*(rec2[3]-rec2[1]) + S_cross = (down_row_min-up_row_max)*(right_column_min-left_column_max) + + return S_cross/(S1+S2-S_cross) + + def evalAxis(self): + + tp = 0 + for u in self.match_list: + if u != 'empty': + tp = tp + 1.0 + + truep = 0 + for i in range(len(self.gt_list)): + sunit = self.match_list[i] + if sunit != 'empty': + tunit = self.gt_list[i] + + saxis = sunit.axis + taxis= tunit.axis + + flag = 1 + for j in range(4): + if saxis[j] != taxis[j]: + flag = 0 + break + + #all four axis are correctly predicted + if flag == 1: + truep = truep + 1.0 + + if len(self.gt_list) == 0: + #return 0 + return 'null' + else: + if tp == 0: + #return 0 + return 'null' + else: + return truep/tp + +class Table(): + def __init__(self, bbox_path, axis_path, file_name): + self.bbox_dir = os.path.join(bbox_path, file_name) + self.axis_dir = os.path.join(axis_path, file_name) + + self.ulist = [] + self.load_tabu(self.bbox_dir, self.axis_dir) + self.ulist = self.bubble_sort(self.ulist) + + def load_tabu(self, bbox_dir, axis_dir): + + f_b = open(self.bbox_dir) + f_a = open(self.axis_dir) + bboxs = f_b.readlines() + axiss = f_a.readlines() + + for bbox, axis in zip(bboxs, axiss): + bbox = list(map(float, re.split(';|,',bbox.strip()))) + axis = list(map(int, axis.strip().split(','))) + unit = TabUnit(bbox, axis) + + self.ulist.append(unit) + + def compute_IOU(self, bbox1, bbox2): + rec1 = (bbox1.point1[0][0], bbox1.point1[0][1], bbox1.point3[0][0], bbox1.point3[0][1]) + rec2 = (bbox2.point1[0][0], bbox2.point1[0][1], bbox2.point3[0][0], bbox2.point3[0][1]) + left_column_max = max(rec1[0],rec2[0]) + right_column_min = min(rec1[2],rec2[2]) + up_row_max = max(rec1[1],rec2[1]) + down_row_min = min(rec1[3],rec2[3]) + + if left_column_max>=right_column_min or down_row_min<=up_row_max: + return 0 + else: + S1 = (rec1[2]-rec1[0])*(rec1[3]-rec1[1]) + S2 = (rec2[2]-rec2[0])*(rec2[3]-rec2[1]) + S_cross = (down_row_min-up_row_max)*(right_column_min-left_column_max) + return S_cross/(S1+S2-S_cross) + + def bubble_sort(self, unit_list): + length = len(unit_list) + for index in range(length): + for j in range(1, length-index): + if self.is_priori(unit_list[j], unit_list[j-1]): + unit_list[j-1], unit_list[j] = unit_list[j], unit_list[j-1] + return unit_list + + def is_priori(self, unit_a, unit_b): + if unit_a.top_idx < unit_b.top_idx : + return True + elif unit_a.top_idx > unit_b.top_idx : + return False + if unit_a.left_idx < unit_b.left_idx : + return True + elif unit_a.left_idx > unit_b.left_idx : + return False + if unit_a.bottom_idx < unit_b.bottom_idx : + return True + elif unit_a.bottom_idx > unit_b.bottom_idx : + return False + if unit_a.right_idx < unit_b.right_idx : + return True + elif unit_a.right_idx > unit_b.right_idx : + return False + +class TabUnit(): + def __init__(self, bbox, axis): + + self.bbox = BBox(bbox) + self.axis = axis + self.top_idx = axis[2] + self.bottom_idx = axis[3] + self.left_idx = axis[0] + self.right_idx = axis[1] + +class BBox(): + def __init__(self, bbox): + self.point1 = np.array([[bbox[0], bbox[1]]]) + self.point2 = np.array([[bbox[2], bbox[3]]]) + self.point3 = np.array([[bbox[4], bbox[5]]]) + self.point4 = np.array([[bbox[6], bbox[7]]]) + + self.col_span = (self.computing_span(self.point1, self.point2) + self.computing_span(self.point3, self.point4))/2 + self.row_span = (self.computing_span(self.point1, self.point3) + self.computing_span(self.point2, self.point4))/2 + + def computing_span(self, pointa, pointb): + span = scipy.spatial.distance.cdist(pointa, pointb, metric = "euclidean") + return span + + + + + \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/image.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/image.py new file mode 100644 index 00000000..38c8e94d --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/image.py @@ -0,0 +1,290 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Xingyi Zhou +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 +import random + +def flip(img): + return img[:, :, ::-1].copy() + +def transform_preds(coords, center, scale, output_size, rot=0): + target_coords = np.zeros(coords.shape) + trans = get_affine_transform(center, scale, rot, output_size, inv=1) + for p in range(coords.shape[0]): + target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) + return target_coords + +def transform_preds_upper_left(coords, center, scale, output_size, rot=0): + target_coords = np.zeros(coords.shape) + trans = get_affine_transform_upper_left(center, scale, rot, output_size, inv=1) + for p in range(coords.shape[0]): + target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) + return target_coords + +def get_affine_transform_upper_left(center, + scale, + rot, + output_size, + shift=np.array([0, 0], dtype=np.float32), + inv=0): + if not isinstance(scale, np.ndarray) and not isinstance(scale, list): + scale = np.array([scale, scale], dtype=np.float32) + + src = np.zeros((3, 2), dtype=np.float32) + dst = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + dst[0, :] = [0,0] + if center[0] < center[1]: + src[1, :] = [scale[0], center[1]] + dst[1, :] = [output_size[0], 0] + else: + src[1, :] = [center[0], scale[0]] + dst[1, :] = [0, output_size[0]] + src[2:, :] = get_3rd_point(src[0, :], src[1, :]) + dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return trans + +def get_affine_transform(center, + scale, + rot, + output_size, + shift=np.array([0, 0], dtype=np.float32), + inv=0): + if not isinstance(scale, np.ndarray) and not isinstance(scale, list): + scale = np.array([scale, scale], dtype=np.float32) + + scale_tmp = scale + src_w = scale_tmp[0] + dst_w = output_size[0] + dst_h = output_size[1] + + rot_rad = np.pi * rot / 180 + src_dir = get_dir([0, src_w * -0.5], rot_rad) + dst_dir = np.array([0, dst_w * -0.5], np.float32) + + src = np.zeros((3, 2), dtype=np.float32) + dst = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale_tmp * shift #[0,0]# + src[1, :] = center + src_dir + scale_tmp * shift #scale# + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] #[0,0]# + + #if scale_tmp < dst_w: #added + #dst[1, :] = scale #added + #else: + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5], np.float32) + dst_dir #output_size # + + src[2:, :] = get_3rd_point(src[0, :], src[1, :]) + dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return trans + + +def affine_transform(pt, t): + new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32).T + new_pt = np.dot(t, new_pt) + return new_pt[:2] + + +def get_3rd_point(a, b): + direct = a - b + return b + np.array([-direct[1], direct[0]], dtype=np.float32) + + +def get_dir(src_point, rot_rad): + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + + src_result = [0, 0] + src_result[0] = src_point[0] * cs - src_point[1] * sn + src_result[1] = src_point[0] * sn + src_point[1] * cs + + return src_result + + +def crop(img, center, scale, output_size, rot=0): + trans = get_affine_transform(center, scale, rot, output_size) + + dst_img = cv2.warpAffine(img, + trans, + (int(output_size[0]), int(output_size[1])), + flags=cv2.INTER_LINEAR) + + return dst_img + + +def gaussian_radius(det_size, min_overlap=0.7): + + height, width = det_size + + a1 = 1 + b1 = (height + width) + c1 = width * height * (1 - min_overlap) / (1 + min_overlap) + sq1 = np.sqrt(b1 ** 2 - 4 * a1 * c1) + r1 = (b1 + sq1) / 2 + + a2 = 4 + b2 = 2 * (height + width) + c2 = (1 - min_overlap) * width * height + sq2 = np.sqrt(b2 ** 2 - 4 * a2 * c2) + r2 = (b2 + sq2) / 2 + + a3 = 4 * min_overlap + b3 = -2 * min_overlap * (height + width) + c3 = (min_overlap - 1) * width * height + sq3 = np.sqrt(b3 ** 2 - 4 * a3 * c3) + r3 = (b3 + sq3) / 2 + return min(r1, r2, r3) + + +def gaussian2D(shape, sigma=1): + m, n = [(ss - 1.) / 2. for ss in shape] + y, x = np.ogrid[-m:m+1,-n:n+1] + + h = np.exp(-(x * x + y * y) / (2 * sigma * sigma)) + h[h < np.finfo(h.dtype).eps * h.max()] = 0 + return h + +def draw_umich_gaussian_wh(heatmap, center, r_w, r_h, k=1): + diameter_x = 2 * r_w + 1 + diameter_y = 2 * r_h + 1 + gaussian = gaussian2D((diameter_y, diameter_x), sigma=min(r_w,r_h) / 6) + + x, y = int(center[0]), int(center[1]) + + height, width = heatmap.shape[0:2] + + left, right = min(x, r_w), min(width - x, r_w + 1) + top, bottom = min(y, r_h), min(height - y, r_h + 1) + + masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right] + masked_gaussian = gaussian[r_h - top:r_h + bottom, r_w - left:r_w + right] + if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug + np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap) + return heatmap + + + +def draw_umich_gaussian(heatmap, center, radius, k=1): + diameter = 2 * radius + 1 + gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6) + + x, y = int(center[0]), int(center[1]) + + height, width = heatmap.shape[0:2] + + left, right = min(x, radius), min(width - x, radius + 1) + top, bottom = min(y, radius), min(height - y, radius + 1) + + masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right] + masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:radius + right] + if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug + np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap) + return heatmap + +def draw_dense_reg(regmap, heatmap, center, value, radius, is_offset=False): + diameter = 2 * radius + 1 + gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6) + value = np.array(value, dtype=np.float32).reshape(-1, 1, 1) + dim = value.shape[0] + reg = np.ones((dim, diameter*2+1, diameter*2+1), dtype=np.float32) * value + if is_offset and dim == 2: + delta = np.arange(diameter*2+1) - radius + reg[0] = reg[0] - delta.reshape(1, -1) + reg[1] = reg[1] - delta.reshape(-1, 1) + + x, y = int(center[0]), int(center[1]) + + height, width = heatmap.shape[0:2] + + left, right = min(x, radius), min(width - x, radius + 1) + top, bottom = min(y, radius), min(height - y, radius + 1) + + masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right] + masked_regmap = regmap[:, y - top:y + bottom, x - left:x + right] + masked_gaussian = gaussian[radius - top:radius + bottom, + radius - left:radius + right] + masked_reg = reg[:, radius - top:radius + bottom, + radius - left:radius + right] + if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug + idx = (masked_gaussian >= masked_heatmap).reshape( + 1, masked_gaussian.shape[0], masked_gaussian.shape[1]) + masked_regmap = (1-idx) * masked_regmap + idx * masked_reg + regmap[:, y - top:y + bottom, x - left:x + right] = masked_regmap + return regmap + + +def draw_msra_gaussian(heatmap, center, sigma): + tmp_size = sigma * 3 + mu_x = int(center[0] + 0.5) + mu_y = int(center[1] + 0.5) + w, h = heatmap.shape[0], heatmap.shape[1] + ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)] + br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)] + if ul[0] >= h or ul[1] >= w or br[0] < 0 or br[1] < 0: + return heatmap + size = 2 * tmp_size + 1 + x = np.arange(0, size, 1, np.float32) + y = x[:, np.newaxis] + x0 = y0 = size // 2 + g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2)) + g_x = max(0, -ul[0]), min(br[0], h) - ul[0] + g_y = max(0, -ul[1]), min(br[1], w) - ul[1] + img_x = max(0, ul[0]), min(br[0], h) + img_y = max(0, ul[1]), min(br[1], w) + heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]] = np.maximum( + heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]], + g[g_y[0]:g_y[1], g_x[0]:g_x[1]]) + return heatmap + +def grayscale(image): + return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +def lighting_(data_rng, image, alphastd, eigval, eigvec): + alpha = data_rng.normal(scale=alphastd, size=(3, )) + image += np.dot(eigvec, eigval * alpha) + +def blend_(alpha, image1, image2): + image1 *= alpha + image2 *= (1 - alpha) + image1 += image2 + +def saturation_(data_rng, image, gs, gs_mean, var): + alpha = 1. + data_rng.uniform(low=-var, high=var) + blend_(alpha, image, gs[:, :, None]) + +def brightness_(data_rng, image, gs, gs_mean, var): + alpha = 1. + data_rng.uniform(low=-var, high=var) + image *= alpha + +def contrast_(data_rng, image, gs, gs_mean, var): + alpha = 1. + data_rng.uniform(low=-var, high=var) + blend_(alpha, image, gs_mean) + +def color_aug(data_rng, image, eig_val, eig_vec): + functions = [brightness_, contrast_, saturation_] + random.shuffle(functions) + + gs = grayscale(image) + gs_mean = gs.mean() + for f in functions: + f(data_rng, image, gs, gs_mean, 0.4) + lighting_(data_rng, image, 0.1, eig_val, eig_vec) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/oracle_utils.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/oracle_utils.py new file mode 100644 index 00000000..d54c35e8 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/oracle_utils.py @@ -0,0 +1,42 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import numba + +@numba.jit(nopython=True, nogil=True) +def gen_oracle_map(feat, ind, w, h): + # feat: B x maxN x featDim + # ind: B x maxN + batch_size = feat.shape[0] + max_objs = feat.shape[1] + feat_dim = feat.shape[2] + out = np.zeros((batch_size, feat_dim, h, w), dtype=np.float32) + vis = np.zeros((batch_size, h, w), dtype=np.uint8) + ds = [(0, 1), (0, -1), (1, 0), (-1, 0)] + for i in range(batch_size): + queue_ind = np.zeros((h*w*2, 2), dtype=np.int32) + queue_feat = np.zeros((h*w*2, feat_dim), dtype=np.float32) + head, tail = 0, 0 + for j in range(max_objs): + if ind[i][j] > 0: + x, y = ind[i][j] % w, ind[i][j] // w + out[i, :, y, x] = feat[i][j] + vis[i, y, x] = 1 + queue_ind[tail] = x, y + queue_feat[tail] = feat[i][j] + tail += 1 + while tail - head > 0: + x, y = queue_ind[head] + f = queue_feat[head] + head += 1 + for (dx, dy) in ds: + xx, yy = x + dx, y + dy + if xx >= 0 and yy >= 0 and xx < w and yy < h and vis[i, yy, xx] < 1: + out[i, :, yy, xx] = f + vis[i, yy, xx] = 1 + queue_ind[tail] = xx, yy + queue_feat[tail] = f + tail += 1 + return out \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/post_process.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/post_process.py new file mode 100644 index 00000000..8fcc12e9 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/post_process.py @@ -0,0 +1,115 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +from .image import transform_preds, transform_preds_upper_left + + +def get_pred_depth(depth): + return depth + +def get_alpha(rot): + # output: (B, 8) [bin1_cls[0], bin1_cls[1], bin1_sin, bin1_cos, + # bin2_cls[0], bin2_cls[1], bin2_sin, bin2_cos] + # return rot[:, 0] + idx = rot[:, 1] > rot[:, 5] + alpha1 = np.arctan(rot[:, 2] / rot[:, 3]) + (-0.5 * np.pi) + alpha2 = np.arctan(rot[:, 6] / rot[:, 7]) + ( 0.5 * np.pi) + return alpha1 * idx + alpha2 * (1 - idx) + + +def ddd_post_process_2d(dets, c, s, opt): + # dets: batch x max_dets x dim + # return 1-based class det list + ret = [] + include_wh = dets.shape[2] > 16 + for i in range(dets.shape[0]): + top_preds = {} + dets[i, :, :2] = transform_preds( + dets[i, :, 0:2], c[i], s[i], (opt.output_w, opt.output_h)) + classes = dets[i, :, -1] + for j in range(opt.num_classes): + inds = (classes == j) + top_preds[j + 1] = np.concatenate([ + dets[i, inds, :3].astype(np.float32), + get_alpha(dets[i, inds, 3:11])[:, np.newaxis].astype(np.float32), + get_pred_depth(dets[i, inds, 11:12]).astype(np.float32), + dets[i, inds, 12:15].astype(np.float32)], axis=1) + if include_wh: + top_preds[j + 1] = np.concatenate([ + top_preds[j + 1], + transform_preds( + dets[i, inds, 15:17], c[i], s[i], (opt.output_w, opt.output_h)) + .astype(np.float32)], axis=1) + ret.append(top_preds) + return ret + +def ctdet_4ps_post_process(dets, c, s, h, w, num_classes,rot=0): + # dets: batch x max_dets x dim + # return 1-based class det dict + ret = [] + + for i in range(dets.shape[0]): + top_preds = {} + dets[i, :, 0:2] = transform_preds(dets[i, :, 0:2], c[i], s[i], (w, h),rot) + dets[i, :, 2:4] = transform_preds(dets[i, :, 2:4], c[i], s[i], (w, h),rot) + dets[i, :, 4:6] = transform_preds(dets[i, :, 4:6], c[i], s[i], (w, h),rot) + dets[i, :, 6:8] = transform_preds(dets[i, :, 6:8], c[i], s[i], (w, h),rot) + classes = dets[i, :, -1] + for j in range(num_classes): + inds = (classes == j) + top_preds[j + 1] = np.concatenate([ + dets[i, inds, :8].astype(np.float32), + dets[i, inds, 8:9].astype(np.float32)], axis=1).tolist() + ret.append(top_preds) + return ret + +def ctdet_4ps_post_process_upper_left(dets, c, s, h, w, num_classes,rot=0): + # dets: batch x max_dets x dim + # return 1-based class det dict + ret = [] + + for i in range(dets.shape[0]): + top_preds = {} + dets[i, :, 0:2] = transform_preds_upper_left(dets[i, :, 0:2], c[i], s[i], (w, h),rot) + dets[i, :, 2:4] = transform_preds_upper_left(dets[i, :, 2:4], c[i], s[i], (w, h),rot) + dets[i, :, 4:6] = transform_preds_upper_left(dets[i, :, 4:6], c[i], s[i], (w, h),rot) + dets[i, :, 6:8] = transform_preds_upper_left(dets[i, :, 6:8], c[i], s[i], (w, h),rot) + classes = dets[i, :, -1] + for j in range(num_classes): + inds = (classes == j) + top_preds[j + 1] = np.concatenate([ + dets[i, inds, :8].astype(np.float32), + dets[i, inds, 8:9].astype(np.float32)], axis=1).tolist() + ret.append(top_preds) + return ret + +def ctdet_post_process(dets, c, s, h, w, num_classes): + # dets: batch x max_dets x dim + # return 1-based class det dict + ret = [] + for i in range(dets.shape[0]): + top_preds = {} + dets[i, :, :2] = transform_preds( + dets[i, :, 0:2], c[i], s[i], (w, h)) + dets[i, :, 2:4] = transform_preds( + dets[i, :, 2:4], c[i], s[i], (w, h)) + classes = dets[i, :, -1] + for j in range(num_classes): + inds = (classes == j) + top_preds[j + 1] = np.concatenate([ + dets[i, inds, :4].astype(np.float32), + dets[i, inds, 4:5].astype(np.float32)], axis=1).tolist() + ret.append(top_preds) + return ret + +def ctdet_corner_post_process(corner_st_reg, c, s, h, w, num_classes): + for i in range(corner_st_reg.shape[0]): + corner_st_reg[i, :, 0:2] = transform_preds(corner_st_reg[i, :, 0:2], c[i], s[i], (w, h)) + corner_st_reg[i, :, 2:4] = transform_preds(corner_st_reg[i, :, 2:4], c[i], s[i], (w, h)) + corner_st_reg[i, :, 4:6] = transform_preds(corner_st_reg[i, :, 4:6], c[i], s[i], (w, h)) + corner_st_reg[i, :, 6:8] = transform_preds(corner_st_reg[i, :, 6:8], c[i], s[i], (w, h)) + corner_st_reg[i, :, 8:10] = transform_preds(corner_st_reg[i, :, 8:10], c[i], s[i], (w, h)) + return corner_st_reg + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/utils.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/utils.py new file mode 100644 index 00000000..fa005740 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/lib/utils/utils.py @@ -0,0 +1,218 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torch +from utils.image import flip, color_aug +from utils.image import get_affine_transform, affine_transform +from utils.image import gaussian_radius, draw_umich_gaussian, draw_umich_gaussian_wh, draw_msra_gaussian +from utils.image import draw_dense_reg +from utils.adjacency import adjacency + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + + self.val = val + self.sum += val * n + if self.val != 0: + self.count += n + if self.count > 0: + self.avg = self.sum / self.count + +def make_batch(opt, path, anns): + max_objs = 300 + max_cors = 1200 + num_objs = min(len(anns), max_objs) + num_classes = 2 + #print(anns[1]) + _valid_ids = [1,2] + cat_ids = {v: i for i, v in enumerate(_valid_ids)} + img_path = path + + mean = np.array([0.40789654, 0.44719302, 0.47026115], dtype=np.float32).reshape(1, 1, 3) + std = np.array([0.28863828, 0.27408164, 0.27809835], dtype=np.float32).reshape(1, 1, 3) + + img = cv2.imread(img_path) + img_size = img.shape + + height, width = img.shape[0], img.shape[1] + c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32) + if opt.keep_res: + input_h = (height | opt.pad)# + 1 + input_w = (width | opt.pad)# + 1 + s = np.array([input_w, input_h], dtype=np.float32) + else: + s = max(img.shape[0], img.shape[1]) * 1.0 + input_h, input_w = opt.input_h, opt.input_w + + flipped = False + + rot = 0 + if opt.rotate==1: + print('----rotate----') + rot = np.random.randint(-15,15) + trans_input = get_affine_transform( + c, s, rot, [input_w, input_h]) + + output_h = input_h // opt.down_ratio + output_w = input_w // opt.down_ratio + num_classes = num_classes + trans_output = get_affine_transform(c, s, rot, [output_w, output_h]) + trans_output_mk = get_affine_transform(c, s, rot, [output_w, output_h]) + + hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32) + wh = np.zeros((max_objs, 8), dtype=np.float32) + reg = np.zeros((max_objs*5, 2), dtype=np.float32) + st = np.zeros((max_cors, 8), dtype=np.float32) + hm_ctxy = np.zeros((max_objs, 2), dtype=np.float32) + hm_xy = np.zeros((max_objs, 10), dtype=np.float32) + hm_ind = np.zeros((max_objs), dtype=np.int64) + hm_mask = np.zeros((max_objs), dtype=np.uint8) + mk_ind = np.zeros((max_cors), dtype=np.int64) + mk_mask = np.zeros((max_cors), dtype=np.uint8) + reg_ind = np.zeros((max_objs*5), dtype=np.int64) + reg_mask = np.zeros((max_objs*5), dtype=np.uint8) + ctr_cro_ind = np.zeros((max_objs*4), dtype=np.int64) + log_ax = np.zeros((max_objs, 4), dtype=np.int64) + box_ind = np.zeros((max_objs, 8), dtype=np.int64) + cc_match = np.zeros((max_objs, 4), dtype=np.int64) + adjacent = np.zeros((max_objs, max_objs), dtype=np.int64) + adj_mask = np.zeros((max_objs, max_objs), dtype=np.int64) + center = np.zeros((max_objs, 2), dtype=np.float32) + + draw_gaussian = draw_msra_gaussian if opt.mse_loss else \ + draw_umich_gaussian + gt_det = [] + corList = [] + point = [] + inp = cv2.warpAffine(img, trans_input, (input_w, input_h),flags=cv2.INTER_LINEAR) + + for k in range(num_objs): + ann = anns[k] + #bbox = self._coco_box_to_bbox(ann['bbox']) + seg_mask = ann['segmentation'][0] #[[351.0, 73.0, 172.0, 70.0, 174.0, 127.0, 351.0, 129.0, 351.0, 73.0]] + x1,y1 = seg_mask[0],seg_mask[1] + x2,y2 = seg_mask[2],seg_mask[3] + x3,y3 = seg_mask[4],seg_mask[5] + x4,y4 = seg_mask[6],seg_mask[7] + + CorNer = np.array([x1,y1,x2,y2,x3,y3,x4,y4]) + boxes = [[CorNer[0],CorNer[1]],[CorNer[2],CorNer[3]],\ + [CorNer[4],CorNer[5]],[CorNer[6],CorNer[7]]] + cls_id = int(cat_ids[ann['category_id']]) + if flipped: + #bbox[[0, 2]] = width - bbox[[2, 0]] - 1 + CorNer[[0,2,4,6]] = width - CorNer[[2,0,6,4]] - 1 + + CorNer[0:2] = affine_transform(CorNer[0:2], trans_output_mk) + CorNer[2:4] = affine_transform(CorNer[2:4], trans_output_mk) + CorNer[4:6] = affine_transform(CorNer[4:6], trans_output_mk) + CorNer[6:8] = affine_transform(CorNer[6:8], trans_output_mk) + CorNer[[0,2,4,6]] = np.clip(CorNer[[0,2,4,6]], 0, output_w - 1) + CorNer[[1,3,5,7]] = np.clip(CorNer[[1,3,5,7]], 0, output_h - 1) + if not _judge(CorNer): + continue + + maxx = max([CorNer[2*I] for I in range(0,4)]) + minx = min([CorNer[2*I] for I in range(0,4)]) + maxy = max([CorNer[2*I+1] for I in range(0,4)]) + miny = min([CorNer[2*I+1] for I in range(0,4)]) + h, w = maxy-miny,maxx-minx#bbox[3] - bbox[1], bbox[2] - bbox[0] + if h > 0 and w > 0: + + radius = gaussian_radius((math.ceil(h), math.ceil(w))) + radius = max(0, int(radius)) + radius = opt.hm_gauss if opt.mse_loss else radius + + ct = np.array([(maxx+minx)/2.0,(maxy+miny)/2.0], dtype=np.float32) + ct_int = ct.astype(np.int32) + draw_gaussian(hm[cls_id], ct_int, radius) + + for i in range(4): + Cor = np.array([CorNer[2*i],CorNer[2*i+1]], dtype=np.float32) + Cor_int = Cor.astype(np.int32) + Cor_key = str(Cor_int[0])+"_"+str(Cor_int[1]) + if Cor_key not in corList: + + corNum = len(corList) + + corList.append(Cor_key) + reg[max_objs+corNum] = np.array([abs(Cor[0]-Cor_int[0]),abs(Cor[1]-Cor_int[1])]) + mk_ind[corNum] = Cor_int[1]*output_w + Cor_int[0] + cc_match[k][i] = mk_ind[corNum] + box_ind[k][i*2:(i+1)*2] = np.array((corNum, corNum)) + reg_ind[max_objs+corNum] = Cor_int[1]*output_w + Cor_int[0] + mk_mask[corNum] = 1 + reg_mask[max_objs+corNum] = 1 + draw_gaussian(hm[num_classes-1], Cor_int, 2) + st[corNum][i*2:(i+1)*2] = np.array([Cor[0]-ct[0],Cor[1]-ct[1]]) + ctr_cro_ind[4*k+i] = corNum*4 + i + + else: + index_of_key = corList.index(Cor_key) + cc_match[k][i] = mk_ind[index_of_key] + box_ind[k][i*2:(i+1)*2] = np.array((index_of_key, index_of_key)) + st[index_of_key][i*2:(i+1)*2] = np.array([Cor[0]-ct[0],Cor[1]-ct[1]]) + ctr_cro_ind[4*k+i] = index_of_key*4 + i + + wh[k] = ct[0] - 1. * CorNer[0], ct[1] - 1. * CorNer[1], \ + ct[0] - 1. * CorNer[2], ct[1] - 1. * CorNer[3], \ + ct[0] - 1. * CorNer[4], ct[1] - 1. * CorNer[5], \ + ct[0] - 1. * CorNer[6], ct[1] - 1. * CorNer[7] + + hm_ind[k] = ct_int[1] * output_w + ct_int[0] + hm_mask[k] = 1 + reg_ind[k] = ct_int[1] * output_w + ct_int[0] + reg_mask[k] = 1 + reg[k] = ct - ct_int + hm_ctxy[k] = ct[0],ct[1] + hm_xy[k] = ct[0], ct[1], CorNer[0], CorNer[1], CorNer[2], CorNer[3], CorNer[4], CorNer[5], CorNer[6], CorNer[7] + center[k] = (x1 + x2 + x3 + x4)/4, (y1 + y2 + y3 + y4)/4 + log_ax[k] = ann['logic_axis'][0][0], ann['logic_axis'][0][1], ann['logic_axis'][0][2], ann['logic_axis'][0][3] + gt_det.append([ct[0] - 1. * CorNer[0], ct[1] - 1. * CorNer[1], + ct[0] - 1. * CorNer[2], ct[1] - 1. * CorNer[3], + ct[0] - 1. * CorNer[4], ct[1] - 1. * CorNer[5], + ct[0] - 1. * CorNer[6], ct[1] - 1. * CorNer[7], 1, cls_id]) + for j in range(k): + box1 = log_ax[k] + box2 = log_ax[j] + if adjacency(box1, box2): + adjacent[j][k] = adjacent[k][j] = 1 + + if j == k: + adjacent[j][k] = 1 + + hm_mask_v = hm_mask.reshape(1, hm_mask.shape[0]) + adj_mask = np.dot(np.transpose(hm_mask_v), hm_mask_v) + inp = (inp.astype(np.float32) / 255.) + + inp = (inp - mean) / std + inp = inp.transpose(2, 0, 1) + hm_xy = torch.FloatTensor(hm_xy) + ret = {'input': inp, 'hm': hm, 'hm_ind':hm_ind, 'hm_mask':hm_mask, 'mk_ind':mk_ind, 'mk_mask':mk_mask, 'reg':reg,'reg_ind':reg_ind,'reg_mask': reg_mask, 'wh': wh,'st':st, 'ctr_cro_ind':ctr_cro_ind,'hm_ctxy':hm_ctxy, 'logic': log_ax, 'adj': adjacent, 'cc_match': cc_match, 'box_ind': box_ind, 'adj_mask': adj_mask, 'hm_xy': hm_xy, 'center': center} + if opt.dense_wh: + hm_a = hm.max(axis=0, keepdims=True) + dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0) + ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask}) + del ret['wh'] + elif opt.cat_spec_wh: + ret.update({'cat_spec_wh': cat_spec_wh, 'cat_spec_mask': cat_spec_mask}) + del ret['wh'] + if opt.reg_offset: + ret.update({'reg': reg}) + + gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \ + np.zeros((1, 10), dtype=np.float32) + meta = {'c': c, 's': s, 'rot':rot, 'gt_det': gt_det} + ret['meta'] = meta + return ret diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/pipeline.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/pipeline.py new file mode 100644 index 00000000..eae94cc3 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/pipeline.py @@ -0,0 +1,409 @@ +import onnxruntime as _ort +import numpy as np +import torch +import cv2 +from .lib.utils.image import get_affine_transform, get_affine_transform_upper_left +from .lib.models.decode import ctdet_decode, corner_decode, ctdet_4ps_decode +from .lib.utils.post_process import ctdet_4ps_post_process_upper_left, ctdet_4ps_post_process, ctdet_corner_post_process +import os +from .table_recover import TableRecover +from .utils_table_recover import match_ocr_cell, plot_html_table, plot_html_wireless_table +from .utils_table_recover import merge_adjacent_polys, sorted_boxes +from qanything_kernel.configs.model_config import PDF_MODEL_PATH +import markdownify +import urllib +import urllib.request +import urllib.parse +import json + + +def pre_process(image, inp_height, inp_width, upper_left=False): + height, width = image.shape[0:2] + + c = np.array([width / 2., height / 2.], dtype=np.float32) + s = max(height, width) * 1.0 + trans_input = get_affine_transform(c, s, 0, [inp_width, inp_height]) + if upper_left: + c = np.array([0, 0], dtype=np.float32) + s = max(height, width) * 1.0 + trans_input = get_affine_transform_upper_left(c, s, 0, [inp_width, inp_height]) + inp_image = cv2.warpAffine( + image, trans_input, (inp_width, inp_height), + flags=cv2.INTER_LINEAR) + + mean = np.array([0.40789654, 0.44719302, 0.47026115], + dtype=np.float32).reshape(1, 1, 3) + std = np.array([0.28863828, 0.27408164, 0.27809835], + dtype=np.float32).reshape(1, 1, 3) + + inp_image = ((inp_image / 255. - mean) / std).astype(np.float32) + + images = inp_image.transpose(2, 0, 1).reshape(1, 3, inp_height, inp_width) + + meta = {'c': c, 's': s, + 'input_height': inp_height, + 'input_width': inp_width, + 'out_height': inp_height // 4, + 'out_width': inp_width // 4} + # images = torch.from_numpy(images) + return images, meta + + +def post_process(dets, meta, corner_st, upper_left=False, scale=1): + device = dets.device + device_str = str(device) + # print('device: ',device) + if device_str.startswith('cuda'): + dets = dets.detach().cpu().numpy() + else: + dets = dets.detach().numpy() + dets = dets.reshape(1, -1, dets.shape[2]) + #return dets is list and what in dets is dict. key of dict is classes, value of dict is [bbox,score] + if upper_left: + dets = ctdet_4ps_post_process_upper_left( + dets.copy(), [meta['c']], [meta['s']], + meta['out_height'], meta['out_width'], 2) + else: + dets = ctdet_4ps_post_process( + dets.copy(), [meta['c']], [meta['s']], + meta['out_height'], meta['out_width'], 2) + corner_st = ctdet_corner_post_process( + corner_st.copy(), [meta['c']], [meta['s']], + meta['out_height'], meta['out_width'], 2) + for j in range(1, 3): + dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 9) + dets[0][j][:, :8] /= scale + return dets[0], corner_st[0] + + +def merge_outputs(detections): + results = {} + for j in range(1, 3): + results[j] = np.concatenate( + [detection[j] for detection in detections], axis=0).astype(np.float32) + scores = np.hstack( + [results[j][:, 8] for j in range(1, 3)]) + if len(scores) > 3000: + kth = len(scores) - 3000 + thresh = np.partition(scores, kth)[kth] + for j in range(1, 3): + keep_inds = (results[j][:, 8] >= thresh) + results[j] = results[j][keep_inds] + return results + + +def filter(results, logi, ps, vis_thresh): + # this function select boxes + device = logi.device + batch_size, feat_dim = logi.shape[0], logi.shape[2] + num_valid = sum(results[1][:, 8] >= vis_thresh) + + #if num_valid <= 900 : #opt.max_objs + slct_logi = np.zeros((batch_size, num_valid, feat_dim), dtype=np.float32) + slct_dets = np.zeros((batch_size, num_valid, 8), dtype=np.int32) + for i in range(batch_size): + for j in range(num_valid): + slct_logi[i, j, :] = logi[i, j, :].cpu() + slct_dets[i, j, :] = ps[i, j, :].cpu() + return torch.Tensor(slct_logi).to(device), torch.Tensor(slct_dets).to(device) + + +def _normalized_ps(ps, vocab_size): + device = ps.device + ps = torch.round(ps).to(torch.int64) + ps = torch.where(ps < vocab_size, ps, (vocab_size - 1) * torch.ones(ps.shape).to(torch.int64).to(device)) + ps = torch.where(ps >= 0, ps, torch.zeros(ps.shape).to(torch.int64).to(device)) + return ps + + +def process_logi(logi): + logi_floor = logi.floor() + dev = logi - logi_floor + logi = torch.where(dev > 0.5, logi_floor + 1, logi_floor) + return logi + + +def add_4ps_coco_bbox(image, bbox, logi=None): + bbox = np.array(bbox, dtype=np.int32) + + if not logi is None: + txt = '{:.0f},{:.0f},{:.0f},{:.0f}'.format(logi[0], logi[1], logi[2], logi[3]) + font = cv2.FONT_HERSHEY_SIMPLEX + cat_size = cv2.getTextSize(txt, font, 0.3, 2)[0] + cv2.line(image, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 0, 255), 2) + cv2.line(image, (bbox[2], bbox[3]), (bbox[4], bbox[5]), (0, 0, 255), 2) + cv2.line(image, (bbox[4], bbox[5]), (bbox[6], bbox[7]), (0, 0, 255), 2) + cv2.line(image, (bbox[6], bbox[7]), (bbox[0], bbox[1]), (0, 0, 255), 2) # 1 - 5 + + if not logi is None: + cv2.rectangle(image, + (bbox[0], bbox[1] - cat_size[1] - 2), + (bbox[0] + cat_size[0], bbox[1] - 2), (255, 128, 128), -1) + cv2.putText(image, txt, (bbox[0], bbox[1] - 2), + font, 0.30, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA) #1 - 5 # 0.20 _ 0.60 + + return image + + +def show_results(results, corner, logi=None): + m, n = corner.shape + polygons = [] + + k = 0 + for m in range(len(results[1])): + bbox = results[1][m] + k = k + 1 + if bbox[8] >= 0.4: + polygons.append([[bbox[0], bbox[1]], [bbox[6], bbox[7]], [bbox[4], bbox[5]], [bbox[2], bbox[3]]]) + # if len(logi.shape) == 1: + # add_4ps_coco_bbox(image, bbox[:8], logi) + # else: + # add_4ps_coco_bbox(image, bbox[:8], logi[m,:]) + return polygons + + +# def sort_logi_by_polygons( +# sorted_polygons: np.ndarray, polygons: np.ndarray, logi_points: np.ndarray +# ) -> np.ndarray: +# sorted_idx = [] +# for v in sorted_polygons: +# loc_idx = np.argwhere(v[0, 0] == polygons[:, 0, 0]).squeeze() +# sorted_idx.append(int(loc_idx)) +# logi_points = logi_points[sorted_idx] +# return logi_points + +def sort_logi_by_polygons( + sorted_polygons: np.ndarray, polygons: np.ndarray, logi_points: np.ndarray +) -> np.ndarray: + sorted_idx = [] + for v in sorted_polygons: + loc_idx = np.argwhere((v[0] == polygons[:, 0]).all(axis=1)).squeeze() + sorted_idx.append(int(loc_idx)) + logi_points = logi_points[sorted_idx] + return logi_points + + +def sort_logi( + sorted_polygons: np.ndarray, logi_points: np.ndarray +) -> np.ndarray: + sorted_idx = [] + sorted_logi_points = np.array(sorted(logi_points, key=lambda x: (x[0], x[2]))) + for v in sorted_logi_points: + loc_idx = np.argwhere((logi_points == v).all(axis=1)).squeeze() + if loc_idx.size > 1: + loc_idx = loc_idx[0] + sorted_idx.append(int(loc_idx)) + sorted_polygons = sorted_polygons[sorted_idx] + return sorted_logi_points, sorted_polygons + + +def wired_table_rec(image_path): + # onnx_model = '/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/wired_model.onnx' + # sess1 = _ort.InferenceSession(onnx_model, None, providers=['CUDAExecutionProvider']) + model = torch.jit.load( + '/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/wired_model.pt').to('cuda') + model.eval() + onnx_processor = '/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/wired_processor.onnx' + sess2 = _ort.InferenceSession(onnx_processor, None, providers=['CUDAExecutionProvider']) + table_recover = TableRecover() + image = cv2.imread(image_path) + input, meta = pre_process(image, 1024, 1024) + # hm, st, wh, ax, cr, reg = sess1.run(None, {'image': input}) + # hm = torch.from_numpy(hm).sigmoid_().cuda() + # st, wh, ax, cr, reg = torch.from_numpy(st).cuda(), torch.from_numpy(wh).cuda(), torch.from_numpy(ax).cuda(), torch.from_numpy(cr).cuda(), torch.from_numpy(reg).cuda() + hm, st, wh, ax, cr, reg = model(torch.from_numpy(input).cuda()) + hm = hm.sigmoid_().detach().cuda() + st, wh, ax, cr, reg = st.detach().cuda(), wh.detach().cuda(), ax.detach().cuda(), cr.detach().cuda(), reg.detach().cuda() + scores, inds, ys, xs, st_reg, corner_dict = corner_decode(hm[:, 1:2, :, :], st, reg, K=1000) + dets, keep, logi, cr = ctdet_4ps_decode(hm[:, 0:1, :, :], wh, ax, cr, corner_dict, reg=reg, K=3000, wiz_rev=True) + raw_dets = dets + corner_output = np.concatenate((np.transpose(xs.cpu().numpy()), np.transpose(ys.cpu().numpy()), + np.array(st_reg.cpu().numpy()), np.transpose(scores.cpu().numpy())), axis=2) + dets, corner_st_reg = post_process(dets, meta, corner_output) + results = merge_outputs([dets]) + logi = logi + cr + slct_logi, slct_dets = filter(results, logi, raw_dets[:, :, :8], 0.15) + slct_dets = _normalized_ps(slct_dets, 256) + _, slct_logi = sess2.run(None, {'vis_feat': slct_logi.cpu().numpy()}) + slct_logi = process_logi(torch.from_numpy(slct_logi).cuda()) + polygons = show_results(results, corner_st_reg, slct_logi.squeeze()) + polygons = np.array(polygons) + polygons = sorted_boxes(polygons) + try: + polygons = merge_adjacent_polys(polygons) + except: + polygons = polygons + table_res = table_recover(polygons) + + return polygons, table_res + + +def wireless_table_rec(image_path): + onnx_model = '/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/wireless_model.onnx' + sess1 = _ort.InferenceSession(onnx_model, None, providers=['CUDAExecutionProvider']) + onnx_processor = '/ssd8/exec/huangjy/AdvancedLiterateMachinery/DocumentUnderstanding/LORE-TSR/src/wireless_processor.onnx' + sess2 = _ort.InferenceSession(onnx_processor, None, providers=['CUDAExecutionProvider']) + image = cv2.imread(image_path) + input, meta = pre_process(image, 768, 768, upper_left=True) + hm, st, wh, ax, cr, reg = sess1.run(None, {'image': input}) + hm = torch.from_numpy(hm).sigmoid_().cuda() + st, wh, ax, cr, reg = torch.from_numpy(st).cuda(), torch.from_numpy(wh).cuda(), torch.from_numpy( + ax).cuda(), torch.from_numpy(cr).cuda(), torch.from_numpy(reg).cuda() + scores, inds, ys, xs, st_reg, corner_dict = corner_decode(hm[:, 1:2, :, :], st, reg, K=5000) + dets, keep, logi, cr = ctdet_4ps_decode(hm[:, 0:1, :, :], wh, ax, cr, corner_dict, reg=reg, K=3000, wiz_rev=False) + raw_dets = dets + corner_output = np.concatenate( + (np.transpose(xs.cpu()), np.transpose(ys.cpu()), np.array(st_reg.cpu()), np.transpose(scores.cpu())), axis=2) + print(dets.device) + dets, corner_st_reg = post_process(dets, meta, corner_output, upper_left=True) + results = merge_outputs([dets]) + logi = logi + cr + slct_logi, slct_dets = filter(results, logi, raw_dets[:, :, :8], 0.2) + slct_dets = _normalized_ps(slct_dets, 256) + # slct_dets = slct_dets.to(torch.float) + _, slct_logi = sess2.run(None, {'slct_logi_feat': slct_logi.cpu().numpy(), 'dets': slct_dets.cpu().numpy()}) + slct_logi = process_logi(torch.from_numpy(slct_logi).cuda()) + polygons = show_results(results, corner_st_reg, slct_logi.squeeze()) + polygons = np.array(polygons) + sorted_polygons = sorted_boxes(polygons) + + return polygons, sorted_polygons, slct_logi[0].cpu().numpy() + + +def request_service(input_dict, url): + data = urllib.parse.urlencode(input_dict).encode("utf-8") + f = urllib.request.urlopen(url=url, data=data) + res_dict = json.loads(json.loads(f.read())['Result']) + + return res_dict + + +class TableParser(object): + def __init__(self, device=torch.device("cpu")): + # self.wired_model_stage1 = torch.jit.load('layout/table_rec/checkpoint/wired_model.pt').to('cuda') + self.device = device + print(self.device) + self.device = device + if self.device == torch.device('cuda'): + onnx_backend = 'CUDAExecutionProvider' + else: + onnx_backend = 'CPUExecutionProvider' + + self.wired_model_stage1 = torch.jit.load(os.path.join(PDF_MODEL_PATH, 'checkpoints/table/wired_model.pt')).to(self.device) + onnx_processor = os.path.join(PDF_MODEL_PATH, 'checkpoints/table/wired_processor.onnx') + self.wired_model_stage2 = _ort.InferenceSession(onnx_processor, None, providers=[onnx_backend]) + wireless_onnx1 = os.path.join(PDF_MODEL_PATH, 'checkpoints/table/wireless_model.onnx') + self.wireless_model_stage1 = _ort.InferenceSession(wireless_onnx1, None, providers=[onnx_backend]) + wireless_onnx2 = os.path.join(PDF_MODEL_PATH, 'checkpoints/table/wireless_processor.onnx') + self.wireless_model_stage2 = _ort.InferenceSession(wireless_onnx2, None, providers=[onnx_backend]) + self.table_recover = TableRecover() + + def wired_rec(self, image): + input, meta = pre_process(image, 1024, 1024) + + hm, st, wh, ax, cr, reg = self.wired_model_stage1(torch.from_numpy(input).to(self.device)) + hm = hm.sigmoid_().detach().to(self.device) + st, wh, ax, cr, reg = st.detach().to(self.device), wh.detach().to(self.device), ax.detach().to( + self.device), cr.detach().to(self.device), reg.detach().to(self.device) + scores, inds, ys, xs, st_reg, corner_dict = corner_decode(hm[:, 1:2, :, :], st, reg, K=1000, device=self.device) + # print(hm.device) + dets, keep, logi, cr = ctdet_4ps_decode(hm[:, 0:1, :, :], wh, ax, cr, corner_dict, reg=reg, K=600, wiz_rev=True) + raw_dets = dets + if self.device == torch.device('cuda'): + corner_output = np.concatenate((np.transpose(xs.cpu().numpy()), np.transpose(ys.cpu().numpy()), + np.array(st_reg.cpu().numpy()), np.transpose(scores.cpu().numpy())), axis=2) + else: + corner_output = np.concatenate(( + np.transpose(xs.numpy()), np.transpose(ys.numpy()), np.array(st_reg.numpy()), + np.transpose(scores.numpy())), axis=2) + dets, corner_st_reg = post_process(dets, meta, corner_output) + results = merge_outputs([dets]) + logi = logi + cr + slct_logi, slct_dets = filter(results, logi, raw_dets[:, :, :8], 0.15) + if self.device == torch.device('cuda'): + _, slct_logi = self.wired_model_stage2.run(None, {'vis_feat': slct_logi.cpu().numpy()}) + slct_logi = process_logi(torch.from_numpy(slct_logi).to(self.device)) + else: + _, slct_logi = self.wired_model_stage2.run(None, {'vis_feat': slct_logi.numpy()}) + slct_logi = process_logi(torch.from_numpy(slct_logi).to(self.device)) + polygons = show_results(results, corner_st_reg, slct_logi.squeeze()) + polygons = np.array(polygons) + polygons = sorted_boxes(polygons) + try: + polygons = merge_adjacent_polys(polygons) + except: + polygons = polygons + table_res = self.table_recover(polygons) + return polygons, table_res + + def wireless_rec(self, image): + input, meta = pre_process(image, 768, 768, upper_left=True) + + hm, st, wh, ax, cr, reg = self.wireless_model_stage1.run(None, {'image': input}) + hm = torch.from_numpy(hm).sigmoid_().to(self.device) + st, wh, ax, cr, reg = torch.from_numpy(st).to(self.device), torch.from_numpy(wh).to( + self.device), torch.from_numpy(ax).to(self.device), torch.from_numpy(cr).to(self.device), torch.from_numpy( + reg).to(self.device) + scores, inds, ys, xs, st_reg, corner_dict = corner_decode(hm[:, 1:2, :, :], st, reg, K=1000, device=self.device) + dets, keep, logi, cr = ctdet_4ps_decode(hm[:, 0:1, :, :], wh, ax, cr, corner_dict, reg=reg, K=600, + wiz_rev=False) + raw_dets = dets + if self.device == torch.device('cuda'): + corner_output = np.concatenate( + (np.transpose(xs.cpu()), np.transpose(ys.cpu()), np.array(st_reg.cpu()), np.transpose(scores.cpu())), + axis=2) + else: + corner_output = np.concatenate((np.transpose(xs), np.transpose(ys), np.array(st_reg), np.transpose(scores)), + axis=2) + dets, corner_st_reg = post_process(dets, meta, corner_output, upper_left=True) + results = merge_outputs([dets]) + logi = logi + cr + slct_logi, slct_dets = filter(results, logi, raw_dets[:, :, :8], 0.2) + slct_dets = _normalized_ps(slct_dets, 256) + if self.device == torch.device('cuda'): + _, slct_logi = self.wireless_model_stage2.run(None, {'slct_logi_feat': slct_logi.cpu().numpy(), + 'dets': slct_dets.cpu().numpy()}) + slct_logi = process_logi(torch.from_numpy(slct_logi).to(self.device)) + else: + _, slct_logi = self.wireless_model_stage2.run(None, {'slct_logi_feat': slct_logi.numpy(), + 'dets': slct_dets.numpy()}) + slct_logi = process_logi(torch.from_numpy(slct_logi).to(self.device)) + polygons = show_results(results, corner_st_reg, slct_logi.squeeze()) + polygons = np.array(polygons) + sorted_polygons = sorted_boxes(polygons) + return polygons, sorted_polygons, slct_logi[0].cpu().numpy() + + def process(self, image, table_type, ocr_result, convert2markdown=True): + if table_type == 'wired': + polygons, table_res = self.wired_rec(image) + else: + polygons, sorted_polygons, slct_logi = self.wireless_rec(image) + if table_type == 'wireless': + slct_logi = sort_logi_by_polygons( + sorted_polygons, polygons, slct_logi + ) + sorted_logi_points, sorted_polygons = sort_logi(sorted_polygons, slct_logi) + cell_box_map, _, _ = match_ocr_cell(sorted_polygons, ocr_result) + table_str = plot_html_wireless_table(sorted_logi_points, cell_box_map) + if convert2markdown: + table_markdown = html2markdown(table_str) + return table_str, table_markdown + else: + cell_box_map, head_box_map, tail_box_map = match_ocr_cell(polygons, ocr_result) + table_str = plot_html_table(table_res, cell_box_map, head_box_map, tail_box_map) + if convert2markdown: + table_markdown = html2markdown(table_str) + return table_str, table_markdown + + +def html2markdown(html_text): + markdown_text = markdownify.markdownify(html_text) + return markdown_text + + +# if __name__ == '__main__': +# parser = TableParser() +# image = cv2.imread('test.jpg') +# table_str, table_markdown = parser.process(image, 'wireless') +# +# print(table_markdown) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wired.html b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wired.html new file mode 100644 index 00000000..a86d9aa2 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wired.html @@ -0,0 +1 @@ +
