mirror of https://github.com/open-mmlab/mmyolo.git
[Improvement] dataset_analysis (#257)
* add ConcatDataset judgment * Update dataset_analysis.pypull/276/head
LinXiaoZheng
huanghaian
parent
1e0cab7371
commit
4c1878ce8b
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@ -115,23 +115,23 @@ Description of the script's functions:
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The data required by each sub function is obtained through the data preparation of `main()`.
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Function 1: Generated by the sub function `show_bbox_num` to display the distribution of categories and bbox instances.
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<img src="https://user-images.githubusercontent.com/90811472/196891728-4c2f1ab3-01cb-445f-a6b8-39752387c40f.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200314770-4fb21626-72f2-4a4c-be5d-bf860ad830ec.jpg"/>
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Function 2: Generated by the sub function `show_bbox_wh` to display the width and height distribution of categories and bbox instances.
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<img src="https://user-images.githubusercontent.com/90811472/199019573-650b9652-eb14-4bc0-a5e8-650dfc578fc8.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200315007-96e8e795-992a-4c72-90fa-f6bc00b3f2c7.jpg"/>
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Function 3: Generated by the sub function `show_bbox_wh_ratio` to display the width to height ratio distribution of categories and bbox instances.
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<img src="https://user-images.githubusercontent.com/90811472/199019593-0f810a21-18d2-41ac-b4fa-baa8288bcb23.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200315044-4bdedcf6-087a-418e-8fe8-c2d3240ceba8.jpg"/>
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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.
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<img src="https://user-images.githubusercontent.com/90811472/199022991-5388db47-d0f3-4201-9eee-13c5fab6bca9.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200315075-71680fe2-db6f-4981-963e-a035c1281fc1.jpg"/>
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Print List: Generated by the sub function `show_class_list` and `show_data_lis`.
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Print List: Generated by the sub function `show_class_list` and `show_data_list`.
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<img src="https://user-images.githubusercontent.com/90811472/199090989-15109bbf-f035-477d-8566-e2a28de0935d.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200315152-9d6df91c-f2d2-4bba-9f95-b790fac37b62.jpg"/>
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```shell
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python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
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@ -145,44 +145,44 @@ python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
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E,g：
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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_dataset`, the area rule is `[0,32,96,1e5]`, generate a result graph containing all functions and save the graph to the current running directory `./dataset_analysis` folder:
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1.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, By default,the data loadingt type is `train_dataset`, the area rule is `[0,32,96,1e5]`, generate a result graph containing all functions and save the graph to the current running directory `./dataset_analysis` folder:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py
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```
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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_dataset` to `val_dataset` through the `--val-dataset` setting:
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2.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the data loading type from the default `train_dataset` to `val_dataset` through the `--val-dataset` setting:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--val-dataset
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```
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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:
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3.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the display of all generated classes to specific classes. Take the display of `person` classes as an example:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--class-name person
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```
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4.Use `config` file `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` analyze the dataset, redefine the area rule through `--area-rule` . Take `30 70 125` as an example, the area rule becomes `[0,30,70,125,1e5]`：
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4.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, redefine the area rule through `--area-rule` . Take `30 70 125` as an example, the area rule becomes `[0,30,70,125,1e5]`：
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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--area-rule 30 70 120
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--area-rule 30 70 125
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```
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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:
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5.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, change the display of four function renderings to only display `Function 1` as an example:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--func show_bbox_num
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```
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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`:
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6.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, modify the picture saving address to `work_ir/dataset_analysis`:
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--output-dir work_dir/dataset_analysis
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```
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@ -108,30 +108,30 @@ python tools/analysis_tools/browse_dataset.py 'configs/yolov5/yolov5_s-v61_syncb
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--not-show
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```
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### 可视化数据集分
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### 可视化数据集分析
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脚本 `tools/analysis_tools/dataset_analysis.py` 能够帮助用户得到四种功能的结果图，并将图片保存到当前运行目录下的 `dataset_analysis` 文件夹中。
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关于该脚本的功能的说明：
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通过 `main()` 的数据准备，得到每个子函数所需要的数据。
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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。
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<img src="https://user-images.githubusercontent.com/90811472/196891728-4c2f1ab3-01cb-445f-a6b8-39752387c40f.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200314770-4fb21626-72f2-4a4c-be5d-bf860ad830ec.jpg"/>
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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。
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<img src="https://user-images.githubusercontent.com/90811472/199019573-650b9652-eb14-4bc0-a5e8-650dfc578fc8.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200315007-96e8e795-992a-4c72-90fa-f6bc00b3f2c7.jpg"/>
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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。
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<img src="https://user-images.githubusercontent.com/90811472/199019593-0f810a21-18d2-41ac-b4fa-baa8288bcb23.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200315044-4bdedcf6-087a-418e-8fe8-c2d3240ceba8.jpg"/>
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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。
