[Feature] Add dataset analysis script

docs/en/user_guides/useful_tools.md

@@ -108,7 +108,85 @@ python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncb
                                                --not-show
 ```
 
-## Convert Dataset
+### Visualize dataset analysis
+
+`tools/analysis_tools/dataset_analysis.py` help users get the renderings of the four functions, and print the list to display the name of the dataset category and the corresponding number, and save the pictures to the `dataset_analysis` folder under the current running directory.
+Description of the script's functions:
+The data required by each sub function is obtained through the data preparation of `main()`.
+Function 1: Generated by the sub function `show_bbox_num` to display the distribution of categories and bbox instances.
+
+<img src="https://user-images.githubusercontent.com/90811472/196891728-4c2f1ab3-01cb-445f-a6b8-39752387c40f.jpg"/>
+
+Function 2: Generated by the sub function `show_bbox_wh` to display the width and height distribution of categories and bbox instances.
+
+<img src="https://user-images.githubusercontent.com/90811472/199019573-650b9652-eb14-4bc0-a5e8-650dfc578fc8.jpg"/>
+
+Function 3: Generated by the sub function `show_bbox_wh_ratio` to display the width to height ratio distribution of categories and bbox instances.
+
+<img src="https://user-images.githubusercontent.com/90811472/199019593-0f810a21-18d2-41ac-b4fa-baa8288bcb23.jpg"/>
+
+Function 3: Generated by the sub function `show_bbox_area` to display the distribution map of category and bbox instance area based on area rules.
+
+<img src="https://user-images.githubusercontent.com/90811472/199022991-5388db47-d0f3-4201-9eee-13c5fab6bca9.jpg"/>
+
+Print List: Generated by the sub function `show_class_list`.
+
+<img src="https://user-images.githubusercontent.com/90811472/199090989-15109bbf-f035-477d-8566-e2a28de0935d.jpg"/>
+
+```shell
+python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
+                                              [-h] \
+                                              [--type ${TYPE}] \
+                                              [--class-name ${CLASS_NAME}] \
+                                              [--area-rule ${AREA_RULE}] \
+                                              [--func ${FUNC}] \
+                                              [--output-dir ${OUTPUT_DIR}]
+```
+
+E,g：
+
+1.Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` analyze the dataset, By default,the data loadingt type is `train`, the area rule interval is `[0,32**2,96**2,1e5**2]`, and all classes and four function diagrams in the dataset are generated and saved to the current running directory `./dataset_analysis` folder:
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
+```
+
+2.Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` analyze the dataset, change the data loading type from the default `train` to `val`:
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --type val
+```
+
+3.Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` analyze the dataset, change the display of all generated classes to specific classes. Take the display of `person` classes as an example:
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --class-name person car
+```
+
+4.Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` analyze the dataset, redefine the area rule. Taking the new value added as `120` as an example, the command entered is ` 32 96 120`, and the area rule interval becomes `[0, 32**2, 96**2，120**2, 1e5**2]`. Only one number can be added. To customize the area rule, the entered command must include `32 96`:
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --area-rule 30 70 120
+```
+
+5.Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` analyze the dataset, change the display of four function renderings to only display `Function 1` as an example:
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --func show_bbox_num
+```
+
+6.Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` analyze the dataset, modify the picture saving address to `work_ir/dataset_analysis`:
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --output-dir work_dir/dataset_analysis
+```
+
+## Dataset Conversion
 
 The folder `tools/data_converters` currently contains `ballon2coco.py` and `yolo2coco.py` two dataset conversion tools.
 

docs/zh_cn/user_guides/useful_tools.md

@@ -14,7 +14,7 @@ mim run mmdet print_config [CONFIG]
 
 ### 可视化 COCO 标签
 
-脚本 `tools/analysis_tools/browse_coco_json.py` 能够使用可视化显示 COCO 标签在图片的情况
+脚本 `tools/analysis_tools/browse_coco_json.py` 能够使用可视化显示 COCO 标签在图片的情况。
 