简介自我
姓名：笔名：绰号：性别：
民族：出生年月：毕业学校：家庭住址：
血型：QQ:E-mail :电话：
家庭成员：姓名：工作单位：联系方式：
父亲
母亲
点击自我描述自我（优美的散文笔调）
爱好：喜欢的食物：
性格：崇拜的偶像：
特长：喜欢阅读的书籍：
喜欢的颜色：喜欢怎样的老师：
\ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wired.jpg b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wired.jpg new file mode 100644 index 00000000..20411f1e Binary files /dev/null and b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wired.jpg differ diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wireless.html b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wireless.html new file mode 100644 index 00000000..ee967181 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wireless.html @@ -0,0 +1,5 @@ +
Block functionHardware Specification level (HSL)
HSL-0HSL-1HSL-2HSL-3
LowTypicalUpLowTypicalUpLowTypicalUpLowTypicalUp
Actuators0.000.000.000.030.060.120.170.331.034.126.19
Casing0.000.000.000.040.270.630.882.133.143.064.265.93
Connectivity0.00 +0.000.000000.080.170.340530.821.261.211361.63
Memory0.000.000.000.050.180.740.821.913.631.883.737.27
Others0.060.110.140.140.220.310.280410.540.490.640.85
Others +PCB0.130.160.240.240.570.811.121.603.202.563.844.80
Power supply0.180.520.660.020.180.380.251.362.710.371.723.32
Processing0.080.170.290.661.051.601.581.942.313.133.98
Security0.000.000.000.010.020.03N/AN/AN/AN/AN/AN/A
Sensing0.000.000.000.010.020.040.000.030.040.190.771.47
Transport0.00 +0.000.000.000.18 +0.030.401.351.072.624.053.346.309.34
UI0.000.000.000.030.060.120.190.752.600.170.612.01
\ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wireless.jpg b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wireless.jpg new file mode 100644 index 00000000..837bc4bb Binary files /dev/null and b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/results/wireless.jpg differ diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/table_recover.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/table_recover.py new file mode 100644 index 00000000..61238703 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/table_recover.py @@ -0,0 +1,217 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +from typing import Dict, List, Tuple + +import numpy as np + + +class TableRecover: + def __init__( + self, + ): + pass + + def __call__(self, polygons: np.ndarray) -> Dict[int, Dict]: + rows = self.get_rows(polygons) + longest_col, each_col_widths, col_nums = self.get_benchmark_cols(rows, polygons) + each_row_heights, row_nums = self.get_benchmark_rows(rows, polygons) + table_res = self.get_merge_cells( + polygons, + rows, + row_nums, + col_nums, + longest_col, + each_col_widths, + each_row_heights, + ) + return table_res + + @staticmethod + def get_rows(polygons: np.array) -> Dict[int, List[int]]: + """对每个框进行行分类，框定哪个是一行的""" + y_axis = polygons[:, 0, 1] + if y_axis.size == 1: + return {0: [0]} + + concat_y = np.array(list(zip(y_axis, y_axis[1:]))) + minus_res = concat_y[:, 1] - concat_y[:, 0] + + result = {} + thresh = 5.0 + split_idxs = np.argwhere(minus_res > thresh).squeeze() + if split_idxs.ndim == 0: + split_idxs = split_idxs[None, ...] + + if max(split_idxs) != len(minus_res): + split_idxs = np.append(split_idxs, len(minus_res)) + + start_idx = 0 + for row_num, idx in enumerate(split_idxs): + if row_num != 0: + start_idx = split_idxs[row_num - 1] + 1 + result.setdefault(row_num, []).extend(range(start_idx, idx + 1)) + + # 计算每一行相邻cell的iou，如果大于0.2，则合并为同一个cell + return result + + def get_benchmark_cols( + self, rows: Dict[int, List], polygons: np.ndarray + ) -> Tuple[np.ndarray, List[float], int]: + longest_col = max(rows.values(), key=lambda x: len(x)) + longest_col_points = polygons[longest_col] + longest_x = longest_col_points[:, 0, 0] + + theta = 10 + for row_value in rows.values(): + cur_row = polygons[row_value][:, 0, 0] + + range_res = {} + for idx, cur_v in enumerate(cur_row): + start_idx, end_idx = None, None + for i, v in enumerate(longest_x): + if cur_v - theta <= v <= cur_v + theta: + break + + if cur_v > v: + start_idx = i + continue + + if cur_v < v: + end_idx = i + break + + range_res[idx] = [start_idx, end_idx] + + sorted_res = dict( + sorted(range_res.items(), key=lambda x: x[0], reverse=True) + ) + for k, v in sorted_res.items(): + if not all(v): + continue + + longest_x = np.insert(longest_x, v[1], cur_row[k]) + longest_col_points = np.insert( + longest_col_points, v[1], polygons[row_value[k]], axis=0 + ) + + # 求出最右侧所有cell的宽，其中最小的作为最后一列宽度 + rightmost_idxs = [v[-1] for v in rows.values()] + rightmost_boxes = polygons[rightmost_idxs] + min_width = min([self.compute_L2(v[3, :], v[0, :]) for v in rightmost_boxes]) + + each_col_widths = (longest_x[1:] - longest_x[:-1]).tolist() + each_col_widths.append(min_width) + + col_nums = longest_x.shape[0] + return longest_col_points, each_col_widths, col_nums + + def get_benchmark_rows( + self, rows: Dict[int, List], polygons: np.ndarray + ) -> Tuple[np.ndarray, List[float], int]: + leftmost_cell_idxs = [v[0] for v in rows.values()] + benchmark_x = polygons[leftmost_cell_idxs][:, 0, 1] + + theta = 10 + # 遍历其他所有的框，按照y轴进行区间划分 + range_res = {} + for cur_idx, cur_box in enumerate(polygons): + if cur_idx in benchmark_x: + continue + + cur_y = cur_box[0, 1] + + start_idx, end_idx = None, None + for i, v in enumerate(benchmark_x): + if cur_y - theta <= v <= cur_y + theta: + break + + if cur_y > v: + start_idx = i + continue + + if cur_y < v: + end_idx = i + break + + range_res[cur_idx] = [start_idx, end_idx] + + sorted_res = dict(sorted(range_res.items(), key=lambda x: x[0], reverse=True)) + for k, v in sorted_res.items(): + if not all(v): + continue + + benchmark_x = np.insert(benchmark_x, v[1], polygons[k][0, 1]) + + each_row_widths = (benchmark_x[1:] - benchmark_x[:-1]).tolist() + + # 求出最后一行cell中，最大的高度作为最后一行的高度 + bottommost_idxs = list(rows.values())[-1] + bottommost_boxes = polygons[bottommost_idxs] + max_height = max([self.compute_L2(v[3, :], v[0, :]) for v in bottommost_boxes]) + each_row_widths.append(max_height) + + row_nums = benchmark_x.shape[0] + return each_row_widths, row_nums + + @staticmethod + def compute_L2(a1: np.ndarray, a2: np.ndarray) -> float: + return np.linalg.norm(a2 - a1) + + def get_merge_cells( + self, + polygons: np.ndarray, + rows: Dict, + row_nums: int, + col_nums: int, + longest_col: np.ndarray, + each_col_widths: List[float], + each_row_heights: List[float], + ) -> Dict[int, Dict[int, int]]: + col_res_merge, row_res_merge = {}, {} + merge_thresh = 20 + for cur_row, col_list in rows.items(): + one_col_result, one_row_result = {}, {} + for one_col in col_list: + box = polygons[one_col] + box_width = self.compute_L2(box[3, :], box[0, :]) + + # 不一定是从0开始的，应该综合已有值和x坐标位置来确定起始位置 + loc_col_idx = np.argmin(np.abs(longest_col[:, 0, 0] - box[0, 0])) + merge_col_cell = max(sum(one_col_result.values()), loc_col_idx) + + # 计算合并多少个列方向单元格 + for i in range(merge_col_cell, col_nums): + col_cum_sum = sum(each_col_widths[merge_col_cell : i + 1]) + if i == merge_col_cell and col_cum_sum > box_width: + one_col_result[one_col] = 1 + break + elif abs(col_cum_sum - box_width) <= merge_thresh: + one_col_result[one_col] = i + 1 - merge_col_cell + break + else: + one_col_result[one_col] = i + 1 - merge_col_cell + 1 + + box_height = self.compute_L2(box[1, :], box[0, :]) + merge_row_cell = cur_row + for j in range(merge_row_cell, row_nums): + row_cum_sum = sum(each_row_heights[merge_row_cell : j + 1]) + # box_height 不确定是几行的高度，所以要逐个试验，找一个最近的几行的高 + # 如果第一次row_cum_sum就比box_height大，那么意味着？丢失了一行 + if j == merge_row_cell and row_cum_sum > box_height: + one_row_result[one_col] = 1 + break + + elif abs(box_height - row_cum_sum) <= merge_thresh: + one_row_result[one_col] = j + 1 - merge_row_cell + break + else: + one_row_result[one_col] = j + 1 - merge_row_cell + 1 + + col_res_merge[cur_row] = one_col_result + row_res_merge[cur_row] = one_row_result + + res = {} + for i, (c, r) in enumerate(zip(col_res_merge.values(), row_res_merge.values())): + res[i] = {k: [cc, r[k]] for k, cc in c.items()} + return res diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/utils_table_recover.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/utils_table_recover.py new file mode 100644 index 00000000..19b44652 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/utils_table_recover.py @@ -0,0 +1,315 @@ +# -*- encoding: utf-8 -*- +# @Author: SWHL +# @Contact: liekkaskono@163.com +import random +from typing import Dict, List, Union + +import cv2 +import numpy as np +import shapely +from shapely.geometry import MultiPoint, Polygon + + +def sorted_boxes(dt_boxes: np.ndarray) -> np.ndarray: + """ + Sort text boxes in order from top to bottom, left to right + args: + dt_boxes(array):detected text boxes with shape (N, 4, 2) + return: + sorted boxes(array) with shape (N, 4, 2) + """ + num_boxes = dt_boxes.shape[0] + dt_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) + _boxes = list(dt_boxes) + + # 解决相邻框，后边比前面y轴小，则会被排到前面去的问题 + for i in range(num_boxes - 1): + for j in range(i, -1, -1): + if ( + abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 + and _boxes[j + 1][0][0] < _boxes[j][0][0] + ): + _boxes[j], _boxes[j + 1] = _boxes[j + 1], _boxes[j] + else: + break + return np.array(_boxes) + + +def compute_poly_iou(a: np.ndarray, b: np.ndarray) -> float: + """计算两个多边形的IOU + + Args: + poly1 (np.ndarray): (4, 2) + poly2 (np.ndarray): (4, 2) + + Returns: + float: iou + """ + poly1 = Polygon(a).convex_hull + poly2 = Polygon(b).convex_hull + + union_poly = np.concatenate((a, b)) + + if not poly1.intersects(poly2): + return 0.0 + + try: + inter_area = poly1.intersection(poly2).area + union_area = MultiPoint(union_poly).convex_hull.area + except shapely.geos.TopologicalError: + print("shapely.geos.TopologicalError occured, iou set to 0") + return 0.0 + + if union_area == 0: + return 0.0 + + return float(inter_area) / union_area + + +def merge_adjacent_polys(polygons: np.ndarray) -> np.ndarray: + """合并相邻iou大于阈值的框""" + combine_iou_thresh = 0.1 + pair_polygons = list(zip(polygons, polygons[1:, ...])) + pair_ious = np.array([compute_poly_iou(p1, p2) for p1, p2 in pair_polygons]) + idxs = np.argwhere(pair_ious >= combine_iou_thresh) + + if idxs.size <= 0: + return polygons + + polygons = combine_two_poly(polygons, idxs) + + # 注意：递归调用 + polygons = merge_adjacent_polys(polygons) + return polygons + + +def combine_two_poly(polygons: np.ndarray, idxs: np.ndarray) -> np.ndarray: + del_idxs, insert_boxes = [], [] + idxs = idxs.squeeze(0) + for idx in idxs: + # idx 和 idx + 1 是重合度过高的 + # 合并，取两者各个点的最大值 + new_poly = [] + pre_poly, pos_poly = polygons[idx], polygons[idx + 1] + + # 四个点，每个点逐一比较 + new_poly.append(np.minimum(pre_poly[0], pos_poly[0])) + + x_2 = min(pre_poly[1][0], pos_poly[1][0]) + y_2 = max(pre_poly[1][1], pos_poly[1][1]) + new_poly.append([x_2, y_2]) + + # 第3个点 + new_poly.append(np.maximum(pre_poly[2], pos_poly[2])) + + # 第4个点 + x_4 = max(pre_poly[3][0], pos_poly[3][0]) + y_4 = min(pre_poly[3][1], pos_poly[3][1]) + new_poly.append([x_4, y_4]) + + new_poly = np.array(new_poly) + + # 删除已经合并的两个框，插入新的框 + del_idxs.extend([idx, idx + 1]) + insert_boxes.append(new_poly) + + # 整合合并后的框 + polygons = np.delete(polygons, del_idxs, axis=0) + + insert_boxes = np.array(insert_boxes) + polygons = np.append(polygons, insert_boxes, axis=0) + polygons = sorted_boxes(polygons) + return polygons + + +def match_ocr_cell( + polygons: np.ndarray, ocr_res: List[Union[List[List[float]], str, str]] +) -> Dict[int, List]: + cell_box_map = {} + head_box_map = {} + tail_box_map = {} + dt_boxes, rec_res, _ = list(zip(*ocr_res)) + dt_boxes = np.array(dt_boxes) + iou_thresh = 0.05 + head_idx = 0 + tail_idx = 0 + for i, dt_box in enumerate(dt_boxes): + ious = [compute_poly_iou(dt_box, cell_box) for cell_box in polygons] + if sum(ious) == 0 and dt_box[-1][1] < polygons[-1][0][1]: + head_box_map.setdefault(head_idx, rec_res[i]) + head_idx += 1 + elif sum(ious) == 0 and dt_box[0][1] > polygons[0][2][1]: + tail_box_map.setdefault(tail_idx, rec_res[i]) + tail_idx += 1 + for i, cell_box in enumerate(polygons): + ious = [compute_poly_iou(dt_box, cell_box) for dt_box in dt_boxes] + + # 对有iou的值，计算是否存在包含关系。如存在→iou=1 + have_iou_idxs = np.argwhere(ious) + if have_iou_idxs.size > 0: + have_iou_idxs = have_iou_idxs.squeeze(1) + for idx in have_iou_idxs: + if is_inclusive_each_other(cell_box, dt_boxes[idx]): + ious[idx] = 1.0 + + if all(x <= iou_thresh for x in ious): + # 说明这个cell中没有文本 + cell_box_map.setdefault(i, []).append("") + continue + + same_cell_idxs = np.argwhere(np.array(ious) >= iou_thresh).squeeze(1) + one_cell_txts = "\n".join([rec_res[idx] for idx in same_cell_idxs]) + cell_box_map.setdefault(i, []).append(one_cell_txts) + return cell_box_map, head_box_map, tail_box_map + + +def is_inclusive_each_other(box1: np.ndarray, box2: np.ndarray): + """判断两个多边形框是否存在包含关系 + + Args: + box1 (np.ndarray): (4, 2) + box2 (np.ndarray): (4, 2) + + Returns: + bool: 是否存在包含关系 + """ + poly1 = Polygon(box1) + poly2 = Polygon(box2) + + poly1_area = poly1.convex_hull.area + poly2_area = poly2.convex_hull.area + + if poly1_area > poly2_area: + box_max = box1 + box_min = box2 + else: + box_max = box2 + box_min = box1 + + x0, y0 = np.min(box_min[:, 0]), np.min(box_min[:, 1]) + x1, y1 = np.max(box_min[:, 0]), np.max(box_min[:, 1]) + + edge_x0, edge_y0 = np.min(box_max[:, 0]), np.min(box_max[:, 1]) + edge_x1, edge_y1 = np.max(box_max[:, 0]), np.max(box_max[:, 1]) + + if x0 >= edge_x0 and y0 >= edge_y0 and x1 <= edge_x1 and y1 <= edge_y1: + return True + return False + + +def plot_html_table(table_res: Dict, cell_box_map: Dict, head_box_map: Dict, tail_box_map: Dict) -> str: + table_str = f"" + for key in head_box_map.keys(): + table_str += f'' + head_box_map[key] + f'
' + table_str = table_str + f"" + for v in table_res.values(): + table_str += "" + for cell_idx, span_info in v.items(): + cur_txt = "".join(cell_box_map.get(cell_idx, "")) + cur_txt = cur_txt.replace('\n', '
') + colspan, rowspan = span_info + if colspan != 1 and rowspan != 1: + table_str += ( + f'' + ) + elif colspan == 1 and rowspan != 1: + table_str += f'' + elif colspan != 1 and rowspan == 1: + table_str += f'' + else: + table_str += f"" + + table_str += "" + table_str += "
{cur_txt}{cur_txt}{cur_txt}{cur_txt}
" + for key in tail_box_map.keys(): + table_str += f'' + tail_box_map[key] + f'
' + table_str += "" + return table_str + + +# def plot_html_wireless_table(logi_points, cell_box_map): +# logi_points = logi_points.astype(np.int32) +# table_dict = {} +# for cell_idx, v in enumerate(logi_points): +# cur_row = v[0] +# cur_txt = "\n".join(cell_box_map.get(cell_idx)) +# sr, er, sc, ec = v.tolist() +# rowspan, colspan = er - sr + 1, ec - sc + 1 +# table_str = f'{cur_txt}' +# # table_str = f'
{cur_txt}
' +# table_dict.setdefault(cur_row, []).append(table_str) + +# new_table_dict = {} +# for k, v in table_dict.items(): +# new_table_dict[k] = [""] + v + [""] + +# html_start = """""" +# # html_start = """
""" +# html_end = "
" +# html_middle = "".join([vv for v in new_table_dict.values() for vv in v]) +# table_str = f"{html_start}{html_middle}{html_end}" +# return table_str + + +def plot_html_wireless_table(logi_points, cell_box_map): + logi_points = logi_points.astype(np.int32) + table_dict = {} + col_dict = {} + colspan_dict = {} + for cell_idx, v in enumerate(logi_points): + cur_row = v[0] + cur_col = v[2] + cur_txt = "\n".join(cell_box_map.get(cell_idx)) + sr, er, sc, ec = v.tolist() + rowspan, colspan = er - sr + 1, ec - sc + 1 + table_str = f'{cur_txt}' + # table_str = f'
{cur_txt}
' + table_dict.setdefault(cur_row, []).append(table_str) + if rowspan != 1 and colspan == 1: + for cur_row in range(v[0], v[1]+1): + col_dict.setdefault(cur_row, []).append(cur_col) + # colspan_dict.setdefault(cur_row, []).append(colspan) + elif rowspan == 1 and colspan != 1: + for cur_col in range(v[2], v[3]+1): + col_dict.setdefault(cur_row, []).append(cur_col) + # colspan_dict.setdefault(cur_row, []).append(colspan) + else: + col_dict.setdefault(cur_row, []).append(cur_col) + # colspan_dict.setdefault(cur_row, []).append(colspan) + + new_table_dict = {} + for k, v in table_dict.items(): + if len(col_dict[k]) < max(col_dict[k]): + for i in range(max(col_dict[k])+1): + if i not in col_dict[k]: + v.insert(i, '') + new_table_dict[k] = [""] + v + [""] + else: + new_table_dict[k] = [""] + v + [""] + + html_start = """""" + # html_start = """
""" + html_end = "
" + html_middle = "".join([vv for v in new_table_dict.values() for vv in v]) + table_str = f"{html_start}{html_middle}{html_end}" + return table_str + + +def vis_table(img: np.ndarray, polygons: np.ndarray) -> np.ndarray: + # mask = img.copy() + for i, poly in enumerate(polygons): + poly = np.round(poly).astype(np.int32).reshape(4, 2) + + random_color = ( + random.randint(0, 255), + random.randint(0, 255), + random.randint(0, 255), + ) + cv2.polylines(img, [poly], 3, random_color) + # cv2.fillPoly(mask, np.array([poly], dtype=np.int32), random_color) + + font = cv2.FONT_HERSHEY_SIMPLEX + cv2.putText(img, str(i), poly[0], font, 1, random_color, 1) + # img = img * 0.67 + mask * 0.33 + + return img diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/vis.jpg b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/vis.jpg new file mode 100644 index 00000000..5237930f Binary files /dev/null and b/qanything_kernel/utils/loader/pdf_to_markdown/core/layout/table_rec/vis.jpg differ diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/nlp/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/nlp/__init__.py new file mode 100644 index 00000000..6d52409d --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/nlp/__init__.py @@ -0,0 +1,363 @@ +import random +from collections import Counter + +from . import huqie +import re +import copy +import tiktoken + +encoder = tiktoken.encoding_for_model("gpt-3.5-turbo") + +def num_tokens_from_string(string: str) -> int: + """Returns the number of tokens in a text string.""" + num_tokens = len(encoder.encode(string)) + return num_tokens + + +BULLET_PATTERN = [[ + r"第[零一二三四五六七八九十百0-9]+(分?编|部分)", + r"第[零一二三四五六七八九十百0-9]+章", + r"第[零一二三四五六七八九十百0-9]+节", + r"第[零一二三四五六七八九十百0-9]+条", + r"[\(（][零一二三四五六七八九十百]+[\)）]", +], [ + r"第[0-9]+章", + r"第[0-9]+节", + r"[0-9]{,2}[\. 、]", + r"[0-9]{,2}\.[0-9]{,2}[^a-zA-Z/%~-]", + r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}", + r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}", +], [ + r"第[零一二三四五六七八九十百0-9]+章", + r"第[零一二三四五六七八九十百0-9]+节", + r"[零一二三四五六七八九十百]+[ 、]", + r"[\(（][零一二三四五六七八九十百]+[\)）]", + r"[\(（][0-9]{,2}[\)）]", +], [ + r"PART (ONE|TWO|THREE|FOUR|FIVE|SIX|SEVEN|EIGHT|NINE|TEN)", + r"Chapter (I+V?|VI*|XI|IX|X)", + r"Section [0-9]+", + r"Article [0-9]+" +] +] + + +def random_choices(arr, k): + k = min(len(arr), k) + return random.choices(arr, k=k) + + +def not_bullet(line): + patt = [ + r"0", r"[0-9]+ +[0-9~个只-]", r"[0-9]+\.{2,}" + ] + return any([re.match(r, line) for r in patt]) + + +def bullets_category(sections): + global BULLET_PATTERN + hits = [0] * len(BULLET_PATTERN) + for i, pro in enumerate(BULLET_PATTERN): + for sec in sections: + for p in pro: + if re.match(p, sec) and not not_bullet(sec): + hits[i] += 1 + break + maxium = 0 + res = -1 + for i, h in enumerate(hits): + if h <= maxium: + continue + res = i + maxium = h + return res + + +def is_english(texts): + eng = 0 + if not texts: return False + for t in texts: + if re.match(r"[a-zA-Z]{2,}", t.strip()): + eng += 1 + if eng / len(texts) > 0.8: + return True + return False + + +def tokenize(d, t, eng): + d["content_with_weight"] = t + t = re.sub(r"]{0,12})?>", " ", t) + d["content_ltks"] = huqie.qie(t) + d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"]) + + +def tokenize_chunks(chunks, doc, eng, pdf_parser): + res = [] + # wrap up as es documents + for ck in chunks: + if len(ck.strip()) == 0:continue + print("--", ck) + d = copy.deepcopy(doc) + if pdf_parser: + try: + d["image"], poss = pdf_parser.crop(ck, need_position=True) + add_positions(d, poss) + ck = pdf_parser.remove_tag(ck) + except NotImplementedError as e: + pass + tokenize(d, ck, eng) + res.append(d) + return res + + +def tokenize_table(tbls, doc, eng, batch_size=10): + res = [] + # add tables + for (img, rows), poss in tbls: + if not rows: + continue + if isinstance(rows, str): + d = copy.deepcopy(doc) + tokenize(d, rows, eng) + d["content_with_weight"] = rows + if img: d["image"] = img + if poss: add_positions(d, poss) + res.append(d) + continue + de = "; " if eng else "； " + for i in range(0, len(rows), batch_size): + d = copy.deepcopy(doc) + r = de.join(rows[i:i + batch_size]) + tokenize(d, r, eng) + d["image"] = img + add_positions(d, poss) + res.append(d) + return res + + +def add_positions(d, poss): + if not poss: + return + d["page_num_int"] = [] + d["position_int"] = [] + d["top_int"] = [] + for pn, left, right, top, bottom in poss: + d["page_num_int"].append(pn + 1) + d["top_int"].append(top) + d["position_int"].append((pn + 1, left, right, top, bottom)) + + +def remove_contents_table(sections, eng=False): + i = 0 + while i < len(sections): + def get(i): + nonlocal sections + return (sections[i] if isinstance(sections[i], + type("")) else sections[i][0]).strip() + + if not re.match(r"(contents|目录|目次|table of contents|致谢|acknowledge)$", + re.sub(r"( | |\u3000)+", "", get(i).split("@@")[0], re.IGNORECASE)): + i += 1 + continue + sections.pop(i) + if i >= len(sections): + break + prefix = get(i)[:3] if not eng else " ".join(get(i).split(" ")[:2]) + while not prefix: + sections.pop(i) + if i >= len(sections): + break + prefix = get(i)[:3] if not eng else " ".join(get(i).split(" ")[:2]) + sections.pop(i) + if i >= len(sections) or not prefix: + break + for j in range(i, min(i + 128, len(sections))): + if not re.match(prefix, get(j)): + continue + for _ in range(i, j): + sections.pop(i) + break + + +def make_colon_as_title(sections): + if not sections: + return [] + if isinstance(sections[0], type("")): + return sections + i = 0 + while i < len(sections): + txt, layout = sections[i] + i += 1 + txt = txt.split("@")[0].strip() + if not txt: + continue + if txt[-1] not in ":：": + continue + txt = txt[::-1] + arr = re.split(r"([。？！!?;；]| .)", txt) + if len(arr) < 2 or len(arr[1]) < 32: + continue + sections.insert(i - 1, (arr[0][::-1], "title")) + i += 1 + + +def title_frequency(bull, sections): + bullets_size = len(BULLET_PATTERN[bull]) + levels = [bullets_size+1 for _ in range(len(sections))] + if not sections or bull < 0: + return bullets_size+1, levels + + for i, (txt, layout) in enumerate(sections): + for j, p in enumerate(BULLET_PATTERN[bull]): + if re.match(p, txt.strip()) and not not_bullet(txt): + levels[i] = j + break + else: + if re.search(r"(title|head)", layout) and not not_title(txt.split("@")[0]): + levels[i] = bullets_size + most_level = bullets_size+1 + for l, c in sorted(Counter(levels).items(), key=lambda x:x[1]*-1): + if l <= bullets_size: + most_level = l + break + return most_level, levels + + +def not_title(txt): + if re.match(r"第[零一二三四五六七八九十百0-9]+条", txt): + return False + if len(txt.split(" ")) > 12 or (txt.find(" ") < 0 and len(txt) >= 32): + return True + return re.search(r"[,;，。；！!]", txt) + + +def hierarchical_merge(bull, sections, depth): + if not sections or bull < 0: + return [] + if isinstance(sections[0], type("")): + sections = [(s, "") for s in sections] + sections = [(t, o) for t, o in sections if + t and len(t.split("@")[0].strip()) > 1 and not re.match(r"[0-9]+$", t.split("@")[0].strip())] + bullets_size = len(BULLET_PATTERN[bull]) + levels = [[] for _ in range(bullets_size + 2)] + + + for i, (txt, layout) in enumerate(sections): + for j, p in enumerate(BULLET_PATTERN[bull]): + if re.match(p, txt.strip()): + levels[j].append(i) + break + else: + if re.search(r"(title|head)", layout) and not not_title(txt): + levels[bullets_size].append(i) + else: + levels[bullets_size + 1].append(i) + sections = [t for t, _ in sections] + + # for s in sections: print("--", s) + + def binary_search(arr, target): + if not arr: + return -1 + if target > arr[-1]: + return len(arr) - 1 + if target < arr[0]: + return -1 + s, e = 0, len(arr) + while e - s > 1: + i = (e + s) // 2 + if target > arr[i]: + s = i + continue + elif target < arr[i]: + e = i + continue + else: + assert False + return s + + cks = [] + readed = [False] * len(sections) + levels = levels[::-1] + for i, arr in enumerate(levels[:depth]): + for j in arr: + if readed[j]: + continue + readed[j] = True + cks.append([j]) + if i + 1 == len(levels) - 1: + continue + for ii in range(i + 1, len(levels)): + jj = binary_search(levels[ii], j) + if jj < 0: + continue + if jj > cks[-1][-1]: + cks[-1].pop(-1) + cks[-1].append(levels[ii][jj]) + for ii in cks[-1]: + readed[ii] = True + + if not cks: + return cks + + for i in range(len(cks)): + cks[i] = [sections[j] for j in cks[i][::-1]] + print("--------------\n", "\n* ".join(cks[i])) + + res = [[]] + num = [0] + for ck in cks: + if len(ck) == 1: + n = num_tokens_from_string(re.sub(r"@@[0-9]+.*", "", ck[0])) + if n + num[-1] < 218: + res[-1].append(ck[0]) + num[-1] += n + continue + res.append(ck) + num.append(n) + continue + res.append(ck) + num.append(218) + + return res + + +def naive_merge(sections, chunk_token_num=128, delimiter="\n。；！？"): + if not sections: + return [] + if isinstance(sections[0], type("")): + sections = [(s, "") for s in sections] + cks = [""] + tk_nums = [0] + + def add_chunk(t, pos): + nonlocal cks, tk_nums, delimiter + tnum = num_tokens_from_string(t) + if tnum < 8: + pos = "" + if tk_nums[-1] > chunk_token_num: + if t.find(pos) < 0: + t += pos + cks.append(t) + tk_nums.append(tnum) + else: + if cks[-1].find(pos) < 0: + t += pos + cks[-1] += t + tk_nums[-1] += tnum + + for sec, pos in sections: + add_chunk(sec, pos) + continue + s, e = 0, 1 + while e < len(sec): + if sec[e] in delimiter: + add_chunk(sec[s: e + 1], pos) + s = e + 1 + e = s + 1 + else: + e += 1 + if s < e: + add_chunk(sec[s: e], pos) + + return cks diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/nlp/huqie.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/nlp/huqie.py new file mode 100644 index 00000000..159808c2 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/nlp/huqie.py @@ -0,0 +1,421 @@ +# -*- coding: utf-8 -*- + +import copy +import datrie +import math +import os +import re +import string +import sys +from hanziconv import HanziConv +from nltk import word_tokenize +from nltk.stem import PorterStemmer, WordNetLemmatizer +from qanything_kernel.configs.model_config import PDF_MODEL_PATH + +class Huqie: + def key_(self, line): + return str(line.lower().encode("utf-8"))[2:-1] + + def rkey_(self, line): + return str(("DD" + (line[::-1].lower())).encode("utf-8"))[2:-1] + + def loadDict_(self, fnm): + print("[HUQIE]:Build trie", fnm, file=sys.stderr) + try: + of = open(fnm, "r") + while True: + line = of.readline() + if not line: + break + line = re.sub(r"[\r\n]+", "", line) + line = re.split(r"[ \t]", line) + k = self.key_(line[0]) + F = int(math.log(float(line[1]) / self.DENOMINATOR) + .5) + if k not in self.trie_ or self.trie_[k][0] < F: + self.trie_[self.key_(line[0])] = (F, line[2]) + self.trie_[self.rkey_(line[0])] = 1 + self.trie_.save(fnm + ".trie") + of.close() + except Exception as e: + print("[HUQIE]:Faild to build trie, ", fnm, e, file=sys.stderr) + + def __init__(self, debug=False): + self.DEBUG = debug + self.DENOMINATOR = 1000000 + self.trie_ = datrie.Trie(string.printable) + self.DIR_ = os.path.join(PDF_MODEL_PATH, "checkpoints/nlp", "huqie") + + self.stemmer = PorterStemmer() + self.lemmatizer = WordNetLemmatizer() + + self.SPLIT_CHAR = r"([ ,\.<>/?;'\[\]\\`!@#$%^&*\(\)\{\}\|_+=《》，。？、；‘’：“”【】~！￥%……（）——-]+|[a-z\.-]+|[0-9,\.-]+)" + try: + self.trie_ = datrie.Trie.load(self.DIR_ + ".txt.trie") + return + except Exception as e: + print("[HUQIE]:Build default trie", file=sys.stderr) + self.trie_ = datrie.Trie(string.printable) + + self.loadDict_(self.DIR_ + ".txt") + + def loadUserDict(self, fnm): + try: + self.trie_ = datrie.Trie.load(fnm + ".trie") + return + except Exception as e: + self.trie_ = datrie.Trie(string.printable) + self.loadDict_(fnm) + + def addUserDict(self, fnm): + self.loadDict_(fnm) + + def _strQ2B(self, ustring): + """把字符串全角转半角""" + rstring = "" + for uchar in ustring: + inside_code = ord(uchar) + if inside_code == 0x3000: + inside_code = 0x0020 + else: + inside_code -= 0xfee0 + if inside_code < 0x0020 or inside_code > 0x7e: # 转完之后不是半角字符返回原来的字符 + rstring += uchar + else: + rstring += chr(inside_code) + return rstring + + def _tradi2simp(self, line): + return HanziConv.toSimplified(line) + + def dfs_(self, chars, s, preTks, tkslist): + MAX_L = 10 + res = s + # if s > MAX_L or s>= len(chars): + if s >= len(chars): + tkslist.append(preTks) + return res + + # pruning + S = s + 1 + if s + 2 <= len(chars): + t1, t2 = "".join(chars[s:s + 1]), "".join(chars[s:s + 2]) + if self.trie_.has_keys_with_prefix(self.key_(t1)) and not self.trie_.has_keys_with_prefix( + self.key_(t2)): + S = s + 2 + if len(preTks) > 2 and len( + preTks[-1][0]) == 1 and len(preTks[-2][0]) == 1 and len(preTks[-3][0]) == 1: + t1 = preTks[-1][0] + "".join(chars[s:s + 1]) + if self.trie_.has_keys_with_prefix(self.key_(t1)): + S = s + 2 + + ################ + for e in range(S, len(chars) + 1): + t = "".join(chars[s:e]) + k = self.key_(t) + + if e > s + 1 and not self.trie_.has_keys_with_prefix(k): + break + + if k in self.trie_: + pretks = copy.deepcopy(preTks) + if k in self.trie_: + pretks.append((t, self.trie_[k])) + else: + pretks.append((t, (-12, ''))) + res = max(res, self.dfs_(chars, e, pretks, tkslist)) + + if res > s: + return res + + t = "".join(chars[s:s + 1]) + k = self.key_(t) + if k in self.trie_: + preTks.append((t, self.trie_[k])) + else: + preTks.append((t, (-12, ''))) + + return self.dfs_(chars, s + 1, preTks, tkslist) + + def freq(self, tk): + k = self.key_(tk) + if k not in self.trie_: + return 0 + return int(math.exp(self.trie_[k][0]) * self.DENOMINATOR + 0.5) + + def tag(self, tk): + k = self.key_(tk) + if k not in self.trie_: + return "" + return self.trie_[k][1] + + def score_(self, tfts): + B = 30 + F, L, tks = 0, 0, [] + for tk, (freq, tag) in tfts: + F += freq + L += 0 if len(tk) < 2 else 1 + tks.append(tk) + F /= len(tks) + L /= len(tks) + if self.DEBUG: + print("[SC]", tks, len(tks), L, F, B / len(tks) + L + F) + return tks, B / len(tks) + L + F + + def sortTks_(self, tkslist): + res = [] + for tfts in tkslist: + tks, s = self.score_(tfts) + res.append((tks, s)) + return sorted(res, key=lambda x: x[1], reverse=True) + + def merge_(self, tks): + patts = [ + (r"[ ]+", " "), + (r"([0-9\+\.,%\*=-]) ([0-9\+\.,%\*=-])", r"\1\2"), + ] + # for p,s in patts: tks = re.sub(p, s, tks) + + # if split chars is part of token + res = [] + tks = re.sub(r"[ ]+", " ", tks).split(" ") + s = 0 + while True: + if s >= len(tks): + break + E = s + 1 + for e in range(s + 2, min(len(tks) + 2, s + 6)): + tk = "".join(tks[s:e]) + if re.search(self.SPLIT_CHAR, tk) and self.freq(tk): + E = e + res.append("".join(tks[s:E])) + s = E + + return " ".join(res) + + def maxForward_(self, line): + res = [] + s = 0 + while s < len(line): + e = s + 1 + t = line[s:e] + while e < len(line) and self.trie_.has_keys_with_prefix( + self.key_(t)): + e += 1 + t = line[s:e] + + while e - 1 > s and self.key_(t) not in self.trie_: + e -= 1 + t = line[s:e] + + if self.key_(t) in self.trie_: + res.append((t, self.trie_[self.key_(t)])) + else: + res.append((t, (0, ''))) + + s = e + + return self.score_(res) + + def maxBackward_(self, line): + res = [] + s = len(line) - 1 + while s >= 0: + e = s + 1 + t = line[s:e] + while s > 0 and self.trie_.has_keys_with_prefix(self.rkey_(t)): + s -= 1 + t = line[s:e] + + while s + 1 < e and self.key_(t) not in self.trie_: + s += 1 + t = line[s:e] + + if self.key_(t) in self.trie_: + res.append((t, self.trie_[self.key_(t)])) + else: + res.append((t, (0, ''))) + + s -= 1 + + return self.score_(res[::-1]) + + def qie(self, line): + line = self._strQ2B(line).lower() + line = self._tradi2simp(line) + zh_num = len([1 for c in line if is_chinese(c)]) + if zh_num < len(line) * 0.2: + return " ".join([self.stemmer.stem(self.lemmatizer.lemmatize(t)) for t in word_tokenize(line)]) + + arr = re.split(self.SPLIT_CHAR, line) + res = [] + for L in arr: + if len(L) < 2 or re.match( + r"[a-z\.-]+$", L) or re.match(r"[0-9\.-]+$", L): + res.append(L) + continue + # print(L) + + # use maxforward for the first time + tks, s = self.maxForward_(L) + tks1, s1 = self.maxBackward_(L) + if self.DEBUG: + print("[FW]", tks, s) + print("[BW]", tks1, s1) + + diff = [0 for _ in range(max(len(tks1), len(tks)))] + for i in range(min(len(tks1), len(tks))): + if tks[i] != tks1[i]: + diff[i] = 1 + + if s1 > s: + tks = tks1 + + i = 0 + while i < len(tks): + s = i + while s < len(tks) and diff[s] == 0: + s += 1 + if s == len(tks): + res.append(" ".join(tks[i:])) + break + if s > i: + res.append(" ".join(tks[i:s])) + + e = s + while e < len(tks) and e - s < 5 and diff[e] == 1: + e += 1 + + tkslist = [] + self.dfs_("".join(tks[s:e + 1]), 0, [], tkslist) + res.append(" ".join(self.sortTks_(tkslist)[0][0])) + + i = e + 1 + + res = " ".join(res) + if self.DEBUG: + print("[TKS]", self.merge_(res)) + return self.merge_(res) + + def qieqie(self, tks): + tks = tks.split(" ") + zh_num = len([1 for c in tks if c and is_chinese(c[0])]) + if zh_num < len(tks) * 0.2: + res = [] + for tk in tks: + res.extend(tk.split("/")) + return " ".join(res) + + res = [] + for tk in tks: + if len(tk) < 3 or re.match(r"[0-9,\.-]+$", tk): + res.append(tk) + continue + tkslist = [] + if len(tk) > 10: + tkslist.append(tk) + else: + self.dfs_(tk, 0, [], tkslist) + if len(tkslist) < 2: + res.append(tk) + continue + stk = self.sortTks_(tkslist)[1][0] + if len(stk) == len(tk): + stk = tk + else: + if re.match(r"[a-z\.-]+$", tk): + for t in stk: + if len(t) < 3: + stk = tk + break + else: + stk = " ".join(stk) + else: + stk = " ".join(stk) + + res.append(stk) + + return " ".join(res) + + +def is_chinese(s): + if s >= u'\u4e00' and s <= u'\u9fa5': + return True + else: + return False + + +def is_number(s): + if s >= u'\u0030' and s <= u'\u0039': + return True + else: + return False + + +def is_alphabet(s): + if (s >= u'\u0041' and s <= u'\u005a') or ( + s >= u'\u0061' and s <= u'\u007a'): + return True + else: + return False + + +def naiveQie(txt): + tks = [] + for t in txt.split(" "): + if tks and re.match(r".*[a-zA-Z]$", tks[-1] + ) and re.match(r".*[a-zA-Z]$", t): + tks.append(" ") + tks.append(t) + return tks + + +hq = Huqie() +qie = hq.qie +qieqie = hq.qieqie +tag = hq.tag +freq = hq.freq +loadUserDict = hq.loadUserDict +addUserDict = hq.addUserDict +tradi2simp = hq._tradi2simp +strQ2B = hq._strQ2B + +if __name__ == '__main__': + huqie = Huqie(debug=True) + # huqie.addUserDict("/tmp/tmp.new.tks.dict") + tks = huqie.qie( + "哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈") + print(huqie.qieqie(tks)) + tks = huqie.qie( + "公开征求意见稿提出，境外投资者可使用自有人民币或外汇投资。使用外汇投资的，可通过债券持有人在香港人民币业务清算行及香港地区经批准可进入境内银行间外汇市场进行交易的境外人民币业务参加行（以下统称香港结算行）办理外汇资金兑换。香港结算行由此所产生的头寸可到境内银行间外汇市场平盘。使用外汇投资的，在其投资的债券到期或卖出后，原则上应兑换回外汇。") + print(huqie.qieqie(tks)) + tks = huqie.qie( + "多校划片就是一个小区对应多个小学初中，让买了学区房的家庭也不确定到底能上哪个学校。目的是通过这种方式为学区房降温，把就近入学落到实处。南京市长江大桥") + print(huqie.qieqie(tks)) + tks = huqie.qie( + "实际上当时他们已经将业务中心偏移到安全部门和针对政府企业的部门 Scripts are compiled and cached aaaaaaaaa") + print(huqie.qieqie(tks)) + tks = huqie.qie("虽然我不怎么玩") + print(huqie.qieqie(tks)) + tks = huqie.qie("蓝月亮如何在外资夹击中生存,那是全宇宙最有意思的") + print(huqie.qieqie(tks)) + tks = huqie.qie( + "涡轮增压发动机num最大功率,不像别的共享买车锁电子化的手段,我们接过来是否有意义,黄黄爱美食,不过，今天阿奇要讲到的这家农贸市场，说实话，还真蛮有特色的！不仅环境好，还打出了") + print(huqie.qieqie(tks)) + tks = huqie.qie("这周日你去吗？这周日你有空吗？") + print(huqie.qieqie(tks)) + tks = huqie.qie("Unity3D开发经验 测试开发工程师 c++双11双11 985 211 ") + print(huqie.qieqie(tks)) + tks = huqie.qie( + "数据分析项目经理|数据分析挖掘|数据分析方向|商品数据分析|搜索数据分析 sql python hive tableau Cocos2d-") + print(huqie.qieqie(tks)) + if len(sys.argv) < 2: + sys.exit() + huqie.DEBUG = False + huqie.loadUserDict(sys.argv[1]) + of = open(sys.argv[2], "r") + while True: + line = of.readline() + if not line: + break + print(huqie.qie(line)) + of.close() diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/__init__.py new file mode 100644 index 00000000..8c4abbbe --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/__init__.py @@ -0,0 +1,3 @@ + + +from .pdf_parser import HuParser as PdfParser, PlainParser diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/pdf_parser.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/pdf_parser.py new file mode 100644 index 00000000..3ae17333 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/pdf_parser.py @@ -0,0 +1,1139 @@ +# -*- coding: utf-8 -*- +import os +import random + +import fitz +import xgboost as xgb +from io import BytesIO +import torch +import re +import pdfplumber +import logging +from PIL import Image +import numpy as np + +from PyPDF2 import PdfReader as pdf2_read + +from qanything_kernel.utils.loader.pdf_to_markdown.core.vision import Recognizer, LayoutRecognizer, TableStructureRecognizer_LORE +from qanything_kernel.utils.loader.pdf_to_markdown.core.nlp import huqie +# from qanything_kernel.dependent_server.ocr_server.ocr import OCRQAnything +from qanything_kernel.configs.model_config import OCR_MODEL_PATH, PDF_MODEL_PATH +from qanything_kernel.utils.custom_log import debug_logger +from tqdm import tqdm +from copy import deepcopy + +logging.getLogger("pdfminer").setLevel(logging.WARNING) + + +class HuParser: + def __init__(self, device='cpu'): + # self.ocr = OCRQAnything(model_dir=OCR_MODEL_PATH, device=device) # 省显存 + if hasattr(self, "model_speciess"): + self.layouter = LayoutRecognizer("layout." + self.model_speciess) + else: + self.layouter = LayoutRecognizer("layout") + self.tbl_det = TableStructureRecognizer_LORE() + + self.updown_cnt_mdl = xgb.Booster() + if torch.cuda.is_available(): + self.updown_cnt_mdl.set_param({"device": "cuda"}) + + model_dir = os.path.join( + PDF_MODEL_PATH, + "checkpoints/updown") + self.updown_cnt_mdl.load_model(os.path.join( + model_dir, "updown_concat_xgb.model")) + self.page_from = 0 + + + def __char_width(self, c): + return (c["x1"] - c["x0"]) // len(c["text"]) + + def __height(self, c): + return c["bottom"] - c["top"] + + def _x_dis(self, a, b): + return min(abs(a["x1"] - b["x0"]), abs(a["x0"] - b["x1"]), + abs(a["x0"] + a["x1"] - b["x0"] - b["x1"]) / 2) + + def _y_dis( + self, a, b): + return ( + b["top"] + b["bottom"] - a["top"] - a["bottom"]) / 2 + + def _match_proj(self, b): + proj_patt = [ + r"第[零一二三四五六七八九十百]+章", + r"第[零一二三四五六七八九十百]+[条节]", + r"[零一二三四五六七八九十百]+[、是 　]", + r"[\(（][零一二三四五六七八九十百]+[）\)]", + r"[\(（][0-9]+[）\)]", + r"[0-9]+(、|\.[　 ]|）|\.[^0-9./a-zA-Z_%><-]{4,})", + r"[0-9]+\.[0-9.]+(、|\.[ 　])", + r"[⚫•➢①② ]", + ] + return any([re.match(p, b["text"]) for p in proj_patt]) + + def _updown_concat_features(self, up, down): + w = max(self.__char_width(up), self.__char_width(down)) + h = max(self.__height(up), self.__height(down)) + y_dis = self._y_dis(up, down) + LEN = 6 + tks_down = huqie.qie(down["text"][:LEN]).split(" ") + tks_up = huqie.qie(up["text"][-LEN:]).split(" ") + tks_all = up["text"][-LEN:].strip() \ + + (" " if re.match(r"[a-zA-Z0-9]+", + up["text"][-1] + down["text"][0]) else "") \ + + down["text"][:LEN].strip() + tks_all = huqie.qie(tks_all).split(" ") + fea = [ + up.get("R", -1) == down.get("R", -1), + y_dis / h, + down["page_number"] - up["page_number"], + up["layout_type"] == down["layout_type"], + up["layout_type"] == "text", + down["layout_type"] == "text", + up["layout_type"] == "table", + down["layout_type"] == "table", + True if re.search( + r"([。？！；!?;+)）]|[a-z]\.)$", + up["text"]) else False, + True if re.search(r"[，：‘“、0-9（+-]$", up["text"]) else False, + True if re.search( + r"(^.?[/,?;:\]，。；：’”？！》】）-])", + down["text"]) else False, + True if re.match(r"[\(（][^\(\)（）]+[）\)]$", up["text"]) else False, + True if re.search(r"[，,][^。.]+$", up["text"]) else False, + True if re.search(r"[，,][^。.]+$", up["text"]) else False, + True if re.search(r"[\(（][^\)）]+$", up["text"]) + and re.search(r"[\)）]", down["text"]) else False, + self._match_proj(down), + True if re.match(r"[A-Z]", down["text"]) else False, + True if re.match(r"[A-Z]", up["text"][-1]) else False, + True if re.match(r"[a-z0-9]", up["text"][-1]) else False, + True if re.match(r"[0-9.%,-]+$", down["text"]) else False, + up["text"].strip()[-2:] == down["text"].strip()[-2:] if len(up["text"].strip() + ) > 1 and len( + down["text"].strip()) > 1 else False, + up["x0"] > down["x1"], + abs(self.__height(up) - self.__height(down)) / min(self.__height(up), + self.