[Improvement] browse_dataset.py

tools/analysis_tools/browse_dataset.py

@@ -1,31 +1,67 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import argparse
+import os
 import os.path as osp
+import sys
+from typing import Tuple
 
+import cv2
+import mmcv
 import numpy as np
 from mmdet.models.utils import mask2ndarray
 from mmdet.structures.bbox import BaseBoxes
 from mmengine.config import Config, DictAction
+from mmengine.dataset import Compose
 from mmengine.utils import ProgressBar
+from mmengine.visualization import Visualizer
 
 from mmyolo.registry import DATASETS, VISUALIZERS
 from mmyolo.utils import register_all_modules
 
 
+# TODO Support for printing the change in key of results
 def parse_args():
     parser = argparse.ArgumentParser(description='Browse a dataset')
     parser.add_argument('config', help='train config file path')
     parser.add_argument(
         '--output-dir',
-        default=None,
+        default='./output',
         type=str,
-        help='If there is no display interface, you can save it')
-    parser.add_argument('--not-show', default=False, action='store_true')
+        help='If there is no display interface, you can save it.')
+    parser.add_argument(
+        '--phase',
+        '-p',
+        default='train',
+        type=str,
+        choices=['train', 'test', 'val'],
+        help='phase of dataset to visualize, accept "train" "test" and "val".'
+        ' Defaults to "train".')
+    parser.add_argument('--show', help='show the pipeline image')
+    parser.add_argument(
+        '--show-number',
+        '-n',
+        type=int,
+        default=sys.maxsize,
+        help='number of images selected to visualize, must bigger than 0. if '
+        'the number is bigger than length of dataset, show all the images in '
+        'dataset; default "sys.maxsize", show all images in dataset')
     parser.add_argument(
         '--show-interval',
+        '-i',
         type=float,
         default=3,
         help='the interval of show (s)')
+    parser.add_argument(
+        '--mode',
+        '-m',
+        default='transformed',
+        type=str,
+        choices=['original', 'transformed', 'pipeline'],
+        help='display mode; display original pictures or transformed pictures'
+        ' or comparison pictures. "original" means show images load from disk'
+        '; "transformed" means to show images after transformed;'
+        '"pipeline" means show all the intermediate images. '
+        'Defaults to "transformed".')
     parser.add_argument(
         '--cfg-options',
         nargs='+',
@@ -40,50 +76,178 @@ def parse_args():
     return args
 
 
+def _get_adaptive_scale(img_shape: Tuple[int, int],
+                        min_scale: float = 0.3,
+                        max_scale: float = 3.0) -> float:
+    """Get adaptive scale according to image shape.
+
+    The target scale depends on the the short edge length of the image. If the
+       short edge length equals 224, the output is 1.0. And output linear
+       scales according the short edge length.
+       You can also specify the minimum scale and the maximum scale to limit
+    the
+       linear scale.
+       Args:
+           img_shape (Tuple[int, int]): The shape of the canvas image.
+           min_size (int): The minimum scale. Defaults to 0.3.
+           max_size (int): The maximum scale. Defaults to 3.0.
+       Returns:
+           int: The adaptive scale.
+    """
+    short_edge_length = min(img_shape)
+    scale = short_edge_length / 224.
+    return min(max(scale, min_scale), max_scale)
+
+
+def make_grid(imgs, names):
+    """Concat list of pictures into a single big picture, align height here."""
+    vis = Visualizer()
+    ori_shapes = [img.shape[:2] for img in imgs]
+    max_height = int(max(img.shape[0] for img in imgs) * 1.1)
+    min_width = min(img.shape[1] for img in imgs)
+    horizontal_gap = min_width // 10
+    img_scale = _get_adaptive_scale((max_height, min_width))
+
+    texts = []
+    text_positions = []
+    start_x = 0
+    for i, img in enumerate(imgs):
+        pad_height = (max_height - img.shape[0]) // 2
+        pad_width = horizontal_gap // 2
+        # make border
+        imgs[i] = cv2.copyMakeBorder(
+            img,
+            pad_height,
+            max_height - img.shape[0] - pad_height + int(img_scale * 30 * 2),
+            pad_width,
+            pad_width,
+            cv2.BORDER_CONSTANT,
+            value=(255, 255, 255))
+        texts.append(f'{"execution: "}{i}\n{names[i]}\n{ori_shapes[i]}')
+        text_positions.append(
+            [start_x + img.shape[1] // 2 + pad_width, max_height])
+        start_x += img.shape[1] + horizontal_gap
+
+    display_img = np.concatenate(imgs, axis=1)
+    vis.set_image(display_img)
+    img_scale = _get_adaptive_scale(display_img.shape[:2])
+    vis.draw_texts(
+        texts,
+        positions=np.array(text_positions),
+        font_sizes=img_scale * 7,
+        colors='black',
+        horizontal_alignments='center',
+        font_families='monospace')
+    return vis.get_image()
+
+
+class InspectCompose(Compose):
+    """Compose multiple transforms sequentially.
+
+    And record "img" field of all results in one list.
+    """
+
+    def __init__(self, transforms, intermediate_imgs):
+        super().__init__(transforms=transforms)
+        self.intermediate_imgs = intermediate_imgs
+
+    def __call__(self, data):
+        if 'img' in data:
+            self.intermediate_imgs.append({
+                'name': 'original',
+                'img': data['img'].copy()
+            })
+        self.ptransforms = [
+            self.transforms[i] for i in range(len(self.transforms) - 1)
+        ]
+        for t in self.ptransforms:
+            data = t(data)
+            self.transforms[-1].meta_keys = [key for key in data]
+            data_sample = self.transforms[-1](data)
+            if data is None:
+                return None
+            if 'img' in data:
+                self.intermediate_imgs.append({
+                    'name':
+                    t.__class__.__name__,
+                    'dataset_sample':
+                    data_sample['data_samples']
+                })
+        return data
+
+
 def main():
     args = parse_args()
     cfg = Config.fromfile(args.config)
     if args.cfg_options is not None:
         cfg.merge_from_dict(args.cfg_options)
 