 ```shell
 python tools/analysis_tools/browse_coco_json.py ${DATA_ROOT} \
@@ -108,6 +108,84 @@ python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncb
                                                --not-show
 ```
 
+### 可视化数据集分析
+
+脚本 `tools/analysis_tools/dataset_analysis.py` 能够帮助用户得到四种功能的效果图，同时打印列表显示该数据集类别名称及对应数量，并将图片保存到当前运行目录下的 `dataset_analysis` 文件夹中。
+关于该脚本的功能的说明：
+通过 `main()` 的数据准备，得到每个子函数所需要的数据。
+功能一：显示类别和 bbox 实例个数的分布图，通过子函数 `show_bbox_num` 生成。
+
+<img src="https://user-images.githubusercontent.com/90811472/196891728-4c2f1ab3-01cb-445f-a6b8-39752387c40f.jpg"/>
+
+功能二：显示类别和 bbox 实例宽、高的分布图，通过子函数 `show_bbox_wh` 生成。
+
+<img src="https://user-images.githubusercontent.com/90811472/199019573-650b9652-eb14-4bc0-a5e8-650dfc578fc8.jpg"/>
+
+功能三：显示类别和 bbox 实例宽/高比例的分布图，通过子函数 `show_bbox_wh_ratio` 生成。
+
+<img src="https://user-images.githubusercontent.com/90811472/199019593-0f810a21-18d2-41ac-b4fa-baa8288bcb23.jpg"/>
+
+功能四：基于面积规则下，显示类别和 bbox 实例面积的分布图，通过子函数 `show_bbox_area` 生成。
+
+<img src="https://user-images.githubusercontent.com/90811472/199022991-5388db47-d0f3-4201-9eee-13c5fab6bca9.jpg"/>
+
+打印列表显示，通过脚本中子函数 `show_class_list` 生成。
+
+<img src="https://user-images.githubusercontent.com/90811472/199090989-15109bbf-f035-477d-8566-e2a28de0935d.jpg"/>
+
+```shell
+python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
+                                              [-h] \
+                                              [--type ${TYPE}] \
+                                              [--class-name ${CLASS_NAME}] \
+                                              [--area-rule ${AREA_RULE}] \
+                                              [--func ${FUNC}] \
+                                              [--output-dir ${OUTPUT_DIR}]
+```
+
+例子：
+
+1.使用 `config` 文件 `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` 分析数据集，其中默认设置，数据加载类型为 `train` ，面积规则区间为 `[0,32**2,96**2,1e5**2]` ,数据集中所有类和四个功能效果图全部生成并将图片保存到当前运行目录下 `./dataset_analysis` 文件夹中：
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
+```
+
+2.使用 `config` 文件 `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` 分析数据集，通过 `--type` 设置将数据加载类型由默认的 `train` 改为 `val`：
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --type val
+```
+
+3.使用 `config` 文件 `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` 分析数据集，通过 `--class-name` 设置将生成所有类显示改为特定类，以显示 `person` 为例：
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --class-name person
+```
+
+4.使用 `config` 文件 `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` 分析数据集，通过 `--area-rule` 重新定义面积规则，以新增值为 `120` 为例，输入的命令为 `32 96 120` ,面积规则区间则变为 `[0, 32**2, 96**2，120**2, 1e5**2]` 。只能添加一个数字，若要自定义面积规则，输入的命令中需要包含 `32 96`：
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --area-rule 32 96 120
+```
+
+5.使用 `config` 文件 `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` 分析数据集，通过 `--func` 设置，将显示四个功能效果图改为只显示 `功能一` 为例：
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --func show_bbox_num
+```
+
+6.使用 `config` 文件 `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` 分析数据集，通过 `--output-dir` 设置修改图片保存地址，以 `work_ir/dataset_analysis` 地址为例：
+
+```shell
+python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
+                                               --output-dir work_dir/dataset_analysis
+```
+
 ## 数据集转换
 
 文件夹 `tools/data_converters/` 目前包含 `ballon2coco.py` 和 `yolo2coco.py` 两个数据集转换工具。