__height(down)), + self._x_dis(up, down) / max(w, 0.000001), + (len(up["text"]) - len(down["text"])) / + max(len(up["text"]), len(down["text"])), + len(tks_all) - len(tks_up) - len(tks_down), + len(tks_down) - len(tks_up), + tks_down[-1] == tks_up[-1], + max(down["in_row"], up["in_row"]), + abs(down["in_row"] - up["in_row"]), + len(tks_down) == 1 and huqie.tag(tks_down[0]).find("n") >= 0, + len(tks_up) == 1 and huqie.tag(tks_up[0]).find("n") >= 0 + ] + return fea + + @staticmethod + def sort_X_by_page(arr, threashold): + # sort using y1 first and then x1 + arr = sorted(arr, key=lambda r: (r["page_number"], r["x0"], r["top"])) + for i in range(len(arr) - 1): + for j in range(i, -1, -1): + # restore the order using th + if abs(arr[j + 1]["x0"] - arr[j]["x0"]) < threashold \ + and arr[j + 1]["top"] < arr[j]["top"] \ + and arr[j + 1]["page_number"] == arr[j]["page_number"]: + tmp = arr[j] + arr[j] = arr[j + 1] + arr[j + 1] = tmp + return arr + + def _has_color(self, o): + if o.get("ncs", "") == "DeviceGray": + if o["stroking_color"] and o["stroking_color"][0] == 1 and o["non_stroking_color"] and \ + o["non_stroking_color"][0] == 1: + if re.match(r"[a-zT_\[\]\(\)-]+", o.get("text", "")): + return False + return True + + def _table_transformer_job(self, ZM): + logging.info("Table processing...") + imgs, pos = [], [] + tbcnt = [0] + MARGIN = 10 + self.tb_cpns = [] + assert len(self.page_layout) == len(self.page_images) + for p, tbls in enumerate(self.page_layout): # for page + tbls = [f for f in tbls if f["type"] == "table"] + tbcnt.append(len(tbls)) + if not tbls: + continue + for tb in tbls: # for table + left, top, right, bott = tb["x0"] - MARGIN, tb["top"] - MARGIN, \ + tb["x1"] + MARGIN, tb["bottom"] + MARGIN + left *= ZM + top *= ZM + right *= ZM + bott *= ZM + pos.append((left, top)) + imgs.append(self.page_images[p].crop((left, top, right, bott))) + + assert len(self.page_images) == len(tbcnt) - 1 + if not imgs: + return + recos = self.tbl_det(imgs) + tbcnt = np.cumsum(tbcnt) + for i in range(len(tbcnt) - 1): # for page + pg = [] + for j, tb_items in enumerate( + recos[tbcnt[i]: tbcnt[i + 1]]): # for table + poss = pos[tbcnt[i]: tbcnt[i + 1]] + for it in tb_items: # for table components + it["x0"] = (it["x0"] + poss[j][0]) + it["x1"] = (it["x1"] + poss[j][0]) + it["top"] = (it["top"] + poss[j][1]) + it["bottom"] = (it["bottom"] + poss[j][1]) + for n in ["x0", "x1", "top", "bottom"]: + it[n] /= ZM + it["top"] += self.page_cum_height[i] + it["bottom"] += self.page_cum_height[i] + it["pn"] = i + it["layoutno"] = j + pg.append(it) + self.tb_cpns.extend(pg) + + def gather(kwd, fzy=10, ption=0.6): + eles = Recognizer.sort_Y_firstly( + [r for r in self.tb_cpns if re.match(kwd, r["label"])], fzy) + eles = Recognizer.layouts_cleanup(self.boxes, eles, 5, ption) + return Recognizer.sort_Y_firstly(eles, 0) + + # add R,H,C,SP tag to boxes within table layout + headers = gather(r".*header$") + rows = gather(r".* (row|header)") + spans = gather(r".*spanning") + clmns = sorted([r for r in self.tb_cpns if re.match( + r"table column$", r["label"])], key=lambda x: (x["pn"], x["layoutno"], x["x0"])) + clmns = Recognizer.layouts_cleanup(self.boxes, clmns, 5, 0.5) + for b in self.boxes: + if b.get("layout_type", "") != "table": + continue + ii = Recognizer.find_overlapped_with_threashold(b, rows, thr=0.3) + if ii is not None: + b["R"] = ii + b["R_top"] = rows[ii]["top"] + b["R_bott"] = rows[ii]["bottom"] + + ii = Recognizer.find_overlapped_with_threashold( + b, headers, thr=0.3) + if ii is not None: + b["H_top"] = headers[ii]["top"] + b["H_bott"] = headers[ii]["bottom"] + b["H_left"] = headers[ii]["x0"] + b["H_right"] = headers[ii]["x1"] + b["H"] = ii + + ii = Recognizer.find_horizontally_tightest_fit(b, clmns) + if ii is not None: + b["C"] = ii + b["C_left"] = clmns[ii]["x0"] + b["C_right"] = clmns[ii]["x1"] + + ii = Recognizer.find_overlapped_with_threashold(b, spans, thr=0.3) + if ii is not None: + b["H_top"] = spans[ii]["top"] + b["H_bott"] = spans[ii]["bottom"] + b["H_left"] = spans[ii]["x0"] + b["H_right"] = spans[ii]["x1"] + b["SP"] = ii + + def __ocr_pdf(self,pagenum,bxs_pymupdf,ZM=3): + """ + use pymupdf parse pdf to save time + """ + bxs = bxs_pymupdf + if not bxs: + self.boxes.append([]) + return + bxs = [(np.array(item[0]),'',item[1]) for item in bxs] + bxs = Recognizer.sort_Y_firstly( + [{"x0": b[0][0] / ZM, "x1": b[1][0] / ZM, + "top": b[0][1] / ZM, "text": rec_text, "txt": t, + "bottom": b[-1][1] / ZM, + "page_number": pagenum} for b, t, rec_text in bxs if b[0][0] <= b[1][0] and b[0][1] <= b[-1][1]], + self.mean_height[-1] / 3 + ) + for b in bxs: + del b["txt"] + bxs = [b for b in bxs if b["text"]] + if self.mean_height[-1] == 0: + self.mean_height[-1] = np.median([b["bottom"] - b["top"] + for b in bxs]) + self.boxes.append(bxs) + + + + def _layouts_rec(self, ZM, drop=True): + assert len(self.page_images) == len(self.boxes) + self.boxes, self.page_layout = self.layouter( + self.page_images, self.boxes, ZM, drop=drop) + # cumlative Y + for i in range(len(self.boxes)): + self.boxes[i]["top"] += \ + self.page_cum_height[self.boxes[i]["page_number"] - 1] + self.boxes[i]["bottom"] += \ + self.page_cum_height[self.boxes[i]["page_number"] - 1] + + def _text_merge(self): + # merge adjusted boxes + bxs = self.boxes + + def end_with(b, txt): + txt = txt.strip() + tt = b.get("text", "").strip() + return tt and tt.find(txt) == len(tt) - len(txt) + + def start_with(b, txts): + tt = b.get("text", "").strip() + return tt and any([tt.find(t.strip()) == 0 for t in txts]) + + # horizontally merge adjacent box with the same layout + i = 0 + while i < len(bxs) - 1: + b = bxs[i] + b_ = bxs[i + 1] + if b.get("layoutno", "0") != b_.get("layoutno", "1") or b.get("layout_type", "") in ["table", "figure", + "equation"]: + i += 1 + continue + if abs(self._y_dis(b, b_) + ) < self.mean_height[bxs[i]["page_number"] - 1] / 3: + # merge + if b_["x0"] > b["x0"]: + bxs[i]["x1"] = b_["x1"] + bxs[i]["top"] = (b["top"] + b_["top"]) / 2 + bxs[i]["bottom"] = (b["bottom"] + b_["bottom"]) / 2 + bxs[i]["text"] += b_["text"] + bxs.pop(i + 1) + else: + bxs[i + 1] = b + bxs[i] = b_ + b = bxs[i] + b_ = bxs[i + 1] + bxs[i]["x1"] = b_["x1"] + bxs[i]["top"] = (b["top"] + b_["top"]) / 2 + bxs[i]["bottom"] = (b["bottom"] + b_["bottom"]) / 2 + bxs[i]["text"] += b_["text"] + bxs.pop(i + 1) + continue + i += 1 + continue + + dis_thr = 1 + dis = b["x1"] - b_["x0"] + if b.get("layout_type", "") != "text" or b_.get( + "layout_type", "") != "text": + if end_with(b, "，") or start_with(b_, "（，"): + dis_thr = -8 + else: + i += 1 + continue + + if abs(self._y_dis(b, b_)) < self.mean_height[bxs[i]["page_number"] - 1] / 5 \ + and dis >= dis_thr and b["x1"] < b_["x1"]: + # merge + bxs[i]["x1"] = b_["x1"] + bxs[i]["top"] = (b["top"] + b_["top"]) / 2 + bxs[i]["bottom"] = (b["bottom"] + b_["bottom"]) / 2 + bxs[i]["text"] += b_["text"] + bxs.pop(i + 1) + continue + i += 1 + self.boxes = bxs + + def _naive_vertical_merge(self): + bxs = Recognizer.sort_Y_firstly( + self.boxes, np.median( + self.mean_height) / 3) + i = 0 + while i + 1 < len(bxs): + b = bxs[i] + b_ = bxs[i + 1] + # if b["page_number"] < b_["page_number"] and re.match( + # r"[0-9 •一—-]+$", b["text"]): + # bxs.pop(i) + # continue + if b["page_number"] < b_["page_number"]: + # bxs.pop(i) + i += 1 + continue + if not b["text"].strip(): + bxs.pop(i) + continue + concatting_feats = [ + b["text"].strip()[-1] in ",;:'\"，、‘“；：-", + len(b["text"].strip()) > 1 and b["text"].strip( + )[-2] in ",;:'\"，‘“、；：", + b["text"].strip()[0] in "。；？！?”）),，、：", + ] + # features for not concating + feats = [ + b.get("layoutno", 0) != b_.get("layoutno", 0), + b["text"].strip()[-1] in "。？！?", + self.is_english and b["text"].strip()[-1] in ".!?", + b["page_number"] == b_["page_number"] and b_["top"] - + b["bottom"] > self.mean_height[b["page_number"] - 1] * 1.5, + b["page_number"] < b_["page_number"] and abs( + b["x0"] - b_["x0"]) > self.mean_width[b["page_number"] - 1] * 4, + ] + # split features + detach_feats = [b["x1"] < b_["x0"], + b["x0"] > b_["x1"]] + if (any(feats) and not any(concatting_feats)) or any(detach_feats): + # print( + # b["text"], + # b_["text"], + # any(feats), + # any(concatting_feats), + # any(detach_feats)) + i += 1 + continue + # merge up and down + b["bottom"] = b_["bottom"] + b["text"] += b_["text"] + b["x0"] = min(b["x0"], b_["x0"]) + b["x1"] = max(b["x1"], b_["x1"]) + bxs.pop(i + 1) + self.boxes = bxs + + def _concat_downward(self, concat_between_pages=True): + blocks = {} + for sec in self.boxes: + pn = sec['page_number'] + if not sec.get('layoutno'): continue + sec_no = str(pn) + '-' + sec['layoutno'] + if sec_no not in blocks.keys(): + blocks[sec_no] = [sec] + else: + blocks[sec_no].append(sec) + boxes = [] + # for b in blocks: + for k, b in blocks.items(): + if len(b) == 1: + boxes.append(b[0]) + continue + t = b[0] + for c in b[1:]: + t["text"] = t["text"].strip() + c["text"] = c["text"].strip() + if not c["text"]: + continue + if t["text"] and re.match( + r"[0-9\.a-zA-Z]+$", t["text"][-1] + c["text"][-1]): + t["text"] += " " + if t["text"] and t['layout_type'] == 'content': + t["text"] += "\n" + t["text"] += c["text"] + t["x0"] = min(t["x0"], c["x0"]) + t["x1"] = max(t["x1"], c["x1"]) + t["page_number"] = min(t["page_number"], c["page_number"]) + t["bottom"] = c["bottom"] + if not t["layout_type"] \ + and c["layout_type"]: + t["layout_type"] = c["layout_type"] + boxes.append(t) + + self.boxes = Recognizer.sort_Y_firstly(boxes, 0) + # print(self.boxes, file=open('boxes3.txt', 'w')) + + def _filter_forpages(self): + if not self.boxes: + return + findit = False + i = 0 + while i < len(self.boxes): + if not re.match(r"(致谢|acknowledge)$", + re.sub(r"( | |\u3000)+", "", self.boxes[i]["text"].lower())): + i += 1 + continue + findit = True + eng = re.match( + r"[0-9a-zA-Z :'.-]{5,}", + self.boxes[i]["text"].strip()) + self.boxes.pop(i) + if i >= len(self.boxes): + break + prefix = self.boxes[i]["text"].strip()[:3] if not eng else " ".join( + self.boxes[i]["text"].strip().split(" ")[:2]) + while not prefix: + self.boxes.pop(i) + if i >= len(self.boxes): + break + prefix = self.boxes[i]["text"].strip()[:3] if not eng else " ".join( + self.boxes[i]["text"].strip().split(" ")[:2]) + self.boxes.pop(i) + if i >= len(self.boxes) or not prefix: + break + for j in range(i, min(i + 128, len(self.boxes))): + if not re.match(prefix, self.boxes[j]["text"]): + continue + for k in range(i, j): + self.boxes.pop(i) + break + if findit: + return + + page_dirty = [0] * len(self.page_images) + for b in self.boxes: + if re.search(r"(··|··|··)", b["text"]): + page_dirty[b["page_number"] - 1] += 1 + page_dirty = set([i + 1 for i, t in enumerate(page_dirty) if t > 3]) + if not page_dirty: + return + i = 0 + while i < len(self.boxes): + if self.boxes[i]["page_number"] in page_dirty: + self.boxes.pop(i) + continue + i += 1 + + def _merge_with_same_bullet(self): + i = 0 + while i + 1 < len(self.boxes): + b = self.boxes[i] + b_ = self.boxes[i + 1] + if not b["text"].strip(): + self.boxes.pop(i) + continue + if not b_["text"].strip(): + self.boxes.pop(i + 1) + continue + + if b["text"].strip()[0] != b_["text"].strip()[0] \ + or b["text"].strip()[0].lower() in set("qwertyuopasdfghjklzxcvbnm") \ + or huqie.is_chinese(b["text"].strip()[0]) \ + or b["top"] > b_["bottom"]: + i += 1 + continue + b_["text"] = b["text"] + "\n" + b_["text"] + b_["x0"] = min(b["x0"], b_["x0"]) + b_["x1"] = max(b["x1"], b_["x1"]) + b_["top"] = b["top"] + self.boxes.pop(i) + + def _extract_table_figure(self, need_image, ZM, + return_html, need_position,image_dir): + tables = {} + figures = {} + captions = {} + merged_captions = {} + # extract figure and table boxes + i = 0 + lst_lout_no = "" + nomerge_lout_no = [] + while i < len(self.boxes): + if "layoutno" not in self.boxes[i]: + i += 1 + continue + lout_no = str(self.boxes[i]["page_number"]) + \ + "-" + str(self.boxes[i]["layoutno"]) + if TableStructureRecognizer_LORE.is_caption(self.boxes[i]) or self.boxes[i]["layout_type"] == 'caption': + # print(self.boxes[i]['text']) + nomerge_lout_no.append(lst_lout_no) + if lout_no not in captions: + captions[lout_no] = [] + captions[lout_no].append(self.boxes[i]) + self.boxes.pop(i) + continue + if self.boxes[i]["layout_type"] == "table": + if re.match(r"(数据|资料|图表)*来源[:： ]", self.boxes[i]["text"]): + self.boxes.pop(i) + continue + if lout_no not in tables: + tables[lout_no] = [] + tables[lout_no].append(self.boxes[i]) + self.boxes.pop(i) + lst_lout_no = lout_no + continue + if need_image and self.boxes[i]["layout_type"] == "figure": + if re.match(r"(数据|资料|图表)*来源[:： ]", self.boxes[i]["text"]): + self.boxes.pop(i) + continue + if lout_no not in figures: + figures[lout_no] = [] + figures[lout_no].append(self.boxes[i]) + self.boxes.pop(i) + lst_lout_no = lout_no + continue + i += 1 + # merge table on different pages + nomerge_lout_no = set(nomerge_lout_no) + tbls = sorted([(k, bxs) for k, bxs in tables.items()], + key=lambda x: (x[1][0]["top"], x[1][0]["x0"])) + i = len(tbls) - 1 + while i - 1 >= 0: + k0, bxs0 = tbls[i - 1] + k, bxs = tbls[i] + i -= 1 + if k0 in nomerge_lout_no: + continue + if bxs[0]["page_number"] == bxs0[0]["page_number"]: + continue + if bxs[0]["page_number"] - bxs0[0]["page_number"] > 1: + continue + mh = self.mean_height[bxs[0]["page_number"] - 1] + if self._y_dis(bxs0[-1], bxs[0]) > mh * 23: + continue + tables[k0].extend(tables[k]) + del tables[k] + + def x_overlapped(a, b): + return not any([a["x1"] < b["x0"], a["x0"] > b["x1"]]) + for k, v in captions.items(): + txt = '' + x0 = min([b['x0'] for b in captions[k]]) + x1 = max([b['x1'] for b in captions[k]]) + top = min([b['top'] for b in captions[k]]) + bottom = max([b['bottom'] for b in captions[k]]) + v = sorted(v, key=lambda x: x['top']) + i = 0 + while i < len(v): + txt += v[i]['text'] + i += 1 + merged_captions[k] = {'x0': x0, 'x1': x1, 'top': top, 'text': txt, 'bottom': bottom, + 'page_number': int(k.split('-')[0]), 'layout_type': 'caption', + 'layoutno': '-'.join(k.split('-')[1:])} + + # find captions and pop out + for ck, cv in captions.items(): + caption = captions[ck] + # mh = self.mean_height[c["page_number"]-1] + # find the nearest layouts + def nearest(tbls): + minks, minvs = [], [] + nonlocal caption + mink = "" + minv = 1000000000 + for k, bxs in tbls.items(): + if k.find('equation') >=0: + continue + for b in bxs: + if b.get("layout_type", "").find("caption") >= 0: + continue + for c in caption: + if b['page_number'] != c['page_number']: + continue + y_dis = self._y_dis(c, b) + x_dis = self._x_dis( + c, b) if not x_overlapped( + c, b) else 0 + dis = y_dis * y_dis + x_dis * x_dis + if dis < minv: + minv = dis + mink = k + minvs.append(minv) + minks.append(mink) + if minvs: + mink = max(minks, key=minks.count) + minv = np.mean([minvs[i] for i in range(len(minvs)) if minks[i] == mink]) + mink2 = minks[minvs.index(min(minvs))] + minv2 = np.mean([minvs[i] for i in range(len(minvs)) if minks[i] == mink2]) + if mink != mink2 and minv > minv2: + mink = mink2 + minv = minv2 + return mink, minv + tk, tv = nearest(tables) + fk, fv = nearest(figures) + # if min(tv, fv) > 2000: + # i += 1 + # continue + if tv < fv and tk: + tables[tk].insert(0, merged_captions[ck]) + logging.debug( + "TABLE:" + + self.boxes[i]["text"] + + "; Cap: " + + tk) + elif fk: + figures[fk].insert(0, merged_captions[ck]) + logging.debug( + "FIGURE:" + + self.boxes[i]["text"] + + "; Cap: " + + tk) + # self.boxes.pop(i) + + res = [] + positions = [] + + def cropout(bxs, ltype, poss): + nonlocal ZM + pn = set([b["page_number"] - 1 for b in bxs]) + if len(pn) < 2: + pn = list(pn)[0] + ht = self.page_cum_height[pn] + b = { + "x0": np.min([b["x0"] for b in bxs if b['layout_type'] != 'caption']), + "top": np.min([b["top"] for b in bxs if b['layout_type'] != 'caption']) - ht, + "x1": np.max([b["x1"] for b in bxs if b['layout_type'] != 'caption']), + "bottom": np.max([b["bottom"] for b in bxs if b['layout_type'] != 'caption']) - ht + } + louts = [l for l in self.page_layout[pn] if l["type"] == ltype] + ii = Recognizer.find_overlapped(b, louts, naive=True) + if ii is not None: + b = louts[ii] + else: + logging.warn( + f"Missing layout match: {pn + 1},%s" % + (bxs[0].get( + "layoutno", ""))) + + left, top, right, bott = b["x0"], b["top"], b["x1"], b["bottom"] + poss.append((pn + self.page_from, left, right, top, bott)) + return self.page_images[pn] \ + .crop((left * ZM, top * ZM, + right * ZM, bott * ZM)) + pn = {} + for b in bxs: + p = b["page_number"] - 1 + if p not in pn: + pn[p] = [] + pn[p].append(b) + pn = sorted(pn.items(), key=lambda x: x[0]) + imgs = [cropout(arr, ltype, poss) for p, arr in pn] + pic = Image.new("RGB", + (int(np.max([i.size[0] for i in imgs])), + int(np.sum([m.size[1] for m in imgs]))), + (245, 245, 245)) + height = 0 + for img in imgs: + pic.paste(img, (0, int(height))) + height += img.size[1] + return pic + + # crop figure out and add caption + for k, bxs in figures.items(): + caption = ''.join([b["text"] for b in bxs if b['layout_type'] == 'caption']) + # txt = "\n".join([b["text"] for b in bxs]) + # if not txt: + # continue + + poss = [] + img = cropout( + bxs, + "figure", poss) + img.save('{}/{}.jpg'.format(image_dir,k)) + res.append( + ( + # img, + caption, + k)) + positions.append(poss) + for k, bxs in tables.items(): + if not bxs: + continue + bxs = Recognizer.sort_Y_firstly(bxs, np.mean( + [(b["bottom"] - b["top"]) / 2 for b in bxs])) + poss = [] + img = cropout(bxs, "table", poss) + pn = list(set([b["page_number"] - 1 for b in bxs]))[0] + res.append(( + # img, self.tbl_det.construct_table(bxs, img, html=return_html, is_english=self.is_english))) + self.tbl_det.construct_table(bxs, img, poss[0][1:], self.page_cum_height[pn], html=return_html, is_english=self.is_english), + k)) + # img.save('{}.jpg'.format(k)) + positions.append(poss) + + assert len(positions) == len(res) + + if need_position: + return list(zip(res, positions)) + return res + + + + def proj_match(self, line): + if len(line) <= 2: + return + if re.match(r"[0-9 ().,%%+/-]+$", line): + return False + for p, j in [ + (r"第[零一二三四五六七八九十百]+章", 1), + (r"第[零一二三四五六七八九十百]+[条节]", 2), + (r"[零一二三四五六七八九十百]+[、 　]", 3), + (r"[\(（][零一二三四五六七八九十百]+[）\)]", 4), + (r"[0-9]+(、|\.[　 ]|\.[^0-9])", 5), + (r"[0-9]+\.[0-9]+(、|[. 　]|[^0-9])", 6), + (r"[0-9]+\.[0-9]+\.[0-9]+(、|[ 　]|[^0-9])", 7), + (r"[0-9]+\.[0-9]+\.[0-9]+\.[0-9]+(、|[ 　]|[^0-9])", 8), + (r".{,48}[：:?？]$", 9), + (r"[0-9]+）", 10), + (r"[\(（][0-9]+[）\)]", 11), + (r"[零一二三四五六七八九十百]+是", 12), + (r"[⚫•➢✓]", 12) + ]: + if re.match(p, line): + return j + return + + def _line_tag(self, bx, ZM): + pn = [bx["page_number"]] + top = bx["top"] - self.page_cum_height[pn[0] - 1] + bott = bx["bottom"] - self.page_cum_height[pn[0] - 1] + while bott * ZM > self.page_images[pn[-1] - 1].size[1]: + bott -= self.page_images[pn[-1] - 1].size[1] / ZM + pn.append(pn[-1] + 1) + + return "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \ + .format("-".join([str(p) for p in pn]), + bx["x0"], bx["x1"], top, bott) + + def __filterout_scraps(self, boxes, ZM): + + def width(b): + return b["x1"] - b["x0"] + + def height(b): + return b["bottom"] - b["top"] + + def usefull(b): + if b.get("layout_type"): + return True + if width( + b) > self.page_images[b["page_number"] - 1].size[0] / ZM / 3: + return True + if b["bottom"] - b["top"] > self.mean_height[b["page_number"] - 1]: + return True + return False + + res = [] + while boxes: + lines = [] + widths = [] + pw = self.page_images[boxes[0]["page_number"] - 1].size[0] / ZM + mh = self.mean_height[boxes[0]["page_number"] - 1] + mj = self.proj_match( + boxes[0]["text"]) or boxes[0].get( + "layout_type", + "") == "title" + + def dfs(line, st): + nonlocal mh, pw, lines, widths + lines.append(line) + widths.append(width(line)) + width_mean = np.mean(widths) + mmj = self.proj_match( + line["text"]) or line.get( + "layout_type", + "") == "title" + for i in range(st + 1, min(st + 20, len(boxes))): + if (boxes[i]["page_number"] - line["page_number"]) > 0: + break + if not mmj and self._y_dis( + line, boxes[i]) >= 3 * mh and height(line) < 1.5 * mh: + break + + if not usefull(boxes[i]): + continue + if mmj or \ + (self._x_dis(boxes[i], line) < pw / 10): \ + # and abs(width(boxes[i])-width_mean)/max(width(boxes[i]),width_mean)<0.5): + # concat following + dfs(boxes[i], i) + boxes.pop(i) + break + + try: + if usefull(boxes[0]): + dfs(boxes[0], 0) + else: + logging.debug("WASTE: " + boxes[0]["text"]) + except Exception as e: + pass + boxes.pop(0) + mw = np.mean(widths) + if mj or mw / pw >= 0.35 or mw > 200: + res.append( + "\n".join([c["text"] + self._line_tag(c, ZM) for c in lines])) + else: + logging.debug("REMOVED: " + + "<<".join([c["text"] for c in lines])) + + return "\n\n".join(res) + + @staticmethod + def total_page_number(fnm, binary=None): + try: + pdf = pdfplumber.open( + fnm) if not binary else pdfplumber.open(BytesIO(binary)) + return len(pdf.pages) + except Exception as e: + pdf = fitz.open(fnm) if not binary else fitz.open( + stream=fnm, filetype="pdf") + return len(pdf) + + def page_ocr(self,page,zoomin): + blocks = page.get_text( + "dict", flags=0, + )["blocks"] + ocr_res = [] + for b in blocks: + for line in b["lines"]: + line_text = '' + line_bbox_lst = [[],[],[],[]] + for item in line['spans']: + line_text += item['text'] + line_bbox_lst[0].append(item['bbox'][0]*zoomin) + line_bbox_lst[1].append(item['bbox'][1]*zoomin) + line_bbox_lst[2].append(item['bbox'][2]*zoomin) + line_bbox_lst[3].append(item['bbox'][3]*zoomin) + line_bbox = [min(line_bbox_lst[0]),min(line_bbox_lst[1]),max(line_bbox_lst[2]),max(line_bbox_lst[3])] + four_point_bbox = [[line_bbox[0],line_bbox[1]],[line_bbox[2],line_bbox[1]], + [line_bbox[2],line_bbox[3]],[line_bbox[0],line_bbox[3]]] + ocr_res.append([four_point_bbox,line_text,1]) + return ocr_res + + + def __images__(self, fnm, zoomin=3, page_from=0, + page_to=299, callback=None): + self.lefted_chars = [] + self.mean_height = [] + self.mean_width = [] + self.boxes = [] + self.garbages = {} + self.page_cum_height = [0] + self.page_layout = [] + self.page_from = page_from + self.pdf = fitz.open(fnm) if isinstance( + fnm, str) else fitz.open( + stream=fnm, filetype="pdf") + self.page_images = [] + self.page_chars = [] + self.ocr_res = [] + mat = fitz.Matrix(zoomin, zoomin) + self.total_page = len(self.pdf) + for i, page in enumerate(self.pdf): + if i < page_from: + continue + if i >= page_to: + break + pix = page.get_pixmap(matrix=mat) + img = Image.frombytes("RGB", [pix.width, pix.height], + pix.samples) + self.page_images.append(img) + self.page_chars.append([]) + page_ocr_res = self.page_ocr(page,zoomin) + self.ocr_res.append(page_ocr_res) + + + self.outlines = [] + try: + self.pdf = pdf2_read(fnm if isinstance(fnm, str) else BytesIO(fnm)) + outlines = self.pdf.outline + + def dfs(arr, depth): + for a in arr: + if isinstance(a, dict): + self.outlines.append((a["/Title"], depth)) + continue + dfs(a, depth + 1) + dfs(outlines, 0) + except Exception as e: + logging.warning(f"Outlines exception: {e}") + if not self.outlines: + logging.warning(f"Miss outlines") + + logging.info("Images converted.") + self.is_english = [re.search(r"[a-zA-Z0-9,/¸;:'\[\]\(\)!@#$%^&*\"?<>._-]{30,}", "".join( + random.choices([c["text"] for c in self.page_chars[i]], k=min(100, len(self.page_chars[i]))))) for i in + range(len(self.page_chars))] + if sum([1 if e else 0 for e in self.is_english]) > len( + self.page_images) / 2: + self.is_english = True + else: + self.is_english = False + + debug_logger.info("Start OCR！") + for i, img in tqdm(enumerate(self.page_images)): + chars = self.page_chars[i] if not self.is_english else [] + self.mean_height.append( + np.median(sorted([c["height"] for c in chars])) if chars else 0 + ) + self.mean_width.append( + np.median(sorted([c["width"] for c in chars])) if chars else 8 + ) + self.page_cum_height.append(img.size[1] / zoomin) + j = 0 + while j + 1 < len(chars): + if chars[j]["text"] and chars[j + 1]["text"] \ + and re.match(r"[0-9a-zA-Z,.:;!%]+", chars[j]["text"] + chars[j + 1]["text"]) \ + and chars[j + 1]["x0"] - chars[j]["x1"] >= min(chars[j + 1]["width"], + chars[j]["width"]) / 2: + chars[j]["text"] += " " + j += 1 + # self.__ocr(i + 1, img, chars, zoomin) + self.__ocr_pdf(i+1, self.ocr_res[i], zoomin) + if callback: + callback(prog=(i + 1) * 0.6 / len(self.page_images), msg="") + + if not self.is_english and not any( + [c for c in self.page_chars]) and self.boxes: + bxes = [b for bxs in self.boxes for b in bxs] + self.is_english = re.search(r"[\na-zA-Z0-9,/¸;:'\[\]\(\)!@#$%^&*\"?<>._-]{30,}", + "".join([b["text"] for b in random.choices(bxes, k=min(30, len(bxes)))])) + + logging.info("Is it English:", self.is_english) + + self.page_cum_height = np.cumsum(self.page_cum_height) + assert len(self.page_cum_height) == len(self.page_images) + 1 + + def __call__(self, fnm, need_image=True, zoomin=3, return_html=False): + self.__images__(fnm, zoomin) + self._layouts_rec(zoomin) + self._table_transformer_job(zoomin) + self._text_merge() + self._concat_downward() + self._filter_forpages() + tbls = self._extract_table_figure( + need_image, zoomin, return_html, False) + return self.__filterout_scraps(deepcopy(self.boxes), zoomin), tbls + + def remove_tag(self, txt): + return re.sub(r"@@[\t0-9.-]+?##", "", txt) + + def crop(self, text, ZM=3, need_position=False): + imgs = [] + poss = [] + for tag in re.findall(r"@@[0-9-]+\t[0-9.\t]+##", text): + pn, left, right, top, bottom = tag.strip( + "#").strip("@").split("\t") + left, right, top, bottom = float(left), float( + right), float(top), float(bottom) + poss.append(([int(p) - 1 for p in pn.split("-")], + left, right, top, bottom)) + if not poss: + if need_position: + return None, None + return + + max_width = max( + np.max([right - left for (_, left, right, _, _) in poss]), 6) + GAP = 6 + pos = poss[0] + poss.insert(0, ([pos[0][0]], pos[1], pos[2], max( + 0, pos[3] - 120), max(pos[3] - GAP, 0))) + pos = poss[-1] + poss.append(([pos[0][-1]], pos[1], pos[2], min(self.page_images[pos[0][-1]].size[1] / ZM, pos[4] + GAP), + min(self.page_images[pos[0][-1]].size[1] / ZM, pos[4] + 120))) + + positions = [] + for ii, (pns, left, right, top, bottom) in enumerate(poss): + right = left + max_width + bottom *= ZM + for pn in pns[1:]: + bottom += self.page_images[pn - 1].size[1] + imgs.append( + self.page_images[pns[0]].crop((left * ZM, top * ZM, + right * + ZM, min( + bottom, self.page_images[pns[0]].size[1]) + )) + ) + if 0 < ii < len(poss) - 1: + positions.append((pns[0] + self.page_from, left, right, top, min( + bottom, self.page_images[pns[0]].size[1]) / ZM)) + bottom -= self.page_images[pns[0]].size[1] + for pn in pns[1:]: + imgs.append( + self.page_images[pn].crop((left * ZM, 0, + right * ZM, + min(bottom, + self.page_images[pn].size[1]) + )) + ) + if 0 < ii < len(poss) - 1: + positions.append((pn + self.page_from, left, right, 0, min( + bottom, self.page_images[pn].size[1]) / ZM)) + bottom -= self.page_images[pn].size[1] + + if not imgs: + if need_position: + return None, None + return + height = 0 + for img in imgs: + height += img.size[1] + GAP + height = int(height) + width = int(np.max([i.size[0] for i in imgs])) + pic = Image.new("RGB", + (width, height), + (245, 245, 245)) + height = 0 + for ii, img in enumerate(imgs): + if ii == 0 or ii + 1 == len(imgs): + img = img.convert('RGBA') + overlay = Image.new('RGBA', img.size, (0, 0, 0, 0)) + overlay.putalpha(128) + img = Image.alpha_composite(img, overlay).convert("RGB") + pic.paste(img, (0, int(height))) + height += img.size[1] + GAP + + if need_position: + return pic, positions + return pic + + def get_position(self, bx, ZM): + poss = [] + pn = bx["page_number"] + top = bx["top"] - self.page_cum_height[pn - 1] + bott = bx["bottom"] - self.page_cum_height[pn - 1] + poss.append((pn, bx["x0"], bx["x1"], top, min( + bott, self.page_images[pn - 1].size[1] / ZM))) + while bott * ZM > self.page_images[pn - 1].size[1]: + bott -= self.page_images[pn - 1].size[1] / ZM + top = 0 + pn += 1 + poss.append((pn, bx["x0"], bx["x1"], top, min( + bott, self.page_images[pn - 1].size[1] / ZM))) + return poss + + + + + +class PlainParser(object): + def __call__(self, filename, from_page=0, to_page=100000, **kwargs): + self.outlines = [] + lines = [] + try: + self.pdf = pdf2_read( + filename if isinstance( + filename, str) else BytesIO(filename)) + for page in self.pdf.pages[from_page:to_page]: + lines.extend([t for t in page.extract_text().split("\n")]) + + outlines = self.pdf.outline + + def dfs(arr, depth): + for a in arr: + if isinstance(a, dict): + self.outlines.append((a["/Title"], depth)) + continue + dfs(a, depth + 1) + + dfs(outlines, 0) + except Exception as e: + logging.warning(f"Outlines exception: {e}") + if not self.outlines: + logging.warning(f"Miss outlines") + + return [(l, "") for l in lines], [] + + def crop(self, ck, need_position): + raise NotImplementedError + + @staticmethod + def remove_tag(txt): + raise NotImplementedError + + +if __name__ == "__main__": + pass diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/__init__.py new file mode 100644 index 00000000..658268d0 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/__init__.py @@ -0,0 +1,52 @@ +import datetime + + +def refactor(cv): + for n in ["raw_txt", "parser_name", "inference", "ori_text", "use_time", "time_stat"]: + if n in cv and cv[n] is not None: del cv[n] + cv["is_deleted"] = 0 + if "basic" not in cv: cv["basic"] = {} + if cv["basic"].get("photo2"): del cv["basic"]["photo2"] + + for n in ["education", "work", "certificate", "project", "language", "skill", "training"]: + if n not in cv or cv[n] is None: continue + if type(cv[n]) == type({}): cv[n] = [v for _, v in cv[n].items()] + if type(cv[n]) != type([]): + del cv[n] + continue + vv = [] + for v in cv[n]: + if "external" in v and v["external"] is not None: del v["external"] + vv.append(v) + cv[n] = {str(i): vv[i] for i in range(len(vv))} + + basics = [ + ("basic_salary_month", "salary_month"), + ("expect_annual_salary_from", "expect_annual_salary"), + ] + for n, t in basics: + if cv["basic"].get(n): + cv["basic"][t] = cv["basic"][n] + del cv["basic"][n] + + work = sorted([v for _, v in cv.get("work", {}).items()], key=lambda x: x.get("start_time", "")) + edu = sorted([v for _, v in cv.get("education", {}).items()], key=lambda x: x.get("start_time", "")) + + if work: + cv["basic"]["work_start_time"] = work[0].get("start_time", "") + cv["basic"]["management_experience"] = 'Y' if any( + [w.get("management_experience", '') == 'Y' for w in work]) else 'N' + cv["basic"]["annual_salary"] = work[-1].get("annual_salary_from", "0") + + for n in ["annual_salary_from", "annual_salary_to", "industry_name", "position_name", "responsibilities", + "corporation_type", "scale", "corporation_name"]: + cv["basic"][n] = work[-1].get(n, "") + + if edu: + for n in ["school_name", "discipline_name"]: + if n in edu[-1]: cv["basic"][n] = edu[-1][n] + + cv["basic"]["updated_at"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + if "contact" not in cv: cv["contact"] = {} + if not cv["contact"].get("name"): cv["contact"]["name"] = cv["basic"].get("name", "") + return cv \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/corporations.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/corporations.py new file mode 100644 index 00000000..6b7b38a5 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/corporations.py @@ -0,0 +1,80 @@ +import re,json,os +import pandas as pd +from rag.nlp import huqie +from . import regions +current_file_path = os.path.dirname(os.path.abspath(__file__)) +GOODS = pd.read_csv(os.path.join(current_file_path, "res/corp_baike_len.csv"), sep="\t", header=0).fillna(0) +GOODS["cid"] = GOODS["cid"].astype(str) +GOODS = GOODS.set_index(["cid"]) +CORP_TKS = json.load(open(os.path.join(current_file_path, "res/corp.tks.freq.json"), "r")) +GOOD_CORP = json.load(open(os.path.join(current_file_path, "res/good_corp.json"), "r")) +CORP_TAG = json.load(open(os.path.join(current_file_path, "res/corp_tag.json"), "r")) + +def baike(cid, default_v=0): + global GOODS + try: + return GOODS.loc[str(cid), "len"] + except Exception as e: + pass + return default_v + + +def corpNorm(nm, add_region=True): + global CORP_TKS + if not nm or type(nm)!=type(""):return "" + nm = huqie.tradi2simp(huqie.strQ2B(nm)).lower() + nm = re.sub(r"&", "&", nm) + nm = re.sub(r"[\(\)（）\+'\"\t \*\\【】-]+", " ", nm) + nm = re.sub(r"([—-]+.*| +co\..*|corp\..*| +inc\..*| +ltd.*)", "", nm, 10000, re.IGNORECASE) + nm = re.sub(r"(计算机|技术|(技术|科技|网络)*有限公司|公司|有限|研发中心|中国|总部)$", "", nm, 10000, re.IGNORECASE) + if not nm or (len(nm)<5 and not regions.isName(nm[0:2])):return nm + + tks = huqie.qie(nm).split(" ") + reg = [t for i,t in enumerate(tks) if regions.isName(t) and (t != "中国" or i > 0)] + nm = "" + for t in tks: + if regions.isName(t) or t in CORP_TKS:continue + if re.match(r"[0-9a-zA-Z\\,.]+", t) and re.match(r".*[0-9a-zA-Z\,.]+$", nm):nm += " " + nm += t + + r = re.search(r"^([^a-z0-9 \(\)&]{2,})[a-z ]{4,}$", nm.strip()) + if r:nm = r.group(1) + r = re.search(r"^([a-z ]{3,})[^a-z0-9 \(\)&]{2,}$", nm.strip()) + if r:nm = r.group(1) + return nm.strip() + (("" if not reg else "(%s)"%reg[0]) if add_region else "") + + +def rmNoise(n): + n = re.sub(r"[\(（][^()（）]+[)）]", "", n) + n = re.sub(r"[,. &（）()]+", "", n) + return n + +GOOD_CORP = set([corpNorm(rmNoise(c), False) for c in GOOD_CORP]) +for c,v in CORP_TAG.items(): + cc = corpNorm(rmNoise(c), False) + if not cc: print (c) +CORP_TAG = {corpNorm(rmNoise(c), False):v for c,v in CORP_TAG.items()} + +def is_good(nm): + global GOOD_CORP + if nm.find("外派")>=0:return False + nm = rmNoise(nm) + nm = corpNorm(nm, False) + for n in GOOD_CORP: + if re.match(r"[0-9a-zA-Z]+$", n): + if n == nm: return True + elif nm.find(n)>=0:return True + return False + +def corp_tag(nm): + global CORP_TAG + nm = rmNoise(nm) + nm = corpNorm(nm, False) + for n in CORP_TAG.keys(): + if re.match(r"[0-9a-zA-Z., ]+$", n): + if n == nm: return CORP_TAG[n] + elif nm.find(n)>=0: + if len(n)<3 and len(nm)/len(n)>=2:continue + return CORP_TAG[n] + return [] + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/degrees.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/degrees.py new file mode 100644 index 00000000..b9b15ef4 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/degrees.py @@ -0,0 +1,24 @@ +TBL = {"94":"EMBA", +"6":"MBA", +"95":"MPA", +"92":"专升本", +"4":"专科", +"90":"中专", +"91":"中技", +"86":"初中", +"3":"博士", +"10":"博士后", +"1":"本科", +"2":"硕士", +"87":"职高", +"89":"高中" +} + +TBL_ = {v:k for k,v in TBL.items()} + +def get_name(id): + return TBL.get(str(id), "") + +def get_id(nm): + if not nm:return "" + return TBL_.get(nm.upper().strip(), "") diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/industries.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/industries.py new file mode 100644 index 00000000..f0fb19c0 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/industries.py @@ -0,0 +1,692 @@ + +TBL = {"1":{"name":"IT/通信/电子","parent":"0"}, +"2":{"name":"互联网","parent":"0"}, +"3":{"name":"电子商务","parent":"2"}, +"4":{"name":"互联网金融","parent":"2"}, +"5":{"name":"网络游戏","parent":"2"}, +"6":{"name":"社交网络平台","parent":"2"}, +"7":{"name":"视频音乐","parent":"2"}, +"9":{"name":"安全","parent":"2"}, +"10":{"name":"云计算","parent":"2"}, +"12":{"name":"工具类客户端应用","parent":"2"}, +"13":{"name":"互联网广告","parent":"2"}, +"14":{"name":"企业互联网服务","parent":"2"}, +"16":{"name":"在线教育","parent":"2"}, +"17":{"name":"在线医疗","parent":"2"}, +"19":{"name":"B2B","parent":"3"}, +"20":{"name":"B2C","parent":"3"}, +"21":{"name":"C2C","parent":"3"}, +"22":{"name":"生活信息本地化","parent":"3"}, +"23":{"name":"在线旅游","parent":"2"}, +"24":{"name":"第三方支付","parent":"4"}, +"26":{"name":"客户端游戏","parent":"5"}, +"27":{"name":"网页游戏","parent":"5"}, +"28":{"name":"手机游戏","parent":"5"}, +"29":{"name":"微博","parent":"6"}, +"30":{"name":"社交网站","parent":"6"}, +"31":{"name":"在线视频","parent":"7"}, +"32":{"name":"在线音乐","parent":"7"}, +"35":{"name":"企业安全","parent":"9"}, +"36":{"name":"个人安全","parent":"9"}, +"37":{"name":"企业级云服务","parent":"10"}, +"38":{"name":"个人级云服务","parent":"10"}, +"43":{"name":"输入法","parent":"12"}, +"44":{"name":"浏览器","parent":"12"}, +"45":{"name":"词典","parent":"12"}, +"46":{"name":"播放器","parent":"12"}, +"47":{"name":"下载器","parent":"12"}, +"48":{"name":"IM","parent":"12"}, +"49":{"name":"广告服务","parent":"13"}, +"50":{"name":"第三方广告网络平台","parent":"13"}, +"51":{"name":"媒体代理","parent":"13"}, +"52":{"name":"创意代理","parent":"13"}, +"53":{"name":"IT-综合","parent":"1"}, +"71":{"name":"团购","parent":"3"}, +"72":{"name":"地图","parent":"2"}, +"73":{"name":"数据存储","parent":"2"}, +"414":{"name":"计算机软件","parent":"1"}, +"415":{"name":"计算机硬件","parent":"1"}, +"416":{"name":"计算机服务(系统、数据服务、维修)","parent":"1"}, +"417":{"name":"通信/电信/网络设备","parent":"1"}, +"418":{"name":"通信/电信运营、增值服务","parent":"1"}, +"419":{"name":"电子技术/半导体/集成电路","parent":"1"}, +"472":{"name":"P2P网贷","parent":"4"}, +"473":{"name":"互联网理财","parent":"4"}, +"474":{"name":"婚恋","parent":"6"}, +"476":{"name":"虚拟化","parent":"10"}, +"477":{"name":"邮箱","parent":"12"}, +"478":{"name":"商业智能","parent":"14"}, +"479":{"name":"企业建站","parent":"14"}, +"480":{"name":"安防","parent":"14"}, +"481":{"name":"网络营销","parent":"2"}, +"487":{"name":"智能终端","parent":"2"}, +"488":{"name":"移动互联网","parent":"2"}, +"489":{"name":"数字城市","parent":"2"}, +"490":{"name":"大数据","parent":"2"}, +"491":{"name":"互联网人力资源","parent":"2"}, +"492":{"name":"舆情监控","parent":"2"}, +"493":{"name":"移动营销","parent":"481"}, +"494":{"name":"微博营销","parent":"481"}, +"495":{"name":"精准营销","parent":"481"}, +"496":{"name":"海外营销","parent":"481"}, +"497":{"name":"微信营销","parent":"481"}, +"498":{"name":"智能手机","parent":"487"}, +"499":{"name":"可穿戴设备","parent":"487"}, +"500":{"name":"智能电视","parent":"487"}, +"501":{"name":"WAP","parent":"488"}, +"502":{"name":"物联网","parent":"489"}, +"503":{"name":"O2O","parent":"489"}, +"504":{"name":"数字出版","parent":"489"}, +"505":{"name":"搜索","parent":"2"}, +"506":{"name":"垂直搜索","parent":"505"}, +"507":{"name":"无线搜索","parent":"505"}, +"508":{"name":"网页搜索","parent":"505"}, +"509":{"name":"网址导航","parent":"2"}, +"510":{"name":"门户","parent":"2"}, +"511":{"name":"网络文学","parent":"2"}, +"512":{"name":"自媒体","parent":"2"}, +"513":{"name":"金融","parent":"0"}, +"514":{"name":"建筑与房地产","parent":"0"}, 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+"1095":{"name":"矿山设备","parent":"727"}, +"1096":{"name":"机床机械","parent":"726"}, +"1097":{"name":"农产品电商","parent":"3"}, +"1098":{"name":"陶瓷装饰材料","parent":"746"}, +"1099":{"name":"车载联网设备","parent":"487"}, +"1100":{"name":"汽车销售电子商务","parent":"3"}, +"1101":{"name":"石油设备","parent":"730"}, +"1102":{"name":"智能家居","parent":"487"}, +"1103":{"name":"散热器","parent":"751"}, +"1104":{"name":"电力工程","parent":"775"}, +"1105":{"name":"生鲜电商","parent":"3"}, +"1106":{"name":"互联网数据服务","parent":"490"}, +"1107":{"name":"房车、商务车销售","parent":"749"}, +"1108":{"name":"茶叶电子商务","parent":"3"}, +"1109":{"name":"酒类电子商务","parent":"3"}, +"1110":{"name":"阀门","parent":"730"}, +"1111":{"name":"食品电商","parent":"3"}, +"1112":{"name":"儿童摄影","parent":"871"}, +"1113":{"name":"广告摄影","parent":"871"}, +"1114":{"name":"婚纱摄影","parent":"871"}, +"1115":{"name":"模具制造","parent":"620"}, +"1116":{"name":"汽车模具","parent":"629"}, +"1117":{"name":"认证咨询","parent":"567"}, +"1118":{"name":"数字视觉制作服务","parent":"590"}, +"1119":{"name":"牙科及医疗器械","parent":"646"}, +"1120":{"name":"猎头招聘","parent":"560"}, +"1121":{"name":"家居","parent":"518"}, +"1122":{"name":"收藏品","parent":"518"}, +"1123":{"name":"首饰","parent":"518"}, +"1124":{"name":"工艺品","parent":"518"}, +"1125":{"name":"财务","parent":"515"}, +"1126":{"name":"税务","parent":"515"}, +"1127":{"name":"分类信息","parent":"2"}, +"1128":{"name":"宠物","parent":"0"}, +"1129":{"name":"快消品","parent":"518"}, +"1130":{"name":"人工智能","parent":"2"}, +"1131":{"name":"农/林/牧/渔","parent":"0"} +} + +def get_names(id): + id = str(id) + nms = [] + d = TBL.get(id) + if not d:return [] + nms.append(d["name"]) + p = get_names(d["parent"]) + if p: nms.extend(p) + return nms + +if __name__ == "__main__": + print(get_names("1119")) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/regions.