-    # register all modules in mmdet into the registries
+    # register all modules in mmyolo into the registries
     register_all_modules()
 
-    dataset = DATASETS.build(cfg.train_dataloader.dataset)
+    dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset')
+    dataset = DATASETS.build(dataset_cfg)
     visualizer = VISUALIZERS.build(cfg.visualizer)
     visualizer.dataset_meta = dataset.metainfo
+    intermediate_imgs = []
+    dataset.pipeline = InspectCompose(dataset.pipeline.transforms,
+                                      intermediate_imgs)
 
-    progress_bar = ProgressBar(len(dataset))
-    for item in dataset:
-        img = item['inputs'].permute(1, 2, 0).numpy()
-        data_samples = item['data_samples'].numpy()
-        gt_instances = data_samples.gt_instances
-        img_path = osp.basename(item['data_samples'].img_path)
-
-        out_file = osp.join(
-            args.output_dir,
-            osp.basename(img_path)) if args.output_dir is not None else None
-
-        img = img[..., [2, 1, 0]]  # bgr to rgb
-        gt_bboxes = gt_instances.get('bboxes', None)
-        if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes):
-            gt_instances.bboxes = gt_bboxes.tensor
-        gt_masks = gt_instances.get('masks', None)
-        if gt_masks is not None:
-            masks = mask2ndarray(gt_masks)
-            gt_instances.masks = masks.astype(np.bool)
-        data_samples.gt_instances = gt_instances
-
-        visualizer.add_datasample(
-            osp.basename(img_path),
-            img,
-            data_samples,
-            draw_pred=False,
-            show=not args.not_show,
-            wait_time=args.show_interval,
-            out_file=out_file)
-
+    # init visualization image number
+    display_number = min(args.show_number, len(dataset))
+    progress_bar = ProgressBar(display_number)
+    for i, item in zip(range(display_number), dataset):
+        image_l = []
+        for k, img in enumerate(
+            [result['dataset_sample'] for result in intermediate_imgs]):
+            image = img.img
+            data_samples = img
+            gt_instances = data_samples.gt_instances
+            image = image[..., [2, 1, 0]]  # bgr to rgb
+            gt_bboxes = gt_instances.get('bboxes', None)
+            if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes):
+                gt_instances.bboxes = gt_bboxes.tensor
+            gt_masks = gt_instances.get('masks', None)
+            if gt_masks is not None:
+                masks = mask2ndarray(gt_masks)
+                gt_instances.masks = masks.astype(np.bool)
+                data_samples.gt_instances = gt_instances
+            # get filename from dataset or just use index as filename
+            visualizer.add_datasample(
+                'result',
+                image,
+                data_samples,
+                draw_pred=False,
+                draw_gt=True,
+                show=False,
+                wait_time=0)
+            image_show = visualizer.get_image()
+            image_l.append(image_show)
+        if args.mode == 'original':
+            image = image_l[0]
+        elif args.mode == 'transformed':
+            image = image_l[-1]
+        else:
+            image = make_grid([result for result in image_l],
+                              [result['name'] for result in intermediate_imgs])
+        if hasattr(data_samples, 'img_path'):
+            filename = osp.basename(data_samples.img_path)
+        else:
+            # some dataset have not image path
+            filename = f'{i}.jpg'
+        out_file = osp.join(args.output_dir,
+                            filename) if args.output_dir is not None else None
+        intermediate_imgs.clear()
         progress_bar.update()
+        if out_file:
+            print('save')
+            mmcv.imwrite(image[..., ::-1], out_file)
+
+        visualizer.set_image(image)
+        visualizer.show()
+
+    print(f'All done!'
+          f'\nResults have been saved at {os.path.abspath(args.out_dir)}')
 
 
 if __name__ == '__main__':