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/regions.py new file mode 100644 index 00000000..54b655e6 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/regions.py @@ -0,0 +1,762 @@ +TBL = { +"2":{"name":"北京","parent":"1"}, +"3":{"name":"天津","parent":"1"}, +"4":{"name":"河北","parent":"1"}, +"5":{"name":"山西","parent":"1"}, +"6":{"name":"内蒙古","parent":"1"}, +"7":{"name":"辽宁","parent":"1"}, +"8":{"name":"吉林","parent":"1"}, +"9":{"name":"黑龙江","parent":"1"}, +"10":{"name":"上海","parent":"1"}, +"11":{"name":"江苏","parent":"1"}, +"12":{"name":"浙江","parent":"1"}, +"13":{"name":"安徽","parent":"1"}, +"14":{"name":"福建","parent":"1"}, +"15":{"name":"江西","parent":"1"}, +"16":{"name":"山东","parent":"1"}, +"17":{"name":"河南","parent":"1"}, +"18":{"name":"湖北","parent":"1"}, +"19":{"name":"湖南","parent":"1"}, +"20":{"name":"广东","parent":"1"}, +"21":{"name":"广西","parent":"1"}, +"22":{"name":"海南","parent":"1"}, +"23":{"name":"重庆","parent":"1"}, +"24":{"name":"四川","parent":"1"}, +"25":{"name":"贵州","parent":"1"}, +"26":{"name":"云南","parent":"1"}, +"27":{"name":"西藏","parent":"1"}, +"28":{"name":"陕西","parent":"1"}, +"29":{"name":"甘肃","parent":"1"}, +"30":{"name":"青海","parent":"1"}, +"31":{"name":"宁夏","parent":"1"}, +"32":{"name":"新疆","parent":"1"}, +"33":{"name":"北京市","parent":"2"}, +"34":{"name":"天津市","parent":"3"}, +"35":{"name":"石家庄市","parent":"4"}, +"36":{"name":"唐山市","parent":"4"}, +"37":{"name":"秦皇岛市","parent":"4"}, +"38":{"name":"邯郸市","parent":"4"}, +"39":{"name":"邢台市","parent":"4"}, +"40":{"name":"保定市","parent":"4"}, +"41":{"name":"张家口市","parent":"4"}, +"42":{"name":"承德市","parent":"4"}, +"43":{"name":"沧州市","parent":"4"}, +"44":{"name":"廊坊市","parent":"4"}, +"45":{"name":"衡水市","parent":"4"}, +"46":{"name":"太原市","parent":"5"}, +"47":{"name":"大同市","parent":"5"}, +"48":{"name":"阳泉市","parent":"5"}, +"49":{"name":"长治市","parent":"5"}, +"50":{"name":"晋城市","parent":"5"}, +"51":{"name":"朔州市","parent":"5"}, +"52":{"name":"晋中市","parent":"5"}, +"53":{"name":"运城市","parent":"5"}, +"54":{"name":"忻州市","parent":"5"}, +"55":{"name":"临汾市","parent":"5"}, 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+"7061":{"name":"特克斯科斯群岛","parent":"3956"}, +"7062":{"name":"图瓦卢","parent":"3956"}, +"7063":{"name":"乌干达","parent":"3956"}, +"7064":{"name":"乌克兰","parent":"3956"}, +"7065":{"name":"阿联酋","parent":"3956"}, +"7066":{"name":"美国本土外小岛屿","parent":"3956"}, +"7067":{"name":"乌拉圭","parent":"3956"}, +"7068":{"name":"乌兹别克斯坦","parent":"3956"}, +"7069":{"name":"瓦努阿图","parent":"3956"}, +"7070":{"name":"梵蒂冈","parent":"3956"}, +"7071":{"name":"委内瑞拉","parent":"3956"}, +"7072":{"name":"越南","parent":"3956"}, +"7073":{"name":"英属维尔京群岛","parent":"3956"}, +"7074":{"name":"美属维尔京群岛","parent":"3956"}, +"7075":{"name":"瓦利斯和富图纳","parent":"3956"}, +"7076":{"name":"西撒哈拉","parent":"3956"}, +"7077":{"name":"也门","parent":"3956"}, +"7078":{"name":"南斯拉夫","parent":"3956"}, +"7079":{"name":"赞比亚","parent":"3956"}, +"7080":{"name":"津巴布韦","parent":"3956"}, +"7081":{"name":"塞尔维亚","parent":"3956"}, +"7082":{"name":"雄安新区","parent":"4"}, +"7084":{"name":"天门市","parent":"18"} +} + +NM_SET = set([v["name"] for _,v in TBL.items()]) + +def get_names(id): + if not id or str(id).lower() == "none":return [] + id = str(id) + if not re.match("[0-9]+$", id.strip()):return [id] + nms = [] + d = TBL.get(id) + if not d:return[] + nms.append(d["name"]) + p = get_names(d["parent"]) + if p: nms.extend(p) + return nms + +import re +def isName(nm): + if nm in NM_SET:return True + if nm + "市" in NM_SET:return True + if re.sub(r"(省|(回族|壮族|维吾尔)*自治区)$", "", nm) in NM_SET:return True + return False diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/corp.tks.freq.json b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/corp.tks.freq.json new file mode 100644 index 00000000..c75e2498 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/corp.tks.freq.json @@ -0,0 +1,65 @@ +[ + "科技", + "集团", + "网络科技", + "技术", + "信息", + "分公司", + "信息技术", + "发展", + "科技股份", + "网络", + "贸易", + "商贸", + "工程", + "企业", + "集团股份", + "商务", + "工业", + "控股集团", + "国际贸易", + "软件技术", + "数码科技", + "软件开发", + "有限", + "经营", + "科技开发", + "股份公司", + "电子技术", + "实业集团", + "责任", + "无限", + "工程技术", + "上市公司", + "技术开发", + "软件系统", + "总公司", + "网络服务", + "ltd.", + "technology", + "company", + "服务公司", + "计算机技术", + "计算机软件", + "电子信息", + "corporation", + "计算机服务", + "计算机系统", + "有限公司", + "事业部", + "公司", + "股份", + "有限责任", + "软件", + "控股", + "高科技", + "房地产", + "事业群", + "部门", + "电子商务", + "人力资源顾问", + "人力资源", + "株式会社", + "网络营销" +] + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/corp_baike_len.csv b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/corp_baike_len.csv new file mode 100644 index 00000000..32b996fb --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/corp_baike_len.csv @@ -0,0 +1,31480 @@ +cid len +376 155 +1003 192 +1236 187 +1306 186 +1512 217 +1600 183 +1695 193 +1869 186 +1959 355 +2559 308 +2751 284 +2782 214 +2883 397 +3475 299 +3834 243 +3846 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"行业好公司" + ], + "happyelements": [ + "好游戏", + "行业好公司" + ], + "hardlight": [ + "好游戏", + "行业好公司" + ], + "haymaker game": [ + "好游戏", + "行业好公司" + ], + "hero entertainment": [ + "好游戏", + "行业好公司" + ], + "hortor": [ + "好游戏", + "行业好公司" + ], + "hortor games": [ + "好游戏", + "行业好公司" + ], + "housemarque": [ + "行业头部", + "好游戏" + ], + "huolieniao network (guangzhou)": [ + "好游戏", + "行业好公司" + ], + "hybride technologies": [ + "行业头部", + "好游戏" + ], + "hypergryph": [ + "好游戏", + "行业好公司" + ], + "i got games": [ + "好游戏", + "行业好公司" + ], + "i love chichi": [ + "好游戏", + "行业好公司" + ], + "id software": [ + "行业头部", + "好游戏" + ], + "id软件": [ + "行业头部", + "好游戏" + ], + "igg": [ + "好游戏", + "行业好公司" + ], + "im30": [ + "好游戏", + "行业好公司" + ], + "inception工作室": [ + "好游戏", + "综合大厂" + ], + "indie built": [ + "好游戏", + "行业好公司" + ], + "insomniac games": [ + "行业头部", + "好游戏" + ], + "instinct工作室": [ + "好游戏", + "综合大厂" + ], + "ique": [ + "好游戏", + "行业好公司" + ], + "ique(china)ltd": [ + "好游戏", + "行业好公司" + ], + "iriam inc.": [ + "好游戏", + "行业好公司" + ], + "irrational games": [ + "好游戏", + "行业好公司" + ], + "jjworld (beijing) network technology": [ + "好游戏", + "行业好公司" + ], + "kesmai": [ + "行业头部", + "好游戏" + ], + "king": [ + "好游戏", + "行业好公司" + ], + "koyosoft": [ + "好游戏", + "行业好公司" + ], + "kunlun tech": [ + "好游戏", + "行业好公司" + ], + "langang online(beijing)technology": [ + "好游戏", + "行业好公司" + ], + "ledao": [ + "好游戏", + "行业好公司" + ], + "legou": [ + "好游戏", + "行业好公司" + ], + "leiting games": [ + "好游戏", + "行业好公司" + ], + "lilith": [ + "行业头部", + "好游戏" + ], + "lingxigames": [ + "行业头部多元化", + "好游戏" + ], + "locojoy": [ + "好游戏", + "行业好公司" + ], + "long tech": [ + "好游戏", + "行业好公司" + ], + "longg entertainment": [ + "好游戏", + "行业好公司" + ], + "luna online": [ + "好游戏", + "行业好公司" + ], + "machine": [ + "行业头部", + "好游戏" + ], + "machinegames": [ + "行业头部", + "好游戏" + ], + "magic tavern": [ + "好游戏", + "行业好公司" + ], + "magnetjoy": [ + "好游戏", + "行业好公司" + ], + "magnetjoy磁聚乐科技": [ + "好游戏", + "行业好公司" + ], + "major league gaming": [ + "好游戏", + "行业好公司" + ], + "manjuu": [ + "好游戏", + "行业好公司" + ], + "media molecule": [ + "行业头部", + "好游戏" + ], + "mihoyo": [ + "行业头部", + "好游戏" + ], + "monster smash": [ + "好游戏", + "行业好公司" + ], + "moonton": [ + "好游戏", + "综合大厂" + ], + "mu77": [ + "好游戏", + "行业好公司" + ], + "mythic entertainment": [ + "行业头部", + "好游戏" + ], + "nadeo": [ + "行业头部", + "好游戏" + ], + "naughty dog": [ + "行业头部", + "好游戏" + ], + "nba street online": [ + "好游戏", + "行业好公司" + ], + "nebulajoy": [ + "好游戏", + "行业好公司" + ], + "nenly": [ + "好游戏", + "行业好公司" + ], + "netease games": [ + "好游戏", + "综合大厂" + ], + "network technology shenzhen downtown and brothers": [ + "好游戏", + "行业好公司" + ], + "newstyle media group": [ + "好游戏", + "行业好公司" + ], + "nintendo": [ + "好游戏", + "行业好公司" + ], + "nufx": [ + "行业头部", + "好游戏" + ], + "oasis绿洲vr": [ + "好游戏", + "行业好公司" + ], + "ohayoo": [ + "好游戏", + "综合大厂" + ], + "online tuyoo (beijing) technology": [ + "好游戏", + "行业好公司" + ], + "pandada studio": [ + "好游戏", + "行业好公司" + ], + "pandemic studios": [ + "行业头部", + "好游戏" + ], + "people can fly": [ + "行业头部", + "好游戏" + ], + "perfect world games": [ + "好游戏", + "行业好公司" + ], + "peroli": [ + "好游戏", + "行业好公司" + ], + "pixelopus": [ + "行业头部", + "好游戏" + ], + "pixmain": [ + "好游戏", + "综合大厂" + ], + "poptop software": [ + "好游戏", + "行业好公司" + ], + "quazal": [ + "行业头部", + "好游戏" + ], + "r2": [ + "好游戏", + "行业好公司" + ], + "r2 game": [ + "好游戏", + "行业好公司" + ], + "r2game": [ + "好游戏", + "行业好公司" + ], + "redlynx": [ + "行业头部", + "好游戏" + ], + "restaurant live": [ + "好游戏", + "行业好公司" + ], + "riot": [ + "行业头部", + "好游戏" + ], + "riot games": [ + "行业头部", + "好游戏" + ], + "roblox": [ + "行业头部", + "好游戏" + ], + "r社": [ + "行业头部", + "好游戏" + ], + "sagi games": [ + "好游戏", + "行业好公司" + ], + "sea ltd": [ + "好游戏", + "行业好公司" + ], + "seasun games": [ + "行业头部多元化", + "好游戏" + ], + "sega": [ + "好游戏", + "行业好公司" + ], + "sega amusements taiwan ltd.": [ + "好游戏", + "行业好公司" + ], + "shanda": [ + "好游戏", + "行业好公司" + ], + "shanghai 37wan network technology": [ + "好游戏", + "行业好公司" + ], + "shanghai bihan internet technology": [ + "好游戏", + "行业好公司" 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"sie": [ + "行业头部", + "好游戏" + ], + "sinister games": [ + "行业头部", + "好游戏" + ], + "snail games": [ + "好游戏", + "行业好公司" + ], + "snda": [ + "好游戏", + "行业好公司" + ], + "sony": [ + "行业头部", + "好游戏" + ], + "sony interactive entertainment llc": [ + "行业头部", + "好游戏" + ], + "sourcetech": [ + "好游戏", + "行业好公司" + ], + "sparx*": [ + "好游戏", + "行业好公司" + ], + "sports interactive": [ + "好游戏", + "行业好公司" + ], + "studiogm": [ + "好游戏", + "行业好公司" + ], + "sucker punch productions": [ + "行业头部", + "好游戏" + ], + "supercell": [ + "行业头部", + "好游戏" + ], + "suzhou gamefriend network technology": [ + "好游戏", + "行业好公司" + ], + "suzhou juhe network technology": [ + "好游戏", + "行业好公司" + ], + "suzhou yunzhi interactive technology": [ + "好游戏", + "行业好公司" + ], + "taiyouxi": [ + "好游戏", + "行业好公司" + ], + "tango": [ + "行业头部", + "好游戏" + ], + "taomee": [ + "好游戏", + "行业好公司" + ], + "taomee inc.": [ + "好游戏", + "行业好公司" + ], + "tap4fun": [ + "好游戏", + "行业好公司" + ], + "tencent games": [ + "好游戏", + "综合大厂" + ], + "the sims studio": [ + "行业头部", + "好游戏" 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], + "上海欢岳网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海欣圣信息科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海沐瞳科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海派酷网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海淘米网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海游久游戏股份有限公司": [ + "好游戏", + "行业好公司" + ], + "上海游奇网络有限公司": [ + "好游戏", + "行业好公司" + ], + "上海游宗网络": [ + "好游戏", + "行业好公司" + ], + "上海游宗网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海游苑网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海源庄数码科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海源稼数码科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海灵擎网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海炽宇网络": [ + "好游戏", + "行业好公司" + ], + "上海炽宇网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海烛龙": [ + "好游戏", + "行业好公司" + ], + "上海热泛信息": [ + "好游戏", + "行业好公司" + ], + "上海热泛信息技术有限公司": [ + "好游戏", + "行业好公司" + ], + "上海爱使股份有限公司": [ + "好游戏", + "行业好公司" + ], + "上海犀牛互动网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海狸猫网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海番糖网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海盛大网络发展有限公司": [ + "好游戏", + "行业好公司" + ], + "上海盛酷": [ + 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"上海达龙": [ + "好游戏", + "行业好公司" + ], + "上海达龙信息科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海追趣网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海那朵花文化传播有限公司": [ + "好游戏", + "行业好公司" + ], + "上海郡阳网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海金蚕网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海银月网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海锐战网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海锦游": [ + "好游戏", + "行业好公司" + ], + "上海阿佩吉网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海雪宝信息科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海霜狼信息科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海顽梦数码科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海鲲鹏数码科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海鹰角网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上海龙魂": [ + "好游戏", + "行业好公司" + ], + "上海龙魂网络": [ + "好游戏", + "行业好公司" + ], + "上海龙魂网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "上禾网络科技（上海）有限公司": [ + "好游戏", + "行业好公司" + ], + "上饶市广丰区贪玩网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "与你游戏": [ + "好游戏", + "行业好公司" + ], + "世嘉": [ + "好游戏", + "行业好公司" + ], + "世嘉株式会社": [ + "好游戏", + "行业好公司" + ], + "世纪华通": [ + "好游戏", + "行业好公司" + ], 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[ + "好游戏", + "行业好公司" + ], + "神仙道": [ + "好游戏", + "行业好公司" + ], + "神兽养成记": [ + "好游戏", + "行业好公司" + ], + "神奇时代": [ + "好游戏", + "行业好公司" + ], + "神庙逃亡": [ + "好游戏", + "行业好公司" + ], + "神戒": [ + "好游戏", + "行业好公司" + ], + "神游科技": [ + "好游戏", + "行业好公司" + ], + "神游科技（中国）有限公司": [ + "好游戏", + "行业好公司" + ], + "神舞幻想": [ + "好游戏", + "行业好公司" + ], + "福州天盟数码有限公司": [ + "好游戏", + "行业好公司" + ], + "福州开发区狼途网络科技有限公司": [ + "好游戏", + "行业好公司" + ], + "福州龙腾中东网络技术有限公司": [ + "好游戏", + "行业好公司" + ], + "福州龙腾简合网络技术有限公司": [ + "好游戏", + "行业好公司" + ], + "福建网龙计算机网络信息技术有限公司": [ + "好游戏", + "行业好公司" + ], + "福建耀星文化发展有限公司": [ + "好游戏", + "行业好公司" + ], + "福建龙岩指悦科技有限公司": [ + "好游戏", + "行业好公司" + ], + "积木堂": [ + "好游戏", + "行业好公司" + ], + "空中网": [ + "好游戏", + "行业好公司" + ], + "穿越君": [ + "好游戏", + "行业好公司" + ], + "立刻玩": [ + "好游戏", + "行业好公司" + ], + "竞技世界": [ + "好游戏", + "猎", + "行业好公司" + ], + "竞技世界（北京）网络技术有限公司": [ + "好游戏", + "行业好公司" + ], + "竞时通": [ + "好游戏", + "行业好公司" + ], + "竞舞": [ + "好游戏", + "行业好公司" + ], + "第七大道": [ + "好游戏", + "行业好公司" + ], + "第九城市": [ + "好游戏", + 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+ ], + "周大福": [ + "阅文偏好" + ], + "chow tai fook": [ + "阅文偏好" + ], + "名创优品": [ + "阅文偏好" + ], + "miniso": [ + "阅文偏好" + ], + "星巴克": [ + "阅文偏好" + ], + "starbucks": [ + "阅文偏好" + ], + "史塔巴克斯": [ + "阅文偏好" + ], + "益海嘉里金龙鱼粮油食品": [ + "阅文偏好" + ], + "金龙鱼": [ + "阅文偏好" + ], + "益海嘉里": [ + "阅文偏好" + ], + "yihai kerry arawana holdings co ,": [ + "阅文偏好" + ], + "迅销公司": [ + "阅文偏好" + ], + "优衣库": [ + "阅文偏好" + ], + "uniqlo": [ + "阅文偏好" + ], + "gu": [ + "阅文偏好" + ], + "comptoir des cotonniers": [ + "阅文偏好" + ], + "princesse tam.tam": [ + "阅文偏好" + ], + "plst": [ + "阅文偏好" + ], + "匡威": [ + "阅文偏好" + ], + "converse": [ + "阅文偏好" + ], + "chuck taylor all star": [ + "阅文偏好" + ], + "jack purcell": [ + "阅文偏好" + ], + "cons": [ + "阅文偏好" + ], + "华彩光影传媒文化": [ + "阅文偏好" + ], + "末那工作": [ + "阅文偏好" + ], + "manas": [ + "阅文偏好" + ], + "萌奇文化": [ + "阅文偏好" + ], + "萌奇meki": [ + "阅文偏好" + ], + "艾漫动漫设计": [ + "阅文偏好" + ], + "艾漫aimon": [ + "阅文偏好" + ], + "aimon shanghai animation design": [ + "阅文偏好" + ], + "娱猫传媒": [ + "阅文偏好" + ], + "娱猫": [ + "阅文偏好" + ], + "网易聚玩": [ + "阅文偏好" + ], + "hobby": [ + "阅文偏好" + ], + "善禧文化": [ + "阅文偏好" + ], + "善禧": [ + "阅文偏好" + ], + "亿福莱": [ + "阅文偏好" + ], + "actoys": [ + "阅文偏好" + ], + "ac模玩网": [ + "阅文偏好" + ], + "actoys模玩网": [ + "阅文偏好" + ], + "凡米粒文化": [ + "阅文偏好" + ], + "凡米粒": [ + "阅文偏好" + ], + "凡米粒familyout": [ + "阅文偏好" + ], + "familyout": [ + "阅文偏好" + ], + "乐自天成文化": [ + "阅文偏好" + ], + "52 toys": [ + "阅文偏好" + ], + "乐自天成": [ + "阅文偏好" + ], + "海脉文化传播": [ + "阅文偏好" + ], + "hobbymax": [ + "阅文偏好" + ], + "泡泡玛特文化创意": [ + "阅文偏好" + ], + "pop mart": [ + "阅文偏好" + ], + "泡泡玛特": [ + "阅文偏好" + ], + "御座文化": [ + "阅文偏好" + ], + "邦策": [ + "阅文偏好" + ], + "漫踪": [ + "阅文偏好" + ], + "monzon": [ + "阅文偏好" + ], + "nanjing bangce trading": [ + "阅文偏好" + ], + "天猫": [ + "阅文偏好", + "微众偏好", + "综合大厂" + ], + "聚划算": [ + "行业头部", + "阅文偏好" + ], + "tmall": [ + "行业头部", + "阅文偏好" + ], + "zhejiang tmall": [ + "行业头部", + "阅文偏好" + ], + "淘宝": [ + "阅文偏好", + "微众偏好", + "综合大厂" + ], + "taobao": [ + "行业头部多元化", + "阅文偏好" + ], + "淘宝网": [ + "行业头部多元化", + "阅文偏好" + ], + "zhejiang taobao network": [ + "行业头部多元化", + "阅文偏好" + ], + "京东叁佰陆拾度电子商务": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "京东": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "beijng jingdong century commerce": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "1号店": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "京东云": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "零机科技": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "京东股票": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "京东科技": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "猫眼文化传媒": [ + "阅文偏好" + ], + "猫眼电影": [ + "阅文偏好", + "微众偏好" + ], + "maoyan": [ + "阅文偏好" + ], + "猫眼娱乐": [ + "阅文偏好", + "微众偏好" + ], + "beijing maoyan culture media": [ + "阅文偏好" + ], + "指点": [ + "阅文偏好" + ], + "抠电影": [ + "阅文偏好" + ], + "guide infinity beijing": [ + "阅文偏好" + ], + "动艺时光": [ + "阅文偏好" + ], + "时光网": [ + "阅文偏好" + ], + "mtime": [ + "阅文偏好" + ], + "beijing dongyi shiguang network": [ + "阅文偏好" + ], + "熵变": [ + "阅文偏好" + ], + "萌趣": [ + "阅文偏好" + ], + "shanghai entropy network": [ + "阅文偏好" + ], + "海拓天行": [ + "阅文偏好" + ], + "漫骆驼": [ + "阅文偏好" + ], + "beijing manluotuo": [ + "阅文偏好" + ], + "shanghai bili bili": [ + "行业头部多元化", + "阅文偏好" + ], + "b站": [ + "行业头部多元化", + "阅文偏好" + ], + "爱奇艺": [ + "行业头部多元化", + "阅文偏好" + ], + "iqiyi": [ + "行业头部多元化", + "阅文偏好" + ], + "times square": [ + "行业头部多元化", + "阅文偏好" + ], + "beijing iqiyi science&": [ + "行业头部多元化", + "阅文偏好" + ], + "巨亿星城": [ + "阅文偏好" + ], + "脑动": [ + "阅文偏好" + ], + "次元仓": [ + "阅文偏好" + ], + "guangzhou naodong network": [ + "阅文偏好" + ], + "葩趣": [ + "阅文偏好" + ], + "beijing paqu": [ + "阅文偏好" + ], + "腾讯视频": [ + "微众偏好", + "阅文偏好", + "综合大厂" + ], + "快乐阳光互动娱乐传媒": [ + "行业头部", + "阅文偏好" + ], + "芒果tv": [ + "行业头部", + "阅文偏好" + ], + "阿里巴巴文化娱乐": [ + "行业头部", + "阅文偏好" + ], + "优酷": [ + "阅文偏好", + "微众偏好", + "综合大厂" + ], + "youku": [ + "行业头部", + "阅文偏好" + ], + "阿里巴巴数字娱乐": [ + "阅文偏好" + ], + "娱乐宝": [ + "阅文偏好" + ], + "闲鱼": [ + "阅文偏好", + "微众偏好", + "综合大厂" + ], + "代拍网": [ + "阅文偏好" + ], + "喵斯拉": [ + "阅文偏好" + ], + "猫耳fm": [ + "阅文偏好" + ], + "missevan": [ + "阅文偏好" + ], + "m站": [ + "阅文偏好" + ], + "网易lofter": [ + "阅文偏好" + ], + "lofter": [ + "阅文偏好" + ], + "老福特": [ + "阅文偏好" + ], + "netease": [ + "阅文偏好", + "综合大厂" + ], + "淘宝众筹": [ + "阅文偏好" + ], + "京东众筹": [ + "阅文偏好" + ], + "摩点会想": [ + "阅文偏好" + ], + "摩点": [ + "阅文偏好" + ], + "万达电影": [ + "阅文偏好" + ], + "万达影城": [ + "阅文偏好" + ], + "wanda cinemas": [ + "阅文偏好" + ], + "横店影视制作": [ + "阅文偏好" + ], + "横店影视": [ + "阅文偏好" + ], + "光逸电影院": [ + "阅文偏好" + ], + "金逸影城": [ + "阅文偏好" + ], + "popmart": [ + "阅文偏好" + ], + "潮漫艾文化传播": [ + "阅文偏好" + ], + "潮玩星球": [ + "阅文偏好" + ], + "ipstar": [ + "阅文偏好" + ], + "国塑唐商社": [ + "阅文偏好" + ], + "塑唐": [ + "阅文偏好" + ], + "筑梦文化": [ + "阅文偏好" + ], + "人潮互娱文化": [ + "阅文偏好" + ], + "人潮互娱": [ + "阅文偏好" + ], + "辉联文化创意": [ + "阅文偏好" + ], + "辉联文化": [ + "阅文偏好" + ], + "万娱引力文化传媒": [ + "阅文偏好" + ], + "万娱引力": [ + "阅文偏好" + ], + "移联创": [ + "阅文偏好" + ], + "wondershow": [ + "阅文偏好" + ], + "快看世界": [ + "阅文偏好" + ], + "快看": [ + "阅文偏好" + ], + "快看漫画": [ + "阅文偏好" + ], + "腾讯动漫": [ + "阅文偏好" + ], + "炫果壳": [ + "阅文偏好" + ], + "微博动漫": [ + "阅文偏好" + ], + "呵呵呵文化传播": [ + "阅文偏好" + ], + "哔哩哔哩漫画": [ + "阅文偏好" + ], + "懒人在线": [ + "阅文偏好" + ], + "懒人听书": [ + "阅文偏好" + ], + "麦克风文化传媒": [ + "阅文偏好" + ], + "蜻蜓fm": [ + "阅文偏好" + ], + "阿里巴巴文学": [ + "阅文偏好" + ], + "书旗小说": [ + "阅文偏好" + ], + "书旗小说网": [ + "阅文偏好" + ], + "喜马拉雅": [ + "阅文偏好" + ], + "himalaya": [ + "阅文偏好" + ], + "天盈九州": [ + "阅文偏好" + ], + "凤凰网": [ + "阅文偏好" + ], + "凤凰新媒体": [ + "阅文偏好" + ], + "凤凰卫视": [ + "阅文偏好" + ], + "tencent": [ + "微众偏好", + "阅文偏好", + "综合大厂" + ], + "鹅厂": [ + "微众偏好", + "阅文偏好", + "综合大厂" + ], + "掌阅科技": [ + "阅文偏好" + ], + "掌阅": [ + "阅文偏好" + ], + "掌阅书城": [ + "阅文偏好" + ], + "掌阅文学": [ + "阅文偏好" + ], + "掌阅精选": [ + "阅文偏好" + ], + "掌阅校园": [ + "阅文偏好" + ], + "掌阅国际": [ + "阅文偏好" + ], + "ireader": [ + "阅文偏好" + ], + "vipshop": [ + "行业头部", + "微众偏好", + "阅文偏好" + ], + "vips": [ + "行业头部", + "微众偏好", + "阅文偏好" + ], + "唯你搭": [ + "行业头部", + "微众偏好", + "阅文偏好" + ], + "花海仓": [ + "行业头部", + "微众偏好", + "阅文偏好" + ], + "唯品支付": [ + "行业头部", + "微众偏好", + "阅文偏好" + ], + "唯品仓": [ + "行业头部", + "微众偏好", + "阅文偏好" + ], + "唯品金融": [ + "行业头部", + "微众偏好", + "阅文偏好" + ], + "美图": [ + "阅文偏好" + ], + "meitu": [ + "阅文偏好" + ], + "美图秀秀": [ + "阅文偏好" + ], + "alibaba": [ + "阅文偏好", + "微众偏好", + "综合大厂" + ], + "ali": [ + "阅文偏好", + "微众偏好", + "综合大厂" + ], + "alibaba group holding limited": [ + "阅文偏好", + "微众偏好", + "综合大厂" + ], + "基分文化传播": [ + "阅文偏好", + "行业好公司" + ], + "qutoutiao": [ + "阅文偏好", + "行业好公司" + ], + "网易传媒": [ + "阅文偏好" + ], + "netease media group": [ + "阅文偏好" + ], + "media. 163. com": [ + "阅文偏好" + ], + "复阅": [ + "阅文偏好" + ], + "妙计": [ + "阅文偏好" + ], + "beijing xiaomi": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "xiaomi": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "小米科技": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "米家": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "小米有品": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "小米集团": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "mi": [ + "行业头部多元化", + "阅文偏好" + ], + "优酷网": [ + "行业头部", + "阅文偏好" + ], + "youku information beijing": [ + "行业头部", + "阅文偏好" + ], + "趣阅数字": [ + "阅文偏好", + "行业好公司" + ], + "shanghai quyue digital": [ + "阅文偏好", + "行业好公司" + ], + "趣阅": [ + "阅文偏好", + "行业好公司" + ], + "麟动": [ + "阅文偏好", + "行业好公司" + ], + "cheetah mobile": [ + "行业头部多元化", + "阅文偏好" + ], + "猎豹": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "cht": [ + "行业头部多元化", + "阅文偏好" + ], + "清理大师": [ + "行业头部多元化", + "阅文偏好" + ], + "nyse": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "cmcm": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "taptap": [ + "行业头部", + "阅文偏好" + ], + "三快": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "美团": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "大众点评": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "美团云": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "美团民宿": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "美团打车": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "美团小贷": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "摩拜单车": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "馒头直播": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "美团优选": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "美团买菜": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "美团单车": [ + "行业头部多元化", + "微众偏好", + "阅文偏好" + ], + "合合": [ + "微众偏好", + "阅文偏好", + "行业好公司" + ], + "合合信息": [ + "微众偏好", + "阅文偏好", + "行业好公司" + ], + "intsig": [ + "阅文偏好", + "行业好公司" + ], + "沪江教育": [ + "行业头部", + "阅文偏好" + ], + "hujiang education&shanghai limited": [ + "行业头部", + "阅文偏好" + ], + "沪江": [ + "行业头部", + "阅文偏好" + ], + "互加文化": [ + "行业头部", + "阅文偏好" + ], + "钧正": [ + "行业头部多元化", + "阅文偏好" + ], + "哈罗单车": [ + "行业头部多元化", + "阅文偏好" + ], + "hellobike": [ + "行业头部多元化", + "阅文偏好" + ], + "携程旅游": [ + "行业头部多元化", + "阅文偏好" + ], + "ctrip travel network shanghai": [ + "行业头部多元化", + "阅文偏好" + ], + "ctrip": [ + "行业头部多元化", + "阅文偏好" + ], + "携程旅行网": [ + "行业头部多元化", + "阅文偏好" + ], + "晁图网络": [ + "阅文偏好", + "行业好公司" + ], + "shanghai chaotu network": [ + "阅文偏好", + "行业好公司" + ], + "晁图": [ + "阅文偏好", + "行业好公司" + ], + "shxgroup": [ + "阅文偏好", + "行业好公司" + ], + "cross 2": [ + "阅文偏好", + "行业好公司" + ], + "搜狐新媒体": [ + "行业头部", + "阅文偏好" + ], + "beijing sohu new media information": [ + "行业头部", + "阅文偏好" + ], + "搜狐": [ + "行业头部", + "阅文偏好" + ], + "sohu": [ + "行业头部", + "阅文偏好" + ], + "沙发视频": [ + "行业头部", + "阅文偏好" + ], + "搜狐新闻": [ + "行业头部", + "阅文偏好" + ], + "搜狐体育": [ + "行业头部", + "阅文偏好" + ], + "搜狐时尚": [ + "行业头部", + "阅文偏好" + ], + "搜狐焦点": [ + "行业头部", + "阅文偏好" + ], + "我爱cba": [ + "行业头部", + "阅文偏好" + ], + "狐友": [ + "行业头部", + "阅文偏好" + ], + "斗鱼网络科技": [ + "阅文偏好", + "行业好公司" + ], + "wuhan douyu network": [ + "阅文偏好", + "行业好公司" + ], + "斗鱼": [ + "阅文偏好", + "行业好公司" + ], + "douyu": [ + "阅文偏好", + "行业好公司" + ], + "鲨鱼网络": [ + "阅文偏好", + "行业好公司" + ], + "五八": [ + "行业头部多元化", + "阅文偏好" + ], + "beijing 58 information ttechnology": [ + "行业头部多元化", + "阅文偏好" + ], + "五八同城": [ + "行业头部多元化", + "阅文偏好" + ], + "beijing 58 information": [ + "行业头部多元化", + "阅文偏好" + ], + "新浪互联服务": [ + "行业头部", + "阅文偏好" + ], + "sina": [ + "行业头部", + "阅文偏好" + ], + "新浪": [ + "行业头部", + "阅文偏好" + ], + "新浪微博": [ + "行业头部", + "阅文偏好" + ], + "新浪新闻": [ + "行业头部", + "阅文偏好" + ], + "绿洲": [ + "行业头部", + "阅文偏好" + ], + "新浪乐居": [ + "行业头部", + "阅文偏好" + ], + "新浪阅读": [ + "行业头部", + "阅文偏好" + ], + "微跳蚤": [ + "行业头部", + "阅文偏好" + ], + "鲜城": [ + "行业头部", + "阅文偏好" + ], + "云从科技": [ + "行业头部", + "微众偏好" + ], + "cloudwalk": [ + "行业头部", + "微众偏好" + ], + "云从": [ + "行业头部", + "微众偏好" + ], + "cloudwalk group": [ + "行业头部", + "微众偏好" + ], + "同盾科技": [ + "微众偏好" + ], + "同盾": [ + "微众偏好" + ], + "海致星图": [ + "微众偏好" + ], + "品钛科技控股公司": [ + "微众偏好" + ], + "品钛": [ + "微众偏好" + ], + "pintec": [ + "微众偏好" + ], + "钒钛智能": [ + "微众偏好" + ], + "慧安金科": [ + "微众偏好" + ], + "冰鉴": [ + "微众偏好" + ], + "ice kredit": [ + "微众偏好" + ], + "冰鉴科技": [ + "微众偏好" + ], + "第四范式": [ + "行业头部", + "微众偏好" + ], + "4 paradigm": [ + "行业头部", + "微众偏好" + ], + "众安服务": [ + "行业头部", + "微众偏好" + ], + "众安科技": [ + "行业头部", + "微众偏好" + ], + "众安": [ + "行业头部", + "微众偏好" + ], + "水滴保险经纪": [ + "微众偏好" + ], + "水滴保险": [ + "微众偏好" + ], + "鲸鹳": [ + "微众偏好" + ], + "保险极客": [ + "微众偏好" + ], + "平安": [ + "行业头部", + "微众偏好" + ], + "pingan": [ + "微众偏好" + ], + "爱保科技": [ + "微众偏好" + ], + "泰康在线财产保险": [ + "微众偏好" + ], + "泰康在线": [ + "微众偏好" + ], + "tk.cn insurance": [ + "微众偏好" + ], + "中国人民保险": [ + "微众偏好" + ], + "中国人保": [ + "微众偏好" + ], + "the people s insurance group of china": [ + "微众偏好" + ], + "栈略数据": [ + "微众偏好" + ], + "leapstack": [ + "微众偏好" + ], + "况客": [ + "微众偏好" + ], + "弘量智能": [ + "微众偏好" + ], + "弘量科技": [ + "微众偏好" + ], + "magnum research limited.": [ + "微众偏好" + ], + "国金涌富资产管理": [ + "微众偏好" + ], + "国金涌富": [ + "微众偏好" + ], + "熵简科技": [ + "微众偏好" + ], + "铂诺计算": [ + "微众偏好" + ], + "标贝": [ + "微众偏好" + ], + "标贝科技": [ + "微众偏好" + ], + "硅基智能": [ + "微众偏好" + ], + "silicon intelligence": [ + "微众偏好" + ], + "智齿博创": [ + "微众偏好" + ], + "智齿科技": [ + "微众偏好" + ], + "容联易通": [ + "微众偏好" + ], + "容联云通讯": [ + "微众偏好" + ], + "iflytek": [ + "行业头部多元化", + "微众偏好" + ], + "沃丰时代数据": [ + "微众偏好" + ], + "udesk": [ + "微众偏好" + ], + "口袋财富": [ + "微众偏好" + ], + "理财魔方": [ + "微众偏好" + ], + "蘑菇人工智能": [ + "微众偏好" + ], + "蘑菇智能": [ + "微众偏好" + ], + "灵犀智投": [ + "微众偏好" + ], + "数行": [ + "微众偏好" + ], + "数行科技": [ + "微众偏好" + ], + "北京知因智慧科技": [ + "微众偏好" + ], + "知因智慧": [ + "微众偏好" + ], + "颖投": [ + "微众偏好" + ], + "妙盈科技": [ + "微众偏好" + ], + "miotech": [ + "微众偏好" + ], + "旷视科技": [ + "行业头部", + "微众偏好" + ], + "megvii": [ + "行业头部", + "微众偏好" + ], + "旷视金智": [ + "行业头部", + "微众偏好" + ], + "旷云金智": [ + "行业头部", + "微众偏好" + ], + "旷镜博煊": [ + "行业头部", + "微众偏好" + ], + "商汤": [ + "行业头部", + "微众偏好" + ], + "beijing sensetime": [ + "行业头部", + "微众偏好" + ], + "sensetime": [ + "行业头部", + "微众偏好" + ], + "拉卡拉支付": [ + "微众偏好" + ], + "拉卡拉": [ + "微众偏好" + ], + "中国银联": [ + "行业头部多元化", + "微众偏好" + ], + "云闪付": [ + "微众偏好" + ], + "依图": [ + "微众偏好" + ], + "yitu": [ + "微众偏好" + ], + "百融云创": [ + "微众偏好" + ], + "交叉": [ + "微众偏好" + ], + "交叉科技": [ + "微众偏好" + ], + "鸟瞰智能": [ + "微众偏好" + ], + "追一": [ + "微众偏好" + ], + "追一科技": [ + "微众偏好" + ], + "zhuiyi": [ + "微众偏好" + ], + "jd digits": [ + "行业头部多元化", + "微众偏好" + ], + "壹账通金融": [ + "微众偏好" + ], + "金融壹账通": [ + "行业头部", + "微众偏好" + ], + "oneconnect": [ + "微众偏好" + ], + "蚂蚁": [ + "行业头部多元化", + "微众偏好" + ], + "蚂蚁集团": [ + "行业头部多元化", + "微众偏好" + ], + "ant": [ + "行业头部多元化", + "微众偏好" + ], + "ant group": [ + "行业头部多元化", + "微众偏好" + ], + "ant financial services group": [ + "行业头部多元化", + "微众偏好" + ], + "支付宝": [ + "微众偏好", + "综合大厂" + ], + "余额宝": [ + "行业头部多元化", + "微众偏好" + ], + "花呗": [ + "行业头部多元化", + "微众偏好" + ], + "相互宝": [ + "行业头部多元化", + "微众偏好" + ], + "网商银行": [ + "行业头部多元化", + "微众偏好" + ], + "芝麻信用": [ + "行业头部多元化", + "微众偏好" + ], + "蚂蚁财富": [ + "行业头部多元化", + "微众偏好" + ], + "罗汉堂": [ + "行业头部多元化", + "微众偏好" + ], + "度小满": [ + "微众偏好" + ], + "度小满金融": [ + "微众偏好" + ], + "du xiaoman financial": [ + "微众偏好" + ], + "百度金融": [ + "微众偏好" + ], + "腾讯金融": [ + "微众偏好" + ], + "tencent financial": [ + "微众偏好" + ], + "fit": [ + "微众偏好" + ], + "财付通": [ + "微众偏好" + ], + "苏宁金融服务": [ + "微众偏好" + ], + "苏宁金融": [ + "微众偏好" + ], + "financesn": [ + "微众偏好" + ], + "陆家嘴国际金融资产交易市场": [ + "行业头部", + "微众偏好" + ], + "lufax": [ + "行业头部", + "微众偏好" + ], + "中移动金融": [ + "微众偏好" + ], + "移动金融": [ + "微众偏好" + ], + "中移金科": [ + "微众偏好" + ], + "china mobile financial": [ + "微众偏好" + ], + "信也": [ + "微众偏好" + ], + "拍拍贷": [ + "微众偏好" + ], + "finvolution group": [ + "微众偏好" + ], + "招商云创": [ + "微众偏好" + ], + "云知声智能": [ + "微众偏好", + "行业好公司" + ], + "云知声": [ + "微众偏好", + "行业好公司" + ], + "unisound ai": [ + "微众偏好" + ], + "the 4 th paradigm": [ + "微众偏好" + ], + "百度网讯": [ + "微众偏好" + ], + "飞桨": [ + "微众偏好" + ], + "paddlepaddle": [ + "微众偏好" + ], + "beijing baidu netcom science and": [ + "微众偏好" + ], + "百度在线网络": [ + "行业头部多元化", + "微众偏好" + ], + "百度": [ + "行业头部多元化", + "微众偏好" + ], + "baidu": [ + "行业头部多元化", + "微众偏好" + ], + "百度文学": [ + "行业头部多元化", + "微众偏好" + ], + "百度地图": [ + "行业头部多元化", + "微众偏好" + ], + "百度视频": [ + "行业头部多元化", + "微众偏好" + ], + "百度钱包": [ + "行业头部多元化", + "微众偏好" + ], + "百度秀": [ + "行业头部多元化", + "微众偏好" + ], + "百度云": [ + "行业头部多元化", + "微众偏好" + ], + "百度网盘": [ + "行业头部多元化", + "微众偏好" + ], + "百度智能云": [ + "行业头部多元化", + "微众偏好" + ], + "百度糯米网": [ + "行业头部多元化", + "微众偏好" + ], + "百度手机": [ + "行业头部多元化", + "微众偏好" + ], + "百度开放平台": [ + "行业头部多元化", + "微众偏好" + ], + "百度apollo": [ + "行业头部多元化", + "微众偏好" + ], + "百度百科": [ + "行业头部多元化", + "微众偏好" + ], + "百度贴吧": [ + "行业头部多元化", + "微众偏好" + ], + "虎博": [ + "微众偏好" + ], + "tigerobo": [ + "微众偏好" + ], + "shanghai yerui information": [ + "微众偏好" + ], + "pactera": [ + "微众偏好" + ], + "甲骨文中国": [ + "微众偏好", + "综合大厂" + ], + "甲骨文": [ + "微众偏好", + "综合大厂" + ], + "oracle": [ + "微众偏好", + "综合大厂" + ], + "oracle china software system": [ + "微众偏好" + ], + "思爱普中国": [ + "微众偏好" + ], + "思爱普": [ + "微众偏好" + ], + "sap": [ + "微众偏好" + ], + "sap china": [ + "微众偏好" + ], + "salesforce.com ,": [ + "微众偏好" + ], + "赛富时": [ + "微众偏好" + ], + "salesforce": [ + "微众偏好", + "综合大厂" + ], + "前海微众银行": [ + "微众偏好", + "综合大厂" + ], + "微众银行": [ + "微众偏好", + "综合大厂" + ], + "webank": [ + "微众偏好", + "综合大厂" + ], + "新网银行": [ + "微众偏好" + ], + "xwbank": [ + "微众偏好" + ], + "sichuan xwbank": [ + "微众偏好" + ], + "中信百信银行": [ + "微众偏好" + ], + "百信银行": [ + "微众偏好" + ], + "citic aibank limited": [ + "微众偏好" + ], + "奇步天下": [ + "微众偏好" + ], + "360数科": [ + "微众偏好" + ], + "beijing odd step the world": [ + "微众偏好" + ], + "马上消费金融": [ + "行业头部", + "微众偏好" + ], + "马上金融": [ + "微众偏好" + ], + "mashang consumer finance": [ + "行业头部", + "微众偏好" + ], + "微众信用": [ + "微众偏好" + ], + "微众信科": [ + "微众偏好" + ], + "中企云链金融信息服务": [ + "微众偏好" + ], + "中企云链": [ + "微众偏好" + ], + "招联消费金融": [ + "行业头部", + "微众偏好" + ], + "mucfc": [ + "微众偏好" + ], + "merchants union consumer finance": [ + "行业头部", + "微众偏好" + ], + "汇立金融": [ + "微众偏好" + ], + "welab": [ + "微众偏好" + ], + "乐信控股": [ + "微众偏好", + "行业好公司" + ], + "乐信": [ + "微众偏好", + "行业好公司" + ], + "lexin": [ + "微众偏好" + ], + "shenzhen lexin holding": [ + "微众偏好", + "行业好公司" + ], + "快钱支付清算": [ + "微众偏好" + ], + "快钱": [ + "微众偏好" + ], + "kuaiqian": [ + "微众偏好" + ], + "kuaiqian payment and settlement service": [ + "微众偏好" + ], + "连连银通电子支付": [ + "微众偏好" + ], + "连连支付": [ + "微众偏好" + ], + "lianlian yintong electronic payment": [ + "微众偏好" + ], + "随行付": [ + "微众偏好" + ], + "呯嘭智能": [ + "微众偏好", + "行业好公司" + ], + "pingpong": [ + "微众偏好", + "行业好公司" + ], + "老虎证券": [ + "微众偏好" + ], + "中证信用增进": [ + "微众偏好" + ], + "中证信用": [ + "微众偏好" + ], + "china securities credit investment": [ + "微众偏好" + ], + "data yes": [ + "微众偏好" + ], + "bitxmesh": [ + "微众偏好" + ], + "bitxhub": [ + "微众偏好" + ], + "布比": [ + "微众偏好" + ], + "布比bubi": [ + "微众偏好" + ], + "万向区块链": [ + "微众偏好" + ], + "wanxiang blockchain": [ + "微众偏好" + ], + "阿里云计算": [ + "行业头部", + "微众偏好" + ], + "阿里云": [ + "微众偏好", + "综合大厂" + ], + "alibaba cloud": [ + "行业头部", + "微众偏好" + ], + "alibaba cloud computing": [ + "行业头部", + "微众偏好" + ], + "腾讯云": [ + "微众偏好", + "综合大厂" + ], + "tencent cloud": [ + "微众偏好" + ], + "来也": [ + "微众偏好" + ], + "金智维": [ + "微众偏好" + ], + "kingsware": [ + "微众偏好" + ], + "艺赛旗": [ + "微众偏好" + ], + "i search": [ + "微众偏好" + ], + "is rpa": [ + "微众偏好" + ], + "is rpm": [ + "微众偏好" + ], + "is cda": [ + "微众偏好" + ], + "奥特曼": [ + "微众偏好" + ], + "automation anywhere": [ + "微众偏好" + ], + "基调": [ + "微众偏好" + ], + "听云": [ + "微众偏好" + ], + "中国工商银行": [ + "微众偏好" + ], + "industrial and commercial bank of china": [ + "微众偏好" + ], + "icbc": [ + "微众偏好" + ], + "工行": [ + "微众偏好" + ], + "中国农业银行": [ + "微众偏好" + ], + "agricultural bank of china": [ + "微众偏好" + ], + "abc": [ + "微众偏好" + ], + "农行": [ + "微众偏好" + ], + "中国银行": [ + "行业头部", + "微众偏好" + ], + "bank of china": [ + "行业头部", + "微众偏好" + ], + "中国建设银行": [ + "微众偏好" + ], + "china construction bank": [ + "微众偏好" + ], + "ccb": [ + "微众偏好" + ], + "建行": [ + "微众偏好" + ], + "建设银行": [ + "微众偏好" + ], + "交通银行": [ + "微众偏好" + ], + "bank of communications": [ + "微众偏好" + ], + "bcm": [ + "微众偏好" + ], + "交行": [ + "微众偏好" + ], + "国家开发银行": [ + "微众偏好" + ], + "china development bank": [ + "微众偏好" + ], + "中国进出口银行": [ + "微众偏好" + ], + "the export import bank of china": [ + "微众偏好" + ], + "中国农业发展银行": [ + "微众偏好" + ], + "agricultural development bank of china": [ + "微众偏好" + ], + "中国中信": [ + "微众偏好" + ], + "中信银行": [ + "微众偏好" + ], + "china citic bank": [ + "微众偏好" + ], + "中国光大银行": [ + "微众偏好" + ], + "光大银行": [ + "微众偏好" + ], + "china everbright bank": [ + "微众偏好" + ], + "华夏银行": [ + "微众偏好" + ], + "广发银行": [ + "微众偏好" + ], + "招商银行": [ + "行业头部", + "微众偏好" + ], + "上海浦东发展银行": [ + "微众偏好" + ], + "浦发银行": [ + "微众偏好" + ], + "兴业银行": [ + "微众偏好" + ], + "中国民生银行": [ + "微众偏好" + ], + "民生银行": [ + "微众偏好" + ], + "恒丰银行": [ + "微众偏好" + ], + "浙商银行": [ + "微众偏好" + ], + "渤海银行": [ + "微众偏好" + ], + "摩根大通银行中国": [ + "微众偏好" + ], + "摩根大通": [ + "微众偏好" + ], + "汇丰银行中国": [ + "微众偏好" + ], + "汇丰银行": [ + "微众偏好" + ], + "花旗银行中国": [ + "微众偏好" + ], + "花旗银行": [ + "微众偏好" + ], + "富国银行集团公司": [ + "微众偏好" + ], + "富国银行": [ + "微众偏好" + ], + "日联金融": [ + "微众偏好" + ], + "三菱日联金融": [ + "微众偏好" + ], + "美国银行": [ + "微众偏好" + ], + "高盛集团": [ + "微众偏好" + ], + "高盛": [ + "微众偏好" + ], + "美林": [ + "微众偏好" + ], + "巴黎国民银行": [ + "微众偏好" + ], + "德累斯顿国际": [ + "微众偏好" + ], + "德累斯顿银行": [ + "微众偏好" + ], + "瑞士联合银行": [ + "微众偏好" + ], + "德意志银行": [ + "微众偏好" + ], + "信贷银行": [ + "微众偏好" + ], + "credit suisse": [ + "微众偏好" + ], + "华云中盛科技": [ + "微众偏好" + ], + "华云中盛": [ + "微众偏好" + ], + "德勤管理咨询": [ + "微众偏好" + ], + "富邦华一银行": [ + "微众偏好", + "行业好公司" + ], + "fubon": [ + "微众偏好", + "行业好公司" + ], + "富邦金控": [ + "微众偏好", + "行业好公司" + ], + "富邦华一": [ + "微众偏好", + "行业好公司" + ], + "富邦银行": [ + "微众偏好", + "行业好公司" + ], + "富邦产险": [ + "微众偏好", + "行业好公司" + ], + "富邦证券": [ + "微众偏好", + "行业好公司" + ], + "富邦投信": [ + "微众偏好", + "行业好公司" + ], + "探迹科技": [ + "微众偏好", + "行业好公司" + ], + "探迹": [ + "微众偏好", + "行业好公司" + ], + "tungee": [ + "微众偏好", + "行业好公司" + ], + "易联支付": [ + "微众偏好", + "行业好公司" + ], + "payeco": [ + "微众偏好", + "行业好公司" + ], + "网联清算": [ + "微众偏好", + "行业好公司" + ], + "nyse:cmcm": [ + "行业头部多元化", + "微众偏好" + ], + "金山网络": [ + "行业头部多元化", + "微众偏好" + ], + "猎户星空": [ + "行业头部多元化", + "微众偏好" + ], + "猎豹浏览器": [ + "行业头部多元化", + "微众偏好" + ], + "猎豹清理大师": [ + "行业头部多元化", + "微众偏好" + ], + "猎豹安全大师": [ + "行业头部多元化", + "微众偏好" + ], + "豹来电": [ + "行业头部多元化", + "微众偏好" + ], + "趣输入": [ + "行业头部多元化", + "微众偏好" + ], + "钢琴块2": [ + "行业头部多元化", + "微众偏好" + ], + "滚动的天空": [ + "行业头部多元化", + "微众偏好" + ], + "跳舞的线": [ + "行业头部多元化", + "微众偏好" + ], + "砖块消消消": [ + "行业头部多元化", + "微众偏好" + ], + "live.me": [ + "行业头部多元化", + "微众偏好" + ], + "豹小秘": [ + "行业头部多元化", + "微众偏好" + ], + "豹咖啡": [ + "行业头部多元化", + "微众偏好" + ], + "豹小贩": [ + "行业头部多元化", + "微众偏好" + ], + "小豹ai翻译棒": [ + "行业头部多元化", + "微众偏好" + ], + "迅雷": [ + "行业头部", + "微众偏好" + ], + "xunlei networking technologies": [ + "行业头部", + "微众偏好" + ], + "xunlei": [ + "行业头部", + "微众偏好" + ], + "迅雷集团": [ + "行业头部", + "微众偏好" + ], + "迅雷网络": [ + "行业头部", + 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], + "幻塔网络": [ + "行业好公司" + ], + "游道易网络文化": [ + "行业好公司" + ], + "beijing yodo 1 network culture": [ + "行业好公司" + ], + "游道易网络": [ + "行业好公司" + ], + "力新": [ + "行业好公司" + ], + "微护照weipass": [ + "行业好公司" + ], + "wayfair": [ + "综合大厂" + ], + "pubg": [ + "行业头部" + ], + "派酷": [ + "行业好公司" + ], + "shanghai shengku network": [ + "行业好公司" + ], + "伽马新媒文化传播": [ + "行业好公司" + ], + "菲鹏": [ + "行业好公司" + ], + "yandex": [ + "综合大厂" + ], + "多益(广州)": [ + "行业好公司" + ], + "黑帆": [ + "行业好公司" + ], + "狸猫": [ + "行业好公司" + ], + "安维尔(上海)": [ + "行业好公司" + ], + "竞技世界(成都)": [ + "行业好公司" + ], + "jjworld beijing network": [ + "行业好公司" + ], + "jjworld": [ + "行业好公司" + ], + "zenimax media": [ + "行业头部" + ], + "贝塞斯达": [ + "行业头部" + ], + "友塔游戏": [ + "行业好公司" + ], + "guangzhou qingtianzhu network": [ + "行业好公司" + ], + "擎天柱(广州市)": [ + "行业好公司" + ], + "星河时代": [ + "行业好公司" + ], + "数说故事(广州)": [ + "行业好公司" + ], + "shanghai leshu information": [ + "行业好公司" + ], + "游苑": [ + "行业好公司" + ], + "灵擎": [ + "行业好公司" + ], + "逗游": [ + "行业好公司" + ], + "亲婴": [ + "行业好公司" + ], + "趣抓": [ + "行业好公司" + ], + "337平台": [ + "行业好公司" + ], + "行云": [ + "行业好公司" + ], + "cn elex": [ + "行业好公司" + ], + "elex wireless": [ + "行业好公司" + ], + "浮云": [ + "行业好公司" + ], + "shenzhen bshark limited": [ + "行业好公司" + ], + "创思教育(东莞市)": [ + "行业好公司" + ], + "创思教育": [ + "行业好公司" + ], + "骏伯网络": [ + "行业好公司" + ], + "游卡": [ + "行业好公司" + ], + "游族网络": [ + "行业好公司" + ], + "游族(南京)": [ + "行业好公司" + ], + "广州要玩娱乐": [ + "行业好公司" + ], + "要玩娱乐(广州)": [ + "行业好公司" + ], + "东方融资网": [ + "行业好公司" + ], + "点点互动(北京)": [ + "行业好公司" + ], + "funplusgame": [ + "行业好公司" + ], + "grubhub": [ + "综合大厂" + ], + "闪殿": [ + "行业好公司" + ], + "popup union": [ + "行业好公司" + ], + "抱一": [ + "行业好公司" + ], + "stitch fix": [ + "综合大厂" + ], + "宝开游戏": [ + "行业头部" + ], + "维瑟罗游戏": [ + "行业头部" + ], + "锦游": [ + "行业好公司" + ], + "佰钧成(苏州)": [ + "行业好公司" + ], + "shanghai 37 wan network": [ + "行业好公司" + ], + "37. com": [ + "行业好公司" + ], + "星众": [ + "行业好公司" + ], + "shanghai hard communication network": [ + "行业好公司" + ], + "37 wan": [ + "行业好公司" + ], + "硬通": [ + "行业好公司" + ], + "冠航": [ + "行业好公司" + ], + "尚轩": [ + "行业好公司" + ], + "三七": [ + "行业好公司" + ], + "尚趣玩": [ + "行业好公司" + ], + "黑湖": [ + "行业好公司" + ], + "星河产业投资运营": [ + "行业好公司" + ], + "星河产业投资运营(深圳市)": [ + "行业好公司" + ], + "asos": [ + "综合大厂" + ], + "微盟新媒体": [ + "行业头部" + ], + "td": [ + "行业好公司" + ], + "育碧电脑软件": [ + "行业头部" + ], + "向日葵娱乐互动": [ + "行业头部" + ], + "育碧娱乐": [ + "行业头部" + ], + "育碧电脑": [ + "行业头部" + ], + "安畅网络": [ + "行业好公司" + ], + "银星": [ + "行业好公司" + ], + "cndw": [ + "行业好公司" + ], + "dovo technology.inc": [ + "行业好公司" + ], + "天象": [ + "行业好公司" + ], + "天象互动": [ + "行业好公司" + ], + "乐牛软件": [ + "行业好公司" + ], + "亿客行咨询服务": [ + "综合大厂" + ], + "亿客行咨询服务(北京)": [ + "综合大厂" + ], + "亿客行": [ + "综合大厂" + ], + "expedia": [ + "综合大厂" + ] +} \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/good_corp.json b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/good_corp.json new file mode 100644 index 00000000..e2a89a46 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/good_corp.json @@ -0,0 +1,911 @@ +[ + "google assistant investments", + "amazon", + "dingtalk china information", + "zhejiang alibaba communication", + "yunos", + "腾讯云", + "新浪新闻", + "网邻通", + "蚂蚁集团", + "大疆", + "恒生股份", + "sf express", + "智者天下", + "shanghai hema network", + "papayamobile", + "lexinfintech", + "industrial consumer finance", + "360搜索", + "世纪光速", + "迅雷区块链", + "赛盒科技", + "齐力电子商务", + "平安养老险", + "平安证券", + "平安好贷", + "五八新服", + "呯嘭智能", + "阿里妈妈", + "mdt", + "tencent", + "weibo", + "浪潮软件", + "阿里巴巴广告", + "mashang consumer finance", + "维沃", + "hqg , limited", + "moodys", + "搜狐支付", + "百度秀", + "新浪服务", + "零售通", + "同城艺龙", + "虾米音乐", + "贝壳集团", + "小米有品", + "滴滴自动驾驶", + "图记", + "阿里影业", + "卓联软件", + "zhejiang tmall", + "谷歌中国", + "hithink flush", + "时装科技", + "程会玩国际旅行社", + "amazon china holding limited", + "中信消金", + "当当比特物流", + "新浪新媒体咨询", + "tongcheng network", + "金山在线", + "shopping cart", + "犀互动", + "五八", + "bilibili", + "阿里星球", + "滴滴金科服务", + "美团", + "哈啰出行", + "face", + "平安健康", + "招商银行", + "连亚", + "盒马网络", + "b站", + "华为机器", + "shanghai mdt infotech", + "ping an healthkonnect", + "beijing home link real estate broker", + "花海仓", + "beijing jingdong shangke information", + "微影智能", + "酷狗游戏", + "health.pingan.com", + "众安", + "陌陌", + "海康威视数字", + "同程网", + "艾丁金融", + "知乎", + " lu", + "国际商业机器公司", + "捷信消费金融", + "恒生利融", + "china merchants bank", + "企鹅电竞", + "捷信信驰", + "360智能家居", + "小桔车服", + "homecredit", + "皮皮虾", + "畅游", + "聚爱聊", + "suning.com", + "途牛旅游网", + "花呗", + "盈店通", + "sina", + "阿里巴巴音乐", + "华为技术有限公司", + "国付宝", + "shanghai lianshang network", + "oppo", + "华为投资控股", + "beijing sohu new media information", + "times square", + "菜鸟物流", + "lingxing", + "jd digits", + "同程旅游", + "分期乐", + "火锅视频", + "天天快报", + "猎豹移动", + "五八人力资源", + "宝宝树", + "顺丰科技", + "上海西翠", + "诗程文化传播", + "dewu", + "领星网络", + "aliexpress", + "贝塔通科技", + "链家", + "花小猪", + "趣输入", + "搜狐新媒体", + "一淘", + "56", + "qq阅读", + "青桔单车", + "iflytek", + "每日优鲜电子商务", + "腾讯觅影", + "微医", + "松果网", + "paypal", + "递瑞供应链管理", + "领星", + "qunar", + "三快", + "lu.com", + "携程旅行网", + "新潮传媒", + "链家经纪", + "景域文化", + "阿里健康", + "pingpeng", + "聚划算", + "零机科技", + "街兔电单车", + "快乐购", + "华为数字能源", + "搜狐", + "陆家嘴国际金融资产交易市场", + "nanjing tuniu", + "亚马逊", + "苏宁易购", + "携程旅游", + "苏宁金服", + "babytree", + "悟空问答", + "同花顺", + "eastmoney", + "浪潮信息", + "滴滴智慧交通", + "beijing ruixun lingtong", + "平安综合金融服务", + "爱奇艺", + "小米集团", + "华为云", + "微店", + "恒生集团", + "网易有道", + "boccfc", + "世纪思速科技", + "海康消防", + "beijing xiaomi", + "众安科技", + "五八同城", + "霆程汽车租赁", + "云卖分销", + "乐信集团", + "蚂蚁", + "舶乐蜜电子商务", + "支付宝中国", + "砖块消消消", + "vivo", + "阿里互娱", + "中国平安", + "lingxihudong", + "百度网盘", + "1号店", + "字节跳动", + "京东科技", + "驴妈妈兴旅国际旅行社", + "hangzhou alibaba music", + "xunlei", + "灵犀互动娱乐", + "快手", + "youtube", + "连尚慧眼", + "腾讯体育", + "爱商在线", + "酷我音乐", + "金融壹账通", + "搜狗服务", + "banma information", + "a站", + "罗汉堂", + "薇仕网络", + "搜狐新闻", + "贝宝", + "薇仕", + "口袋时尚科技", + "穆迪咨询", + "新狐投资管理", + "hikvision", + "alimama china holding limited", + "超聚变数字", + "腾讯视频", + "恒生电子", + "百度游戏", + "绿洲", + "木瓜移动", + "红袖添香", + "店匠科技", + "易贝", + "一淘网", + "博览群书", + "唯品会", + "lazglobal", + "amap", + "芒果网", + "口碑", + "海康慧影", + "腾讯音乐娱乐", + "网易严选", + "微信", + "shenzhen lexin holding", + "hangzhou pingpeng intelligent", + "连尚网络", + "海思", + "isunor", + "蝉翼", + "阿里游戏", + "广州优视", + "优视", + "腾讯征信", + "识装", + "finserve.pingan.com", + "papaya", + "阅文", + "平安健康保险", + "考拉海购", + "网易印象", + "wifi万能钥匙", + "新浪互联服务", + "亚马逊云科技", + "迅雷看看", + "华为朗新科技", + "adyen hong kong limited", + "谷歌", + "得物", + "网心", + "cainiao network", + "沐瞳", + "linkedln", + "hundsun", + "阿里旅行", + "珍爱网", + "阿里巴巴通信", + "金山奇剑", + "tongtool", + "华为安捷信电气", + "快乐时代", + "平安寿险", + "微博", + "微跳蚤", + "oppo移动通信", + "毒", + "alimama", + "shoplazza", + "shenzhen dianjiang science and", + "众鸣世科", + "平安金融", + "狐友", + "维沃移动通信", + "tobosoft", + "齐力电商", + "ali", + "诚信通", + "行吟", + "跳舞的线", + "橙心优选", + "众安健康", + "亚马逊中国投资", + "德絮投资管理中心合伙", + "招联消费金融", + "百度文学", + "芝麻信用", + "阿里零售通", + "时装", + "花样直播", + "sogou", + "uc", + "海思半导体", + "zhongan online p&c insurance", + "新浪数字", + "驴妈妈旅游网", + "华为数字能源技术", + "京东数科", + "oracle", + "xiaomi", + "nyse", + "阳光消费金融", + "天天动听", + "大众点评", + "上海瑞家", + "trustpass", + "hundsun technologies", + "美团小贷", + "ebay", + "通途", + "tcl", + "鸿蒙", + "酷狗计算机", + "品诺保险", + "capitalg", + "康盛创想", + "58同城", + "闲鱼", + "微软", + "吉易付科技", + "理财通", + "ctrip", + "yy", + "华为数字", + "kingsoft", + "孙宁金融", + "房江湖经纪", + "youku", + "ant financial services group", + "盒马", + "sensetime", + "伊千网络", + "小豹ai翻译棒", + "shopify", + "前海微众银行", + "qd", + "gmail", + "pingpong", + "alibaba group holding limited", + "捷信时空电子商务", + "orientsec", + "乔戈里管理咨询", + "ant", + "锐讯灵通", + "兴业消费金融", + "京东叁佰陆拾度电子商务", + "新浪", + "优酷土豆", + "海康机器人", + "美团单车", + "海康存储", + "领英", + "阿里全球速卖通", + "美菜网", + "京邦达", + "安居客", + "阿里体育", + "相互宝", + "cloudwalk", + "百度智能云", + "贝壳", + "酷狗", + "sunshine consumer finance", + "掌宜", + "奇酷网", + "核新同花顺", + "阿里巴巴影业", + "节创", + "学而思网校", + "速途", + "途牛", + "阿里云计算", + "beijing sensetime", + "alibaba cloud", + "西瓜视频", + "美团优选", + "orient securities limited", + "华为朗新", + "店匠", + "shanghai weishi network", + "友盟", + "飞猪旅行", + "滴滴出行", + "alipay", + "mogu", + "dangdang", + "大麦网", + "汉军智能系统", + "百度地图", + "货车帮", + "狐狸金服", + "众安在线保险经纪", + "华为通信", + "新浪支付", + "zhihu", + "alibaba cloud computing", + "沙发视频", + "金山软件", + "ping an good doctor", + "携程", + "脉脉", + "youku information beijing", + "zhongan", + "艾丁软件", + "乒乓智能", + "蘑菇街", + "taobao", + "华为技术服务", + "仕承文化传播", + "安捷信", + "狐狸互联网小额贷款", + "节点迅捷", + "中国银行", + "搜镇", + "众安在线", + "dingtalk", + "云从科技", + "beijing jingbangda trade", + "moody s", + "滚动的天空", + "yl.pingan.com", + "奇虎", + "alihealth", + "芒果tv", + "lufax", + "美团打车", + "小桔", + "贝壳找房网", + "小米科技", + "vips", + "kindle", + "亚马逊服务", + "citic consumer finance", + "微众", + "搜狗智慧互联网医院", + "盒马鲜生", + "life.pinan.com", + "ph.com.cn", + "银联", + "cmbchina", + "平安金融科技咨询", + "微保", + "甲骨文中国", + "飞书", + "koubei shanghai information", + "企鹅辅导", + "斑马", + "平安租赁", + "云从", + "马上消费", + "hangzhou ali baba advertising", + "金山", + "赛盒", + "科大讯飞", + "金星创业投资", + "平安国际融资租赁", + "360你财富", + "西山居", + "shenzhen qianhai fourth paradigm data", + "海思光电子", + "猎户星空", + "网易公司", + "浪潮", + "粒粒橙传媒", + "招联金融", + "100. me", + "捷信信驰咨询", + "唯品仓", + "orient", + "趣拿", + "摩拜单车", + "天猫精灵", + "菜鸟", + "豹小贩", + "去哪儿", + "米家", + "哈啰单车", + "搜狐体育", + "shopify payments usa", + "高德软件", + "讯联智付", + "乐信", + "唯你搭", + "第四范式", + "菜鸟网络", + "同程", + "yy语音", + "浪潮云", + "东财", + "淘宝", + "寻梦", + "citic securities limited", + "青橙之旅", + "阿里巴巴", + "番茄小说", + "上海亿贝", + "inspur", + "babytree inc", + "海康智慧产业股权投资基金合伙合伙", + "adyen", + "艺龙", + "蚂蚁金服", + "平安金服", + "百度百科", + "unionpay", + "当当", + "阅文集团", + "东方财富", + "东方证券", + "哈罗单车", + "优酷", + "海康", + "alipay china network", + "网商银行", + "钧正", + "property.pingan.com", + "豹咖啡", + "网易", + "我爱cba", + "theduapp", + "360", + "金山数字娱乐", + "新浪阅读", + "alibabagames", + "顺丰", + "支点商贸", + "同程旅行", + "citic securities", + "ele.com", + "tal", + "fresh hema", + "运满满", + "贝壳网", + "酷狗音乐", + "鲜城", + "360健康", + "浪潮世科", + "迅雷网络", + "哔哩哔哩", + "华为电动", + "淘友天下", + "华多网络", + "xunlei networking technologies", + "云杉", + "当当网电子商务", + "津虹网络", + "wedoc cloud hangzhou holdings", + "alisports shanghai", + "旷视金智", + "钉钉中国", + "微影", + "金山快快", + "亿贝", + "wedoc", + "autonavi", + "哈啰助力车", + "google cloud", + "新浪乐居", + "京东股票", + "搜狗智慧远程医疗中心", + "中银消金", + "merchants union consumer finance", + "王者荣耀", + "百度手机", + "美团民宿", + "kaola", + "小屋", + "金山网络", + "来往", + "顺丰速运", + "腾讯课堂", + "百度在线网络", + "美团买菜", + "威视汽车", + "uc mobile", + "来赞达", + "平安健康医疗", + "豹小秘", + "尚网", + "哈勃投资", + " ping an insurance group of china ,", + "小米", + "360好药", + "qq音乐", + "lingxigames", + "faceu激萌", + "搜狗", + "sohu", + "满帮", + "vipshop", + "wishpost", + "金山世游", + "shanghai yibaimi network", + "1688", + "海康汽车", + "顺丰控股", + "华为", + "妙镜vr", + "paybkj.com", + "hellobike", + "豹来电", + "京东", + "驴妈妈", + "momo", + "平安健康险", + "哈勃科技", + "美菜", + "众安在线财产保险", + "海康威视", + "east money information", + "阿里云", + "蝉游记", + "余额宝", + "屋客", + "滴滴", + "shopify international limited", + "百度", + "阿里健康中国", + "阿里通信", + "微梦创科", + "微医云", + "轻颜相机", + "搜易居", + "趣店集团", + "美团云", + "ant group", + "金山云", + "beijing express hand", + "觅觅", + "支付宝", + "滴滴承信科技咨询服务", + "拼多多", + "众安运动", + "乞力电商", + "youcash", + "唯品金融", + "陆金所", + "本地生活", + "sz dji", + "海康智能", + "魔方网聘", + "青藤大学", + "international business machines", + "学而思", + "beijing zhongming century science and", + "猎豹清理大师", + "asinking", + "高德", + "苏宁", + "优酷网", + "艾丁", + "中银消费金融", + "京东健康", + "五八教育", + "pingpongx", + "搜狐时尚", + "阿里广告", + "平安财险", + "中邮消金", + "etao", + "怕怕", + "nyse:cmcm", + "华为培训中心", + "高德地图", + "云狐天下征信", + "大疆创新", + "连尚", + "壹佰米", + "康健公司", + "iqiyi.com", + "360安全云盘", + "馒头直播", + "淘友网", + "东方赢家", + "bank of china", + "微众银行", + "阿里巴巴国际站", + "虾米", + "去哪儿网", + "ctrip travel network shanghai", + "潇湘书院", + "腾讯", + "快乐阳光互动娱乐传媒", + "迅雷", + "weidian", + "滴滴货运", + "ping an puhui enterprise management", + "新浪仓石基金销售", + "搜狐焦点", + "alibaba pictures", + "wps", + "平安", + "lazmall", + "百度开放平台", + "兴业消金", + " 珍爱网", + "京东云", + "小红书", + "1688. com", + "如视智数", + "missfresh", + "pazl.pingan.cn", + "平安集团", + "kugou", + "懂车帝", + "斑马智行", + "浪潮集团", + "netease hangzhou network", + "pagd.net", + "探探", + "chinaliterature", + "amazon亚马逊", + "alphabet", + "当当文创手工艺品电子商务", + "五八邦", + "shenzhen zhenai network information", + "lingshoutong", + "字节", + "lvmama", + "金山办公", + "众安保险", + "时装信息", + "优视科技", + "guangzhou kugou", + "ibm", + "滴滴打车", + "beijing sogou information service", + "megvii", + "健谈哥", + "cloudwalk group", + "蜂联科技", + "冬云", + "京东尚科", + "钢琴块2", + "京东世纪", + "商汤", + "众鸣世纪", + "腾讯音乐", + "迅雷网文化", + "华为云计算技术", + "live.me", + "全球速卖通", + "快的打车", + "hello group inc", + "美丽说", + "suning", + "opengauss", + "lazada", + "tmall", + "acfun", + "当当网", + "中银", + "旷视科技", + "百度钱包", + "淘宝网", + "新浪微博", + "迅雷集团", + "中信消费金融", + "学而思教育", + "平安普惠", + "悟空跨境", + "irobotbox", + "平安产险", + "inspur group", + "世纪卓越快递服务", + "奇虎360", + "webank", + "偶藻", + "唯品支付", + "腾讯云计算", + "众安服务", + "亿之唐", + "beijing 58 information ttechnology", + "平安好医生", + "迅雷之锤", + "旅行小账本", + "芒果游戏", + "新浪传媒", + "旷镜博煊", + "全民k歌", + "滴滴支付", + "北京网心科技", + "挂号网", + "萤石", + "chinavision media group limited", + "猎豹安全大师", + "cmcm", + "趣店", + "蚂蚁财富", + "商汤科技", + "甲骨文", + "百度云", + "百度apollo", + "19 pay", + "stock.pingan.com", + "tiktok", + "alibaba pictures group limited", + "ele", + "考拉", + "天猫", + "腾讯优图", + "起点中文网", + "百度视频", + "shanghai bili bili", + "京东物流", + "ebay marketplaces gmbh", + "alibaba sport", + "wish", + "阿里巴巴中国", + "中国银联", + "alibaba china network", + "china ping an property insurance", + "百度糯米网", + "微软中国", + "一九付", + "4 paradigm", + "叮咚买菜", + "umeng", + "众鸣科技", + "平安财富通", + "google", + "巨量引擎", + "百度贴吧", + "beijing jingdong century information", + "讯飞", + "beijing yunshan information", + "满运软件", + "中邮消费金融", + "饿了么", + "alios", + "腾讯ai实验室", + "第四范式智能", + "瀚星创业投资", + "gradient ventures", + "microsoft", + "哈啰共享汽车", + "乞力电子商务", + "mscf", + "网易影业文化", + "铁友旅游咨询", + "kilimall", + "云企互联投资", + "ping an financial consulting", + "beijng jingdong century commerce", + "高德威智能交通系统", + "中友信息", + "平安医疗健康管理", + "eciticcfc", + "中信证券", + "fliggy", + "电子湾", + "旷云金智", + "微粒贷", + "rsi", + "滴滴云计算", + "google ventures", + "箐程", + "每日优鲜", + "音兔", + "拉扎斯", + "今日头条", + "乐信控股", + "猎豹浏览器", + "细微咨询", + "好未来", + "我乐", + "绘声绘色", + "抖音", + "搜狐新时代", + "飞猪", + "鹅厂", + "贝壳找房", + "tuniu", + "红马传媒文化", + "钉钉", + "马上消费金融", + "360手机", + "平安医保", + "快途", + "alibaba", + "小哈换电", + "大麦", + "恒睿人工智能研究院", + "谷歌资本", + "猎豹", + "穆迪信息" +] diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/good_sch.json b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/good_sch.json new file mode 100644 index 00000000..14b8ecfe --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/good_sch.json @@ -0,0 +1,595 @@ +[ +"中国科技大学", +"国防科学技术大学", +"清华大学", +"清华", +"tsinghua university", +"thu", +"北京大学", +"北大", +"beijing university", +"pku", +"中国科学技术大学", +"中国科大", +"中科大", +"china science & technology university", +"ustc", +"复旦大学", +"复旦", +"fudan university", +"fdu", +"中国人民大学", +"人大", +"人民大学", +"renmin university of china", +"ruc", +"上海交通大学", +"上海交大", +"shanghai jiao tong university", +"sjtu", +"南京大学", +"南大", +"nanjing university", +"nju", +"同济大学", +"同济", +"tongji university", +"tongji", +"浙江大学", +"浙大", +"zhejiang university", +"zju", +"南开大学", +"南开", +"nankai university", +"nku", +"北京航空航天大学", +"北航", +"beihang university", +"buaa", +"北京师范大学", +"北师", +"北师大", +"beijing normal university", +"bnu", +"武汉大学", +"武大", +"wuhan university", +"whu", +"西安交通大学", +"西安交大", +"xi’an jiaotong university", +"xjtu", +"天津大学", +"天大", +"university of tianjin", +"tju", +"华中科技大学", +"华中大", +"central china university science and technology", +"hust", +"北京理工大学", +"北理", +"beijing institute of technology", +"bit", +"东南大学", +"东大", +"southeast china university", +"seu", +"中山大学", +"中大", +"zhongshan university", +"sysu", +"华东师范大学", +"华师大", +"east china normal university", +"ecnu", +"哈尔滨工业大学", +"哈工大", +"harbin institute of technology", +"hit", +"厦门大学", +"厦大", +"xiamen university", +"xmu", +"西北工业大学", +"西工大", +"西北工大", +"northwestern polytechnical university", +"npu", +"中南大学", +"中南", +"middle and southern university", +"csu", +"大连理工大学", +"大工", +"institute of technology of dalian", +"dut", +"四川大学", +"川大", +"sichuan university", +"scu", +"电子科技大学", +"电子科大", +"university of electronic science and technology of china", +"uestc", +"华南理工大学", +"华南理工", +"institutes of technology of south china", +"scut", +"吉林大学", +"吉大", +"jilin university", +"jlu", +"湖南大学", +"湖大", +"hunan university", +"hnu", +"重庆大学", +"重大", +"university of chongqing", +"cqu", +"山东大学", +"山大", +"shandong university", +"sdu", +"中国农业大学", +"中国农大", +"china agricultural university", +"cau", +"中国海洋大学", +"中国海大", +"chinese marine university", +"ouc", +"中央民族大学", +"中央民大", +"central university for nationalities", +"muc", +"东北大学", +"东北工学院", +"northeastern university", +"neu 或 nu", +"兰州大学", +"兰大", +"lanzhou university", +"lzu", +"西北农林科技大学", +"西农","西北农大", +"northwest a&f university", +"nwafu", +"中国人民解放军国防科技大学", +"国防科技大学","国防科大", +"national university of defense technology", +"nudt", +"郑州大学", +"郑大", +"zhengzhou university", +"zzu", +"云南大学", +"云大", +"yunnan university", +"ynu", +"新疆大学", +"新大", +"xinjiang university", +"xju", +"北京交通大学", +"北京交大", +"beijing jiaotong university", +"bjtu", +"北京工业大学", +"北工大", +"beijing university of technology", +"bjut", +"北京科技大学", +"北科大","北京科大", +"university of science and technology beijing", +"ustb", +"北京化工大学", +"北化", +"beijing university of chemical technology", +"buct", +"北京邮电大学", +"北邮", +"beijing university of posts and telecommunications", +"beijing university of post and telecommunications", +"beijing university of post and telecommunication", +"beijing university of posts and telecommunication", +"bupt", +"北京林业大学", +"北林", +"beijing forestry university", +"bfu", +"北京协和医学院", +"协和医学院", +"peking union medical college", +"pumc", +"北京中医药大学", +"北中医", +"beijing university of chinese medicine", +"bucm", +"首都师范大学", +"首师大", +"capital normal university", +"cnu", +"北京外国语大学", +"北外", +"beijing foreign studies university", +"bfsu", +"中国传媒大学", +"中媒", +"中传", +"北京广播学院", +"communication university of china", +"cuc", +"中央财经大学", +"中央财大", +"中财大", +"the central university of finance and economics", +"cufe", +"对外经济贸易大学", +"对外经贸大学", +"贸大", +"university of international business and economics", +"uibe", +"外交学院", +"外院", +"china foreign affairs university", +"cfau", +"中国人民公安大学", +"公安大学", +"people's public security university of china", +"ppsuc", +"北京体育大学", +"北体大", +"beijing sport university", +"bsu", +"中央音乐学院", +"央音", +"中央院", +"central conservatory of music", +"ccom", +"中国音乐学院", +"国音", +"中国院", +"china conservatory of music", +"ccmusic", +"中央美术学院", +"央美", +"central academy of fine art", +"cafa", +"中央戏剧学院", +"中戏", +"the central academy of drama", +"tcad", +"中国政法大学", +"法大", +"china university of political science and law", +"zuc", +"cupl", +"中国科学院大学", +"国科大", +"科院大", +"university of chinese academy of sciences", +"ucas", +"福州大学", +"福大", +"university of fuzhou", +"fzu", +"暨南大学", +"暨大", +"ji'nan university", +"jnu", +"广州中医药大学", +"广中医", +"traditional chinese medicine university of guangzhou", +"gucm", +"华南师范大学", +"华南师大", +"south china normal university", +"scnu", +"广西大学", +"西大", +"guangxi university", +"gxu", +"贵州大学", +"贵大", +"guizhou university", +"gzu", +"海南大学", +"海大", +"university of hainan", +"hainu", +"河南大学", +"河大", +"he'nan university", +"henu", +"哈尔滨工程大学", +"哈工程", +"harbin engineering university", +"heu", +"东北农业大学", +"东北农大", +"northeast agricultural university", +"neau", +"东北林业大学", +"东北林大", +"northeast forestry university", +"nefu", +"中国地质大学", +"地大", +"china university of geosciences", +"cug", +"武汉理工大学", +"武汉理工", +"wuhan university of technology", +"wut", +"华中农业大学", +"华中农大", +"华农", +"central china agricultural university", +"hzau", +"华中师范大学", +"华中师大", +"华大", +"central china normal university", +"ccnu", +"中南财经政法大学", +"中南大", +"zhongnan university of economics & law", +"zuel", +"湖南师范大学", +"湖南师大", +"hunan normal university", +"hunnu", +"延边大学", +"延大", +"yanbian university", +"ybu", +"东北师范大学", +"东北师大", +"northeast normal university", +"nenu", +"苏州大学", +"苏大", +"soochow university", +"suda", +"南京航空航天大学", +"南航", +"nanjing aero-space university", +"nuaa", +"南京理工大学", +"南理工", +"institutes of technology of nanjing", +"njust", +"中国矿业大学", +"中国矿大", +"china mining university", +"cumt", +"南京邮电大学", +"南邮", +"nanjing university of posts and telecommunications", +"njupt", +"河海大学", +"河海", +"river sea university", +"hhu", +"江南大学", +"江南大", +"jiangnan university", +"jiangnan", +"南京林业大学", +"南林", +"nanjing forestry university", +"njfu", +"南京信息工程大学", +"南信大", +"nanjing university of information science and technology", +"nuist", +"南京农业大学", +"南农", +"南农大", +"南京农大", +"agricultural university of nanjing", +"njau", +"nau", +"南京中医药大学", +"南中医", +"nanjing university of chinese medicine", +"njucm", +"中国药科大学", +"中国药大", +"china medicine university", +"cpu", +"南京师范大学", +"南京师大", +"南师大", +"南师", +"nanjing normal university", +"nnu", +"南昌大学", +"昌大", +"university of nanchang","nanchang university", +"ncu", +"辽宁大学", +"辽大", +"liaoning university", +"lnu", +"大连海事大学", +"大连海大", +"海大", +"maritime affairs university of dalian", +"dmu", +"内蒙古大学", +"内大", +"university of the inner mongol","inner mongolia university", +"imu", +"宁夏大学", +"宁大", +"ningxia university", +"nxu", +"青海大学", +"清大", +"qinghai university", +"qhu", +"中国石油大学", +"中石大", +"china university of petroleum beijing", +"upc", +"太原理工大学", +"太原理工", +"institutes of technology of taiyuan","taiyuan university of technology", +"tyut", +"西北大学", +"西大", +"northwest university", +"nwu", +"西安电子科技大学", +"西电", +"xidian university", +"xdu", +"长安大学", +"长大", +"chang`an university", +"chu", +"陕西师范大学", +"陕西师大", +"陕师大", +"shaanxi normal university", +"snnu", +"第四军医大学", +"空军军医大学","四医大", +"air force medical university", +"fmmu", +"华东理工大学", +"华理", +"east china university of science", +"ecust", +"东华大学", +"东华", +"donghua university", +"dhu", +"上海海洋大学", +"上海海大", +"shanghai ocean university", +"shou", +"上海中医药大学", +"上中医", +"shanghai university of traditional chinese medicine", +"shutcm", +"上海外国语大学", +"上外", +"shanghai international studies university", +"sisu", +"上海财经大学", +"上海财大", +"上财", +"shanghai university of finance", +"sufe", +"上海体育学院", +"shanghai university of sport", +"上海音乐学院", +"上音", +"shanghai conservatory of music", +"shcm", +"上海大学", +"上大", +"shanghai university", +"第二军医大学", +"海军军医大学", +"naval medical university", +"西南交通大学", +"西南交大", +"southwest jiaotong university", +"swjtu", +"西南石油大学", +"西南石大", +"southwest petroleum university", +"swpu", +"成都理工大学", +"成都理工", +"chengdu university of technology", +"cdut ", +"四川农业大学", +"川农", +"川农大", +"sichuan agricultural university", +"sicau", +"成都中医药大学", +"成中医", +"chengdu university of tcm", +"cdutcm", +"西南财经大学", +"西南财大", +"西财", +"southwestern university of finance and economics", +"swufe", +"天津工业大学", +"天工大", +"tianjin university of technology", +"tgu", +"天津医科大学", +"天津医大", +"medical university of tianjin", +"tmu", +"天津中医药大学", +"天中", +"tianjin university of traditional chinese medicine", +"tutcm", +"华北电力大学", +"华电", +"north china electric power university", +"ncepu", +"河北工业大学", +"河工大", +"hebei university of technology", +"hebut", +"西藏大学", +"藏大", +"tibet university", +"tu", +"石河子大学", +"石大", +"shihezi university", +"中国美术学院", +"中国美院", +"国美", +"china academy of art", +"caa", +"宁波大学", +"宁大", +"ningbo university", +"nbu", +"西南大学", +"西大", +"southwest university", +"swu", +"安徽大学", +"安大", +"university of anhui", +"ahu", +"合肥工业大学", +"合肥工大", +"合工大", +"hefei university of technology", +"hfut", +"中国地质大学", +"地大", +"china university of geosciences", +"cug", +"中国地质大学", +"地大", +"北京地大", +"cugb", +"中国矿业大学", +"中国矿大", +"china university of mining & technology", +"cumtb", +"中国石油大学", +"中石大", +"石大", +"china university of petroleum", +"cup", +"中国石油大学", +"中石大", +"cup"] diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/school.rank.csv b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/school.rank.csv new file mode 100644 index 00000000..4dab9cb9 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/school.rank.csv @@ -0,0 +1,1627 @@ +清华大学,2,985,清华 +清华大学,2,985,Tsinghua University +清华大学,2,985,THU +北京大学,1,985,北大 +北京大学,1,985,Beijing University +北京大学,1,985,PKU +中国科学技术大学,14,985,中国科大 +中国科学技术大学,14,985,中科大 +中国科学技术大学,14,985,China Science & Technology University +中国科学技术大学,14,985,USTC +复旦大学,5,985,复旦 +复旦大学,5,985,Fudan University +复旦大学,5,985,FDU +中国人民大学,15,985,人大 +中国人民大学,15,985,人民大学 +中国人民大学,15,985,Renmin University Of China +中国人民大学,15,985,RUC +上海交通大学,4,985,上海交大 +上海交通大学,4,985,Shanghai Jiao Tong University +上海交通大学,4,985,SJTU +南京大学,11,985,南大 +南京大学,11,985,Nanjing University +南京大学,11,985,NJU +同济大学,17,985,同济 +同济大学,17,985,Tongji University +同济大学,17,985,Tongji +浙江大学,3,985,浙大 +浙江大学,3,985,Zhejiang University +浙江大学,3,985,ZJU +南开大学,27,985,南开 +南开大学,27,985,Nankai University +南开大学,27,985,NKU +北京航空航天大学,21,985,北航 +北京航空航天大学,21,985,Beihang University +北京航空航天大学,21,985,BUAA +北京师范大学,23,985,北师 +北京师范大学,23,985,北师大 +北京师范大学,23,985,Beijing Normal University +北京师范大学,23,985,BNU +武汉大学,6,985,武大 +武汉大学,6,985,Wuhan University +武汉大学,6,985,WHU +西安交通大学,13,985,西安交大 +西安交通大学,13,985,Xi’an Jiaotong University +西安交通大学,13,985,XJTU +天津大学,22,985,天大 +天津大学,22,985,University Of Tianjin +天津大学,22,985,TJU +华中科技大学,8,985,华中大 +华中科技大学,8,985,Central China University Science and Technology +华中科技大学,8,985,HUST +北京理工大学,24,985,北理 +北京理工大学,24,985,Beijing Institute of Technology +北京理工大学,24,985,BIT +东南大学,20,985,东大 +东南大学,20,985,Southeast China University +东南大学,20,985,SEU +中山大学,9,985,中大 +中山大学,9,985,Zhongshan University +中山大学,9,985,SYSU +华东师范大学,30,985,华师大 +华东师范大学,30,985,East China Normal University +华东师范大学,30,985,ECNU +哈尔滨工业大学,10,985,哈工大 +哈尔滨工业大学,10,985,Harbin Institute Of Technology +哈尔滨工业大学,10,985,HIT +厦门大学,28,985,厦大 +厦门大学,28,985,Xiamen University +厦门大学,28,985,XMU +西北工业大学,31,985,西工大 +西北工业大学,31,985,西北工大 +西北工业大学,31,985,Northwestern Polytechnical University +西北工业大学,31,985,NPU +中南大学,19,985,中南 +中南大学,19,985,Middle and Southern University +中南大学,19,985,CSU +大连理工大学,26,985,大工 +大连理工大学,26,985,Institute Of Technology Of Dalian +大连理工大学,26,985,DUT +四川大学,7,985,川大 +四川大学,7,985,Sichuan University +四川大学,7,985,SCU +电子科技大学,32,985,电子科大 +电子科技大学,32,985,University of Electronic Science and Technology of China +电子科技大学,32,985,UESTC +华南理工大学,25,985,华南理工 +华南理工大学,25,985,Institutes Of Technology Of South China +华南理工大学,25,985,SCUT +吉林大学,12,985,吉大 +吉林大学,12,985,Jilin University +吉林大学,12,985,JLU +湖南大学,38,985,湖大 +湖南大学,38,985,Hunan University +湖南大学,38,985,HNU +重庆大学,29,985,重大 +重庆大学,29,985,University Of Chongqing +重庆大学,29,985,CQU +山东大学,16,985,山大 +山东大学,16,985,Shandong University +山东大学,16,985,SDU +中国农业大学,37,985,中国农大 +中国农业大学,37,985,China Agricultural University +中国农业大学,37,985,CAU +中国海洋大学,60,985,中国海大 +中国海洋大学,60,985,Chinese Marine University +中国海洋大学,60,985,OUC +中央民族大学,106,985,中央民大 +中央民族大学,106,985,Central University For Nationalities +中央民族大学,106,985,MUC +东北大学,41,985,东北工学院 +东北大学,41,985,Northeastern University +东北大学,41,985,NEU +东北大学,41,985,NU +兰州大学,39,985,兰大 +兰州大学,39,985,Lanzhou University +兰州大学,39,985,LZU +西北农林科技大学,78,985,西农、西北农大 +西北农林科技大学,78,985,Northwest A&F University +西北农林科技大学,78,985,NWAFU +中国人民解放军国防科技大学,89,985,国防科技大学、国防科大、国防科学技术大学 +中国人民解放军国防科技大学,89,985,National University of Defense Technology +中国人民解放军国防科技大学,89,985,NUDT +郑州大学,34,211,郑大 +郑州大学,34,211,Zhengzhou University +郑州大学,34,211,ZZU +云南大学,75,211,云大 +云南大学,75,211,Yunnan University +云南大学,75,211,YNU +新疆大学,114,211,新大 +新疆大学,114,211,Xinjiang University +新疆大学,114,211,XJU +北京交通大学,49,211,北京交大 +北京交通大学,49,211,Beijing Jiaotong University +北京交通大学,49,211,BJTU +北京工业大学,63,211,北工大 +北京工业大学,63,211,Beijing University Of Technology +北京工业大学,63,211,BJUT +北京科技大学,48,211,北科大、北京科大 +北京科技大学,48,211,University Of Science and Technology Beijing +北京科技大学,48,211,USTB +北京化工大学,82,211,北化 +北京化工大学,82,211,Beijing University of Chemical Technology +北京化工大学,82,211,BUCT +北京邮电大学,76,211,北邮 +北京邮电大学,76,211,Beijing University Of Posts and Telecommunications +北京邮电大学,76,211,BUPT +北京林业大学,88,211,北林 +北京林业大学,88,211,Beijing Forestry University +北京林业大学,88,211,BFU +北京协和医学院,,双一流,协和医学院 +北京协和医学院,,双一流,Peking Union Medical College +北京协和医学院,,双一流,PUMC +北京中医药大学,121,211,北中医 +北京中医药大学,121,211,Beijing University Of Chinese Medicine +北京中医药大学,121,211,BUCM +首都师范大学,,双一流,首师大 +首都师范大学,,双一流,Capital Normal University +首都师范大学,,双一流,CNU +北京外国语大学,124,211,北外 +北京外国语大学,124,211,Beijing Foreign Studies University +北京外国语大学,124,211,BFSU +中国传媒大学,96,211,中媒 +中国传媒大学,96,211,中传 +中国传媒大学,96,211,北京广播学院 +中国传媒大学,96,211,Communication University Of China +中国传媒大学,96,211,CUC +中央财经大学,79,211,中央财大 +中央财经大学,79,211,中财大 +中央财经大学,79,211,The Central University Of Finance and Economics +中央财经大学,79,211,CUFE +对外经济贸易大学,99,211,对外经贸大学 +对外经济贸易大学,99,211,贸大 +对外经济贸易大学,99,211,University Of International Business and Economics +对外经济贸易大学,99,211,UIBE +外交学院,,双一流,外院 +外交学院,,双一流,China Foreign Affairs University +外交学院,,双一流,CFAU +中国人民公安大学,,双一流,公安大学 +中国人民公安大学,,双一流,People's Public Security University of China +中国人民公安大学,,双一流,PPSUC +北京体育大学,122,211,北体大 +北京体育大学,122,211,Beijing Sport University +北京体育大学,122,211,BSU +中央音乐学院,103,211,央音 +中央音乐学院,103,211,中央院 +中央音乐学院,103,211,Central Conservatory Of Music +中央音乐学院,103,211,CCOM +中国音乐学院,,双一流,国音 +中国音乐学院,,双一流,中国院 +中国音乐学院,,双一流,China Conservatory of Music +中国音乐学院,,双一流,CCMUSIC +中央美术学院,104,双一流,央美 +中央美术学院,104,双一流,Central Academy of Fine Art +中央美术学院,104,双一流,CAFA +中央戏剧学院,133,双一流,中戏 +中央戏剧学院,133,双一流,The Central Academy Of Drama +中央戏剧学院,133,双一流,TCAD +中国政法大学,90,211,法大 +中国政法大学,90,211,China University Of Political Science and Law +中国政法大学,90,211,ZUC +中国政法大学,90,211,CUPL +中国科学院大学,18,双一流,国科大 +中国科学院大学,18,双一流,科院大 +中国科学院大学,18,双一流,University of Chinese Academy of Sciences +中国科学院大学,18,双一流,UCAS +福州大学,72,211,福大 +福州大学,72,211,University Of Fuzhou +福州大学,72,211,FZU +暨南大学,44,211,暨大 +暨南大学,44,211,Ji'nan University +暨南大学,44,211,JNU +广州中医药大学,,双一流,广中医 +广州中医药大学,,双一流,Traditional Chinese Medicine University Of Guangzhou +广州中医药大学,,双一流,GUCM +华南师范大学,55,211,华南师大 +华南师范大学,55,211,South China Normal University +华南师范大学,55,211,SCNU +广西大学,71,211,广西大 +广西大学,71,211,Guangxi University +广西大学,71,211,GXU +贵州大学,94,211,贵大 +贵州大学,94,211,Guizhou University +贵州大学,94,211,GZU +海南大学,101,211,海大 +海南大学,101,211,University Of Hainan +海南大学,101,211,HAINU +河南大学,85,双一流,河大 +河南大学,85,双一流,He'nan University +河南大学,85,双一流,HENU +哈尔滨工程大学,65,211,哈工程 +哈尔滨工程大学,65,211,Harbin Engineering University +哈尔滨工程大学,65,211,HEU +东北农业大学,98,211,东北农大 +东北农业大学,98,211,Northeast Agricultural University +东北农业大学,98,211,NEAU +东北林业大学,93,211,东北林大 +东北林业大学,93,211,Northeast Forestry University +东北林业大学,93,211,NEFU +中国地质大学,80,211,地大 +中国地质大学,80,211,China University Of Geosciences +中国地质大学,80,211,CUG +武汉理工大学,40,211,武汉理工 +武汉理工大学,40,211,Wuhan University of Technology +武汉理工大学,40,211,WUT +华中农业大学,52,211,华中农大 +华中农业大学,52,211,华农 +华中农业大学,52,211,Central China Agricultural University +华中农业大学,52,211,HZAU +华中师范大学,58,211,华中师大 +华中师范大学,58,211,华大 +华中师范大学,58,211,Central China Normal University +华中师范大学,58,211,CCNU +中南财经政法大学,105,211,中南大 +中南财经政法大学,105,211,Zhongnan University Of Economics & Law +中南财经政法大学,105,211,ZUEL +湖南师范大学,68,211,湖南师大 +湖南师范大学,68,211,Hunan Normal University +湖南师范大学,68,211,HUNNU +延边大学,130,211,延大 +延边大学,130,211,Yanbian University +延边大学,130,211,YBU +东北师范大学,69,211,东北师大 +东北师范大学,69,211,Northeast Normal University +东北师范大学,69,211,NENU +苏州大学,35,211,苏大 +苏州大学,35,211,Soochow University +苏州大学,35,211,SUDA +南京航空航天大学,33,211,南航 +南京航空航天大学,33,211,Nanjing Aero-Space University +南京航空航天大学,33,211,NUAA +南京理工大学,42,211,南理工 +南京理工大学,42,211,Institutes Of Technology Of Nanjing +南京理工大学,42,211,NJUST +中国矿业大学,61,211,中国矿大 +中国矿业大学,61,211,China Mining University +中国矿业大学,61,211,CUMT +南京邮电大学,,双一流,南邮 +南京邮电大学,,双一流,Nanjing University of Posts and Telecommunications +南京邮电大学,,双一流,NJUPT +河海大学,54,211,河海 +河海大学,54,211,River Sea University +河海大学,54,211,HHU +江南大学,57,211,江南大 +江南大学,57,211,Jiangnan University +江南大学,57,211,Jiangnan +南京林业大学,,双一流,南林 +南京林业大学,,双一流,Nanjing Forestry University +南京林业大学,,双一流,NJFU +南京信息工程大学,91,双一流,南信大 +南京信息工程大学,91,双一流,Nanjing University of Information Science and Technology +南京信息工程大学,91,双一流,NUIST +南京农业大学,53,211,南农 +南京农业大学,53,211,南农大 +南京农业大学,53,211,南京农大 +南京农业大学,53,211,Agricultural University Of Nanjing +南京农业大学,53,211,NJAU +南京农业大学,53,211,NAU +南京中医药大学,,双一流,南中医 +南京中医药大学,,双一流,Nanjing University Of Chinese Medicine +南京中医药大学,,双一流,NJUCM +中国药科大学,119,211,中国药大 +中国药科大学,119,211,China Medicine University +中国药科大学,119,211,CPU +南京师范大学,56,211,南京师大 +南京师范大学,56,211,南师大 +南京师范大学,56,211,南师 +南京师范大学,56,211,Nanjing Normal University +南京师范大学,56,211,NNU +南昌大学,47,211,昌大 +南昌大学,47,211,University Of Nanchang、Nanchang University +南昌大学,47,211,NCU +辽宁大学,118,211,辽大 +辽宁大学,118,211,Liaoning University +辽宁大学,118,211,LNU +大连海事大学,111,211,大连海大 +大连海事大学,111,211,Maritime Affairs University Of Dalian +大连海事大学,111,211,DMU +内蒙古大学,116,211,内大 +内蒙古大学,116,211,University Of The Inner Mongol、Inner Mongolia University +内蒙古大学,116,211,IMU +宁夏大学,125,211,Ningxia University +宁夏大学,125,211,NXU +青海大学,129,211,清大 +青海大学,129,211,Qinghai University +青海大学,129,211,QHU +中国石油大学,77,双一流,中石大 +中国石油大学,77,双一流,China University Of Petroleum Beijing +中国石油大学,77,双一流,UPC +太原理工大学,84,211,太原理工 +太原理工大学,84,211,Institutes Of Technology Of Taiyuan、Taiyuan University of Technology +太原理工大学,84,211,TYUT +西北大学,59,211,西北大 +西北大学,59,211,Northwest University +西北大学,59,211,NWU +西安电子科技大学,50,211,西电 +西安电子科技大学,50,211,Xidian University +西安电子科技大学,50,211,XDU +长安大学,83,211,长大 +长安大学,83,211,Chang`an University +长安大学,83,211,CHU +陕西师范大学,67,211,陕西师大 +陕西师范大学,67,211,陕师大 +陕西师范大学,67,211,Shaanxi Normal University +陕西师范大学,67,211,SNNU +第四军医大学,,211,空军军医大学、四医大 +第四军医大学,,211,Air Force Medical University +第四军医大学,,211,FMMU +华东理工大学,45,211,华理 +华东理工大学,45,211,East China University Of Science +华东理工大学,45,211,ECUST +东华大学,74,211,东华 +东华大学,74,211,Donghua University +东华大学,74,211,DHU +上海海洋大学,,双一流,上海海大 +上海海洋大学,,双一流,Shanghai Ocean University +上海海洋大学,,双一流,SHOU +上海中医药大学,,211,上中医 +上海中医药大学,,211,Shanghai University of Traditional Chinese Medicine +上海中医药大学,,211,SHUTCM +上海外国语大学,123,211,上外 +上海外国语大学,123,211,Shanghai International Studies University +上海外国语大学,123,211,SISU +上海财经大学,95,211,上海财大 +上海财经大学,95,211,上财 +上海财经大学,95,211,Shanghai University Of Finance +上海财经大学,95,211,SUFE +上海体育学院,,双一流,Shanghai University Of Sport +上海音乐学院,,双一流,上音 +上海音乐学院,,双一流,Shanghai Conservatory Of Music +上海音乐学院,,双一流,SHCM +上海大学,43,211,上大 +上海大学,43,211,Shanghai University +第二军医大学,,211,海军军医大学 +第二军医大学,,211,Naval Medical University +西南交通大学,36,211,西南交大 +西南交通大学,36,211,Southwest Jiaotong University +西南交通大学,36,211,SWJTU +西南石油大学,,双一流,西南石大 +西南石油大学,,双一流,Southwest Petroleum University +西南石油大学,,双一流,SWPU +成都理工大学,,双一流,成都理工 +成都理工大学,,双一流,Chengdu University Of Technology +成都理工大学,,双一流,CDUT +四川农业大学,113,211,川农 +四川农业大学,113,211,川农大 +四川农业大学,113,211,Sichuan Agricultural University +四川农业大学,113,211,SICAU +成都中医药大学,,双一流,成中医 +成都中医药大学,,双一流,Chengdu University of TCM +成都中医药大学,,双一流,CDUTCM +西南财经大学,97,211,西南财大 +西南财经大学,97,211,西财 +西南财经大学,97,211,Southwestern University Of Finance And Economics +西南财经大学,97,211,SWUFE +天津工业大学,,双一流,天工大 +天津工业大学,,双一流,Tianjin University of Technology +天津工业大学,,双一流,TGU +天津医科大学,107,211,天津医大 +天津医科大学,107,211,Medical University Of Tianjin +天津医科大学,107,211,TMU +天津中医药大学,,双一流,天中 +天津中医药大学,,双一流,Tianjin University of Traditional Chinese Medicine +天津中医药大学,,双一流,TUTCM +华北电力大学,73,211,华电 +华北电力大学,73,211,North China Electric Power University +华北电力大学,73,211,NCEPU +河北工业大学,92,211,河工大 +河北工业大学,92,211,Hebei University of Technology +河北工业大学,92,211,HEBUT +西藏大学,135,211,藏大 +西藏大学,135,211,Tibet University +西藏大学,135,211,TU +石河子大学,117,211,石大 +石河子大学,117,211,Shihezi University +中国美术学院,,双一流,中国美院 +中国美术学院,,双一流,国美 +中国美术学院,,双一流,China Academy of Art +中国美术学院,,双一流,CAA +宁波大学,70,双一流,Ningbo University +宁波大学,70,双一流,NBU +西南大学,46,985,西南大 +西南大学,46,985,Southwest University +西南大学,46,985,SWU +安徽大学,81,211,安大 +安徽大学,81,211,University Of Anhui +安徽大学,81,211,AHU +合肥工业大学,51,211,合肥工大 +合肥工业大学,51,211,合工大 +合肥工业大学,51,211,HeFei University of Technology +合肥工业大学,51,211,HFUT +麻省理工学院,1,海外名校,Massachusetts Institute of Technology +麻省理工学院,1,海外名校,MIT +麻省理工学院,1,海外名校,麻省 +麻省理工学院,1,海外名校,马萨诸塞理工学院 +牛津大学,2,海外名校,University of Oxford +牛津大学,2,海外名校,Oxford +牛津大学,2,海外名校,Oxon +牛津大学,2,海外名校,牛津 +斯坦福大学,3,海外名校,Stanford University +斯坦福大学,3,海外名校,Leland Stanford Junior University +斯坦福大学,3,海外名校,斯坦福 +斯坦福大学,3,海外名校,Stanford +剑桥大学,3,海外名校,University of Cambridge +剑桥大学,3,海外名校,剑桥 +哈佛大学,5,海外名校,Harvard University +哈佛大学,5,海外名校,哈佛 +加州理工学院,6,海外名校,California Institute of Technology +加州理工学院,6,海外名校,Caltech +加州理工学院,6,海外名校,加州理工 +帝国理工学院,,海外名校,Imperial College London +帝国理工学院,,海外名校,帝国理工医学院 +帝国理工学院,,海外名校,Imperial College of Science +帝国理工学院,,海外名校,Technology and Medicine +帝国理工学院,,海外名校,帝国学院 +帝国理工学院,,海外名校,帝国理工 +帝国理工学院,,海外名校,Imperial +帝国理工学院,,海外名校,IC +苏黎世联邦理工学院,8,海外名校,苏黎世理工 +苏黎世联邦理工学院,8,海外名校,ETH +苏黎世联邦理工学院,8,海外名校,Swiss Federal Institute of Technology in Zurich +伦敦大学学院,8,海外名校,UCL +伦敦大学学院,8,海外名校,University College London +芝加哥大学,10,海外名校,University of Chicago +芝加哥大学,10,海外名校,芝大 +芝加哥大学,10,海外名校,UChicago +新加坡国立大学,11,海外名校,National University of Singapore +新加坡国立大学,11,海外名校,NUS +南洋理工大学,12,海外名校,Nanyang Technological University +南洋理工大学,12,海外名校,Singapore +南洋理工大学,12,海外名校,NTU +宾夕法尼亚大学,13,海外名校,University of Pennsylvania +宾夕法尼亚大学,13,海外名校,UPenn +宾夕法尼亚大学,13,海外名校,宾大 +洛桑联邦理工学院,14,海外名校,EPFL +洛桑联邦理工学院,14,海外名校,Swiss federal Institute of Technology in Lausanne +耶鲁大学,14,海外名校,Yale University +耶鲁大学,14,海外名校,耶鲁 +耶鲁大学,14,海外名校,Yale +爱丁堡大学,16,海外名校,The University of Edinburgh +爱丁堡大学,16,海外名校,Edin +爱丁堡大学,16,海外名校,爱大 +哥伦比亚大学,19,海外名校,Columbia University +哥伦比亚大学,19,海外名校,哥大 +哥伦比亚大学,19,海外名校,Columbia University in the City of New York +普林斯顿大学,20,海外名校,Princeton University +普林斯顿大学,20,海外名校,普林斯顿 +康奈尔大学,21,海外名校,Cornell University +康奈尔大学,21,海外名校,Cornell +康奈尔大学,21,海外名校,康奈尔 +香港大学,22,海外名校,The University of Hong Kong +香港大学,22,海外名校,HKU +香港大学,22,海外名校,港大 +东京大学,23,海外名校,The University of Tokyo +东京大学,23,海外名校,東京大学 +东京大学,23,海外名校,UTokyo +密歇根大学安娜堡分校,,海外名校,University of Michigan-Ann Arbor +密歇根大学安娜堡分校,,海外名校,UMich +约翰霍普金斯大学,25,海外名校,Johns Hopkins University +约翰霍普金斯大学,25,海外名校,JHU +约翰霍普金斯大学,25,海外名校,Hopkins +约翰霍普金斯大学,25,海外名校,霍普金斯大学 +多伦多大学,26,海外名校,University of Toronto +多伦多大学,26,海外名校,UofT +多伦多大学,26,海外名校,UToronto +麦吉尔大学,27,海外名校,McGill University +麦吉尔大学,27,海外名校,McGill +澳洲国立大学,,海外名校,The Australian National University +澳洲国立大学,,海外名校,ANU +曼彻斯特大学,27,海外名校,The University of Manchester +曼彻斯特大学,27,海外名校,UoM +京都大学,33,海外名校,Kyoto University +香港科技大学,34,海外名校,The Hong Kong University of Science and Technology +香港科技大学,34,海外名校,HKUST +伦敦大学国王学院,,海外名校,King's College London +伦敦大学国王学院,,海外名校,King's +伦敦大学国王学院,,海外名校,KCL +首尔国立大学,36,海外名校,Seoul National University +首尔国立大学,36,海外名校,SNU +墨尔本大学,37,海外名校,The University of Melbourne +墨尔本大学,37,海外名校,UniMelb +墨尔本大学,37,海外名校,Melb Uni +悉尼大学,38,海外名校,The University of Sydney +悉尼大学,38,海外名校,USYD +悉尼大学,38,海外名校,Sydney U +香港中文大学,39,海外名校,The Chinese University of Hong Kong +香港中文大学,39,海外名校,CUHK +韩国高等科技学院,,海外名校,Korea Advanced Institute of Science And Technology +韩国高等科技学院,,海外名校,KAIST +纽约大学,42,海外名校,New York University +纽约大学,42,海外名校,NYU +新南威尔士大学,43,海外名校,The University of New South Wales +新南威尔士大学,43,海外名校,UNSW +巴黎第九大学,,海外名校,University Paris Dauphine +英属哥伦比亚大学,,海外名校,University of British Columbia +英属哥伦比亚大学,,海外名校,UBC +昆士兰大学,47,海外名校,The University of Queensland +昆士兰大学,47,海外名校,UQ +巴黎理工学院,49,海外名校,Institut Polytechnique de Paris +巴黎理工学院,49,海外名校,IP-Paris +伦敦政治经济学院,49,海外名校,The London School of Economics and Political Science +伦敦政治经济学院,49,海外名校,LSE +伦敦政治经济学院,49,海外名校,London School of Economics +慕尼黑工业大学,,海外名校,Technical University of Munich +杜克大学,52,海外名校,Duke University +卡耐基梅隆大学,,海外名校,Carnegie Mellon University +卡耐基梅隆大学,,海外名校,TU München +卡耐基梅隆大学,,海外名校,TUM +香港城市大学,53,海外名校,City University of Hong Kong +香港城市大学,53,海外名校,CityU +阿姆斯特丹大学,55,海外名校,University of Amsterdam +阿姆斯特丹大学,55,海外名校,UvA +东京工业大学,56,海外名校,Tokyo Institute of Technology +东京工业大学,56,海外名校,Tokyo Tech +代尔夫特理工大学,57,海外名校,Delft University of Technology +莫纳什大学,,海外名校,Monash University +莫纳什大学,,海外名校,Monash +莫纳什大学,,海外名校,蒙纳士 +莫纳什大学,,海外名校,莫纳什 +莫纳什大学,,海外名校,莫大 +布朗大学,60,海外名校,Brown University +布朗大学,60,海外名校,Brown +布朗大学,60,海外名校,布朗 +华威大学,61,海外名校,The University of Warwick +华威大学,61,海外名校,华威 +华威大学,61,海外名校,Warwick +华威大学,61,海外名校,UoW +布里斯托大学,62,海外名校,University of Bristol +布里斯托大学,62,海外名校,布大 +海德堡大学,63,海外名校,Heidelberg University +海德堡大学,63,海外名校,海德堡大学 +慕尼黑大学,64,海外名校,LMU +慕尼黑大学,64,海外名校,University of Munich +马来亚大学,65,海外名校,University of Malaya +马来亚大学,65,海外名校,马大 +香港理工大学,66,海外名校,The Hong Kong Polytechnic University +香港理工大学,66,海外名校,PolyU +香港理工大学,66,海外名校,理大 +香港理工大学,66,海外名校,港理工 +德克萨斯大学奥斯汀分校,,海外名校,University of Texas at Austin +德克萨斯大学奥斯汀分校,,海外名校,得州大学 +德克萨斯大学奥斯汀分校,,海外名校,UT-Austin +国立台湾大学,68,海外名校,National Taiwan University +国立台湾大学,68,海外名校,台大 +布宜诺斯艾利斯大学,69,海外名校,Universidad de Buenos Aires +布宜诺斯艾利斯大学,69,海外名校,UBA +鲁汶大学（荷语）,,海外名校,University of Leuven +鲁汶大学（荷语）,,海外名校,KU Leuven +苏黎世大学,70,海外名校,University of Zurich +苏黎世大学,70,海外名校,UZH +索邦大学,72,海外名校,Sorbonne University +索邦大学,72,海外名校,索邦 +索邦大学,72,海外名校,Sorbonne +格拉斯哥大学,73,海外名校,University of Glasgow +格拉斯哥大学,73,海外名校,格大 +高丽大学,,海外名校,Korea University +高丽大学,,海外名校,高丽大 +大阪大学,75,海外名校,Osaka University +大阪大学,75,海外名校,阪大 +威斯康辛大学麦迪逊分校,,海外名校,University of Wisconsin-Madison +威斯康辛大学麦迪逊分校,,海外名校,UW-Madison +南安普敦大学,,海外名校,University of Southampton +南安普敦大学,,海外名校,Soton +莫斯科国立大学,,海外名校,Lomonosov Moscow State University +莫斯科国立大学,,海外名校,莫斯科大学 +哥本哈根大学,79,海外名校,University of Copenhagen +哥本哈根大学,79,海外名校,UCPH +延世大学,79,海外名校,Yonsei University +延世大学,79,海外名校,연세대학교 +浦项科技大学,81,海外名校,Pohang University of Science And Technology +浦项科技大学,81,海外名校,POSTECH +杜伦大学,,海外名校,Durham University +杜伦大学,,海外名校,Durham +伊利诺伊大学厄巴纳-香槟分校,,海外名校,University of Illinois at Urbana-Champaign +伊利诺伊大学厄巴纳-香槟分校,,海外名校,UIUC +奥克兰大学,85,海外名校,The University of Auckland +奥克兰大学,85,海外名校,UoA +华盛顿大学,85,海外名校,University of Washington +华盛顿大学,85,海外名校,UWashington +华盛顿大学,85,海外名校,UW +巴黎萨克雷大学,86,海外名校,Université Paris-Saclay +巴黎萨克雷大学,86,海外名校,UPSaclay +巴黎萨克雷大学,86,海外名校,UPS +隆德大学,87,海外名校,Lund University +佐治亚理工学院,88,海外名校,Georgia Institute of Technology +佐治亚理工学院,88,海外名校,Georgia Tech +佐治亚理工学院,88,海外名校,Gatech +佐治亚理工学院,88,海外名校,GT +瑞典皇家理工学院,89,海外名校,KTH Royal Institute of Technology +瑞典皇家理工学院,89,海外名校,皇家理工学院 +瑞典皇家理工学院,89,海外名校,KTH +伯明翰大学,90,海外名校,University of Birmingham +伯明翰大学,90,海外名校,伯大 +伯明翰大学,90,海外名校,UoB +圣安德鲁斯大学,91,海外名校,University of St Andrews +圣安德鲁斯大学,91,海外名校,St Andrews +圣安德鲁斯大学,91,海外名校,St And +圣安德鲁斯大学,91,海外名校,圣安 +利兹大学,92,海外名校,University of Leeds +西澳大学,93,海外名校,The University of Western Australia +西澳大学,93,海外名校,UWA +莱斯大学,94,海外名校,Rice University +莱斯大学,94,海外名校,Rice +谢菲尔德大学,95,海外名校,The University of Sheffield +谢菲尔德大学,95,海外名校,谢菲 +谢菲尔德大学,95,海外名校,谢大 +谢菲尔德大学,95,海外名校,TUoS +宾州州立大学公园分校,,海外名校,Pennsylvania State University +宾州州立大学公园分校,,海外名校,University Park +成均馆大学,97,海外名校,Sungkyunkwan University(SKKU) +成均馆大学,97,海外名校,成均馆大 +成均馆大学,97,海外名校,SKKU +丹麦技术大学,99,海外名校,Technical University of Denmark +丹麦技术大学,99,海外名校,DTU +北卡罗来纳大学教堂山分校,100,海外名校,University of North Carolina at Chapel Hill +北卡罗来纳大学教堂山分校,100,海外名校,UNC +都柏林三一学院,,海外名校,Trinity College Dublin +都柏林三一学院,,海外名校,The University of Dublin +都柏林三一学院,,海外名校,TCD +奥斯陆大学,102,海外名校,University of Oslo +奥斯陆大学,102,海外名校,UiO +奥斯陆大学,102,海外名校,奥大 +诺丁汉大学,103,海外名校,University of Nottingham +诺丁汉大学,103,海外名校,UoN +诺丁汉大学,103,海外名校,诺大 +赫尔辛基大学,104,海外名校,University of Helsinki +赫尔辛基大学,104,海外名校,UH +墨西哥国立自治大学,105,海外名校,Universidad Nacional Autónoma de México +墨西哥国立自治大学,105,海外名校,墨国大 +墨西哥国立自治大学,105,海外名校,UNAM +日内瓦大学,105,海外名校,University of Geneva +日内瓦大学,105,海外名校,UNIGE +圣路易斯华盛顿大学,107,海外名校,Washington University in St. Louis +圣路易斯华盛顿大学,107,海外名校,WashU +圣路易斯华盛顿大学,107,海外名校,WUSTL +阿德雷德大学,,海外名校,The University of Adelaide +阿德雷德大学,,海外名校,阿大 +阿卜杜勒阿齐兹国王大学,109,海外名校,King Abdulaziz University (KAU) +乌得勒支大学,,海外名校,Utrecht University +乌得勒支大学,,海外名校,UU +蒙特利尔大学,111,海外名校,Université de Montréal +蒙特利尔大学,111,海外名校,UdeM +蒙特利尔大学,111,海外名校,蒙大 +阿尔托大学,112,海外名校,Aalto University +阿尔托大学,112,海外名校,Aalto +波士顿大学,112,海外名校,Boston University +波士顿大学,112,海外名校,BU +莱顿大学,112,海外名校,Leiden University +南加州大学,11,海外名校,University of Southern California +南加州大学,11,海外名校,南加州大学 +南加州大学,11,海外名校,南加大 +南加州大学,11,海外名校,USC +普渡大学西拉法叶分校,116,海外名校,Purdue University +普渡大学西拉法叶分校,116,海外名校,普渡大学 +伦敦大学玛丽女王学院,,海外名校,Queen Mary University of London +伦敦大学玛丽女王学院,,海外名校,QMUL +伦敦大学玛丽女王学院,,海外名校,Queen Mary +名古屋大学,118,海外名校,Nagoya University +名古屋大学,118,海外名校,名大 +伯尔尼大学,119,海外名校,University of Bern +伯尔尼大学,119,海外名校,UniBe +俄亥俄州立大学,120,海外名校,The Ohio State University +俄亥俄州立大学,120,海外名校,Ohio State +俄亥俄州立大学,120,海外名校,OSU +查尔姆斯理工大学,121,海外名校,Chalmers University of Technology +查尔姆斯理工大学,121,海外名校,Chalmers +圣保罗大学,121,海外名校,Universidade de São Paulo +圣保罗大学,121,海外名校,USP +圣保罗大学,121,海外名校,圣大 +瓦格宁根大学,,海外名校,Wageningen University & Research +瓦格宁根大学,,海外名校,Wageningen UR +乌普萨拉大学,124,海外名校,Uppsala University +乌普萨拉大学,124,海外名校,乌大 +埃因霍温理工大学,,海外名校,Eindhoven University of Technology +埃因霍温理工大学,,海外名校,TU/e +埃因霍温理工大学,,海外名校,TU Eindhoven +柏林自由大学,127,海外名校,Freie Universitaet Berlin +柏林自由大学,127,海外名校,FU Berlin +柏林洪堡大学,128,海外名校,Humboldt-Universität zu Berlin +柏林洪堡大学,128,海外名校,洪堡大学 +柏林洪堡大学,128,海外名校,柏林大学 +格罗宁根大学,128,海外名校,University of Groningen +格罗宁根大学,128,海外名校,RuG +里昂高等师范学院,,海外名校,École Normale Supérieure de Lyon +里昂高等师范学院,,海外名校,ENS Lyon +兰卡斯特大学,,海外名校,Lancaster University +兰卡斯特大学,,海外名校,兰卡 +悉尼科技大学,133,海外名校,University of Technology Sydney +悉尼科技大学,133,海外名校,UTS +悉尼科技大学,133,海外名校,悉尼科大 +纽卡斯尔大学,134,海外名校,Newcastle University +纽卡斯尔大学,134,海外名校,NCL +纽卡斯尔大学,134,海外名校,纽大 +智利天主大学,,海外名校,Pontificia Universidad Católica de Chile (UC) +卡尔斯鲁厄理工学院,136,海外名校,KIT +卡尔斯鲁厄理工学院,136,海外名校,Karlsruhe Institute of Technology +九州大学,137,海外名校,Kyushu University +九州大学,137,海外名校,九大 +巴塞尔大学,138,海外名校,University of Basel +巴塞尔大学,138,海外名校,Unibasel +麦克马斯特大学,140,海外名校,McMaster University +麦克马斯特大学,140,海外名校,麦马 +根特大学,141,海外名校,Ghent University +根特大学,141,海外名校,UGent +米兰理工大学,142,海外名校,Politecnico di Milano +米兰理工大学,142,海外名校,POLIMI +米兰理工大学,142,海外名校,米理 +米兰理工大学,142,海外名校,米兰理工 +马来西亚博特拉大学,143,海外名校,Universiti Putra Malaysia (UPM) +马来西亚博特拉大学,143,海外名校,UPM +马来西亚国民大学,144,海外名校,Universiti Kebangsaan Malaysia (UKM) +马来西亚国民大学,144,海外名校,UKM +北海道大学,145,海外名校,Hokkaido University +北海道大学,145,海外名校,北大（ほくだい） +马来西亚理科大学,147,海外名校,Universiti Sains Malaysia (USM) +马来西亚理科大学,147,海外名校,USM +马来西亚理科大学,147,海外名校,槟城理大 +斯德哥尔摩大学,148,海外名校,Stockholm University +斯德哥尔摩大学,148,海外名校,SU +埃克塞特大学,149,海外名校,The University of Exeter +埃克塞特大学,149,海外名校,Exon +滑铁卢大学,149,海外名校,University of Waterloo +滑铁卢大学,149,海外名校,Waterloo（UW） +卡迪夫大学,151,海外名校,Cardiff University +卡迪夫大学,151,海外名校,卡大 +维也纳大学,151,海外名校,University of Vienna +维也纳大学,151,海外名校,univie +约克大学（英国）,,海外名校,University of York +约克大学（英国）,,海外名校,York +罗切斯特大学,,海外名校,University of Rochester +罗切斯特大学,,海外名校,U of R +奥胡斯大学,155,海外名校,Aarhus University +奥胡斯大学,155,海外名校,AU +汉阳大学,156,海外名校,Hanyang University +汉阳大学,156,海外名校,汉阳大 +密歇根州立大学,157,海外名校,Michigan State University +密歇根州立大学,157,海外名校,MSU +马里兰大学学院公园分校,,海外名校,University of Maryland +马里兰大学学院公园分校,,海外名校,College Park +马里兰大学学院公园分校,,海外名校,UMD +马里兰大学学院公园分校,,海外名校,UMCP +柏林工业大学,159,海外名校,Technische Universität Berlin (TU Berlin) +柏林工业大学,159,海外名校,TUB +柏林工业大学,159,海外名校,TU Berlin +埃默里大学,160,海外名校,Emory University +埃默里大学,160,海外名校,Emory +凯斯西储大学,161,海外名校,Case Western Reserve University +凯斯西储大学,161,海外名校,CWRU +凯斯西储大学,161,海外名校,CASE +蒙特雷理工学院,,海外名校,Tecnológico de Monterrey +法赫德国王石油与矿产大学,,海外名校,King Fahd University of Petroleum & Minerals +法赫德国王石油与矿产大学,,海外名校,KFUPM +匹兹堡大学,163,海外名校,University of Pittsburgh +匹兹堡大学,163,海外名校,PITT +匹兹堡大学,163,海外名校,匹大 +亚琛工业大学,165,海外名校,RWTH Aachen University +亚琛工业大学,165,海外名校,RWTH Aachen +亚琛工业大学,165,海外名校,RWTH +博洛尼亚大学,166,海外名校,Alma Mater Studiorum - University of Bologna +博洛尼亚大学,166,海外名校,Unibo +博洛尼亚大学,166,海外名校,博大 +巴斯大学,166,海外名校,University of Bath +德州农工大学,168,海外名校,Texas A&M University +德州农工大学,168,海外名校,TAMU +巴塞罗那大学,168,海外名校,Universitat de Barcelona +西安大略大学,,海外名校,Western University +西安大略大学,,海外名校,韦仕敦大学 +西安大略大学,,海外名校,UWO +罗马第一大学,,海外名校,Sapienza University of Rome +罗马第一大学,,海外名校,罗马一大 +弗莱堡大学,,海外名校,Albert-Ludwigs-Universitaet Freiburg +都柏林大学学院,,海外名校,University College Dublin +都柏林大学学院,,海外名校,UCD +都柏林大学学院,,海外名校,UC Dublin +佛罗里达大学,173,海外名校,University of Florida +佛罗里达大学,173,海外名校,UF +佛罗里达大学,173,海外名校,UFL +国立哈萨克大学,,海外名校,Al-Farabi Kazakh National University +洛桑大学,176,海外名校,University of Lausanne +蒂宾根大学,177,海外名校,Eberhard Karls Universität Tübingen +印度理工学院孟买分校,,海外名校,Indian Institute of Technology Bombay +印度理工学院孟买分校,,海外名校,IITB +伊拉斯姆斯大学,,海外名校,Erasmus University Rotterdam +国立清华大学,180,海外名校,National Tsing Hua University +维也纳技术大学,,海外名校,Technische Universität Wien +哥德堡大学,180,海外名校,University of Gothenburg +哈里发大学,,海外名校,Khalifa University +智利大学,183,海外名校,Universidad de Chile +印度理工学院德里分校,,海外名校,Indian Institute of Technology Delhi +印度理工学院德里分校,,海外名校,IITD +印度科学学院,,海外名校,Indian Institute of Science +明尼苏达大学双城分校,186,海外名校,University of Minnesota Twin Cities +鲁汶大学（法语）,,海外名校,Université catholique de Louvain +鲁汶大学（法语）,,海外名校,UCLouvain +利物浦大学,189,海外名校,University of Liverpool +特文特大学,,海外名校,University of Twente +达特茅斯学院,191,海外名校,Dartmouth College +马来西亚理工大学,,海外名校,Universiti Teknologi Malaysia +卧龙岗大学,193,海外名校,University of Wollongong +科廷大学,194,海外名校,Curtin University +德累斯顿工业大学,194,海外名校,Technische Universität Dresden +奥塔戈大学,,海外名校,University of Otago +纽卡斯尔大学（澳洲）,,海外名校,The University of Newcastle +纽卡斯尔大学（澳洲）,,海外名校,Australia +纽卡斯尔大学（澳洲）,,海外名校,UON +耶路撒冷希伯来大学,198,海外名校,The Hebrew University of Jerusalem +卑尔根大学,199,海外名校,University of Bergen +麦考瑞大学,200,海外名校,Macquarie University +加州大学伯克利分校,32,海外名校,"University of California, Berkeley" +加州大学伯克利分校,32,海外名校,UC Berkeley +加州大学伯克利分校,32,海外名校,Cal +加州大学圣地亚哥分校,48,海外名校,"University of California, San Diego" +加州大学圣地亚哥分校,48,海外名校,UCSD +加州大学圣地亚哥分校,48,海外名校,UC San Diego +加州大学戴维斯分校,138,海外名校,"University of California, Davis" +加州大学戴维斯分校,138,海外名校,UC Davis +加州大学圣塔芭芭拉分校,146,海外名校,"University of California, Santa Barbara" +加州大学圣塔芭芭拉分校,146,海外名校,UCSB +加州大学圣塔芭芭拉分校,146,海外名校,UC Santa Barbara +上海体育学院,,双一流,SUS +密歇根大学-安娜堡,23,海外名校,University of Michigan - Ann Arbor +伦敦国王学院,35,海外名校,King’s College London +加州大学洛杉矶分校,40,海外名校,"University of California, Los Angeles" +韩国科学技术院,41,海外名校,KAIST - Korea Advanced Institute of Science & Technology +巴黎文理研究大学,44,海外名校,PSL University +德克萨斯州大学奥斯汀分校,67,海外名校,The University of Texas at Austin +威斯康星大学-麦迪逊,75,海外名校,University of Wisconsin - Madison +宾夕法尼亚州立大学,96,海外名校,Penn State (Main campus) +里昂高等师范学校,130,海外名校,Ecole Normale Superieure - Lyon +智利天主教大学,135,海外名校,Pontifical Catholic University of Chile +约克大学,494,海外名校,York University +马里兰大学帕克分校,158,海外名校,"University of Maryland, College Park" +蒙特雷技术学院,161,海外名校,Tecnologico de Monterrey +天主教鲁汶大学（法语区）,188,海外名校,Université Catholique de Louvain +澳大利亚纽卡斯尔大学,197,海外名校,"The University of Newcastle, Australia" +庆应义塾大学,201,海外名校,Keio University +雷丁大学,202,海外名校,University of Reading +早稻田大学,203,海外名校,Waseda University +哥廷根大学,204,海外名校,University of Göttingen +阿伯丁大学,205,海外名校,University of Aberdeen +墨尔本皇家理工大学,206,海外名校,RMIT University +马德里自治大学,207,海外名校,Autonomous University of Madrid +布鲁塞尔自由大学,207,海外名校,Universite libre de Bruxelles +阿姆斯特丹自由大学,209,海外名校,Vrije Universiteit Amsterdam +巴塞罗那自治大学,209,海外名校,Autonomous University of Barcelona +蔚山国立科学技术研究所,212,海外名校,Ulsan National Institute of Science & Technology +昆士兰科技大学,213,海外名校,Queensland University of Technology +汉堡大学,214,海外名校,Universität Hamburg +朱拉隆功大学,215,海外名校,Chulalongkorn University +亚利桑那州立大学,216,海外名校,Arizona State University +贝尔法斯特女王大学,216,海外名校,Queen’s University Belfast +范德堡大学,218,海外名校,Vanderbilt University +坎皮纳斯州立大学,219,海外名校,State University of Campinas +拉德堡德大学,220,海外名校,Radboud University +布鲁塞尔大学,221,海外名校,Vrije Universiteit Brussel +圣母大学,701,海外名校,Notre Dame University-Louaize NDU +马德里康普顿斯大学,223,海外名校,Complutense University of Madrid +卡塔尔大学,224,海外名校,Qatar University +波恩大学,226,海外名校,Rheinische Friedrich-Wilhelms-Universität Bonn +开普敦大学,226,海外名校,University of Cape Town +萨塞克斯大学,226,海外名校,University of Sussex +弗吉尼亚大学,226,海外名校,University of Virginia +渥太华大学,230,海外名校,University of Ottawa +拉夫堡大学,231,海外名校,Loughborough University +加州大学欧文分校,232,海外名校,"University of California, Irvine" +安特卫普大学,233,海外名校,University of Antwerp +马斯特里赫特大学,233,海外名校,Maastricht University +卡尔加里大学,235,海外名校,University of Calgary +安德斯大学,236,海外名校,Universidad de los Andes +莱斯特大学,236,海外名校,University of Leicester +维多利亚大学,601,海外名校,Victoria University +金斯敦女王大学,240,海外名校,Queen's University at Kingston +瑞士提契诺大学,240,海外名校,USI - Università della Svizzera italiana +贝鲁特美国大学,242,海外名校,American University of Beirut +帕多瓦大学,242,海外名校,Università di Padova +圣彼得堡国立大学,242,海外名校,Saint Petersburg State University +巴黎高科桥梁学院,245,海外名校,Ecole des Ponts ParisTech +新西伯利亚州立大学,246,海外名校,Novosibirsk State University +马萨诸塞大学安姆斯特分校,246,海外名校,University of Massachusetts +乔治敦大学,248,海外名校,Georgetown University +庞培法布拉大学,248,海外名校,Pompeu Fabra University +文莱达鲁萨兰国大学,250,海外名校,Universiti Brunei Darussalam (UBD) +科罗拉多大学-玻尔得,251,海外名校,University of Colorado at Boulder +国立成功大学,252,海外名校,National Cheng Kung University +叶史瓦大学,252,海外名校,Yeshiva University +加查马达大学,254,海外名校,Gadjah Mada University +印度马德拉斯技术学院,255,海外名校,Indian Institute of Technology Madras +玛希隆大学,255,海外名校,Mahidol University +特拉维夫大学,255,海外名校,Tel Aviv University +哥伦比亚国立大学,258,海外名校,National University of Colombia +爱尔兰国立大学戈尔韦分校,258,海外名校,National University of Ireland Galway +坎特伯雷大学,258,海外名校,University of Canterbury +科学宝,261,海外名校,Sciences Po +巴黎大学,261,海外名校,Université de Paris +庆熙大学,264,海外名校,Kyung Hee University +罗格斯大学,264,海外名校,Rutgers University–New Brunswick +纳瓦拉大学,266,海外名校,University of Navarra +布拉格查理大学,266,海外名校,Charles University +国立阳明大学,268,海外名校,National Yang Ming Chiao Tung University +亚利桑那大学,268,海外名校,The University of Arizona +达姆施塔特工业大学,269,海外名校,Technical University Darmstadt +赫瑞瓦特大学,270,海外名校,Heriot Watt University +达尔豪斯大学,272,海外名校,Dalhousie University +托木斯克州立大学,272,海外名校,Tomsk State University +萨里大学,272,海外名校,University of Surrey +南方科技大学,275,海外名校,Southern University of Science & Technology +塔夫茨大学,275,海外名校,Tufts University +格拉茨科技大学,277,海外名校,Graz University of Technology +印度理工学院坎普尔,277,海外名校,Indian Institute of Technology Kanpur +沙特国王大学,277,海外名校,King Saud University +印度理工学院卡拉格普尔分校,280,海外名校,Indian Institute of Technology Kharagpur +鲍曼莫斯科国立技术大学,281,海外名校,Bauman Moscow State Technical University +因斯布鲁克大学,281,海外名校,University of Innsbruck +迪肯大学,283,海外名校,Deakin University +梅西大学,284,海外名校,Massey University +伊利诺伊大学芝加哥分校,285,海外名校,University of Illinois at Chicago +筑波大学,285,海外名校,University of Tsukuba +香港浸会大学,287,海外名校,Hong Kong Baptist University +阿联酋大学,288,海外名校,United Arab Emirates University +印度尼西亚大学,290,海外名校,University of Indonesia +格里菲斯大学,290,海外名校,Griffith University +莫斯科物理科学与技术学院,290,海外名校,Moscow Institute of Physics and Technology +巴黎第一大学,290,海外名校,Panthéon-Sorbonne University – Paris 1 +白俄罗斯国立大学,295,海外名校,Belarusian State University +波尔图大学,295,海外名校,University of Porto +图尔库大学,295,海外名校,University of Turku +科克大学,298,海外名校,University College Cork +西蒙弗雷泽大学,298,海外名校,Simon Fraser University +北卡罗来纳州立大学,300,海外名校,North Carolina State University +塔尔图大学,300,海外名校,University of Tartu +斯特拉斯克莱德大学,302,海外名校,University of Strathclyde +塔斯马尼亚大学,303,海外名校,University of Tasmania +万隆理工学院（ITB）,303,海外名校,Bandung Institute of Technology (ITB) +光州科学技术学院,305,海外名校,Gwangju Institute of Science and Technology +俄罗斯国立高等经济大学,305,海外名校,HSE University +东英吉利大学,307,海外名校,University of East Anglia +华沙大学,308,海外名校,University of Warsaw +南丹麦大学,309,海外名校,University of Southern Denmark +雅盖隆大学,309,海外名校,Jagiellonian University +印第安纳大学伯明顿分校,311,海外名校,Indiana University +科隆大学,311,海外名校,University of Cologne +迈阿密大学,311,海外名校,University of Miami +格勒诺布尔阿尔卑斯大学,314,海外名校,Grenoble Alpes University +国立台湾科技大学,314,海外名校,National Taiwan University of Science and Technology +米兰大学,316,海外名校,University of Milan +俄罗斯人民友谊大学,317,海外名校,RUDN University +IE大学,317,海外名校,IE University +国立核研究大学-莫斯科工程物理学院,319,海外名校,National Research Nuclear University MEPhI +加泰罗尼亚理工大学,319,海外名校,Polytechnic University of Catalonia +斯威本科技大学,321,海外名校,Swinburne University of Technology +阿根廷天主教大学,322,海外名校,Pontificia Universidad Católica Argentina +邓迪大学,322,海外名校,University of Dundee +澳门大学,322,海外名校,University of Macau +埃尔朗根-纽伦堡大学,322,海外名校,Friedrich-Alexander-Universität Erlangen-Nürnberg +奥尔堡大学,326,海外名校,Aalborg University +南澳大利亚大学,326,海外名校,University of South Australia +国立欧亚大学,328,海外名校,L.N. Gumilyov Eurasian National University +林雪平大学,329,海外名校,Linkoping University +以色列理工学院,330,海外名校,Technion - Israel Institute of Technology +伦敦大学城市学院,330,海外名校,City University London +伦敦大学伯贝克学院,332,海外名校,Birkbeck University London +圣光机大学,365,海外名校,ITMO University +国立台湾师范大学,334,海外名校,National Taiwan Normal University +都灵理工大学,334,海外名校,Politecnico di Torino +伦敦大学皇家霍洛威学院,334,海外名校,Royal Holloway University of London +法兰克福大学,340,海外名校,Goethe University Frankfurt +夏威夷大学马诺阿分校,340,海外名校,University of Hawai’i at Mānoa +广岛大学,343,海外名校,Hiroshima University +耶拿大学,344,海外名校,Universität Jena +文莱大学,344,海外名校,Universiti Teknologi Brunei +弗吉尼亚理工,346,海外名校,Virginia Polytechnic Institute and State University +斯图加特大学,347,海外名校,Universität Stuttgart +加州大学圣克鲁兹分校,347,海外名校,"University of California, Santa Cruz" +喀山联邦大学,347,海外名校,Kazan Federal University +斯特雅大学,347,海外名校,UCSI University +马德里卡洛斯三世大学,351,海外名校,Universidad Carlos III de Madrid +乌拉尔联邦大学,351,海外名校,Ural Federal University +伦敦布鲁内尔大学,351,海外名校,Brunel University London +约翰内斯开普勒大学林茨,354,海外名校,Johannes Kepler University Linz +乔治华盛顿大学,355,海外名校,George Washington University +里斯本大学,356,海外名校,University of Lisbon +蒂尔堡大学,356,海外名校,Tilburg University +国立科技大学-巴基斯坦,358,海外名校,National University of Sciences & Technology - Pakistan +于韦斯屈莱大学,358,海外名校,University of Jyvaskyla +犹他大学,358,海外名校,University of Utah +拉筹伯大学,362,海外名校,La Trobe University +莫斯科国立国际关系学院,362,海外名校,MGIMO University +梨花女子大学,362,海外名校,Ewha Womans University +乌尔姆大学,365,海外名校,Ulm University +于默奥大学,365,海外名校,Umea University +苏丹卡布斯大学,368,海外名校,Sultan Qaboos University +挪威科技大学,369,海外名校,Norwegian University of Science and Technology +里约热内卢联邦大学,369,海外名校,Federal University of Rio de Janeiro +瓦伦西亚理工大学,371,海外名校,Polytechnic University of Valencia +林肯大学,801,海外名校,University of Lincoln +布拉格化工大学,373,海外名校,"University of Chemistry & Technology, Prague" +康涅狄格大学,373,海外名校,University of Connecticut +怀卡托大学,373,海外名校,University of Waikato +奥卢大学,377,海外名校,University of Oulu +维尔茨堡大学,378,海外名校,Julius-Maximilians-Universität Würzburg +真纳大学,378,海外名校,Quaid I Azam University +纽约州立大学石溪分校,378,海外名校,Stony Brook University +谢里夫科技大学,381,海外名校,Sharif University of Technology +东京医科齿科大学,381,海外名校,Tokyo Medical and Dental University +美国沙迦大学,383,海外名校,American University of Sharjah +肯特大学,383,海外名校,University of Kent +神户大学,386,海外名校,Kobe University +堪萨斯大学,387,海外名校,University of Kansas +纽约州立大学布法罗分校,388,海外名校,University at Buffalo +比萨大学,388,海外名校,University of Pisa +圣拉斐尔生命健康大学,390,海外名校,Università Vita-Salute San Raffaele +伦敦大学亚非学院,391,海外名校,SOAS University of London +巴勒莫大学,801,海外名校,University of Palermo +彼得大帝圣彼得堡理工大学,393,海外名校,Peter the Great St Petersburg Polytechnic University +波鸿大学,393,海外名校,University of Bochum +国立研究托木斯克理工大学,395,海外名校,National Research Tomsk Polytechnic University +秘鲁天主教大学,395,海外名校,Pontifical Catholic University of Peru +印度理工学院古瓦哈提,395,海外名校,Indian Institute of Technology Guwahati +巴基斯坦工程与应用科学研究所,398,海外名校,Pakistan Institute of Engineering and Applied Sciences (PIEAS) +菲律宾大学,399,海外名校,University of the Philippines +南方大学,400,海外名校,Universidad Austral +印度理工学院鲁尔基校区,400,海外名校,Indian Institute of Technology Roorkee +维尔纽斯大学,400,海外名校,Vilnius University +加州大学河滨分校,403,海外名校,"University of California, Riverside" +布拉格捷克技术大学,403,海外名校,Czech Technical University in Prague +弗林德斯大学,407,海外名校,Flinders University +台北医科大学,407,海外名校,Taipei Medical University +明斯特大学,411,海外名校,University of Muenster +国立中山大学,412,海外名校,National Sun Yat-Sen University +哈韦里亚纳大学,412,海外名校,Pontificia Universidad Javeriana +拉彭兰塔理工大学,414,海外名校,Lappeenranta University of Technology +拉瓦尔大学,414,海外名校,Université Laval +国油大学,414,海外名校,Universiti Teknologi Petronas +科罗拉多大学-丹佛,414,海外名校,University of Colorado at Denver +坦佩雷大学,414,海外名校,University of Tampere +中央大学,414,海外名校,Chung-Ang University +斯特拉斯堡大学,421,海外名校,Université de Strasbourg +雅典国立技术大学,421,海外名校,National Technical University of Athens +曼海姆大学,423,海外名校,Universität Mannheim +那不勒斯菲里德里克第二大学,424,海外名校,University of Naples - Federico II +威特沃特斯兰德大学,424,海外名校,University of the Witwatersrand +詹姆斯库克大学,424,海外名校,James Cook University +约翰内斯古腾堡美因茨大学,427,海外名校,Johannes Gutenberg University of Mainz +华盛顿州立大学,427,海外名校,Washington State University +维克森林大学,429,海外名校,Wake Forest University +牛津布鲁克斯大学,429,海外名校,Oxford Brookes University +新里斯本大学,431,海外名校,NOVA University Lisbon +伦斯勒理工学院,431,海外名校,Rensselaer Polytechnic Institute +科罗拉多州立大学,431,海外名校,Colorado State University +圣保罗联邦大学,434,海外名校,Federal University of Sao Paulo +约翰内斯堡大学,434,海外名校,University of Johannesburg +堪培拉大学,436,海外名校,University of Canberra +杜兰大学,436,海外名校,Tulane University +埃塞克斯大学,439,海外名校,University of Essex +塞浦路斯大学,440,海外名校,University of Cyprus +特伦托大学,440,海外名校,University of Trento +特罗姆瑟大学挪威北极大学,440,海外名校,University of Tromsø The Arctic University of Norway +斯旺西大学,440,海外名校,Swansea University +伊利诺伊理工学院,444,海外名校,Illinois Institute of Technology +韩国外国语大学,445,海外名校,HUFS - Hankuk (Korea) University of Foreign Studies +开罗美国大学,445,海外名校,The American University in Cairo +萨尔大学,447,海外名校,Saarland University +乌姆古拉大学,447,海外名校,Umm Al-Qura University +莱比锡大学,447,海外名校,Universität Leipzig +米兰 - 比可卡大学,450,海外名校,University of Milan-Bicocca +佛罗伦萨大学,451,海外名校,University of Florence +奥克兰理工大学,451,海外名校,Auckland University of Technology +邦德大学,451,海外名校,Bond University +科英布拉大学,455,海外名校,University of Coimbra +爱荷华大学,455,海外名校,University of Iowa +布兰迪斯大学,455,海外名校,Brandeis University +萨斯喀彻温大学,458,海外名校,University of Saskatchewan +马德里理工大学,459,海外名校,Universidad Politécnica de Madrid +圣加仑大学,459,海外名校,University of St Gallen +马丁·路德·哈勒-威登堡大学,461,海外名校,Martin Luther University of Halle-Wittenberg +科罗拉多矿业大学,461,海外名校,Colorado School of Mines +远东联邦大学,461,海外名校,Far Eastern Federal University +伦敦大学金史密斯学院,461,海外名校,Goldsmiths University London +爱兰加大学,465,海外名校,Airlangga University +阿米尔卡比尔理工大学,465,海外名校,Amirkabir University of Technology +贝尔格拉诺大学,465,海外名校,Universidad de Belgrano +国立台北科技大学,469,海外名校,National Taipei University of Technology +乌拉圭蒙得维的亚大学,469,海外名校,Universidad de Montevideo (UM) +乌拉圭大学,471,海外名校,Universidad ORT Uruguay +斯特灵大学,471,海外名校,University of Stirling +内盖夫本古里安大学,471,海外名校,Ben-Gurion University of the Negev +哈瑟尔特大学,471,海外名校,Hasselt University +佛罗里达州立大学,475,海外名校,Florida State University +密苏里大学,476,海外名校,Mizzou - University of Missouri +德克萨斯大学-达拉斯,477,海外名校,The University of Texas at Dallas +巴伊兰大学,477,海外名校,Bar-Ilan University +千叶大学,477,海外名校,Chiba University +长庚大学,480,海外名校,Chang Gung University +列日大学,480,海外名校,University of Liege +南哈萨克斯坦大学,482,海外名校,Auezov South Kazakhstan University (SKU) +斯泰伦博斯大学,482,海外名校,Stellenbosch University +韩国天主教大学,482,海外名校,The Catholic University of Korea +都灵大学,485,海外名校,University of Turin +阿斯顿大学,485,海外名校,Aston University +俄罗斯国立科技大学,487,海外名校,The National University of Science and Technology MISIS +横滨市立大学,487,海外名校,Yokohama City University +智利圣地亚哥大学,487,海外名校,Universidad de Santiago de Chile +都柏林城市大学,490,海外名校,Dublin City University +贾森·利比希大学,490,海外名校,Justus Liebig University Giessen +圣保罗州立大学,492,海外名校,UNESP +格拉纳达大学,492,海外名校,University of Granada +罗马第二大学,494,海外名校,University of Roma - Tor Vergata +西悉尼大学,494,海外名校,University of Western Sydney +波士顿学院,494,海外名校,Boston College +基尔大学,751,海外名校,Keele University +东国大学,494,海外名校,Dongguk University +爱荷华州立大学,494,海外名校,Iowa State University +西江大学,494,海外名校,Sogang University +华沙工业大学,501,海外名校,Warsaw University of Technology +艾克斯 - 马赛大学,501,海外名校,Aix-Marseille University +哈瓦那大学,501,海外名校,Universidad de La Habana +康斯坦茨大学,501,海外名校,Universität Konstanz +波尔多大学,501,海外名校,Universite de Bordeaux +德里大学,501,海外名校,University of Delhi +克拉根福大学,501,海外名校,University of Klagenfurt +利默里克大学,501,海外名校,University of Limerick +萨特巴耶夫大学,501,海外名校,Satbayev University +密苏里科技大学,511,海外名校,Missouri University of Science & Technology +新加坡管理大学,511,海外名校,Singapore Management University +茂物农业大学,511,海外名校,IPB University +科奇大学,511,海外名校,Koç University +哈尔科夫大学,511,海外名校,V. N. Karazin Kharkiv National University +韦恩州立大学,511,海外名校,Wayne State University +阿尔卡拉大学,511,海外名校,Universidad de Alcala +马来西亚北方大学,511,海外名校,Universiti Utara Malaysia +巴拉曼德大学,511,海外名校,University of Balamand +中央大学“阿伯勒玛塔”德拉斯别墅,521,海外名校,"Universidad Central ""Marta Abreu"" de Las Villas" +拜罗伊特大学,521,海外名校,University of Bayreuth +东芬兰大学,521,海外名校,University of Eastern Finland +德黑兰大学,521,海外名校,University of Tehran +康考迪亚大学,521,海外名校,Concordia University +阿卜杜勒拉曼费萨尔大学,521,海外名校,Imam Abdulrahman Bin Faisal University +蒙彼利埃大学,521,海外名校,Montpellier University +国立中央大学,521,海外名校,National Central University +理海大学,531,海外名校,Lehigh University +长崎大学,531,海外名校,Nagasaki University +国家研究萨拉托夫州立大学,531,海外名校,National Research Saratov State University +俄勒冈州立大学,531,海外名校,Oregon State University +贝鲁特圣约瑟夫大学,531,海外名校,Saint Joseph University of Beirut (USJ) +南方联邦大学,531,海外名校,Southern Federal University +韩国亚洲大学,531,海外名校,Ajou University +一桥大学,531,海外名校,Hitotsubashi University +里昂国家科学研究院,531,海外名校,Institut National des Sciences Appliquées de Lyon (INSA) +哥斯达黎加大学,531,海外名校,Universidad de Costa Rica +萨拉戈萨大学,531,海外名校,Universidad de Zaragoza +特拉华大学,531,海外名校,University of Delaware +蔚山大学,541,海外名校,University of Ulsan +阿伯里斯特威斯大学,541,海外名校,Aberystwyth University +伊朗科技大学,541,海外名校,Iran University of Science & Technology +庆北国立大学,541,海外名校,Kyungpook National University +新泻大学,541,海外名校,Niigata University +萨班哲大学,541,海外名校,Sabanci University +佐治亚大学,541,海外名校,The University of Georgia +田纳西大学,541,海外名校,"The University of Tennessee, Knoxville" +马萨里克大学,551,海外名校,Masaryk University +中东技术大学,551,海外名校,Middle East Technical University +图卢兹第三大学,551,海外名校,Paul Sabatier University (Toulouse 3) +阿拜哈萨克斯坦国立师范大学,551,海外名校,Abai Kazakh National Pedagogical University +塞萨洛尼基亚里士多德大学,551,海外名校,Aristotle University of Thessaloniki +哈萨克国立农业大学,551,海外名校,Kazakh National Agrarian University KazNAU +塞维利亚大学,551,海外名校,Universidad de Sevilla +泛美大学,551,海外名校,Universidad Panamericana (UP) +米兰圣心天主教大学,551,海外名校,Università Cattolica del Sacro Cuore +塞格德大学,551,海外名校,University of Szeged +帕维亚大学研究学院,561,海外名校,Università degli Studi di Pavia +弗里堡大学,561,海外名校,Université de Fribourg +阿尔泰州立大学,561,海外名校,Altai State University +哈林大学,561,海外名校,Hallym University +仁荷大学,561,海外名校,Inha University +贾瓦哈拉尔尼赫鲁大学,561,海外名校,Jawaharlal Nehru University +布伦瑞克工业大学,561,海外名校,Technical University of Braunschweig +新学院大学,561,海外名校,The New School +大阪市立大学,571,海外名校,Osaka City University +开罗大学,571,海外名校,Cairo University +卡斯里克圣灵大学,571,海外名校,Holy Spirit University of Kaslik +伊万第比利斯国立大学,571,海外名校,Ivane Javakhishvili Tbilisi State University +全北国立大学,571,海外名校,Jeonbuk National University +不来梅大学,571,海外名校,Universität Bremen +瓦伦西亚大学,571,海外名校,Universitat de Valencia +米尼奥大学,571,海外名校,University of Minho +内布拉斯加大学-林肯,571,海外名校,University of Nebraska - Lincoln +拉普拉塔国立大学,581,海外名校,Universidad Nacional de La Plata (UNLP) +圭尔夫大学,581,海外名校,University of Guelph +南佛罗里达大学,581,海外名校,University of South Florida +黎巴嫩美国大学,581,海外名校,Lebanese American University +香港岭南大学,581,海外名校,"Lingnan University, Hong Kong" +默多克大学,581,海外名校,Murdoch University +冈山大学,581,海外名校,Okayama University +萨马拉国立研究大学（Samara University）,581,海外名校,Samara National Research University (Samara University) +国立政治大学,591,海外名校,National Chengchi University +Savitribai普鲁大学,591,海外名校,Savitribai Phule Pune University +索非亚大学,591,海外名校,Sofia University +哥伦比亚外事大学,591,海外名校,Universidad Externado de Colombia +雷根斯堡大学,591,海外名校,Universität Regensburg +魁北克大学,591,海外名校,Université du Québec +德布勒森大学,591,海外名校,University of Debrecen +卢布尔雅那大学,591,海外名校,University of Ljubljana +巴林应用科学大学,591,海外名校,Applied Science University - Bahrain +比尔肯特大学,591,海外名校,Bilkent University +印度理工学院海德拉巴,591,海外名校,Indian Institute of Technology Hyderabad +熊本大学,591,海外名校,Kumamoto University +阿博大学,601,海外名校,Abo Akademi University +迪拜美国大学,601,海外名校,American University in Dubai +马尼拉雅典大学,601,海外名校,Ateneo de Manila University +班戈大学,601,海外名校,Bangor University +迪拜加拿大大学,601,海外名校,Canadian University Dubai +卡尔顿大学,601,海外名校,Carleton University +中央昆士兰大学,601,海外名校,Central Queensland University +清迈大学,601,海外名校,Chiang Mai University +克拉克大学,601,海外名校,Clark University +科米利亚斯宗座大学,601,海外名校,Comillas Pontifical University +考文垂大学,601,海外名校,Coventry University +岐阜大学,601,海外名校,Gifu University +金泽大学,601,海外名校,Kanazawa University +金斯顿大学,601,海外名校,Kingston University +建国大学,601,海外名校,Konkuk University +墨西哥阿那瓦克大学,601,海外名校,Universidad Anáhuac México +康塞普西翁大学,601,海外名校,Universidad de Concepción +布宜诺斯艾利斯省中央大学 (UNICEN,601,海外名校,Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN) +拉蒙鲁尔大学,601,海外名校,Universitat Ramon Llull +霍恩海姆大学,601,海外名校,University Hohenheim +阿威罗大学,601,海外名校,University of Aveiro +辛辛那提大学,601,海外名校,University of Cincinnati +热那亚大学,601,海外名校,University of Genoa +曼尼托巴大学,601,海外名校,University of Manitoba +普利茅斯大学,601,海外名校,University of Plymouth +比勒陀利亚大学,601,海外名校,University of Pretoria +沙迦大学,601,海外名校,University of Sharjah +锡耶纳大学,601,海外名校,University of Siena +南卡罗来纳大学,601,海外名校,University of South Carolina +汉诺威莱布尼兹大学,601,海外名校,Leibniz University Hannover +管理科学大学,601,海外名校,Management and Science University +南京科技大学,601,海外名校,Nanjing University of Science & Technology +帕拉茨基大学,601,海外名校,Palacky University Olomouc +Pavol JozefŠafárikUniversity位于科希策,601,海外名校,Pavol Jozef Šafárik University in Košice +釜山国立大学,601,海外名校,Pusan National University +S.D.阿斯芬迪亚罗夫哈萨克国立医科大学,601,海外名校,S.D. Asfendiyarov Kazakh National Medical University +世宗大学,601,海外名校,Sejong University +深圳大学,601,海外名校,Shenzhen University +史密斯学院,601,海外名校,Smith College +基辅国立大学,601,海外名校,Taras Shevchenko National University Kiev +法政大学,601,海外名校,Thammasat University +约旦大学,601,海外名校,The University of Jordan +东京农工大学,601,海外名校,Tokyo University of Agriculture and Technology +巴黎第二大学,601,海外名校,University Paris 2 Panthéon-Assas +扎耶德大学,651,海外名校,Zayed University +安蒂奥基亚大学,651,海外名校,Universidad de Antioquia +ICESI大学,651,海外名校,Universidad ICESI +秘鲁秘鲁卡耶塔诺大学,651,海外名校,Universidad Peruana Cayetano Heredia +玻利瓦尔纳天主教大学,651,海外名校,Universidad Pontificia Bolivariana +罗斯托克大学,651,海外名校,Universität Rostock +克劳德·伯纳德·里昂大学,651,海外名校,Université Claude Bernard Lyon 1 +玛拉工艺大学,651,海外名校,Universiti Teknologi MARA - UiTM +克里特岛大学,651,海外名校,University of Crete +赫尔大学,651,海外名校,University of Hull +海德拉巴大学,651,海外名校,University of Hyderabad +肯塔基大学,651,海外名校,University of Kentucky +马萨诸塞大学波士顿分校,651,海外名校,University of Massachusetts Boston +蒙斯大学,651,海外名校,University of Mons +新不伦瑞克大学,651,海外名校,University of New Brunswick +俄克拉荷马大学,651,海外名校,University of Oklahoma +俄勒冈大学,651,海外名校,University of Oregon +佩奇大学,651,海外名校,University of Pecs +朴茨茅斯大学,651,海外名校,University of Portsmouth +萨拉曼卡大学,651,海外名校,University of Salamanca +的里雅斯特大学,651,海外名校,University of Trieste +佛蒙特大学,651,海外名校,University of Vermont +美利坚大学,651,海外名校,American University +威廉与玛丽学院,651,海外名校,College of William and Mary +布拉迪斯拉发的夸美纽斯大学,651,海外名校,Comenius University in Bratislava +德雷塞尔大学,651,海外名校,Drexel University +伊迪丝科文大学,651,海外名校,Edith Cowan University +罗兰大学,651,海外名校,Eotvos Lorand University +米纳斯吉拉斯州联邦大学,651,海外名校,Universidade Federal de Minas Gerais +博尔扎诺自由大学,651,海外名校,Free University of Bozen-Bolzano +霍华德大学,651,海外名校,Howard University +伊曼纽尔·康德波罗的海联邦大学,651,海外名校,Immanuel Kant Baltic Federal University +马来西亚国际伊斯兰大学,651,海外名校,International Islamic University Malaysia +迦达浦大学,651,海外名校,Jadavpur University +鹿儿岛大学,651,海外名校,Kagoshima University +卡尔·弗朗岑斯大学格拉茨大学,651,海外名校,Karl-Franzens-Universitaet Graz +哈立德国王大学,651,海外名校,King Khalid University +拉合尔管理科学大学,651,海外名校,Lahore University of Management Sciences +洛巴切夫斯基大学,651,海外名校,Lobachevsky University +澳门科技大学,651,海外名校,Macau University of Science & Technology +密歇根理工大学,651,海外名校,Michigan Technological University +雅典大学,651,海外名校,National and Kapodistrian University of Athens +国立中兴大学,651,海外名校,National Chung Hsing University +国立哈尔科夫工业大学,651,海外名校,"National Technical University ""Kharkiv Polytechnic Institute""" +里约热内卢天主教大学,651,海外名校,Pontifícia Universidade Católica do Rio de Janeiro +谢切诺夫大学,651,海外名校,Sechenov University +双威大学,651,海外名校,Sunway University +雪城大学,651,海外名校,Syracuse University +新墨西哥大学-阿尔伯克基,651,海外名校,The University of New Mexico - Albuquerque +德岛大学,651,海外名校,Tokushima University +阿尔斯特大学,651,海外名校,Ulster University +黎巴嫩大学,701,海外名校,Lebanese University +米德尔塞克斯大学,701,海外名校,Middlesex University +乌克兰国立技术大学 – 伊戈尔·西科斯基基辅理工学院,701,海外名校,National Technical University of Ukraine – Igor Sikorsky Kyiv Poly +新泽西理工大学,701,海外名校,New Jersey Institute of Technology +诺桑比亚大学,701,海外名校,Northumbria University +印度金德而全球大学,701,海外名校,O.P. Jindal Global University +大阪府大学,701,海外名校,Osaka Prefecture University +马尔堡菲尔兹大学,701,海外名校,Philipps University of Marburg +普列汉诺夫俄罗斯经济大学,701,海外名校,Plekhanov Russian University of Economics +巴里理工大学,701,海外名校,Politecnico di Bari +穆罕默德·本·法赫德王子大学,701,海外名校,Prince Mohammad Bin Fahd university +罗维拉-威尔吉利大学,701,海外名校,Rovira i Virgili University +圣彼得堡国立电子科技大学,701,海外名校,Saint Petersburg Electrotechnical University ETU-LETI +史蒂文斯理工学院,701,海外名校,Stevens Institute of Technology +苏美州立大学,701,海外名校,Sumy State University +东京都立大学,701,海外名校,Tokyo Metropolitan University +乌法国立航空技术大学,701,海外名校,Ufa State Aviation Technical University +委内瑞拉中央大学,701,海外名校,Universidad Central de Venezuela +萨瓦纳大学,701,海外名校,Universidad de La Sabana +圣安德烈斯大学,701,海外名校,Universidad de San Andrés - UdeSA +伊比利亚美洲大学,701,海外名校,Universidad Iberoamericana IBERO +圣地亚哥德孔波斯特拉大学,701,海外名校,Universidade de Santiago de Compostela +波茨坦大学,701,海外名校,Universität Potsdam +蔚蓝海岸大学,701,海外名校,Université Côte d'Azur +苏塞大学,701,海外名校,Université de Sousse +布拉德福德大学,701,海外名校,University of Bradford +海法大学,701,海外名校,University of Haifa +休斯敦大学,701,海外名校,University of Houston +哈德斯菲尔德大学,701,海外名校,University of Huddersfield +摩德纳大学和雷焦艾米利亚,701,海外名校,University of Modena and Reggio Emilia +南昆士兰大学,701,海外名校,University of Southern Queensland +巴斯克大学,701,海外名校,University of the Basque Country +威斯敏斯特大学,701,海外名校,University of Westminster +温莎大学,701,海外名校,University of Windsor +弗吉尼亚联邦大学,701,海外名校,Virginia Commonwealth University +阿布扎比大学,701,海外名校,Abu Dhabi University +阿治曼大学,701,海外名校,Ajman University +艾因大学,701,海外名校,Al Ain University +海峡大学,701,海外名校,Bogaziçi Üniversitesi +布尔诺工业大学,701,海外名校,Brno University of Technology +查尔斯达尔文大学,701,海外名校,Charles Darwin University +纽约城市大学,701,海外名校,City University of New York +群马大学,701,海外名校,Gunma University +印度理工学院布巴内斯瓦尔,701,海外名校,Indian Institute of Technology Bhubaneswar +布宜诺斯艾利斯技术大学,701,海外名校,Instituto Tecnológico de Buenos Aires (ITBA) +伊斯坦布尔理工大学,701,海外名校,Istanbul Technical University +约夫大学,701,海外名校,Jouf University +阿道夫·伊巴涅斯大学,751,海外名校,Adolfo Ibáñez University +中东美国大学,751,海外名校,American University of the Middle East +白俄罗斯国立技术大学（BNTU）,751,海外名校,Belarusian National Technical University (BNTU) +布雷西亚大学,751,海外名校,Brescia University +全南国立大学,751,海外名校,Chonnam National University +忠南国立大学,751,海外名校,Chungnam National University +克拉克森大学,751,海外名校,Clarkson University +檀国大学,751,海外名校,Dankook University +南里奥格兰德联邦大学,751,海外名校,Federal University of Rio Grande do Sul +佐治亚州立大学,751,海外名校,Georgia State University +杜塞尔多夫大学,751,海外名校,Heinrich Heine University Duesseldorf +印尼泗水理工大学,751,海外名校,Institut Teknologi Sepuluh Nopember +国立理工学院 (IPN),751,海外名校,Instituto Politécnico Nacional (IPN) +墨西哥自治技术学院,751,海外名校,Instituto Tecnológico Autónomo de México (ITAM) +印度新德里国立伊斯兰大学,751,海外名校,Jamia Millia Islamia +卡拉干达国立技术大学,751,海外名校,Karaganda State Technical University +República大学（乌德拉尔）,751,海外名校,Universidad de la República (Udelar) +罗萨里奥大学,751,海外名校,Universidad del Rosario +基多圣弗朗西斯科大学,751,海外名校,Universidad San Francisco de Quito +托拉卡托迪泰拉大学,751,海外名校,Universidad Torcuato Di Tella +佩鲁贾研究大学,751,海外名校,Università degli Studi di Perugia +舍布鲁克大学,751,海外名校,Université de Sherbrooke +国能大学,751,海外名校,Universiti Tenaga Nasional (UNITEN) +中佛罗里达大学,751,海外名校,University of Central Florida +丹佛大学,751,海外名校,University of Denver +格林威治大学,751,海外名校,University of Greenwich +马里兰大学巴尔的摩,751,海外名校,"University of Maryland, Baltimore County" +萨格勒布大学,751,海外名校,University of Zagreb +曼尼珀尔高等教育学院,751,海外名校,Manipal Academy of Higher Education +梅努斯大学,751,海外名校,Maynooth University +纽芬兰纪念大学,751,海外名校,Memorial University of Newfoundland +瓦尔帕莱索天主教大学,751,海外名校,Pontificia Universidad Catolica de Valparaiso +努拉·本·公主阿卜杜勒拉曼大学,751,海外名校,Princess Nourah bint Abdulrahman University +里加技术大学,751,海外名校,Riga Technical University +立命馆大学,751,海外名校,Ritsumeikan University +设拉子大学,751,海外名校,Shiraz University +南十字大学,751,海外名校,Southern Cross University +纽约州立大学奥尔巴尼分校,751,海外名校,State University of New York Albany +塔林科技大学,751,海外名校,Tallinn University of Technology +天普大学,751,海外名校,Temple University +维尔纽斯Gediminas技术大学,751,海外名校,Vilnius Gediminas Technical University +伍斯特理工学院,751,海外名校,Worcester Polytechnic Institute +越南国立大学胡志明市,801,海外名校,Viet Nam National University Ho Chi Minh City (VNU-HCM) +越南河内国立大学,801,海外名校,Vietnam National University Hanoi +维陶塔斯·马格努斯大学,801,海外名校,Vytautas Magnus University +西弗吉尼亚大学,801,海外名校,West Virginia University +弗罗茨瓦夫科技大学,801,海外名校,Wroclaw University of Science and Technology +西交利物浦大学,801,海外名校,Xi‘an Jiaotong-Liverpool University +山口大学,801,海外名校,Yamaguchi University +埃里温州立大学,801,海外名校,Yerevan State University +岭南大学,801,海外名校,Yeungnam University +横滨国立大学,801,海外名校,Yokohama National University +查平戈自治大学,801,海外名校,Universidad Autónoma de Chapingo +伊达尔戈自治大学,801,海外名校,Universidad Autónoma del Estado de Hidalgo +墨西哥自治大学,801,海外名校,Universidad Autonoma del Estado de Mexico +大都会大学（UAM）,801,海外名校,Universidad Autónoma Metropolitana (UAM) +卡塔利卡大学安德烈斯·贝洛大学,801,海外名校,Universidad Católica Andres Bello +乌拉圭卡托利卡大学（UCU）,801,海外名校,Universidad Católica del Uruguay (UCU) +瓜达拉哈拉大学,801,海外名校,Universidad de Guadalajara +美洲普埃布拉大学（UDLAP）,801,海外名校,Universidad de las Américas Puebla (UDLAP) +洛斯安第斯大学-（ULA）梅里达,801,海外名校,Universidad de Los Andes - (ULA) Mérida +洛斯安第斯大学-智利,801,海外名校,Universidad de los Andes - Chile +奥维耶多大学,801,海外名校,Universidad de Oviedo +瓦莱大学,801,海外名校,Universidad del Valle +圣地亚哥大学门户网站,801,海外名校,University Diego Portales +EAFIT大学,801,海外名校,Universidad EAFIT +国立圣路易斯大学,801,海外名校,Universidad Nacional de San Luis +圣马科斯国立大学市长,801,海外名校,Universidad Nacional Mayor de San Marcos +西蒙玻利瓦尔大学（USB）,801,海外名校,Universidad Simón Bolívar (USB) +巴拿马巴拿马技术大学（UTP）,801,海外名校,Universidad Tecnológica de Panamá (UTP) +全国技术大学（UTN）,801,海外名校,Universidad Tecnológica Nacional (UTN) +卡图里卡葡萄牙大学-UCP,801,海外名校,Universidade Católica Portuguesa - UCP +拉科鲁尼亚大学,801,海外名校,Universidade da Coruna +巴西利亚大学,801,海外名校,Universidade de Brasília +费拉拉大学,801,海外名校,Universita' degli Studi di Ferrara +乌迪内大学研究,801,海外名校,Università degli Studi di Udine +意大利马尔凯理工大学,801,海外名校,Universita' Politecnica delle Marche +帕贾扎兰大学,801,海外名校,Universitas Padjadjaran +杜伊斯堡 - 埃森大学,801,海外名校,University of Duisburg-Essen +里尔大学,801,海外名校,Université de Lille +洛林大学,801,海外名校,Universite de Lorraine +南特大学,801,海外名校,Université de Nantes +雷恩第一大学,801,海外名校,Université de Rennes 1 +图卢兹大学1 Capitole,801,海外名校,Université Toulouse 1 Capitole +吉隆坡大学,801,海外名校,Universiti Kuala Lumpur +马来西亚彭亨大学,801,海外名校,Universiti Malaysia Pahang +马来西亚玻璃市大学,801,海外名校,Universiti Malaysia Perlis +拉曼大学,801,海外名校,Universiti Tunku Abdul Rahman (UTAR) +阿利坎特大学,801,海外名校,University of Alicante +巴格达大学,801,海外名校,University of Baghdad +巴林大学,801,海外名校,University of Bahrain +巴里大学,801,海外名校,University of Bari +布莱顿大学,801,海外名校,University of Brighton +加尔各答大学,801,海外名校,University of Calcutta +中央兰开夏大学,801,海外名校,University of Central Lancashire +达卡大学,801,海外名校,University of Dhaka +东伦敦大学,801,海外名校,University of East London +拉合尔工程技术大学（UET）,801,海外名校,University of Engineering & Technology (UET) Lahore +格但斯克大学,801,海外名校,University of Gdansk +哈特福德大学,801,海外名校,University of Hartford +赫特福德大学,801,海外名校,University of Hertfordshire +赫拉德茨克拉洛夫大学,801,海外名校,University of Hradec Kralove +夸祖鲁纳塔尔大学,801,海外名校,University of KwaZulu Natal +罗兹大学,801,海外名校,University of Lodz +马耳他大学,801,海外名校,University of Malta +马里博尔大学,801,海外名校,University of Maribor +墨西拿大学,801,海外名校,University of Messina +密西西比大学,801,海外名校,University of Mississippi +密苏里大学堪萨斯城分校,801,海外名校,"University of Missouri, Kansas City" +穆尔西亚大学,801,海外名校,University of Murcia +新英格兰大学,801,海外名校,University of New England +新罕布什尔大学,801,海外名校,University of New Hampshire +帕尔马大学,801,海外名校,University of Parma +帕特雷大学,801,海外名校,University of Patras +萨勒诺大学,801,海外名校,University of Salerno +索尔福德大学,801,海外名校,University of Salford +首尔大学,801,海外名校,University of Seoul +旁遮普大学,801,海外名校,University of the Punjab +英格兰西部大学,801,海外名校,University of the West of England +塔尔萨大学,801,海外名校,University of Tulsa +秋明大学,801,海外名校,University of Tyumen +维罗纳大学,801,海外名校,University of Verona +威斯康星大学-密尔沃基,801,海外名校,University of Wisconsin - Milwaukee +弗罗茨瓦夫大学,801,海外名校,University of Wroclaw +怀俄明大学,801,海外名校,University of Wyoming +齐里纳大学,801,海外名校,University of Žilina +Y.A.院士布克托夫卡拉干达大学,801,海外名校,Academician Y.A. Buketov Karaganda University +亚当·米基维奇大学,801,海外名校,Adam Mickiewicz University +AGH科技大学,801,海外名校,AGH University of Science & Technology +艾恩夏姆斯大学,801,海外名校,Ain Shams University +安卡拉大学,801,海外名校,Ankara Üniversitesi +安娜大学,801,海外名校,Anna University +奥本大学,801,海外名校,Auburn University +澳洲天主教大学,801,海外名校,Australian Catholic University +孟加拉国工程技术大学,801,海外名校,Bangladesh University of Engineering and Technology +贝鲁特阿拉伯大学,801,海外名校,Beirut Arab University +比勒费尔德大学,801,海外名校,Bielefeld University +纽约州立大学宾汉姆顿大学,801,海外名校,Binghamton University +布达佩斯科技经济大学,801,海外名校,Budapest University of Technology & Economics +威尼斯福斯卡里宫大学,801,海外名校,Ca’ Foscari University of Venice +卡塔尼亚大学,801,海外名校,Catania University +查尔斯斯特大学,801,海外名校,Charles Sturt University +克莱姆森大学,801,海外名校,Clemson University +布达佩斯科维努斯大学,801,海外名校,Corvinus University of Budapest +克拉科夫工业大学,801,海外名校,Cracow University of Technology +CY塞尔吉巴黎大学,801,海外名校,CY Cergy Paris Université +布拉格捷克生命科学大学,801,海外名校,Czech University of Life Sciences in Prague +德拉萨大学,801,海外名校,De La Salle University +德蒙福特大学,801,海外名校,De Montfort University +多特蒙德工业大学,801,海外名校,Technical University of Dortmund +爱丁堡纳皮尔大学,801,海外名校,Edinburgh Napier University +巴拉那联邦大学,801,海外名校,Federal University of Parana +伯南布哥联邦大学,801,海外名校,Federal University of Pernambuco +圣卡塔琳娜联邦大学,801,海外名校,Federal University of Santa Catarina +圣卡洛斯联邦大学,801,海外名校,Federal University of Sao Carlos +费德里科圣玛丽亚技术大学,801,海外名校,Federico Santa María Technical University +俄罗斯联邦政府金融大学,801,海外名校,Financial University under the Government of the Russian Federation +佛罗里达国际大学,801,海外名校,Florida International University +福特汉姆大学,801,海外名校,Fordham University +格但斯克工业大学,801,海外名校,Gdansk University of Technology +乔治梅森大学,801,海外名校,George Mason University +德国约旦大学,801,海外名校,German Jordanian University +海湾科技大学,801,海外名校,Gulf University for Science and Technology +哈斯特帕大学,801,海外名校,Hacettepe University +印第安纳大学-印第安纳波利斯,801,海外名校,Indiana University-Purdue University at Indianapolis +国际基督教大学,801,海外名校,International Christian University +麦地那伊斯兰大学,801,海外名校,Islamic University of Madinah +伊斯坦布尔大学,801,海外名校,Istanbul University +约旦科技大学,801,海外名校,Jordan University of Science & Technology +堪萨斯州立大学,801,海外名校,Kansas State University +卡塞萨特大学,801,海外名校,Kasetsart University +考纳斯工业大学,801,海外名校,Kaunas University of Technology +哈萨克阿布赖汗国际关系与世界语言大学,801,海外名校,Kazakh Ablai Khan University of International Relations and World Languages +哈萨克斯坦-英国技术大学,801,海外名校,Kazakh-British Technical University +喀山国立研究技术大学,801,海外名校,Kazan National Research Technical University +孔敬大学,801,海外名校,Khon Kaen University +费萨尔国王大学,801,海外名校,King Faisal University +泰国国王科技大学,801,海外名校,King Mongkut’s University of Technology Thonburi +京都工业大学,801,海外名校,Kyoto Institute of Technology +九州工业大学,801,海外名校,Kyushu Institute of Technology +利物浦约翰摩尔斯大学,801,海外名校,Liverpool John Moores University +罗兹工业大学,801,海外名校,Lodz University of Technology +伦敦都会大学,801,海外名校,London Metropolitan University +伦敦南岸大学,801,海外名校,London South Bank University +路易斯安那州立大学,801,海外名校,Louisiana State University +芝加哥洛约拉大学,801,海外名校,Loyola University Chicago +利沃夫理工大学,801,海外名校,Lviv Polytechnic National University +曼彻斯特城市大学,801,海外名校,Manchester Metropolitan University +布尔诺的孟德尔大学,801,海外名校,Mendel University Brno +门捷列夫化工大学,801,海外名校,Mendeleev University of Chemical Technology +国立中正大学,801,海外名校,National Chung Cheng University +科尔多瓦国立大学,801,海外名校,National University of Cordoba +罗萨里奥国立大学,801,海外名校,National University of Rosario +尼古拉·哥白尼大学,801,海外名校,Nicolaus Copernicus University +NJSC KIMEP大学,801,海外名校,NJSC KIMEP University +诺丁汉特伦特大学,801,海外名校,Nottingham Trent University +新西伯利亚国立技术大学,801,海外名校,Novosibirsk State Technical University +俄克拉荷马州立大学,801,海外名校,Oklahoma State University +萨尔茨堡巴黎罗德隆大学,801,海外名校,Paris Lodron University of Salzburg +那不勒斯帕斯诺普大学,801,海外名校,Parthenope University Naples +彼尔姆国立研究大学,801,海外名校,Perm State National Research University +本地治里大学,801,海外名校,Pondicherry University +圣保罗天主教大学,801,海外名校,Pontifícia Universidade Católica de São Paulo +波兹南理工大学,801,海外名校,Poznan University of Technology +宋卡王子大学,801,海外名校,Prince of Songkla University +苏美亚公主科技大学,801,海外名校,Princess Sumaya University for Technology +卡西姆大学,801,海外名校,Qassim University +英国爱丁堡玛格丽特女王大学,801,海外名校,"Queen Margaret University , Edinburgh" +罗得大学,801,海外名校,Rhodes University +里加斯特拉丁斯大学,801,海外名校,Riga Stradins University +罗伯特·戈登大学,801,海外名校,Robert Gordon University +罗马特雷大学,801,海外名校,Roma Tre University +俄罗斯总统国民经济与公共行政学院,801,海外名校,Russian Presidential Academy of National Economy and Public Admini +罗格斯大学纽瓦克分校,801,海外名校,Rutgers University - Newark +瑞尔森大学,801,海外名校,Ryerson University +西雅图大学,801,海外名校,Seattle University +信州大学,801,海外名校,Shinshu University +Siksha'O'Anusandhan,801,海外名校,Siksha ‘O’ Anusandhan +西里西亚工业大学,801,海外名校,Silesian University of Technology +斯洛伐克工业大学布拉迪斯拉发,801,海外名校,Slovak University of Technology Bratislava +上智大学,801,海外名校,Sophia University +南乌拉尔州立大学,801,海外名校,South Ural State University +南卫理公会大学,801,海外名校,Southern Methodist University +塞切尼伊斯特万大学,801,海外名校,Széchenyi István University +圣伊斯特万大学,801,海外名校,Szent Istvan University +科希策技术大学,801,海外名校,Technical University of Kosice +利贝雷茨技术大学,801,海外名校,Technical University of Liberec +都柏林科技大学,801,海外名校,Technological University Dublin +哥斯达黎加技术中心（TECN）,801,海外名校,Tecnológico de Costa Rica -TEC +德克萨斯理工大学,801,海外名校,Texas Tech University +阿拉巴马大学,801,海外名校,The University of Alabama +阿尔伯塔大学,126,海外名校, +加州艺术学院,1,海外名校,California Institute of the Arts +萨凡纳艺术学院,2,海外名校,Savannah College of Art and Design +瑞格林艺术设计学院,3,海外名校,Ringling College of Art and Design +纽约视觉艺术学院,4,海外名校,School of Visual Art +谢尔丹学院,6,海外名校,Sheridan College +伯恩茅斯大学,7,海外名校,Bournemouth University +提赛德大学,8,海外名校,Teesside University +高布兰学院,9,海外名校,Gobelins diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/schools.csv b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/schools.csv new file mode 100644 index 00000000..7ed0c84a --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/res/schools.csv @@ -0,0 +1,5713 @@ +id type parent_id name_cn name_en alias is_abroad is_world_known school_type is_double_first education_type province city is_985 +102982 0 0 ABC联邦大学 Federal University of ABC (UFABC) 1 0 NULL 0 NULL NULL NULL 0 +105232 0 0 LEC东京法律思维大学 1 0 NULL 0 NULL NULL NULL 0 +104244 0 0 LEC东京职业发展大学 1 0 NULL 0 NULL NULL NULL 0 +105334 0 0 Pontifícia里约热内卢天主教大学 Pontifical Catholic University of Rio de Janeiro 1 0 NULL 0 NULL NULL NULL 0 +104995 0 0 PSG理工学院 PSG College of Technology 1 0 NULL 0 NULL NULL NULL 0 +105336 0 0 Universite libre de Bruxelles Free University of Brussels 1 0 NULL 0 NULL NULL NULL 0 +104629 0 0 一桥大学 Hitotsubashi University 1 0 NULL 0 NULL NULL NULL 0 +100722 0 0 七台河职业学院 0 0 高职类 0 专科 黑龙江 七台河市 0 +105002 0 0 万隆理工学院 Bandung Institute of Technology (ITB) 1 0 NULL 0 NULL NULL NULL 0 +103586 0 0 三一学院 Trinity College 1 0 NULL 0 NULL NULL NULL 0 +105583 0 0 三亚中瑞酒店管理职业学院 0 0 高职类 0 民办(专科) 海南 三亚市 0 +102247 0 0 三亚城市职业学院 0 0 高职类 0 民办(专科) 海南 三亚市 0 +102245 0 0 三亚学院 0 0 综合类 0 民办(本科) 海南 三亚市 0 +102256 0 0 三亚理工职业学院 0 0 高职类 0 民办(专科) 海南 三亚市 0 +102254 0 0 三亚航空旅游职业学院 0 0 高职类 0 民办(专科) 海南 三亚市 0 +101763 0 0 三峡大学 0 0 综合类 0 本科 湖北 宜昌市 0 +101852 0 0 三峡大学科技学院 0 0 理工类 0 独立学院 湖北 宜昌市 0 +101829 0 0 三峡旅游职业技术学院 0 0 高职类 0 专科 湖北 宜昌市 0 +101825 0 0 三峡电力职业学院 0 0 高职类 0 专科 湖北 宜昌市 0 +101307 0 0 三明医学科技职业学院 三明职业技术学院 0 0 高职类 0 专科 福建 三明市 0 +101262 0 0 三明学院 0 0 综合类 0 本科 福建 三明市 0 +100877 0 0 三江学院 0 0 综合类 0 民办(本科) 江苏 南京市 0 +104285 0 0 三重中京大学 1 0 NULL 0 NULL NULL NULL 0 +104540 0 0 三重县立看护大学 1 0 NULL 0 NULL NULL NULL 0 +104675 0 0 三重大学 Mie University 1 0 NULL 0 NULL NULL NULL 0 +105612 0 0 三门峡社会管理职业学院 0 0 高职类 0 专科 河南 三门峡市 0 +101639 0 0 三门峡职业技术学院 0 0 高职类 0 专科 河南 三门峡市 0 +104643 0 0 上智大学 Sophia University 1 0 NULL 0 NULL NULL NULL 0 +104099 0 0 上武大学 1 0 NULL 0 NULL NULL NULL 0 +100797 0 0 上海东海职业技术学院 0 0 高职类 0 民办(专科) 上海 上海市 0 +100816 0 0 上海中侨职业技术学院 0 0 高职类 0 民办(专科) 上海 上海市 0 +100769 0 0 上海中医药大学 0 0 医科类 1 本科 上海 上海市 0 +100819 0 0 上海中华职业技术学院 0 0 高职类 0 民办(专科) 上海 上海市 0 +100761 2 0 上海交通大学 0 1 综合类 1 本科 上海 上海市 1 +100802 0 0 上海交通职业技术学院 0 0 高职类 0 专科 上海 上海市 0 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University 1 0 NULL 0 NULL NULL NULL 0 +102262 1 0 西南大学 0 0 综合类 1 本科 重庆 重庆市 0 +104464 0 0 西南女学院大学 1 0 NULL 0 NULL NULL NULL 0 +104463 0 0 西南学院大学 1 0 NULL 0 NULL NULL NULL 0 +102268 0 0 西南政法大学 0 0 政法类 0 本科 重庆 重庆市 0 +102512 0 0 西南林业大学 0 0 农林类 0 本科 云南 昆明市 0 +102350 0 0 西南民族大学 0 0 民族类 0 本科 四川 成都市 0 +102329 0 0 西南石油大学 0 0 理工类 1 本科 四川 成都市 0 +102331 0 0 西南科技大学 0 0 理工类 0 本科 四川 绵阳市 0 +102446 0 0 西南科技大学城市学院 0 0 综合类 0 独立学院 四川 绵阳市 0 +102328 1 0 西南财经大学 0 0 财经类 1 本科 四川 成都市 0 +102448 0 0 西南财经大学天府学院 0 0 财经类 0 独立学院 四川 绵阳市 0 +103719 0 0 西卡罗来纳大学 Western Carolina University 1 0 NULL 0 NULL NULL NULL 0 +102541 0 0 西双版纳职业技术学院 0 0 高职类 0 专科 云南 西双版纳傣族自治州 0 +103947 0 0 西奈山伊坎医学院 Icahn School of Medicine at Mount Sinai 1 0 NULL 0 NULL NULL NULL 0 +102749 0 0 西宁城市职业技术学院 0 0 高职类 0 专科 青海 西宁市 0 +102645 0 0 西安东方亚太职业技术学院 0 0 综合类 0 专科（高职） 陕西 西安市 0 +102584 2 0 西安交通大学 0 0 理工类 1 本科 陕西 西安市 1 +102680 0 0 西安交通大学城市学院 0 0 综合类 0 独立学院 陕西 西安市 0 +102625 0 0 西安交通工程学院 0 0 理工类 0 民办(本科) 陕西 西安市 0 +102606 0 0 西安体育学院 0 0 体育类 0 本科 陕西 西安市 0 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Ruhr-University Bochum 1 0 NULL 0 NULL NULL NULL 0 +105337 0 0 鲁汶天主教大学 Catholic University of Louvain 1 0 NULL 0 NULL NULL NULL 0 +100481 0 0 鲁迅美术学院 0 0 艺术类 0 本科 辽宁 沈阳市 0 +103135 0 0 鲍曼大学 Bauman Moscow State Technical University 1 0 NULL 0 NULL NULL NULL 0 +103659 0 0 鲍登大学 Bowdoin College 1 0 NULL 0 NULL NULL NULL 0 +103477 0 0 鲍耶大学 Babes-Bolyai University 1 0 NULL 0 NULL NULL NULL 0 +103319 0 0 鲜文大学 1 0 NULL 0 NULL NULL NULL 0 +104705 0 0 鸟取大学 Tottori University 1 0 NULL 0 NULL NULL NULL 0 +104611 0 0 鸣门教育大学 1 0 NULL 0 NULL NULL NULL 0 +101700 0 0 鹤壁汽车工程职业学院 0 0 高职类 0 民办(专科) 河南 鹤壁市 0 +101657 0 0 鹤壁职业技术学院 0 0 高职类 0 专科 河南 鹤壁市 0 +101712 0 0 鹤壁能源化工职业学院 0 0 NULL 0 其他 河南 鹤壁市 0 +100705 0 0 鹤岗师范高等专科学校 0 0 高职类 0 专科 黑龙江 鹤岗市 0 +100742 0 0 鹤岗矿务局职工大学 0 0 其他 0 专科（高职） 黑龙江 鹤岗市 0 +104203 0 0 鹤见大学 1 0 NULL 0 NULL NULL NULL 0 +101377 0 0 鹰潭职业技术学院 0 0 高职类 0 专科 江西 鹰潭市 0 +104501 0 0 鹿儿岛国际大学 1 0 NULL 0 NULL NULL NULL 0 +104639 0 0 鹿儿岛大学 Kagoshima University 1 0 NULL 0 NULL NULL NULL 0 +104502 0 0 鹿儿岛纯心女子大学 1 0 NULL 0 NULL NULL NULL 0 +104613 0 0 鹿屋体育大学 1 0 NULL 0 NULL NULL NULL 0 +104794 0 0 麦克雷雷大学 Makerere University 1 0 NULL 0 NULL NULL NULL 0 +103400 0 0 麦克马斯特大学 McMaster University 1 1 NULL 0 NULL NULL NULL 0 +104740 0 0 麦加大学 Umm Al-Qura University 1 0 NULL 0 NULL NULL NULL 0 +103396 0 0 麦吉尔大学 McGill University 1 1 NULL 0 NULL NULL NULL 0 +102918 0 0 麦考瑞大学 Macquarie University 1 0 NULL 0 NULL NULL NULL 0 +102888 0 0 麦觉理大学 Macquarie University 1 0 NULL 0 NULL NULL NULL 0 +103761 0 0 麦迪逊分校 University of Wisconsin, Madison 1 0 NULL 0 NULL NULL NULL 0 +104188 0 0 麻布大学 1 0 NULL 0 NULL NULL NULL 0 +105242 0 0 麻省大学波士顿分校 University of Massachusetts Boston 1 0 NULL 0 NULL NULL NULL 0 +103764 0 0 麻省理工学院 Massachusetts Institute of Technology (MIT) 1 1 NULL 0 NULL NULL NULL 0 +101750 0 0 黄冈师范学院 0 0 师范类 0 本科 湖北 黄冈市 0 +101822 0 0 黄冈科技职业学院 0 0 高职类 0 民办(专科) 湖北 黄冈市 0 +101780 0 0 黄冈职业技术学院 0 0 高职类 0 骨干(高职) 湖北 黄冈市 0 +101138 0 0 黄山学院 0 0 师范类 0 本科 安徽 黄山市 0 +101218 0 0 黄山职业技术学院 0 0 高职类 0 专科 安徽 黄山市 0 +101632 0 0 黄河交通学院 0 0 理工类 0 民办(本科) 河南 焦作市 0 +101648 0 0 黄河水利职业技术学院 0 0 高职类 0 示范(高职) 河南 开封市 0 +101626 0 0 黄河科技学院 0 0 理工类 0 民办(本科) 河南 郑州市 0 +101616 0 0 黄淮学院 0 0 综合类 0 本科 河南 驻马店市 0 +1464 0 0 黄石广播电视大学 HUANGSHI RADIO&TV UNIVERSITY 黄石电大 0 0 综合类 0 专科（高职） 湖北 黄石市 0 +103469 0 0 黎巴嫩美国大学 Lebanese American University 1 0 NULL 0 NULL NULL NULL 0 +101275 0 0 黎明职业大学 0 0 高职类 0 专科 福建 泉州市 0 +100692 0 0 黑河学院 0 0 综合类 0 本科 黑龙江 黑河市 0 +100719 0 0 黑龙江三江美术职业学院 0 0 高职类 0 民办(专科) 黑龙江 佳木斯市 0 +100681 0 0 黑龙江东方学院 0 0 综合类 0 民办(本科) 黑龙江 哈尔滨市 0 +100668 0 0 黑龙江中医药大学 0 0 医科类 0 本科 黑龙江 哈尔滨市 0 +100725 0 0 黑龙江交通职业技术学院 0 0 高职类 0 专科 黑龙江 齐齐哈尔市 0 +100715 0 0 黑龙江信息技术职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100664 0 0 黑龙江八一农垦大学 0 0 农林类 0 本科 黑龙江 大庆市 0 +100714 0 0 黑龙江公安警官职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100735 0 0 黑龙江兵器工业职工大学 0 0 军事类 0 专科（高职） 黑龙江 哈尔滨市 0 +100702 0 0 黑龙江农业工程职业学院 0 0 高职类 0 示范(高职) 黑龙江 哈尔滨市 0 +100709 0 0 黑龙江农业经济职业学院 0 0 高职类 0 示范(高职) 黑龙江 牡丹江市 0 +100701 0 0 黑龙江农业职业技术学院 0 0 高职类 0 专科 黑龙江 佳木斯市 0 +100717 0 0 黑龙江农垦科技职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100744 0 0 黑龙江农垦管理干部学院 0 0 综合类 0 专科（高职） 黑龙江 哈尔滨市 0 +100703 0 0 黑龙江农垦职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +105504 0 0 黑龙江冰雪体育职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100704 0 0 黑龙江司法警官职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100713 0 0 黑龙江商业职业学院 0 0 高职类 0 专科 黑龙江 牡丹江市 0 +100685 0 0 黑龙江外国语学院 0 0 外语类 0 民办(本科) 黑龙江 哈尔滨市 0 +100657 0 0 黑龙江大学 0 0 综合类 0 本科 黑龙江 哈尔滨市 0 +100680 0 0 黑龙江工业学院 0 0 理工类 0 本科 黑龙江 鸡西市 0 +100757 0 0 黑龙江工商学院 东北农业大学成栋学院 0 0 财经类 0 民办(本科) 黑龙江 哈尔滨市 0 +100683 0 0 黑龙江工程学院 0 0 理工类 0 本科 黑龙江 哈尔滨市 0 +100758 0 0 黑龙江工程学院昆仑旅游学院 0 0 理工类 0 独立学院 黑龙江 哈尔滨市 0 +100727 0 0 黑龙江幼儿师范高等专科学校 0 0 高职类 0 专科 黑龙江 牡丹江市 0 +100751 0 0 黑龙江广播电视大学/黑龙江电大 黑龙江省广播电视大学 0 0 综合类 0 专科（高职） 黑龙江 哈尔滨市 0 +100697 0 0 黑龙江建筑职业技术学院 0 0 高职类 0 示范(高职) 黑龙江 哈尔滨市 0 +100731 0 0 黑龙江护理高等专科学校 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100718 0 0 黑龙江旅游职业技术学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100700 0 0 黑龙江林业职业技术学院 0 0 高职类 0 专科 黑龙江 牡丹江市 0 +100723 0 0 黑龙江民族职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100720 0 0 黑龙江生态工程职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100712 0 0 黑龙江生物科技职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100752 0 0 黑龙江省商业职工大学 0 0 其他 0 专科（高职） 黑龙江 哈尔滨市 0 +100746 0 0 黑龙江省政法管理干部学院 0 0 政法类 0 专科（高职） 黑龙江 哈尔滨市 0 +100747 0 0 黑龙江省教育学院 0 0 师范类 0 专科（高职） 黑龙江 哈尔滨市 0 +100739 0 0 黑龙江省电力职工大学 0 0 理工类 0 专科（高职） 黑龙江 哈尔滨市 0 +100753 0 0 黑龙江省社会科学院职工大学 0 0 其他 0 专科（高职） 黑龙江 哈尔滨市 0 +100745 0 0 黑龙江省经济管理干部学院 0 0 政法类 0 专科（高职） 黑龙江 哈尔滨市 0 +100755 0 0 黑龙江省职工体育运动技术学院 0 0 体育类 0 专科（高职） 黑龙江 哈尔滨市 0 +100661 0 0 黑龙江科技大学 0 0 理工类 0 本科 黑龙江 哈尔滨市 0 +100729 0 0 黑龙江粮食职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100696 0 0 黑龙江职业学院 0 0 高职类 0 骨干(高职) 黑龙江 哈尔滨市 0 +100721 0 0 黑龙江能源职业学院 黑龙江煤炭职业技术学院 0 0 高职类 0 专科 黑龙江 双鸭山市 0 +100698 0 0 黑龙江艺术职业学院 0 0 高职类 0 专科 黑龙江 哈尔滨市 0 +100686 0 0 黑龙江财经学院 0 0 财经类 0 民办(本科) 黑龙江 哈尔滨市 0 +102475 0 0 黔东南民族职业技术学院 0 0 高职类 0 专科 贵州 黔东南苗族侗族自治州 0 +102468 0 0 黔南民族医学高等专科学校 0 0 高职类 0 专科 贵州 黔南布依族苗族自治州 0 +102461 0 0 黔南民族师范学院 0 0 师范类 0 本科 贵州 黔南布依族苗族自治州 0 +102494 0 0 黔南民族幼儿师范高等专科学校 0 0 高职类 0 专科 贵州 黔南布依族苗族自治州 0 +102476 0 0 黔南民族职业技术学院 0 0 高职类 0 专科 贵州 黔南布依族苗族自治州 0 +102483 0 0 黔西南民族职业技术学院 0 0 高职类 0 专科 贵州 黔西南布依族苗族自治州 0 +102930 0 0 默多克大学 Murdoch University 1 0 NULL 0 NULL NULL NULL 0 +103643 0 0 默海尔州立大学 Morehead State University 1 0 NULL 0 NULL NULL NULL 0 +101470 0 0 齐鲁医药学院 山东万杰医学院 0 0 医科类 0 民办(本科) 山东 淄博市 0 +101448 0 0 齐鲁工业大学 0 0 理工类 0 本科 山东 济南市 0 +101491 0 0 齐鲁师范学院 0 0 师范类 0 本科 山东 济南市 0 +101489 0 0 齐鲁理工学院 0 0 综合类 0 民办(本科) 山东 济南市 0 +100679 0 0 齐齐哈尔医学院 0 0 医科类 0 本科 黑龙江 齐齐哈尔市 0 +100671 0 0 齐齐哈尔大学 0 0 综合类 0 本科 黑龙江 齐齐哈尔市 0 +100684 0 0 齐齐哈尔工程学院 0 0 理工类 0 民办(本科) 黑龙江 齐齐哈尔市 0 +100741 0 0 齐齐哈尔市职工大学 0 0 其他 0 专科（高职） 黑龙江 齐齐哈尔市 0 +100733 0 0 齐齐哈尔理工职业学院 齐齐哈尔市建设职工大学 0 0 高职类 0 民办(专科) 黑龙江 齐齐哈尔市 0 +100693 0 0 齐齐哈尔高等师范专科学校 0 0 高职类 0 专科 黑龙江 齐齐哈尔市 0 +103307 0 0 龙仁大学 1 0 NULL 0 NULL NULL NULL 0 +101263 0 0 龙岩学院 0 0 师范类 0 本科 福建 龙岩市 0 +105146 0 0 龙比亚大学 Edinburgh Napier University 1 0 NULL 0 NULL NULL NULL 0 +104330 0 0 龙谷大学 1 0 NULL 0 NULL NULL NULL 0 +203765 0 0 加州艺术学院 California Institute of the Arts 1 1 NULL 0 NULL NULL NULL 0 +213765 0 0 萨凡纳艺术学院 Savannah College of Art and Design 1 1 NULL 0 NULL NULL NULL 0 +223765 0 0 瑞格林艺术设计学院 Ringling College of Art and Design 1 1 NULL 0 NULL NULL NULL 0 +233765 0 0 纽约视觉艺术学院 School of Visual Art 1 1 NULL 0 NULL NULL NULL 0 +253765 0 0 谢尔丹学院 Sheridan College 1 1 NULL 0 NULL NULL NULL 0 +263765 0 0 伯恩茅斯大学 Bournemouth University 1 1 NULL 0 NULL NULL NULL 0 +273765 0 0 提赛德大学 Teesside University 1 1 NULL 0 NULL NULL NULL 0 +283765 0 0 高布兰学院 Gobelins 1 1 NULL 0 NULL NULL NULL 0 diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/schools.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/schools.py new file mode 100644 index 00000000..e1abfa51 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/entities/schools.py @@ -0,0 +1,62 @@ +# -*- coding: UTF-8 -*- +import os, json,re,copy +import pandas as pd +current_file_path = os.path.dirname(os.path.abspath(__file__)) +TBL = pd.read_csv(os.path.join(current_file_path, "res/schools.csv"), sep="\t", header=0).fillna("") +TBL["name_en"] = TBL["name_en"].map(lambda x: x.lower().strip()) +GOOD_SCH = json.load(open(os.path.join(current_file_path, "res/good_sch.json"), "r")) +GOOD_SCH = set([re.sub(r"[,. &（）()]+", "", c) for c in GOOD_SCH]) + + +def loadRank(fnm): + global TBL + TBL["rank"] = 1000000 + with open(fnm, "r",encoding='UTF-8') as f: + while True: + l = f.readline() + if not l:break + l = l.strip("\n").split(",") + try: + nm,rk = l[0].strip(),int(l[1]) + #assert len(TBL[((TBL.name_cn == nm) | (TBL.name_en == nm))]),f"<{nm}>" + TBL.loc[((TBL.name_cn == nm) | (TBL.name_en == nm)), "rank"] = rk + except Exception as e: + pass + + +loadRank(os.path.join(current_file_path, "res/school.rank.csv")) + + +def split(txt): + tks = [] + for t in re.sub(r"[ \t]+", " ",txt).split(" "): + if tks and re.match(r".*[a-zA-Z]$", tks[-1]) and \ + re.match(r"[a-zA-Z]", t) and tks: + tks[-1] = tks[-1] + " " + t + else:tks.append(t) + return tks + + +def select(nm): + global TBL + if not nm:return + if isinstance(nm, list):nm = str(nm[0]) + nm = split(nm)[0] + nm = str(nm).lower().strip() + nm = re.sub(r"[(（][^()（）]+[)）]", "", nm.lower()) + nm = re.sub(r"(^the |[,.&（）();；·]+|^(英国|美国|瑞士))", "", nm) + nm = re.sub(r"大学.*学院", "大学", nm) + tbl = copy.deepcopy(TBL) + tbl["hit_alias"] = tbl["alias"].map(lambda x:nm in set(x.split("+"))) + res = tbl[((tbl.name_cn == nm) | (tbl.name_en == nm) | (tbl.hit_alias == True))] + if res.empty:return + + return json.loads(res.to_json(orient="records"))[0] + + +def is_good(nm): + global GOOD_SCH + nm = re.sub(r"[(（][^()（）]+[)）]", "", nm.lower()) + nm = re.sub(r"[''`‘’“”,. &（）();；]+", "", nm) + return nm in GOOD_SCH + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/step_one.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/step_one.py new file mode 100644 index 00000000..7ae9839b --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/step_one.py @@ -0,0 +1,174 @@ +# -*- coding: utf-8 -*- +import json +from deepdoc.parser.resume.entities import degrees, regions, industries + +FIELDS = [ +"address STRING", +"annual_salary int", +"annual_salary_from int", +"annual_salary_to int", +"birth STRING", +"card STRING", +"certificate_obj string", +"city STRING", +"corporation_id int", +"corporation_name STRING", +"corporation_type STRING", +"degree STRING", +"discipline_name STRING", +"education_obj string", +"email STRING", +"expect_annual_salary int", +"expect_city_names string", +"expect_industry_name STRING", +"expect_position_name STRING", +"expect_salary_from int", +"expect_salary_to int", +"expect_type STRING", +"gender STRING", +"industry_name STRING", +"industry_names STRING", +"is_deleted STRING", +"is_fertility STRING", +"is_house STRING", +"is_management_experience STRING", +"is_marital STRING", +"is_oversea STRING", +"language_obj string", +"name STRING", +"nation STRING", +"phone STRING", +"political_status STRING", +"position_name STRING", +"project_obj string", +"responsibilities string", +"salary_month int", +"scale STRING", +"school_name STRING", +"self_remark string", +"skill_obj string", +"title_name STRING", +"tob_resume_id STRING", +"updated_at Timestamp", +"wechat STRING", +"work_obj string", +"work_experience int", +"work_start_time BIGINT" +] + +def refactor(df): + def deal_obj(obj, k, kk): + if not isinstance(obj, type({})): + return "" + obj = obj.get(k, {}) + if not isinstance(obj, type({})): + return "" + return obj.get(kk, "") + + def loadjson(line): + try: + return json.loads(line) + except Exception as e: + pass + return {} + + df["obj"] = df["resume_content"].map(lambda x: loadjson(x)) + df.fillna("", inplace=True) + + clms = ["tob_resume_id", "updated_at"] + + def extract(nms, cc=None): + nonlocal clms + clms.extend(nms) + for c in nms: + if cc: + df[c] = df["obj"].map(lambda x: deal_obj(x, cc, c)) + else: + df[c] = df["obj"].map( + lambda x: json.dumps( + x.get( + c, + {}), + ensure_ascii=False) if isinstance( + x, + type( + {})) and ( + isinstance( + x.get(c), + type( + {})) or not x.get(c)) else str(x).replace( + "None", + "")) + + extract(["education", "work", "certificate", "project", "language", + "skill"]) + extract(["wechat", "phone", "is_deleted", + "name", "tel", "email"], "contact") + extract(["nation", "expect_industry_name", "salary_month", + "industry_ids", "is_house", "birth", "annual_salary_from", + "annual_salary_to", "card", + "expect_salary_to", "expect_salary_from", + "expect_position_name", "gender", "city", + "is_fertility", "expect_city_names", + "political_status", "title_name", "expect_annual_salary", + "industry_name", "address", "position_name", "school_name", + "corporation_id", + "is_oversea", "responsibilities", + "work_start_time", "degree", "management_experience", + "expect_type", "corporation_type", "scale", "corporation_name", + "self_remark", "annual_salary", "work_experience", + "discipline_name", "marital", "updated_at"], "basic") + + df["degree"] = df["degree"].map(lambda x: degrees.get_name(x)) + df["address"] = df["address"].map(lambda x: " ".join(regions.get_names(x))) + df["industry_names"] = df["industry_ids"].map(lambda x: " ".join([" ".join(industries.get_names(i)) for i in + str(x).split(",")])) + clms.append("industry_names") + + def arr2str(a): + if not a: + return "" + if isinstance(a, list): + a = " ".join([str(i) for i in a]) + return str(a).replace(",", " ") + + df["expect_industry_name"] = df["expect_industry_name"].map( + lambda x: arr2str(x)) + df["gender"] = df["gender"].map( + lambda x: "男" if x == 'M' else ( + "女" if x == 'F' else "")) + for c in ["is_fertility", "is_oversea", "is_house", + "management_experience", "marital"]: + df[c] = df[c].map( + lambda x: '是' if x == 'Y' else ( + '否' if x == 'N' else "")) + df["is_management_experience"] = df["management_experience"] + df["is_marital"] = df["marital"] + clms.extend(["is_management_experience", "is_marital"]) + + df.fillna("", inplace=True) + for i in range(len(df)): + if not df.loc[i, "phone"].strip() and df.loc[i, "tel"].strip(): + df.loc[i, "phone"] = df.loc[i, "tel"].strip() + + for n in ["industry_ids", "management_experience", "marital", "tel"]: + for i in range(len(clms)): + if clms[i] == n: + del clms[i] + break + + clms = list(set(clms)) + + df = df.reindex(sorted(clms), axis=1) + #print(json.dumps(list(df.columns.values)), "LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL") + for c in clms: + df[c] = df[c].map( + lambda s: str(s).replace( + "\t", + " ").replace( + "\n", + "\\n").replace( + "\r", + "\\n")) + # print(df.values.tolist()) + return dict(zip([n.split(" ")[0] for n in FIELDS], df.values.tolist()[0])) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/step_two.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/step_two.py new file mode 100644 index 00000000..207cc791 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/parser/resume/step_two.py @@ -0,0 +1,580 @@ +# -*- coding: utf-8 -*- +import re, copy, time, datetime, demjson, \ + traceback, signal +import numpy as np +from deepdoc.parser.resume.entities import degrees, schools, corporations +from rag.nlp import huqie, surname +from xpinyin import Pinyin +from contextlib import contextmanager + + +class TimeoutException(Exception): pass + + +@contextmanager +def time_limit(seconds): + def signal_handler(signum, frame): + raise TimeoutException("Timed out!") + + signal.signal(signal.SIGALRM, signal_handler) + signal.alarm(seconds) + try: + yield + finally: + signal.alarm(0) + + +ENV = None +PY = Pinyin() + + +def rmHtmlTag(line): + return re.sub(r"<[a-z0-9.\"=';,:\+_/ -]+>", " ", line, 100000, re.IGNORECASE) + + +def highest_degree(dg): + if not dg: return "" + if type(dg) == type(""): dg = [dg] + m = {"初中": 0, "高中": 1, "中专": 2, "大专": 3, "专升本": 4, "本科": 5, "硕士": 6, "博士": 7, "博士后": 8} + return sorted([(d, m.get(d, -1)) for d in dg], key=lambda x: x[1] * -1)[0][0] + + +def forEdu(cv): + if not cv.get("education_obj"): + cv["integerity_flt"] *= 0.8 + return cv + + first_fea, fea, maj, fmaj, deg, fdeg, sch, fsch, st_dt, ed_dt = [], [], [], [], [], [], [], [], [], [] + edu_nst = [] + edu_end_dt = "" + cv["school_rank_int"] = 1000000 + for ii, n in enumerate(sorted(cv["education_obj"], key=lambda x: x.get("start_time", "3"))): + e = {} + if n.get("end_time"): + if n["end_time"] > edu_end_dt: edu_end_dt = n["end_time"] + try: + dt = n["end_time"] + if re.match(r"[0-9]{9,}", dt): dt = turnTm2Dt(dt) + y, m, d = getYMD(dt) + ed_dt.append(str(y)) + e["end_dt_kwd"] = str(y) + except Exception as e: + pass + if n.get("start_time"): + try: + dt = n["start_time"] + if re.match(r"[0-9]{9,}", dt): dt = turnTm2Dt(dt) + y, m, d = getYMD(dt) + st_dt.append(str(y)) + e["start_dt_kwd"] = str(y) + except Exception as e: + pass + + r = schools.select(n.get("school_name", "")) + if r: + if str(r.get("type", "")) == "1": fea.append("211") + if str(r.get("type", "")) == "2": fea.append("211") + if str(r.get("is_abroad", "")) == "1": fea.append("留学") + if str(r.get("is_double_first", "")) == "1": fea.append("双一流") + if str(r.get("is_985", "")) == "1": fea.append("985") + if str(r.get("is_world_known", "")) == "1": fea.append("海外知名") + if r.get("rank") and cv["school_rank_int"] > r["rank"]: cv["school_rank_int"] = r["rank"] + + if n.get("school_name") and isinstance(n["school_name"], str): + sch.append(re.sub(r"(211|985|重点大学|[,&;；-])", "", n["school_name"])) + e["sch_nm_kwd"] = sch[-1] + fea.append(huqie.qieqie(huqie.qie(n.get("school_name", ""))).split(" ")[-1]) + + if n.get("discipline_name") and isinstance(n["discipline_name"], str): + maj.append(n["discipline_name"]) + e["major_kwd"] = n["discipline_name"] + + if not n.get("degree") and "985" in fea and not first_fea: n["degree"] = "1" + + if n.get("degree"): + d = degrees.get_name(n["degree"]) + if d: e["degree_kwd"] = d + if d == "本科" and ("专科" in deg or "专升本" in deg or "中专" in deg or "大专" in deg or re.search(r"(成人|自考|自学考试)", + n.get( + "school_name", + ""))): d = "专升本" + if d: deg.append(d) + + # for first degree + if not fdeg and d in ["中专", "专升本", "专科", "本科", "大专"]: + fdeg = [d] + if n.get("school_name"): fsch = [n["school_name"]] + if n.get("discipline_name"): fmaj = [n["discipline_name"]] + first_fea = copy.deepcopy(fea) + + edu_nst.append(e) + + cv["sch_rank_kwd"] = [] + if cv["school_rank_int"] <= 20 \ + or ("海外名校" in fea and cv["school_rank_int"] <= 200): + cv["sch_rank_kwd"].append("顶尖学校") + elif cv["school_rank_int"] <= 50 and cv["school_rank_int"] > 20 \ + or ("海外名校" in fea and cv["school_rank_int"] <= 500 and \ + cv["school_rank_int"] > 200): + cv["sch_rank_kwd"].append("精英学校") + elif cv["school_rank_int"] > 50 and ("985" in fea or "211" in fea) \ + or ("海外名校" in fea and cv["school_rank_int"] > 500): + cv["sch_rank_kwd"].append("优质学校") + else: + cv["sch_rank_kwd"].append("一般学校") + + if edu_nst: cv["edu_nst"] = edu_nst + if fea: cv["edu_fea_kwd"] = list(set(fea)) + if first_fea: cv["edu_first_fea_kwd"] = list(set(first_fea)) + if maj: cv["major_kwd"] = maj + if fsch: cv["first_school_name_kwd"] = fsch + if fdeg: cv["first_degree_kwd"] = fdeg + if fmaj: cv["first_major_kwd"] = fmaj + if st_dt: cv["edu_start_kwd"] = st_dt + if ed_dt: cv["edu_end_kwd"] = ed_dt + if ed_dt: cv["edu_end_int"] = max([int(t) for t in ed_dt]) + if deg: + if "本科" in deg and "专科" in deg: + deg.append("专升本") + deg = [d for d in deg if d != '本科'] + cv["degree_kwd"] = deg + cv["highest_degree_kwd"] = highest_degree(deg) + if edu_end_dt: + try: + if re.match(r"[0-9]{9,}", edu_end_dt): edu_end_dt = turnTm2Dt(edu_end_dt) + if edu_end_dt.strip("\n") == "至今": edu_end_dt = cv.get("updated_at_dt", str(datetime.date.today())) + y, m, d = getYMD(edu_end_dt) + cv["work_exp_flt"] = min(int(str(datetime.date.today())[0:4]) - int(y), cv.get("work_exp_flt", 1000)) + except Exception as e: + print("EXCEPTION: ", e, edu_end_dt, cv.get("work_exp_flt")) + if sch: + cv["school_name_kwd"] = sch + if (len(cv.get("degree_kwd", [])) >= 1 and "本科" in cv["degree_kwd"]) \ + or all([c.lower() in ["硕士", "博士", "mba", "博士后"] for c in cv.get("degree_kwd", [])]) \ + or not cv.get("degree_kwd"): + for c in sch: + if schools.is_good(c): + if "tag_kwd" not in cv: cv["tag_kwd"] = [] + cv["tag_kwd"].append("好学校") + cv["tag_kwd"].append("好学历") + break + if (len(cv.get("degree_kwd", [])) >= 1 and \ + "本科" in cv["degree_kwd"] and \ + any([d.lower() in ["硕士", "博士", "mba", "博士"] for d in cv.get("degree_kwd", [])])) \ + or all([d.lower() in ["硕士", "博士", "mba", "博士后"] for d in cv.get("degree_kwd", [])]) \ + or any([d in ["mba", "emba", "博士后"] for d in cv.get("degree_kwd", [])]): + if "tag_kwd" not in cv: cv["tag_kwd"] = [] + if "好学历" not in cv["tag_kwd"]: cv["tag_kwd"].append("好学历") + + if cv.get("major_kwd"): cv["major_tks"] = huqie.qie(" ".join(maj)) + if cv.get("school_name_kwd"): cv["school_name_tks"] = huqie.qie(" ".join(sch)) + if cv.get("first_school_name_kwd"): cv["first_school_name_tks"] = huqie.qie(" ".join(fsch)) + if cv.get("first_major_kwd"): cv["first_major_tks"] = huqie.qie(" ".join(fmaj)) + + return cv + + +def forProj(cv): + if not cv.get("project_obj"): return cv + + pro_nms, desc = [], [] + for i, n in enumerate( + sorted(cv.get("project_obj", []), key=lambda x: str(x.get("updated_at", "")) if type(x) == type({}) else "", + reverse=True)): + if n.get("name"): pro_nms.append(n["name"]) + if n.get("describe"): desc.append(str(n["describe"])) + if n.get("responsibilities"): desc.append(str(n["responsibilities"])) + if n.get("achivement"): desc.append(str(n["achivement"])) + + if pro_nms: + # cv["pro_nms_tks"] = huqie.qie(" ".join(pro_nms)) + cv["project_name_tks"] = huqie.qie(pro_nms[0]) + if desc: + cv["pro_desc_ltks"] = huqie.qie(rmHtmlTag(" ".join(desc))) + cv["project_desc_ltks"] = huqie.qie(rmHtmlTag(desc[0])) + + return cv + + +def json_loads(line): + return demjson.decode(re.sub(r": *(True|False)", r": '\1'", line)) + + +def forWork(cv): + if not cv.get("work_obj"): + cv["integerity_flt"] *= 0.7 + return cv + + flds = ["position_name", "corporation_name", "corporation_id", "responsibilities", + "industry_name", "subordinates_count"] + duas = [] + scales = [] + fea = {c: [] for c in flds} + latest_job_tm = "" + goodcorp = False + goodcorp_ = False + work_st_tm = "" + corp_tags = [] + for i, n in enumerate( + sorted(cv.get("work_obj", []), key=lambda x: str(x.get("start_time", "")) if type(x) == type({}) else "", + reverse=True)): + if type(n) == type(""): + try: + n = json_loads(n) + except Exception as e: + continue + + if n.get("start_time") and (not work_st_tm or n["start_time"] < work_st_tm): work_st_tm = n["start_time"] + for c in flds: + if not n.get(c) or str(n[c]) == '0': + fea[c].append("") + continue + if c == "corporation_name": + n[c] = corporations.corpNorm(n[c], False) + if corporations.is_good(n[c]): + if i == 0: + goodcorp = True + else: + goodcorp_ = True + ct = corporations.corp_tag(n[c]) + if i == 0: + corp_tags.extend(ct) + elif ct and ct[0] != "软外": + corp_tags.extend([f"{t}(曾)" for t in ct]) + + fea[c].append(rmHtmlTag(str(n[c]).lower())) + + y, m, d = getYMD(n.get("start_time")) + if not y or not m: continue + st = "%s-%02d-%02d" % (y, int(m), int(d)) + latest_job_tm = st + + y, m, d = getYMD(n.get("end_time")) + if (not y or not m) and i > 0: continue + if not y or not m or int(y) > 2022: y, m, d = getYMD(str(n.get("updated_at", ""))) + if not y or not m: continue + ed = "%s-%02d-%02d" % (y, int(m), int(d)) + + try: + duas.append((datetime.datetime.strptime(ed, "%Y-%m-%d") - datetime.datetime.strptime(st, "%Y-%m-%d")).days) + except Exception as e: + print("kkkkkkkkkkkkkkkkkkkk", n.get("start_time"), n.get("end_time")) + + if n.get("scale"): + r = re.search(r"^([0-9]+)", str(n["scale"])) + if r: scales.append(int(r.group(1))) + + if goodcorp: + if "tag_kwd" not in cv: cv["tag_kwd"] = [] + cv["tag_kwd"].append("好公司") + if goodcorp_: + if "tag_kwd" not in cv: cv["tag_kwd"] = [] + cv["tag_kwd"].append("好公司(曾)") + + if corp_tags: + if "tag_kwd" not in cv: cv["tag_kwd"] = [] + cv["tag_kwd"].extend(corp_tags) + cv["corp_tag_kwd"] = [c for c in corp_tags if re.match(r"(综合|行业)", c)] + + if latest_job_tm: cv["latest_job_dt"] = latest_job_tm + if fea["corporation_id"]: cv["corporation_id"] = fea["corporation_id"] + + if fea["position_name"]: + cv["position_name_tks"] = huqie.qie(fea["position_name"][0]) + cv["position_name_sm_tks"] = huqie.qieqie(cv["position_name_tks"]) + cv["pos_nm_tks"] = huqie.qie(" ".join(fea["position_name"][1:])) + + if fea["industry_name"]: + cv["industry_name_tks"] = huqie.qie(fea["industry_name"][0]) + cv["industry_name_sm_tks"] = huqie.qieqie(cv["industry_name_tks"]) + cv["indu_nm_tks"] = huqie.qie(" ".join(fea["industry_name"][1:])) + + if fea["corporation_name"]: + cv["corporation_name_kwd"] = fea["corporation_name"][0] + cv["corp_nm_kwd"] = fea["corporation_name"] + cv["corporation_name_tks"] = huqie.qie(fea["corporation_name"][0]) + cv["corporation_name_sm_tks"] = huqie.qieqie(cv["corporation_name_tks"]) + cv["corp_nm_tks"] = huqie.qie(" ".join(fea["corporation_name"][1:])) + + if fea["responsibilities"]: + cv["responsibilities_ltks"] = huqie.qie(fea["responsibilities"][0]) + cv["resp_ltks"] = huqie.qie(" ".join(fea["responsibilities"][1:])) + + if fea["subordinates_count"]: fea["subordinates_count"] = [int(i) for i in fea["subordinates_count"] if + re.match(r"[^0-9]+$", str(i))] + if fea["subordinates_count"]: cv["max_sub_cnt_int"] = np.max(fea["subordinates_count"]) + + if type(cv.get("corporation_id")) == type(1): cv["corporation_id"] = [str(cv["corporation_id"])] + if not cv.get("corporation_id"): cv["corporation_id"] = [] + for i in cv.get("corporation_id", []): + cv["baike_flt"] = max(corporations.baike(i), cv["baike_flt"] if "baike_flt" in cv else 0) + + if work_st_tm: + try: + if re.match(r"[0-9]{9,}", work_st_tm): work_st_tm = turnTm2Dt(work_st_tm) + y, m, d = getYMD(work_st_tm) + cv["work_exp_flt"] = min(int(str(datetime.date.today())[0:4]) - int(y), cv.get("work_exp_flt", 1000)) + except Exception as e: + print("EXCEPTION: ", e, work_st_tm, cv.get("work_exp_flt")) + + cv["job_num_int"] = 0 + if duas: + cv["dua_flt"] = np.mean(duas) + cv["cur_dua_int"] = duas[0] + cv["job_num_int"] = len(duas) + if scales: cv["scale_flt"] = np.max(scales) + return cv + + +def turnTm2Dt(b): + if not b: return + b = str(b).strip() + if re.match(r"[0-9]{10,}", b): b = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(b[:10]))) + return b + + +def getYMD(b): + y, m, d = "", "", "01" + if not b: return (y, m, d) + b = turnTm2Dt(b) + if re.match(r"[0-9]{4}", b): y = int(b[:4]) + r = re.search(r"[0-9]{4}.?([0-9]{1,2})", b) + if r: m = r.group(1) + r = re.search(r"[0-9]{4}.?[0-9]{,2}.?([0-9]{1,2})", b) + if r: d = r.group(1) + if not d or int(d) == 0 or int(d) > 31: d = "1" + if not m or int(m) > 12 or int(m) < 1: m = "1" + return (y, m, d) + + +def birth(cv): + if not cv.get("birth"): + cv["integerity_flt"] *= 0.9 + return cv + y, m, d = getYMD(cv["birth"]) + if not m or not y: return cv + b = "%s-%02d-%02d" % (y, int(m), int(d)) + cv["birth_dt"] = b + cv["birthday_kwd"] = "%02d%02d" % (int(m), int(d)) + + cv["age_int"] = datetime.datetime.now().year - int(y) + return cv + + +def parse(cv): + for k in cv.keys(): + if cv[k] == '\\N': cv[k] = '' + # cv = cv.asDict() + tks_fld = ["address", "corporation_name", "discipline_name", "email", "expect_city_names", + "expect_industry_name", "expect_position_name", "industry_name", "industry_names", "name", + "position_name", "school_name", "self_remark", "title_name"] + small_tks_fld = ["corporation_name", "expect_position_name", "position_name", "school_name", "title_name"] + kwd_fld = ["address", "city", "corporation_type", "degree", "discipline_name", "expect_city_names", "email", + "expect_industry_name", "expect_position_name", "expect_type", "gender", "industry_name", + "industry_names", "political_status", "position_name", "scale", "school_name", "phone", "tel"] + num_fld = ["annual_salary", "annual_salary_from", "annual_salary_to", "expect_annual_salary", "expect_salary_from", + "expect_salary_to", "salary_month"] + + is_fld = [ + ("is_fertility", "已育", "未育"), + ("is_house", "有房", "没房"), + ("is_management_experience", "有管理经验", "无管理经验"), + ("is_marital", "已婚", "未婚"), + ("is_oversea", "有海外经验", "无海外经验") + ] + + rmkeys = [] + for k in cv.keys(): + if cv[k] is None: rmkeys.append(k) + if (type(cv[k]) == type([]) or type(cv[k]) == type("")) and len(cv[k]) == 0: rmkeys.append(k) + for k in rmkeys: del cv[k] + + integerity = 0. + flds_num = 0. + + def hasValues(flds): + nonlocal integerity, flds_num + flds_num += len(flds) + for f in flds: + v = str(cv.get(f, "")) + if len(v) > 0 and v != '0' and v != '[]': integerity += 1 + + hasValues(tks_fld) + hasValues(small_tks_fld) + hasValues(kwd_fld) + hasValues(num_fld) + cv["integerity_flt"] = integerity / flds_num + + if cv.get("corporation_type"): + for p, r in [(r"(公司|企业|其它|其他|Others*|\n|未填写|Enterprises|Company|companies)", ""), + (r"[／/．·　<\(（]+.*", ""), + (r".*(合资|民企|股份制|中外|私营|个体|Private|创业|Owned|投资).*", "民营"), + (r".*(机关|事业).*", "机关"), + (r".*(非盈利|Non-profit).*", "非盈利"), + (r".*(外企|外商|欧美|foreign|Institution|Australia|港资).*", "外企"), + (r".*国有.*", "国企"), + (r"[ （）\(\)人/·0-9-]+", ""), + (r".*(元|规模|于|=|北京|上海|至今|中国|工资|州|shanghai|强|餐饮|融资|职).*", "")]: + cv["corporation_type"] = re.sub(p, r, cv["corporation_type"], 1000, re.IGNORECASE) + if len(cv["corporation_type"]) < 2: del cv["corporation_type"] + + if cv.get("political_status"): + for p, r in [ + (r".*党员.*", "党员"), + (r".*(无党派|公民).*", "群众"), + (r".*团员.*", "团员")]: + cv["political_status"] = re.sub(p, r, cv["political_status"]) + if not re.search(r"[党团群]", cv["political_status"]): del cv["political_status"] + + if cv.get("phone"): cv["phone"] = re.sub(r"^0*86([0-9]{11})", r"\1", re.sub(r"[^0-9]+", "", cv["phone"])) + + keys = list(cv.keys()) + for k in keys: + # deal with json objects + if k.find("_obj") > 0: + try: + cv[k] = json_loads(cv[k]) + cv[k] = [a for _, a in cv[k].items()] + nms = [] + for n in cv[k]: + if type(n) != type({}) or "name" not in n or not n.get("name"): continue + n["name"] = re.sub(r"(（442）|\t )", "", n["name"]).strip().lower() + if not n["name"]: continue + nms.append(n["name"]) + if nms: + t = k[:-4] + cv[f"{t}_kwd"] = nms + cv[f"{t}_tks"] = huqie.qie(" ".join(nms)) + except Exception as e: + print("【EXCEPTION】:", str(traceback.format_exc()), cv[k]) + cv[k] = [] + + # tokenize fields + if k in tks_fld: + cv[f"{k}_tks"] = huqie.qie(cv[k]) + if k in small_tks_fld: cv[f"{k}_sm_tks"] = huqie.qie(cv[f"{k}_tks"]) + + # keyword fields + if k in kwd_fld: cv[f"{k}_kwd"] = [n.lower() + for n in re.split(r"[\t,，；;. ]", + re.sub(r"([^a-zA-Z])[ ]+([^a-zA-Z ])", r"\1，\2", cv[k]) + ) if n] + + if k in num_fld and cv.get(k): cv[f"{k}_int"] = cv[k] + + cv["email_kwd"] = cv.get("email_tks", "").replace(" ", "") + # for name field + if cv.get("name"): + nm = re.sub(r"[\n——\-\(（\+].*", "", cv["name"].strip()) + nm = re.sub(r"[ \t　]+", " ", nm) + if re.match(r"[a-zA-Z ]+$", nm): + if len(nm.split(" ")) > 1: + cv["name"] = nm + else: + nm = "" + elif nm and (surname.isit(nm[0]) or surname.isit(nm[:2])): + nm = re.sub(r"[a-zA-Z]+.*", "", nm[:5]) + else: + nm = "" + cv["name"] = nm.strip() + name = cv["name"] + + # name pingyin and its prefix + cv["name_py_tks"] = " ".join(PY.get_pinyins(nm[:20], '')) + " " + " ".join(PY.get_pinyins(nm[:20], ' ')) + cv["name_py_pref0_tks"] = "" + cv["name_py_pref_tks"] = "" + for py in PY.get_pinyins(nm[:20], ''): + for i in range(2, len(py) + 1): cv["name_py_pref_tks"] += " " + py[:i] + for py in PY.get_pinyins(nm[:20], ' '): + py = py.split(" ") + for i in range(1, len(py) + 1): cv["name_py_pref0_tks"] += " " + "".join(py[:i]) + + cv["name_kwd"] = name + cv["name_pinyin_kwd"] = PY.get_pinyins(nm[:20], ' ')[:3] + cv["name_tks"] = ( + huqie.qie(name) + " " + (" ".join(list(name)) if not re.match(r"[a-zA-Z ]+$", name) else "") + ) if name else "" + else: + cv["integerity_flt"] /= 2. + + if cv.get("phone"): + r = re.search(r"(1[3456789][0-9]{9})", cv["phone"]) + if not r: + cv["phone"] = "" + else: + cv["phone"] = r.group(1) + + # deal with date fields + if cv.get("updated_at") and isinstance(cv["updated_at"], datetime.datetime): + cv["updated_at_dt"] = cv["updated_at"].strftime('%Y-%m-%d %H:%M:%S') + else: + y, m, d = getYMD(str(cv.get("updated_at", ""))) + if not y: y = "2012" + if not m: m = "01" + if not d: d = "01" + cv["updated_at_dt"] = f"%s-%02d-%02d 00:00:00" % (y, int(m), int(d)) + # long text tokenize + + if cv.get("responsibilities"): cv["responsibilities_ltks"] = huqie.qie(rmHtmlTag(cv["responsibilities"])) + + # for yes or no field + fea = [] + for f, y, n in is_fld: + if f not in cv: continue + if cv[f] == '是': fea.append(y) + if cv[f] == '否': fea.append(n) + + if fea: cv["tag_kwd"] = fea + + cv = forEdu(cv) + cv = forProj(cv) + cv = forWork(cv) + cv = birth(cv) + + cv["corp_proj_sch_deg_kwd"] = [c for c in cv.get("corp_tag_kwd", [])] + for i in range(len(cv["corp_proj_sch_deg_kwd"])): + for j in cv.get("sch_rank_kwd", []): cv["corp_proj_sch_deg_kwd"][i] += "+" + j + for i in range(len(cv["corp_proj_sch_deg_kwd"])): + if cv.get("highest_degree_kwd"): cv["corp_proj_sch_deg_kwd"][i] += "+" + cv["highest_degree_kwd"] + + try: + if not cv.get("work_exp_flt") and cv.get("work_start_time"): + if re.match(r"[0-9]{9,}", str(cv["work_start_time"])): + cv["work_start_dt"] = turnTm2Dt(cv["work_start_time"]) + cv["work_exp_flt"] = (time.time() - int(int(cv["work_start_time"]) / 1000)) / 3600. / 24. / 365. + elif re.match(r"[0-9]{4}[^0-9]", str(cv["work_start_time"])): + y, m, d = getYMD(str(cv["work_start_time"])) + cv["work_start_dt"] = f"%s-%02d-%02d 00:00:00" % (y, int(m), int(d)) + cv["work_exp_flt"] = int(str(datetime.date.today())[0:4]) - int(y) + except Exception as e: + print("【EXCEPTION】", e, "==>", cv.get("work_start_time")) + if "work_exp_flt" not in cv and cv.get("work_experience", 0): cv["work_exp_flt"] = int(cv["work_experience"]) / 12. + + keys = list(cv.keys()) + for k in keys: + if not re.search(r"_(fea|tks|nst|dt|int|flt|ltks|kwd|id)$", k): del cv[k] + for k in cv.keys(): + if not re.search("_(kwd|id)$", k) or type(cv[k]) != type([]): continue + cv[k] = list(set([re.sub("(市)$", "", str(n)) for n in cv[k] if n not in ['中国', '0']])) + keys = [k for k in cv.keys() if re.search(r"_feas*$", k)] + for k in keys: + if cv[k] <= 0: del cv[k] + + cv["tob_resume_id"] = str(cv["tob_resume_id"]) + cv["id"] = cv["tob_resume_id"] + print("CCCCCCCCCCCCCCC") + + return dealWithInt64(cv) + + +def dealWithInt64(d): + if isinstance(d, dict): + for n, v in d.items(): + d[n] = dealWithInt64(v) + + if isinstance(d, list): + d = [dealWithInt64(t) for t in d] + + if isinstance(d, np.integer): d = int(d) + return d + diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/__init__.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/__init__.py new file mode 100644 index 00000000..5468f367 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/__init__.py @@ -0,0 +1,47 @@ +from .recognizer import Recognizer +from .layout_recognizer import LayoutRecognizer +from .table_structure_recognizer_lore import TableStructureRecognizer_LORE + +def init_in_out(args): + from PIL import Image + import fitz + import os + import traceback + from core.utils.utils import traversal_files + images = [] + outputs = [] + + if not os.path.exists(args.output_dir): + os.mkdir(args.output_dir) + + def pdf_pages(fnm, zoomin=3): + nonlocal outputs, images + pdf = fitz.open(fnm) + mat = fitz.Matrix(zoomin, zoomin) + for i, page in enumerate(pdf): + pix = page.get_pixmap(matrix=mat) + img = Image.frombytes("RGB", [pix.width, pix.height], + pix.samples) + images.append(img) + outputs.append(os.path.split(fnm)[-1] + f"_{i}.jpg") + + def images_and_outputs(fnm): + nonlocal outputs, images + if fnm.split(".")[-1].lower() == "pdf": + pdf_pages(fnm) + return + try: + images.append(Image.open(fnm)) + outputs.append(os.path.split(fnm)[-1]) + except Exception as e: + traceback.print_exc() + + if os.path.isdir(args.inputs): + for fnm in traversal_files(args.inputs): + images_and_outputs(fnm) + else: + images_and_outputs(args.inputs) + + for i in range(len(outputs)): outputs[i] = os.path.join(args.output_dir, outputs[i]) + + return images, outputs \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/layout_recognizer.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/layout_recognizer.py new file mode 100644 index 00000000..c3130bf7 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/layout_recognizer.py @@ -0,0 +1,134 @@ +import os +import re +from collections import Counter +from copy import deepcopy +import numpy as np +from qanything_kernel.utils.loader.pdf_to_markdown.core.vision import Recognizer +from qanything_kernel.configs.model_config import PDF_MODEL_PATH +from tqdm import tqdm + + +class LayoutRecognizer(Recognizer): + labels = ['Text', 'Title', 'Figure', 'Equation', 'Table', + 'Caption', 'Header', 'Footer', 'BibInfo', 'Reference', + 'Content', 'Code', 'Other', 'Item', 'Author'] + + def __init__(self, domain): + model_dir = os.path.join( + PDF_MODEL_PATH, + "checkpoints/layout") + super().__init__(self.labels, domain, model_dir) + self.garbage_layouts = ["footer", "header"] + + def __call__(self, image_list, ocr_res, scale_factor=3, thr=0.25, batch_size=16, drop=True): + def __is_garbage(b): + patt = ['\* Corresponding Author', '\*Corresponding to'] + return any([re.search(p, b["text"]) for p in patt]) + + layouts = super().__call__(image_list, thr, batch_size) + # save_results(image_list, layouts, self.labels, output_dir='output/', threshold=0.7) + assert len(image_list) == len(ocr_res) + # Tag layout type + boxes = [] + assert len(image_list) == len(layouts) + garbages = {} + page_layout = [] + for pn, lts in tqdm(enumerate(layouts)): + bxs = ocr_res[pn] + lts = [{"type": b["type"], + "score": float(b["score"]), + "x0": b["bbox"][0] / scale_factor, "x1": b["bbox"][2] / scale_factor, + "top": b["bbox"][1] / scale_factor, "bottom": b["bbox"][-1] / scale_factor, + "page_number": pn, + } for b in lts] + lts = self.sort_Y_firstly(lts, np.mean( + [l["bottom"] - l["top"] for l in lts]) / 2) + lts = self.layouts_cleanup(bxs, lts) + if pn == 0: + try: + idx = [b['x0'] for b in lts].index(min([b['x0'] for b in lts if b['type'] == 'text'])) + if (lts[idx]['bottom']-lts[idx]['top'])/(lts[idx]['x1']-lts[idx]['x0']) > 15: + lts.pop(idx) + except: + lts = lts + page_layout.append(lts) + + # Tag layout type, layouts are ready + def findLayout(ty): + nonlocal bxs, lts, self + lts_ = [lt for lt in lts if lt["type"] == ty] + i = 0 + while i < len(bxs): + if bxs[i].get("layout_type"): + i += 1 + continue + if __is_garbage(bxs[i]): + bxs.pop(i) + continue + + ii = self.find_overlapped_with_threashold(bxs[i], lts_, + thr=0.4) + + if ii is None: # belong to nothing + bxs[i]["layout_type"] = "" + i += 1 + continue + lts_[ii]["visited"] = True + keep_feats = [ + lts_[ + ii]["type"] == "footer" and bxs[i]["bottom"] < image_list[pn].size[1] * 0.9 / scale_factor, + lts_[ + ii]["type"] == "header" and bxs[i]["top"] > image_list[pn].size[1] * 0.1 / scale_factor, + ] + if drop and lts_[ + ii]["type"] in self.garbage_layouts and not any(keep_feats): + if lts_[ii]["type"] not in garbages: + garbages[lts_[ii]["type"]] = [] + garbages[lts_[ii]["type"]].append(bxs[i]["text"]) + bxs.pop(i) + continue + + bxs[i]["layoutno"] = f"{ty}-{ii}" + bxs[i]["layout_type"] = lts_[ii]["type"] if lts_[ + ii]["type"] != "equation" else "figure" + i += 1 + + for ty in ["footer", "header", "reference", "caption", "author", + "title", "table", "text", "figure", "equation", "content"]: + findLayout(ty) + # add box to figure layouts which has not text box + for i, lt in enumerate( + [lt for lt in lts if lt["type"] in ["figure", "equation"]]): + if lt.get("visited"): + continue + lt = deepcopy(lt) + del lt["type"] + lt["text"] = "" + lt["layout_type"] = "figure" + lt["layoutno"] = f"figure-{i}" + lt["page_number"] = pn + 1 + bxs.append(lt) + + lts_ = [lt for lt in lts if lt["type"] == 'item'] + for i, bx in enumerate(bxs): + if bx["layout_type"] != 'reference': continue + ii = self.find_overlapped_with_threashold(bx, lts_, + thr=0.4) + if ii is None: + continue + layoutno = bx["layoutno"] + bxs[i]["layoutno"] = f"{layoutno}-item-{ii}" + + boxes.extend(bxs) + + ocr_res = boxes + + garbag_set = set() + for k in garbages.keys(): + garbages[k] = Counter(garbages[k]) + for g, c in garbages[k].items(): + if c > 1: + garbag_set.add(g) + + ocr_res = [b for b in ocr_res if b["text"].strip() not in garbag_set] + return ocr_res, page_layout diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/operators.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/operators.py new file mode 100644 index 00000000..5f5736f4 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/operators.py @@ -0,0 +1,695 @@ +import sys +import six +import cv2 +import numpy as np +import math +from PIL import Image + + +class DecodeImage(object): + """ decode image """ + + def __init__(self, + img_mode='RGB', + channel_first=False, + ignore_orientation=False, + **kwargs): + self.img_mode = img_mode + self.channel_first = channel_first + self.ignore_orientation = ignore_orientation + + def __call__(self, data): + img = data['image'] + if six.PY2: + assert isinstance(img, str) and len( + img) > 0, "invalid input 'img' in DecodeImage" + else: + assert isinstance(img, bytes) and len( + img) > 0, "invalid input 'img' in DecodeImage" + img = np.frombuffer(img, dtype='uint8') + if self.ignore_orientation: + img = cv2.imdecode(img, cv2.IMREAD_IGNORE_ORIENTATION | + cv2.IMREAD_COLOR) + else: + img = cv2.imdecode(img, 1) + if img is None: + return None + if self.img_mode == 'GRAY': + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + elif self.img_mode == 'RGB': + assert img.shape[2] == 3, 'invalid shape of image[%s]' % ( + img.shape) + img = img[:, :, ::-1] + + if self.channel_first: + img = img.transpose((2, 0, 1)) + + data['image'] = img + return data + + +class StandardizeImage(object): + """normalize image + Args: + mean (list): im - mean + std (list): im / std + is_scale (bool): whether need im / 255 + norm_type (str): type in ['mean_std', 'none'] + """ + + def __init__(self, mean, std, is_scale=True, norm_type='mean_std'): + self.mean = mean + self.std = std + self.is_scale = is_scale + self.norm_type = norm_type + + def __call__(self, im, im_info): + """ + Args: + im (np.ndarray): image (np.ndarray) + im_info (dict): info of image + Returns: + im (np.ndarray): processed image (np.ndarray) + im_info (dict): info of processed image + """ + im = im.astype(np.float32, copy=False) + if self.is_scale: + scale = 1.0 / 255.0 + im *= scale + + if self.norm_type == 'mean_std': + mean = np.array(self.mean)[np.newaxis, np.newaxis, :] + std = np.array(self.std)[np.newaxis, np.newaxis, :] + im -= mean + im /= std + return im, im_info + + +class NormalizeImage(object): + """ normalize image such as substract mean, divide std + """ + + def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs): + if isinstance(scale, str): + scale = eval(scale) + self.scale = np.float32(scale if scale is not None else 1.0 / 255.0) + mean = mean if mean is not None else [0.485, 0.456, 0.406] + std = std if std is not None else [0.229, 0.224, 0.225] + + shape = (3, 1, 1) if order == 'chw' else (1, 1, 3) + self.mean = np.array(mean).reshape(shape).astype('float32') + self.std = np.array(std).reshape(shape).astype('float32') + + def __call__(self, data): + img = data['image'] + from PIL import Image + if isinstance(img, Image.Image): + img = np.array(img) + assert isinstance(img, + np.ndarray), "invalid input 'img' in NormalizeImage" + data['image'] = ( + img.astype('float32') * self.scale - self.mean) / self.std + return data + + +class ToCHWImage(object): + """ convert hwc image to chw image + """ + + def __init__(self, **kwargs): + pass + + def __call__(self, data): + img = data['image'] + from PIL import Image + if isinstance(img, Image.Image): + img = np.array(img) + data['image'] = img.transpose((2, 0, 1)) + return data + + +class Fasttext(object): + def __init__(self, path="None", **kwargs): + import fasttext + self.fast_model = fasttext.load_model(path) + + def __call__(self, data): + label = data['label'] + fast_label = self.fast_model[label] + data['fast_label'] = fast_label + return data + + +class KeepKeys(object): + def __init__(self, keep_keys, **kwargs): + self.keep_keys = keep_keys + + def __call__(self, data): + data_list = [] + for key in self.keep_keys: + data_list.append(data[key]) + return data_list + + +class Pad(object): + def __init__(self, size=None, size_div=32, **kwargs): + if size is not None and not isinstance(size, (int, list, tuple)): + raise TypeError("Type of target_size is invalid. Now is {}".format( + type(size))) + if isinstance(size, int): + size = [size, size] + self.size = size + self.size_div = size_div + + def __call__(self, data): + + img = data['image'] + img_h, img_w = img.shape[0], img.shape[1] + if self.size: + resize_h2, resize_w2 = self.size + assert ( + img_h < resize_h2 and img_w < resize_w2 + ), '(h, w) of target size should be greater than (img_h, img_w)' + else: + resize_h2 = max( + int(math.ceil(img.shape[0] / self.size_div) * self.size_div), + self.size_div) + resize_w2 = max( + int(math.ceil(img.shape[1] / self.size_div) * self.size_div), + self.size_div) + img = cv2.copyMakeBorder( + img, + 0, + resize_h2 - img_h, + 0, + resize_w2 - img_w, + cv2.BORDER_CONSTANT, + value=0) + data['image'] = img + return data + + +class LinearResize(object): + """resize image by target_size and max_size + Args: + target_size (int): the target size of image + keep_ratio (bool): whether keep_ratio or not, default true + interp (int): method of resize + """ + + def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR): + if isinstance(target_size, int): + target_size = [target_size, target_size] + self.target_size = target_size + self.keep_ratio = keep_ratio + self.interp = interp + + def __call__(self, im, im_info): + """ + Args: + im (np.ndarray): image (np.ndarray) + im_info (dict): info of image + Returns: + im (np.ndarray): processed image (np.ndarray) + im_info (dict): info of processed image + """ + assert len(self.target_size) == 2 + assert self.target_size[0] > 0 and self.target_size[1] > 0 + im_channel = im.shape[2] + im_scale_y, im_scale_x = self.generate_scale(im) + im = cv2.resize( + im, + None, + None, + fx=im_scale_x, + fy=im_scale_y, + interpolation=self.interp) + im_info['im_shape'] = np.array(im.shape[:2]).astype('float32') + im_info['scale_factor'] = np.array( + [im_scale_y, im_scale_x]).astype('float32') + return im, im_info + + def generate_scale(self, im): + """ + Args: + im (np.ndarray): image (np.ndarray) + Returns: + im_scale_x: the resize ratio of X + im_scale_y: the resize ratio of Y + """ + origin_shape = im.shape[:2] + im_c = im.shape[2] + if self.keep_ratio: + im_size_min = np.min(origin_shape) + im_size_max = np.max(origin_shape) + target_size_min = np.min(self.target_size) + target_size_max = np.max(self.target_size) + im_scale = float(target_size_min) / float(im_size_min) + if np.round(im_scale * im_size_max) > target_size_max: + im_scale = float(target_size_max) / float(im_size_max) + im_scale_x = im_scale + im_scale_y = im_scale + else: + resize_h, resize_w = self.target_size + im_scale_y = resize_h / float(origin_shape[0]) + im_scale_x = resize_w / float(origin_shape[1]) + return im_scale_y, im_scale_x + + +class Resize(object): + def __init__(self, size=(640, 640), **kwargs): + self.size = size + + def resize_image(self, img): + resize_h, resize_w = self.size + ori_h, ori_w = img.shape[:2] # (h, w, c) + ratio_h = float(resize_h) / ori_h + ratio_w = float(resize_w) / ori_w + img = cv2.resize(img, (int(resize_w), int(resize_h))) + return img, [ratio_h, ratio_w] + + def __call__(self, data): + img = data['image'] + if 'polys' in data: + text_polys = data['polys'] + + img_resize, [ratio_h, ratio_w] = self.resize_image(img) + if 'polys' in data: + new_boxes = [] + for box in text_polys: + new_box = [] + for cord in box: + new_box.append([cord[0] * ratio_w, cord[1] * ratio_h]) + new_boxes.append(new_box) + data['polys'] = np.array(new_boxes, dtype=np.float32) + data['image'] = img_resize + return data + + +class DetResizeForTest(object): + def __init__(self, **kwargs): + super(DetResizeForTest, self).__init__() + self.resize_type = 0 + self.keep_ratio = False + if 'image_shape' in kwargs: + self.image_shape = kwargs['image_shape'] + self.resize_type = 1 + if 'keep_ratio' in kwargs: + self.keep_ratio = kwargs['keep_ratio'] + elif 'limit_side_len' in kwargs: + self.limit_side_len = kwargs['limit_side_len'] + self.limit_type = kwargs.get('limit_type', 'min') + elif 'resize_long' in kwargs: + self.resize_type = 2 + self.resize_long = kwargs.get('resize_long', 960) + else: + self.limit_side_len = 736 + self.limit_type = 'min' + + def __call__(self, data): + img = data['image'] + src_h, src_w, _ = img.shape + if sum([src_h, src_w]) < 64: + img = self.image_padding(img) + + if self.resize_type == 0: + # img, shape = self.resize_image_type0(img) + img, [ratio_h, ratio_w] = self.resize_image_type0(img) + elif self.resize_type == 2: + img, [ratio_h, ratio_w] = self.resize_image_type2(img) + else: + # img, shape = self.resize_image_type1(img) + img, [ratio_h, ratio_w] = self.resize_image_type1(img) + data['image'] = img + data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w]) + return data + + def image_padding(self, im, value=0): + h, w, c = im.shape + im_pad = np.zeros((max(32, h), max(32, w), c), np.uint8) + value + im_pad[:h, :w, :] = im + return im_pad + + def resize_image_type1(self, img): + resize_h, resize_w = self.image_shape + ori_h, ori_w = img.shape[:2] # (h, w, c) + if self.keep_ratio is True: + resize_w = ori_w * resize_h / ori_h + N = math.ceil(resize_w / 32) + resize_w = N * 32 + ratio_h = float(resize_h) / ori_h + ratio_w = float(resize_w) / ori_w + img = cv2.resize(img, (int(resize_w), int(resize_h))) + # return img, np.array([ori_h, ori_w]) + return img, [ratio_h, ratio_w] + + def resize_image_type0(self, img): + """ + resize image to a size multiple of 32 which is required by the network + args: + img(array): array with shape [h, w, c] + return(tuple): + img, (ratio_h, ratio_w) + """ + limit_side_len = self.limit_side_len + h, w, c = img.shape + + # limit the max side + if self.limit_type == 'max': + if max(h, w) > limit_side_len: + if h > w: + ratio = float(limit_side_len) / h + else: + ratio = float(limit_side_len) / w + else: + ratio = 1. + elif self.limit_type == 'min': + if min(h, w) < limit_side_len: + if h < w: + ratio = float(limit_side_len) / h + else: + ratio = float(limit_side_len) / w + else: + ratio = 1. + elif self.limit_type == 'resize_long': + ratio = float(limit_side_len) / max(h, w) + else: + raise Exception('not support limit type, image ') + resize_h = int(h * ratio) + resize_w = int(w * ratio) + + resize_h = max(int(round(resize_h / 32) * 32), 32) + resize_w = max(int(round(resize_w / 32) * 32), 32) + + try: + if int(resize_w) <= 0 or int(resize_h) <= 0: + return None, (None, None) + img = cv2.resize(img, (int(resize_w), int(resize_h))) + except BaseException: + print(img.shape, resize_w, resize_h) + sys.exit(0) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + return img, [ratio_h, ratio_w] + + def resize_image_type2(self, img): + h, w, _ = img.shape + + resize_w = w + resize_h = h + + if resize_h > resize_w: + ratio = float(self.resize_long) / resize_h + else: + ratio = float(self.resize_long) / resize_w + + resize_h = int(resize_h * ratio) + resize_w = int(resize_w * ratio) + + max_stride = 128 + resize_h = (resize_h + max_stride - 1) // max_stride * max_stride + resize_w = (resize_w + max_stride - 1) // max_stride * max_stride + img = cv2.resize(img, (int(resize_w), int(resize_h))) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + + return img, [ratio_h, ratio_w] + + +class E2EResizeForTest(object): + def __init__(self, **kwargs): + super(E2EResizeForTest, self).__init__() + self.max_side_len = kwargs['max_side_len'] + self.valid_set = kwargs['valid_set'] + + def __call__(self, data): + img = data['image'] + src_h, src_w, _ = img.shape + if self.valid_set == 'totaltext': + im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext( + img, max_side_len=self.max_side_len) + else: + im_resized, (ratio_h, ratio_w) = self.resize_image( + img, max_side_len=self.max_side_len) + data['image'] = im_resized + data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w]) + return data + + def resize_image_for_totaltext(self, im, max_side_len=512): + + h, w, _ = im.shape + resize_w = w + resize_h = h + ratio = 1.25 + if h * ratio > max_side_len: + ratio = float(max_side_len) / resize_h + resize_h = int(resize_h * ratio) + resize_w = int(resize_w * ratio) + + max_stride = 128 + resize_h = (resize_h + max_stride - 1) // max_stride * max_stride + resize_w = (resize_w + max_stride - 1) // max_stride * max_stride + im = cv2.resize(im, (int(resize_w), int(resize_h))) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + return im, (ratio_h, ratio_w) + + def resize_image(self, im, max_side_len=512): + """ + resize image to a size multiple of max_stride which is required by the network + :param im: the resized image + :param max_side_len: limit of max image size to avoid out of memory in gpu + :return: the resized image and the resize ratio + """ + h, w, _ = im.shape + + resize_w = w + resize_h = h + + # Fix the longer side + if resize_h > resize_w: + ratio = float(max_side_len) / resize_h + else: + ratio = float(max_side_len) / resize_w + + resize_h = int(resize_h * ratio) + resize_w = int(resize_w * ratio) + + max_stride = 128 + resize_h = (resize_h + max_stride - 1) // max_stride * max_stride + resize_w = (resize_w + max_stride - 1) // max_stride * max_stride + im = cv2.resize(im, (int(resize_w), int(resize_h))) + ratio_h = resize_h / float(h) + ratio_w = resize_w / float(w) + + return im, (ratio_h, ratio_w) + + +class KieResize(object): + def __init__(self, **kwargs): + super(KieResize, self).__init__() + self.max_side, self.min_side = kwargs['img_scale'][0], kwargs[ + 'img_scale'][1] + + def __call__(self, data): + img = data['image'] + points = data['points'] + src_h, src_w, _ = img.shape + im_resized, scale_factor, [ratio_h, ratio_w + ], [new_h, new_w] = self.resize_image(img) + resize_points = self.resize_boxes(img, points, scale_factor) + data['ori_image'] = img + data['ori_boxes'] = points + data['points'] = resize_points + data['image'] = im_resized + data['shape'] = np.array([new_h, new_w]) + return data + + def resize_image(self, img): + norm_img = np.zeros([1024, 1024, 3], dtype='float32') + scale = [512, 1024] + h, w = img.shape[:2] + max_long_edge = max(scale) + max_short_edge = min(scale) + scale_factor = min(max_long_edge / max(h, w), + max_short_edge / min(h, w)) + resize_w, resize_h = int(w * float(scale_factor) + 0.5), int(h * float( + scale_factor) + 0.5) + max_stride = 32 + resize_h = (resize_h + max_stride - 1) // max_stride * max_stride + resize_w = (resize_w + max_stride - 1) // max_stride * max_stride + im = cv2.resize(img, (resize_w, resize_h)) + new_h, new_w = im.shape[:2] + w_scale = new_w / w + h_scale = new_h / h + scale_factor = np.array( + [w_scale, h_scale, w_scale, h_scale], dtype=np.float32) + norm_img[:new_h, :new_w, :] = im + return norm_img, scale_factor, [h_scale, w_scale], [new_h, new_w] + + def resize_boxes(self, im, points, scale_factor): + points = points * scale_factor + img_shape = im.shape[:2] + points[:, 0::2] = np.clip(points[:, 0::2], 0, img_shape[1]) + points[:, 1::2] = np.clip(points[:, 1::2], 0, img_shape[0]) + return points + + +class SRResize(object): + def __init__(self, + imgH=32, + imgW=128, + down_sample_scale=4, + keep_ratio=False, + min_ratio=1, + mask=False, + infer_mode=False, + **kwargs): + self.imgH = imgH + self.imgW = imgW + self.keep_ratio = keep_ratio + self.min_ratio = min_ratio + self.down_sample_scale = down_sample_scale + self.mask = mask + self.infer_mode = infer_mode + + def __call__(self, data): + imgH = self.imgH + imgW = self.imgW + images_lr = data["image_lr"] + transform2 = ResizeNormalize( + (imgW // self.down_sample_scale, imgH // self.down_sample_scale)) + images_lr = transform2(images_lr) + data["img_lr"] = images_lr + if self.infer_mode: + return data + + images_HR = data["image_hr"] + label_strs = data["label"] + transform = ResizeNormalize((imgW, imgH)) + images_HR = transform(images_HR) + data["img_hr"] = images_HR + return data + + +class ResizeNormalize(object): + def __init__(self, size, interpolation=Image.BICUBIC): + self.size = size + self.interpolation = interpolation + + def __call__(self, img): + img = img.resize(self.size, self.interpolation) + img_numpy = np.array(img).astype("float32") + img_numpy = img_numpy.transpose((2, 0, 1)) / 255 + return img_numpy + + +class GrayImageChannelFormat(object): + """ + format gray scale image's channel: (3,h,w) -> (1,h,w) + Args: + inverse: inverse gray image + """ + + def __init__(self, inverse=False, **kwargs): + self.inverse = inverse + + def __call__(self, data): + img = data['image'] + img_single_channel = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + img_expanded = np.expand_dims(img_single_channel, 0) + + if self.inverse: + data['image'] = np.abs(img_expanded - 1) + else: + data['image'] = img_expanded + + data['src_image'] = img + return data + + +class Permute(object): + """permute image + Args: + to_bgr (bool): whether convert RGB to BGR + channel_first (bool): whether convert HWC to CHW + """ + + def __init__(self, ): + super(Permute, self).__init__() + + def __call__(self, im, im_info): + """ + Args: + im (np.ndarray): image (np.ndarray) + im_info (dict): info of image + Returns: + im (np.ndarray): processed image (np.ndarray) + im_info (dict): info of processed image + """ + im = im.transpose((2, 0, 1)).copy() + return im, im_info + + +class PadStride(object): + """ padding image for model with FPN, instead PadBatch(pad_to_stride) in original config + Args: + stride (bool): model with FPN need image shape % stride == 0 + """ + + def __init__(self, stride=0): + self.coarsest_stride = stride + + def __call__(self, im, im_info): + """ + Args: + im (np.ndarray): image (np.ndarray) + im_info (dict): info of image + Returns: + im (np.ndarray): processed image (np.ndarray) + im_info (dict): info of processed image + """ + coarsest_stride = self.coarsest_stride + if coarsest_stride <= 0: + return im, im_info + im_c, im_h, im_w = im.shape + pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride) + pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride) + padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32) + padding_im[:, :im_h, :im_w] = im + return padding_im, im_info + + +def decode_image(im_file, im_info): + """read rgb image + Args: + im_file (str|np.ndarray): input can be image path or np.ndarray + im_info (dict): info of image + Returns: + im (np.ndarray): processed image (np.ndarray) + im_info (dict): info of processed image + """ + if isinstance(im_file, str): + with open(im_file, 'rb') as f: + im_read = f.read() + data = np.frombuffer(im_read, dtype='uint8') + im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode + im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + else: + im = im_file + im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32) + im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32) + return im, im_info + + +def preprocess(im, preprocess_ops): + # process image by preprocess_ops + im_info = { + 'scale_factor': np.array( + [1., 1.], dtype=np.float32), + 'im_shape': None, + } + im, im_info = decode_image(im, im_info) + for operator in preprocess_ops: + im, im_info = operator(im, im_info) + return im, im_info diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/recognizer.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/recognizer.py new file mode 100644 index 00000000..13968f67 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/recognizer.py @@ -0,0 +1,444 @@ +import os +from copy import deepcopy +import onnxruntime as ort +from qanything_kernel.utils.loader.pdf_to_markdown.core.vision.operators import * + +class Recognizer(object): + def __init__(self, label_list, task_name, model_dir=None): + """ + If you have trouble downloading HuggingFace models, -_^ this might help!! + + For Linux: + export HF_ENDPOINT=https://hf-mirror.com + + For Windows: + Good luck + ^_- + + """ + model_file_path = os.path.join(model_dir, task_name + ".onnx") + if not os.path.exists(model_file_path): + raise ValueError("not find model file path {}".format( + model_file_path)) + if False and ort.get_device() == "GPU": + options = ort.SessionOptions() + options.enable_cpu_mem_arena = False + self.ort_sess = ort.InferenceSession(model_file_path, options=options, providers=[('CUDAExecutionProvider')]) + else: + self.ort_sess = ort.InferenceSession(model_file_path, providers=['CPUExecutionProvider']) + self.input_names = [node.name for node in self.ort_sess.get_inputs()] + self.output_names = [node.name for node in self.ort_sess.get_outputs()] + self.input_shape = self.ort_sess.get_inputs()[0].shape[2:4] + self.label_list = label_list + + @staticmethod + def sort_Y_firstly(arr, threashold): + # sort using y1 first and then x1 + arr = sorted(arr, key=lambda r: (r["top"], r["x0"])) + for i in range(len(arr) - 1): + for j in range(i, -1, -1): + # restore the order using th + if abs(arr[j + 1]["top"] - arr[j]["top"]) < threashold \ + and arr[j + 1]["x0"] < arr[j]["x0"]: + tmp = deepcopy(arr[j]) + arr[j] = deepcopy(arr[j + 1]) + arr[j + 1] = deepcopy(tmp) + return arr + + @staticmethod + def sort_X_firstly(arr, threashold, copy=True): + # sort using y1 first and then x1 + arr = sorted(arr, key=lambda r: (r["x0"], r["top"])) + for i in range(len(arr) - 1): + for j in range(i, -1, -1): + # restore the order using th + if abs(arr[j + 1]["x0"] - arr[j]["x0"]) < threashold \ + and arr[j + 1]["top"] < arr[j]["top"]: + tmp = deepcopy(arr[j]) if copy else arr[j] + arr[j] = deepcopy(arr[j + 1]) if copy else arr[j + 1] + arr[j + 1] = deepcopy(tmp) if copy else tmp + return arr + + @staticmethod + def sort_C_firstly(arr, thr=0): + # sort using y1 first and then x1 + # sorted(arr, key=lambda r: (r["x0"], r["top"])) + arr = Recognizer.sort_X_firstly(arr, thr) + for i in range(len(arr) - 1): + for j in range(i, -1, -1): + # restore the order using th + if "C" not in arr[j] or "C" not in arr[j + 1]: + continue + if arr[j + 1]["C"] < arr[j]["C"] \ + or ( + arr[j + 1]["C"] == arr[j]["C"] + and arr[j + 1]["top"] < arr[j]["top"] + ): + tmp = arr[j] + arr[j] = arr[j + 1] + arr[j + 1] = tmp + return arr + + return sorted(arr, key=lambda r: (r.get("C", r["x0"]), r["top"])) + + @staticmethod + def sort_R_firstly(arr, thr=0): + # sort using y1 first and then x1 + # sorted(arr, key=lambda r: (r["top"], r["x0"])) + arr = Recognizer.sort_Y_firstly(arr, thr) + for i in range(len(arr) - 1): + for j in range(i, -1, -1): + if "R" not in arr[j] or "R" not in arr[j + 1]: + continue + if arr[j + 1]["R"] < arr[j]["R"] \ + or ( + arr[j + 1]["R"] == arr[j]["R"] + and arr[j + 1]["x0"] < arr[j]["x0"] + ): + tmp = arr[j] + arr[j] = arr[j + 1] + arr[j + 1] = tmp + return arr + + @staticmethod + def overlapped_area(a, b, ratio=True): + tp, btm, x0, x1 = a["top"], a["bottom"], a["x0"], a["x1"] + if b["x0"] > x1 or b["x1"] < x0: + return 0 + if b["bottom"] < tp or b["top"] > btm: + return 0 + x0_ = max(b["x0"], x0) + x1_ = min(b["x1"], x1) + assert x0_ <= x1_, "Fuckedup! T:{},B:{},X0:{},X1:{} ==> {}".format( + tp, btm, x0, x1, b) + tp_ = max(b["top"], tp) + btm_ = min(b["bottom"], btm) + assert tp_ <= btm_, "Fuckedup! T:{},B:{},X0:{},X1:{} => {}".format( + tp, btm, x0, x1, b) + ov = (btm_ - tp_) * (x1_ - x0_) if x1 - \ + x0 != 0 and btm - tp != 0 else 0 + if ov > 0 and ratio: + ov /= (x1 - x0) * (btm - tp) + return ov + + @staticmethod + def layouts_cleanup(boxes, layouts, far=5, thr=0.7): + def notOverlapped(a, b): + return any([a["x1"] < b["x0"], + a["x0"] > b["x1"], + a["bottom"] < b["top"], + a["top"] > b["bottom"]]) + + i = 0 + while i + 1 < len(layouts): + j = i + 1 + while j < min(i + far, len(layouts)) \ + and (layouts[i].get("type", "") != layouts[j].get("type", "") + or notOverlapped(layouts[i], layouts[j])): + j += 1 + if j >= min(i + far, len(layouts)): + i += 1 + continue + if Recognizer.overlapped_area(layouts[i], layouts[j]) < thr \ + and Recognizer.overlapped_area(layouts[j], layouts[i]) < thr: + i += 1 + continue + + if layouts[i].get("score") and layouts[j].get("score"): + if layouts[i]["type"] == 'figure' or layouts[i]["type"] == 'equation': + if Recognizer.overlapped_area(layouts[j], layouts[i]) > Recognizer.overlapped_area(layouts[i], layouts[j]): + layouts.pop(j) + else: + layouts.pop(i) + else: + if layouts[i]["score"] > layouts[j]["score"]: + layouts.pop(j) + else: + layouts.pop(i) + continue + + area_i, area_i_1 = 0, 0 + for b in boxes: + if not notOverlapped(b, layouts[i]): + area_i += Recognizer.overlapped_area(b, layouts[i], False) + if not notOverlapped(b, layouts[j]): + area_i_1 += Recognizer.overlapped_area(b, layouts[j], False) + + if area_i > area_i_1: + layouts.pop(j) + else: + layouts.pop(i) + + return layouts + + def create_inputs(self, imgs, im_info): + """generate input for different model type + Args: + imgs (list(numpy)): list of images (np.ndarray) + im_info (list(dict)): list of image info + Returns: + inputs (dict): input of model + """ + inputs = {} + + im_shape = [] + scale_factor = [] + if len(imgs) == 1: + inputs['image'] = np.array((imgs[0],)).astype('float32') + inputs['im_shape'] = np.array( + (im_info[0]['im_shape'],)).astype('float32') + inputs['scale_factor'] = np.array( + (im_info[0]['scale_factor'],)).astype('float32') + return inputs + + for e in im_info: + im_shape.append(np.array((e['im_shape'],)).astype('float32')) + scale_factor.append(np.array((e['scale_factor'],)).astype('float32')) + + inputs['im_shape'] = np.concatenate(im_shape, axis=0) + inputs['scale_factor'] = np.concatenate(scale_factor, axis=0) + + imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs] + max_shape_h = max([e[0] for e in imgs_shape]) + max_shape_w = max([e[1] for e in imgs_shape]) + padding_imgs = [] + for img in imgs: + im_c, im_h, im_w = img.shape[:] + padding_im = np.zeros( + (im_c, max_shape_h, max_shape_w), dtype=np.float32) + padding_im[:, :im_h, :im_w] = img + padding_imgs.append(padding_im) + inputs['image'] = np.stack(padding_imgs, axis=0) + return inputs + + @staticmethod + def find_overlapped(box, boxes_sorted_by_y, naive=False): + if not boxes_sorted_by_y: + return + bxs = boxes_sorted_by_y + s, e, ii = 0, len(bxs), 0 + while s < e and not naive: + ii = (e + s) // 2 + pv = bxs[ii] + if box["bottom"] < pv["top"]: + e = ii + continue + if box["top"] > pv["bottom"]: + s = ii + 1 + continue + break + while s < ii: + if box["top"] > bxs[s]["bottom"]: + s += 1 + break + while e - 1 > ii: + if box["bottom"] < bxs[e - 1]["top"]: + e -= 1 + break + + max_overlaped_i, max_overlaped = None, 0 + for i in range(s, e): + ov = Recognizer.overlapped_area(bxs[i], box) + if ov <= max_overlaped: + continue + max_overlaped_i = i + max_overlaped = ov + + return max_overlaped_i + + @staticmethod + def find_horizontally_tightest_fit(box, boxes): + if not boxes: + return + min_dis, min_i = 1000000, None + for i,b in enumerate(boxes): + if box.get("layoutno", "0") != b.get("layoutno", "0"): continue + dis = min(abs(box["x0"] - b["x0"]), abs(box["x1"] - b["x1"]), abs(box["x0"]+box["x1"] - b["x1"] - b["x0"])/2) + if dis < min_dis: + min_i = i + min_dis = dis + return min_i + + @staticmethod + def find_overlapped_with_threashold(box, boxes, thr=0.3): + if not boxes: + return + max_overlapped_i, max_overlapped, _max_overlapped = None, thr, 0 + s, e = 0, len(boxes) + for i in range(s, e): + ov = Recognizer.overlapped_area(box, boxes[i]) + _ov = Recognizer.overlapped_area(boxes[i], box) + if (ov, _ov) < (max_overlapped, _max_overlapped): + continue + max_overlapped_i = i + max_overlapped = ov + _max_overlapped = _ov + + return max_overlapped_i + + def preprocess(self, image_list): + inputs = [] + if "scale_factor" in self.input_names: + preprocess_ops = [] + for op_info in [ + {'interp': 2, 'keep_ratio': False, 'target_size': [800, 608], 'type': 'LinearResize'}, + {'is_scale': True, 'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225], 'type': 'StandardizeImage'}, + {'type': 'Permute'}, + {'stride': 32, 'type': 'PadStride'} + ]: + new_op_info = op_info.copy() + op_type = new_op_info.pop('type') + preprocess_ops.append(eval(op_type)(**new_op_info)) + + for im_path in image_list: + im, im_info = preprocess(im_path, preprocess_ops) + inputs.append({"image": np.array((im,)).astype('float32'), + "scale_factor": np.array((im_info["scale_factor"],)).astype('float32')}) + else: + hh, ww = self.input_shape + for img in image_list: + h, w = img.shape[:2] + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + scale = min(hh / h, ww / w) + img = cv2.resize(np.array(img).astype('float32'), (int(round(scale * w)), int(round(scale * h)))) + dw, dh = hh - img.shape[1], ww - img.shape[0] + dw /= 2 # divide padding into 2 sides + dh /= 2 + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) + # img = cv2.resize(np.array(img).astype('float32'), (ww, hh)) + # Scale input pixel values to 0 to 1 + img /= 255.0 + img = img.transpose(2, 0, 1) + img = img[np.newaxis, :, :, :].astype(np.float32) + inputs.append({self.input_names[0]: img, "scale_factor": [h/hh, w/ww]}) + return inputs + + def postprocess(self, boxes, inputs, thr): + if "scale_factor" in self.input_names: + bb = [] + for b in boxes: + clsid, bbox, score = int(b[0]), b[2:], b[1] + if score < thr: + continue + if clsid >= len(self.label_list): + # cron_logger.warning(f"bad category id") + continue + bb.append({ + "type": self.label_list[clsid].lower(), + "bbox": [float(t) for t in bbox.tolist()], + "score": float(score) + }) + return bb + + def xywh2xyxy(x): + # [x, y, w, h] to [x1, y1, x2, y2] + y = np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 + y[:, 1] = x[:, 1] - x[:, 3] / 2 + y[:, 2] = x[:, 0] + x[:, 2] / 2 + y[:, 3] = x[:, 1] + x[:, 3] / 2 + return y + + def scale_boxes(img1_shape, boxes, img0_shape): + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + # clip_boxes(boxes, img0_shape) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, img0_shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, img0_shape[0]) + return boxes + + def compute_iou(box, boxes): + # Compute xmin, ymin, xmax, ymax for both boxes + xmin = np.maximum(box[0], boxes[:, 0]) + ymin = np.maximum(box[1], boxes[:, 1]) + xmax = np.minimum(box[2], boxes[:, 2]) + ymax = np.minimum(box[3], boxes[:, 3]) + + # Compute intersection area + intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin) + + # Compute union area + box_area = (box[2] - box[0]) * (box[3] - box[1]) + boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + union_area = box_area + boxes_area - intersection_area + + # Compute IoU + iou = intersection_area / union_area + + return iou + + def iou_filter(boxes, scores, iou_threshold): + sorted_indices = np.argsort(scores)[::-1] + + keep_boxes = [] + while sorted_indices.size > 0: + # Pick the last box + box_id = sorted_indices[0] + keep_boxes.append(box_id) + + # Compute IoU of the picked box with the rest + ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :]) + + # Remove boxes with IoU over the threshold + keep_indices = np.where(ious < iou_threshold)[0] + + # print(keep_indices.shape, sorted_indices.shape) + sorted_indices = sorted_indices[keep_indices + 1] + + return keep_boxes + + boxes = np.squeeze(boxes).T + # Filter out object confidence scores below threshold + scores = np.max(boxes[:, 4:], axis=1) + boxes = boxes[scores > thr, :] + scores = scores[scores > thr] + if len(boxes) == 0: return [] + + # Get the class with the highest confidence + class_ids = np.argmax(boxes[:, 4:], axis=1) + boxes = boxes[:, :4] + input_shape = np.array([inputs["scale_factor"][0]*self.input_shape[0], inputs["scale_factor"][1]*self.input_shape[1]]) + # boxes = np.multiply(boxes, input_shape, dtype=np.float32) + boxes = xywh2xyxy(boxes) + boxes = scale_boxes(self.input_shape, boxes, input_shape) + + unique_class_ids = np.unique(class_ids) + indices = [] + for class_id in unique_class_ids: + class_indices = np.where(class_ids == class_id)[0] + class_boxes = boxes[class_indices, :] + class_scores = scores[class_indices] + class_keep_boxes = iou_filter(class_boxes, class_scores, 0.7) + indices.extend(class_indices[class_keep_boxes]) + + return [{ + "type": self.label_list[class_ids[i]].lower(), + "bbox": [float(t) for t in boxes[i].tolist()], + "score": float(scores[i]) + } for i in indices] + + def __call__(self, image_list, thr=0.7, batch_size=16): + res = [] + imgs = [] + for i in range(len(image_list)): + if not isinstance(image_list[i], np.ndarray): + imgs.append(np.array(image_list[i])) + else: imgs.append(image_list[i]) + + batch_loop_cnt = math.ceil(float(len(imgs)) / batch_size) + for i in range(batch_loop_cnt): + start_index = i * batch_size + end_index = min((i + 1) * batch_size, len(imgs)) + batch_image_list = imgs[start_index:end_index] + inputs = self.preprocess(batch_image_list) + print("preprocess") + for ins in inputs: + bb = self.postprocess(self.ort_sess.run(None, {k:v for k,v in ins.items() if k in self.input_names})[0], ins, thr) + res.append(bb) + + return res diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/seeit.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/seeit.py new file mode 100644 index 00000000..07c2f7e0 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/seeit.py @@ -0,0 +1,70 @@ +import os +import PIL +from PIL import ImageDraw + + +def save_results(image_list, results, labels, output_dir='output/', threshold=0.5): + if not os.path.exists(output_dir): + os.makedirs(output_dir) + for idx, im in enumerate(image_list): + im = draw_box(im, results[idx], labels, threshold=threshold) + + out_path = os.path.join(output_dir, f"{idx}.jpg") + im.save(out_path, quality=95) + print("save result to: " + out_path) + + +def draw_box(im, result, lables, threshold=0.5): + draw_thickness = min(im.size) // 320 + draw = ImageDraw.Draw(im) + color_list = get_color_map_list(len(lables)) + clsid2color = {n.lower():color_list[i] for i,n in enumerate(lables)} + result = [r for r in result if r["score"] >= threshold] + + for dt in result: + color = tuple(clsid2color[dt["type"]]) + xmin, ymin, xmax, ymax = dt["bbox"] + draw.line( + [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), + (xmin, ymin)], + width=draw_thickness, + fill=color) + + # draw label + text = "{} {:.4f}".format(dt["type"], dt["score"]) + tw, th = imagedraw_textsize_c(draw, text) + draw.rectangle( + [(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color) + draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255)) + return im + + +def get_color_map_list(num_classes): + """ + Args: + num_classes (int): number of class + Returns: + color_map (list): RGB color list + """ + color_map = num_classes * [0, 0, 0] + for i in range(0, num_classes): + j = 0 + lab = i + while lab: + color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j)) + color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j)) + color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j)) + j += 1 + lab >>= 3 + color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)] + return color_map + + +def imagedraw_textsize_c(draw, text): + if int(PIL.__version__.split('.')[0]) < 10: + tw, th = draw.textsize(text) + else: + left, top, right, bottom = draw.textbbox((0, 0), text) + tw, th = right - left, bottom - top + + return tw, th diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/table_structure_recognizer_lore.py b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/table_structure_recognizer_lore.py new file mode 100644 index 00000000..01c424b2 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/core/vision/table_structure_recognizer_lore.py @@ -0,0 +1,50 @@ +import re +from qanything_kernel.utils.loader.pdf_to_markdown.core.layout import TableRecognizer +import numpy as np +import torch +import cv2 + + +class TableStructureRecognizer_LORE(): + def __init__(self, device=torch.device('cpu')): + self.table_rec = TableRecognizer(device=device) + + @staticmethod + def is_caption(bx): + patt = [ + r"[图表]+[ 0-9:：]{2,}" + ] + if any([re.match(p, bx["text"].strip()) for p in patt]) \ + or bx["layout_type"].find("caption") >= 0: + return True + return False + + def construct_table(self, boxes, image, table_box, height, is_english=False, html=False): + """ + 接收一个表格的ocr结果，同时接收一个表格的图片，最终需要返回该表格的html结果 + """ + ocr_result = [] + table_caption = '' + zoomin = 3 + t_x1, t_x2, t_y1, t_y2 = table_box + table_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) + vis_image = table_image.copy() + for item in boxes: + if item['layout_type'] == 'caption': + table_caption += item['text'] + else: + new_item = [] + x0, y0, x1, y1 = item['x0'], item['top'], item['x1'], item['bottom'] + new_item.append([[zoomin * (x0 - t_x1), zoomin * (y0 - t_y1 - height)], + [zoomin * (x1 - t_x1), zoomin * (y0 - t_y1 - height)], + [zoomin * (x1 - t_x1), zoomin * (y1 - t_y1 - height)], + [zoomin * (x0 - t_x1), zoomin * (y1 - t_y1 - height)]]) + new_item.append(item['text']) + new_item.append(1.0) + ocr_result.append(new_item) + table_html, table_markdown = self.table_rec.extract_table(table_image, ocr_result) + return { + 'table_html': table_html, + 'table_caption': table_caption, + 'table_markdown': table_markdown + } diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/excel_loader.py b/qanything_kernel/utils/loader/pdf_to_markdown/excel_loader.py new file mode 100644 index 00000000..3e5efcfd --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/excel_loader.py @@ -0,0 +1,13 @@ +import pandas as pd + +def excel2html(xlsx_path): + """ + convert excel file to html + """ + df = pd.read_excel(xlsx_path) + html = df.to_html() + return html + + +if __name__ =='__main__': + excel2html('/ssd8/exec/qinhaibo/code/RAG/release/git/document-layout-parser/第三次培训_数字人产品销售素材20240314.xlsx') diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/file_parser.py b/qanything_kernel/utils/loader/pdf_to_markdown/file_parser.py new file mode 100644 index 00000000..5289b02a --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/file_parser.py @@ -0,0 +1,166 @@ +import json +import re + +from qanything_kernel.utils.loader.pdf_to_markdown.core.parser import PdfParser, PlainParser +from qanything_kernel.utils.loader.pdf_to_markdown.convert2markdown import json2markdown +import numpy as np +import os + +import time + + +class Pdf(PdfParser): + def __init__(self): + super().__init__() + + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None): + res_dir = 'results/' + timestamp = int(time.time()) + save_dir = os.path.join(res_dir,filename.split('.')[0]+'_'+str(timestamp)) + os.makedirs(save_dir,exist_ok=True) + + json_dir = os.path.join(save_dir,os.path.basename(filename)[:-4]) + '.json' + basedir = os.path.dirname(json_dir) + basename = os.path.basename(json_dir) + markdown_path = os.path.join(basedir,basename.split('.')[0]+'_md') + os.makedirs(markdown_path,exist_ok=True) + markdown_dir = os.path.join(markdown_path, basename.split('.')[0]+'.md') + # image_dir = os.path.join(markdown_path, 'image') + # os.makedirs(image_dir,exist_ok=True) + + + callback(msg="OCR is running...") + print("OCR is running...") + self.__images__( + filename if not binary else binary, + zoomin, + from_page, + to_page, + callback + ) + callback(msg="OCR finished.") + print("OCR finished.") + + np.set_printoptions(threshold=np.inf) + from timeit import default_timer as timer + start = timer() + self._layouts_rec(zoomin) + + callback(0.63, "Layout analysis finished") + # self._table_transformer_job(zoomin) + callback(0.68, "Table analysis finished") + self._text_merge() + tbls = self._extract_table_figure(True, zoomin, True, True, markdown_path) + page_width = max([b["x1"] for b in self.boxes if b['layout_type'] == 'text']) - min([b["x0"] for b in self.boxes if b['layout_type'] == 'text']) + # self._naive_vertical_merge() + self._concat_downward() + self._filter_forpages() + callback(0.75, "Text merging finished.") + column_width = np.median([b["x1"] - b["x0"] for b in self.boxes if b['layout_type'] == 'text']) + text_width = np.argmax(np.bincount([b["x1"] - b["x0"] for b in self.boxes if b['layout_type'] == 'text'])) + + + # clean mess + if column_width < page_width / 2 and text_width < page_width / 2: + # if column_width < self.page_images[0].size[0] / zoomin / 2: + # print("two_column...................", column_width, + # self.page_images[0].size[0] / zoomin / 2) + self.boxes = self.sort_X_by_page(self.boxes, 0.9 * column_width) + + + for b in self.boxes: + b["text"] = re.sub(r"([\t 　]|\u3000){2,}", " ", b["text"].strip()) + + def _begin(txt): + return re.match( + "[0-9. 一、i]*(introduction|abstract|摘要|引言|keywords|key words|关键词|background|背景|目录|前言|contents)", + txt.lower().strip()) + + if from_page > 0: + return { + "title": "", + "authors": "", + "abstract": "", + "sections": [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) for b in self.boxes if + re.match(r"(text|title)", b.get("layoutno", "text"))], + "tables": tbls + } + i = 0 + sections = [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) for b in self.boxes[i:] if + re.match(r"(text|title|author|reference|content)", b.get("layoutno", "text"))] + new_sections = {} + + for sec in sections: + i = 0 + pn = int(sec[0].split('@@')[-1].split('\t')[0]) + top = float(sec[0].split('@@')[-1].split('\t')[3]) + self.page_cum_height[pn - 1] + right = float(sec[0].split('@@')[-1].split('\t')[2]) + sec_no = str(pn) + '-' + sec[1] + while i < len(tbls): + tbl = tbls[i] + t_pn = int(tbl[1][0][0]) + 1 + t_bottom = float(tbl[1][0][4]) + self.page_cum_height[t_pn - 1] + t_left = float(tbl[1][0][1]) + tbl_no = tbl[0][1] + if t_pn > pn: + i += 1 + continue + if t_bottom < top and t_left < right: + new_sections[tbl_no] = {'text': tbl[0][0], 'type': tbl[0][1]} + tbls.pop(i) + continue + if t_bottom < top and t_left > right and t_pn < pn: + new_sections[tbl_no] = {'text': tbl[0][0], 'type': tbl[0][1]} + tbls.pop(i) + continue + i += 1 + if sec_no not in new_sections.keys(): + new_sections[sec_no] = {'text': sec[0].split('@@')[0], 'type': sec[1]} + else: + new_sections[sec_no]['text'] += sec[0].split('@@')[0] + if tbls: + for tbl in tbls: + tbl_no = tbl[0][1] + new_sections[tbl_no] = {'text': tbl[0][0], 'type': tbl[0][1]} + + json.dump(new_sections,open(json_dir,'w')) + json2markdown(json_dir,markdown_dir) + print("pdf parser:", timer() - start) + + + return new_sections + + + +def chunk(filename, binary=None, from_page=0, to_page=100000, + lang="Chinese", callback=None, **kwargs): + """ + Only pdf is supported. + """ + pdf_parser = None + if re.search(r"\.pdf$", filename, re.IGNORECASE): + if not kwargs.get("parser_config", {}).get("layout_recognize", True): + pdf_parser = PlainParser() + paper = { + "title": filename, + "authors": " ", + "abstract": "", + "sections": pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page)[0], + "tables": [] + } + else: + pdf_parser = Pdf() + paper = pdf_parser(filename if not binary else binary, + from_page=from_page, to_page=to_page, callback=callback) + else: + raise NotImplementedError("file type not supported yet(pdf supported)") + return paper + + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + chunk(sys.argv[1], callback=dummy, layout_recognize=True) diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/filetype.py b/qanything_kernel/utils/loader/pdf_to_markdown/filetype.py new file mode 100644 index 00000000..a5826d1f --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/filetype.py @@ -0,0 +1,27 @@ +import pandas as pd +import markdownify + + +def html2markdown(html_text): + markdown_text = markdownify.markdownify(html_text) + return markdown_text + + +def excel2html(xlsx_path): + """ + convert excel file to html + """ + df = pd.read_excel(xlsx_path) + html = df.to_html() + return html + + +def excel2markdown(xlsx_path): + df = pd.read_excel(xlsx_path) + html = df.to_html() + markdown = html2markdown(html) + return markdown + + +if __name__ =='__main__': + excel2html('/ssd8/exec/qinhaibo/code/RAG/release/git/document-layout-parser/第三次培训_数字人产品销售素材20240314.xlsx') \ No newline at end of file diff --git a/qanything_kernel/utils/loader/pdf_to_markdown/pptx_loader.py b/qanything_kernel/utils/loader/pdf_to_markdown/pptx_loader.py new file mode 100644 index 00000000..d290d383 --- /dev/null +++ b/qanything_kernel/utils/loader/pdf_to_markdown/pptx_loader.py @@ -0,0 +1,118 @@ +from io import BytesIO +from pptx import Presentation +from pptx.enum.shapes import MSO_SHAPE_TYPE +from rapidocr_onnxruntime import RapidOCR +import numpy as np +import cv2 +from PIL import Image +from qanything_kernel.utils.loader.pdf_to_markdown.filetype import html2markdown + + +def pptx_table_to_html(table): + html_table = '\n' + for row in table.rows: + html_table += ' \n' + for cell in row.cells: + cell_text = ''.join(text_frame.text for text_frame in cell.text_frame.paragraphs) + html_table += ' \n'.format(cell_text) + html_table += ' \n' + html_table += '
{}
\n' + return html_table + + +class PPTParser(object): + def __init__(self): + super().__init__() + self.ocr_rapid = RapidOCR() + + def __extract(self, shape): + if shape.shape_type == 19: + tb = shape.table + tb_html = pptx_table_to_html(tb) + new_html = tb_html + + tb_markdown = html2markdown(new_html) + + return tb_markdown + + if shape.has_text_frame: + return shape.text_frame.text + + if shape.shape_type == 6: + texts = [] + for p in sorted(shape.shapes, key=lambda x: (x.top // 10, x.left)): + t = self.__extract(p) + if t: + texts.append(t) + return "\n".join(texts) + + def __extract_img(self, slide, shape, array_lst): + if shape.shape_type == MSO_SHAPE_TYPE.PICTURE: + image_data = shape.image.blob + image = Image.open(BytesIO(image_data)) + array = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) + # cv2.imwrite(f'./tmp/{slide.slide_id}_{shape.shape_id}.png',array) + array_lst.append(array) + return array_lst, True + elif shape.shape_type == MSO_SHAPE_TYPE.GROUP: + for s in shape.shapes: + self.__extract_img(slide, s, array_lst) + return array_lst, True + else: + return [], False + + def __call__(self, fnm, from_page, to_page, callback=None): + ppt = Presentation(fnm) if isinstance( + fnm, str) else Presentation( + BytesIO(fnm)) + txts = [] + self.total_page = len(ppt.slides) + # print(self.total_page) + for i, slide in enumerate(ppt.slides): + if i < from_page: + continue + if i >= to_page: + break + texts = [] + for shape in sorted( + slide.shapes, key=lambda x: (x.top // 10, x.left)): + txt = self.__extract(shape) + array_lst = [] + image_lst, has_image = self.__extract_img(slide, shape, array_lst) + if has_image: + ocr_res_lst = [] + for img in image_lst: + ocr_res = self.__ocr(img) + if ocr_res != '': + ocr_res_lst.append(ocr_res) + if len(ocr_res_lst) != 0: + ocr_res = '\n'.join(ocr_res_lst) + else: + ocr_res = None + else: + ocr_res = None + if txt: + texts.append(txt) + if ocr_res: + texts.append(ocr_res) + txts.append("\n".join(texts)) + return txts + + def __ocr(self, image): + bxs_rapid = self.ocr_rapid(image) + text = '' + if bxs_rapid[0] == None: + return '' + for box in bxs_rapid[0]: + text += (box[1] + '\n') + return text + + +if __name__ == '__main__': + path = '第一次培训_2024年第一次培训大模型业务介绍V1.0.pptx' + parser = PPTParser() + res = parser(fnm=path, from_page=12, to_page=13) + # print(res) + for text in res: + print('********************') + print(text) diff --git a/qanything_kernel/utils/loader/self_pdf_loader.py b/qanything_kernel/utils/loader/self_pdf_loader.py new file mode 100644 index 00000000..d3d8c992 --- /dev/null +++ b/qanything_kernel/utils/loader/self_pdf_loader.py @@ -0,0 +1,116 @@ +import json +import re +from qanything_kernel.utils.loader.pdf_to_markdown.core.parser import PdfParser +from qanything_kernel.utils.loader.pdf_to_markdown.convert2markdown import json2markdown +from qanything_kernel.utils.custom_log import debug_logger +from timeit import default_timer as timer +import numpy as np +import os +import time + + +class PdfLoader(PdfParser): + def __init__(self, filename, binary=None, from_page=0, to_page=10000, zoomin=3, root_dir='results/', callback=None): + super().__init__() + timestamp = int(time.time()) + save_dir = os.path.join(root_dir, filename.split('.')[0] + '_' + str(timestamp)) + os.makedirs(save_dir, exist_ok=True) + + self.json_dir = os.path.join(save_dir, os.path.basename(filename)[:-4]) + '.json' + basedir = os.path.dirname(self.json_dir) + basename = os.path.basename(self.json_dir) + self.markdown_path = os.path.join(basedir, basename.split('.')[0] + '_md') + os.makedirs(self.markdown_path, exist_ok=True) + self.markdown_dir = os.path.join(self.markdown_path, basename.split('.')[0] + '.md') + + self.filename = filename + self.binary = binary + self.from_page = from_page + self.to_page = to_page + self.zoomin = zoomin + self.callback = callback + + def load_to_markdown(self): + ocr_start = timer() + self.__images__( + self.filename if self.binary is None else self.binary, + self.zoomin, + self.from_page, + self.to_page, + self.callback + ) + debug_logger.info("OCR finished in %s seconds" % (timer() - ocr_start)) + + np.set_printoptions(threshold=np.inf) + start = timer() + self._layouts_rec(self.zoomin) + + self._text_merge() + tbls = self._extract_table_figure(True, self.zoomin, True, True, self.markdown_path) + page_width = max([b["x1"] for b in self.boxes if b['layout_type'] == 'text']) - min( + [b["x0"] for b in self.boxes if b['layout_type'] == 'text']) + self._concat_downward() + self._filter_forpages() + column_width = np.median([b["x1"] - b["x0"] for b in self.boxes if b['layout_type'] == 'text']) + text_width = np.argmax(np.bincount([b["x1"] - b["x0"] for b in self.boxes if b['layout_type'] == 'text'])) + + # clean mess + if column_width < page_width / 2 and text_width < page_width / 2: + self.boxes = self.sort_X_by_page(self.boxes, 0.9 * column_width) + + for b in self.boxes: + b["text"] = re.sub(r"([\t 　]|\u3000){2,}", " ", b["text"].strip()) + + if self.from_page > 0: + return { + "title": "", + "authors": "", + "abstract": "", + "sections": [(b["text"] + self._line_tag(b, self.zoomin), b.get("layoutno", "")) for b in self.boxes if + re.match(r"(text|title)", b.get("layoutno", "text"))], + "tables": tbls + } + i = 0 + sections = [(b["text"] + self._line_tag(b, self.zoomin), b.get("layoutno", "")) for b in self.boxes[i:] if + re.match(r"(text|title|author|reference|content)", b.get("layoutno", "text"))] + new_sections = {} + + for sec in sections: + i = 0 + pn = int(sec[0].split('@@')[-1].split('\t')[0]) + top = float(sec[0].split('@@')[-1].split('\t')[3]) + self.page_cum_height[pn - 1] + right = float(sec[0].split('@@')[-1].split('\t')[2]) + sec_no = str(pn) + '-' + sec[1] + while i < len(tbls): + tbl = tbls[i] + t_pn = int(tbl[1][0][0]) + 1 + t_bottom = float(tbl[1][0][4]) + self.page_cum_height[t_pn - 1] + t_left = float(tbl[1][0][1]) + tbl_no = tbl[0][1] + if t_pn > pn: + i += 1 + continue + if t_bottom < top and t_left < right: + new_sections[tbl_no] = {'text': tbl[0][0], 'type': tbl[0][1]} + tbls.pop(i) + continue + if t_bottom < top and t_left > right and t_pn < pn: + new_sections[tbl_no] = {'text': tbl[0][0], 'type': tbl[0][1]} + tbls.pop(i) + continue + i += 1 + if sec_no not in new_sections.keys(): + new_sections[sec_no] = {'text': sec[0].split('@@')[0], 'type': sec[1]} + else: + new_sections[sec_no]['text'] += sec[0].split('@@')[0] + if tbls: + for tbl in tbls: + tbl_no = tbl[0][1] + new_sections[tbl_no] = {'text': tbl[0][0], 'type': tbl[0][1]} + + json.dump(new_sections, open(self.json_dir, 'w')) + markdown_str = json2markdown(self.json_dir, self.markdown_dir) + debug_logger.info("PDF Parse finished in %s seconds" % (timer() - start)) + # print(new_sections, flush=True) + return self.markdown_dir + diff --git a/requirements.txt b/requirements.txt index 3ca058cd..d41e1389 100644 --- a/requirements.txt +++ b/requirements.txt @@ -29,3 +29,4 @@ faster_whisper==1.0.1 python-dotenv==1.0.1 duckduckgo-search==5.3.0b4 html2text==2024.2.26 +mistune-3.0.2