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<img src="https://user-images.githubusercontent.com/90811472/199022991-5388db47-d0f3-4201-9eee-13c5fab6bca9.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200315075-71680fe2-db6f-4981-963e-a035c1281fc1.jpg"/>
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打印列表显示，通过脚本中子函数 `show_class_list` 和 `show_data_lis` 生成。
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打印列表显示，通过脚本中子函数 `show_class_list` 和 `show_data_list` 生成。
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<img src="https://user-images.githubusercontent.com/90811472/199090989-15109bbf-f035-477d-8566-e2a28de0935d.jpg"/>
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<img src="https://user-images.githubusercontent.com/90811472/200315152-9d6df91c-f2d2-4bba-9f95-b790fac37b62.jpg"/>
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```shell
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python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
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@ -145,44 +145,44 @@ python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
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例子：
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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_dataset` ，面积规则设置为 `[0,32,96,1e5]` ,生成包含所有类的结果图并将图片保存到当前运行目录下 `./dataset_analysis` 文件夹中：
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1.使用 `config` 文件 `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` 分析数据集，其中默认设置:数据加载类型为 `train_dataset` ，面积规则设置为 `[0,32,96,1e5]` ,生成包含所有类的结果图并将图片保存到当前运行目录下 `./dataset_analysis` 文件夹中：
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py
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```
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2.使用 `config` 文件 `configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py` 分析数据集，通过 `--val-dataset` 设置将数据加载类型由默认的 `train_dataset` 改为 `val_dataset`：
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2.使用 `config` 文件 `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` 分析数据集，通过 `--val-dataset` 设置将数据加载类型由默认的 `train_dataset` 改为 `val_dataset`：
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--val-dataset
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```
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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` 设置将生成所有类改为特定类显示，以显示 `person` 为例：
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3.使用 `config` 文件 `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` 分析数据集，通过 `--class-name` 设置将生成所有类改为特定类显示，以显示 `person` 为例：
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--class-name person
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```
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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 `30 70 125` 为例,面积规则变为 `[0,30,70,125,1e5]`：
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4.使用 `config` 文件 `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` 分析数据集，通过 `--area-rule` 重新定义面积规则，以 `30 70 125` 为例,面积规则变为 `[0,30,70,125,1e5]`：
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--area-rule 30 70 125
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```
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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：
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5.使用 `config` 文件 `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.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：
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--func show_bbox_num
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```
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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：
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6.使用 `config` 文件 `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.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：
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```shell
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
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python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
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--output-dir work_dir/dataset_analysis
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```
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@ -7,6 +7,7 @@ import matplotlib.patches as mpatches
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import matplotlib.pyplot as plt
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import numpy as np
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from mmengine.config import Config
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from mmengine.dataset.dataset_wrapper import ConcatDataset
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from mmengine.utils import ProgressBar
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from prettytable import PrettyTable
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@ -61,12 +62,12 @@ def show_bbox_num(cfg, args, fig_set, class_name, class_num):
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print('\n\nDrawing bbox_num figure:')
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# Draw designs
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fig = plt.figure(
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figsize=(fig_set['figsize'][0], fig_set['figsize'][1]), dpi=600)
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figsize=(fig_set['figsize'][0], fig_set['figsize'][1]), dpi=300)
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plt.bar(class_name, class_num, align='center')
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# Draw titles, labels and so on
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for x, y in enumerate(class_num):
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plt.text(x, y, '%s' % y, ha='center', fontsize=fig_set['fontsize'])
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plt.text(x, y, '%s' % y, ha='center', fontsize=fig_set['fontsize'] + 3)
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plt.xticks(rotation=fig_set['xticks_angle'])
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plt.xlabel('Category Name')
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plt.ylabel('Num of instances')
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@ -77,7 +78,10 @@ def show_bbox_num(cfg, args, fig_set, class_name, class_num):
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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out_name = fig_set['out_name']
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fig.savefig(f'{out_dir}/{out_name}_bbox_num.jpg') # Save Image
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fig.savefig(
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f'{out_dir}/{out_name}_bbox_num.jpg',
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bbox_inches='tight',
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pad_inches=0.1) # Save Image
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plt.close()
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print(f'End and save in {out_dir}/{out_name}_bbox_num.jpg')
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@ -88,7 +92,7 @@ def show_bbox_wh(args, fig_set, class_bbox_w, class_bbox_h, class_name):
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print('\n\nDrawing bbox_wh figure:')
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# Draw designs
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fig, ax = plt.subplots(
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figsize=(fig_set['figsize'][0], fig_set['figsize'][1]), dpi=600)
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figsize=(fig_set['figsize'][0], fig_set['figsize'][1]), dpi=300)
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# Set the position of the map and label on the x-axis
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positions_w = list(range(0, 12 * len(class_name), 12))
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@ -165,7 +169,10 @@ def show_bbox_wh(args, fig_set, class_bbox_w, class_bbox_h, class_name):
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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out_name = fig_set['out_name']
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fig.savefig(f'{out_dir}/{out_name}_bbox_wh.jpg') # Save Image
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fig.savefig(
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f'{out_dir}/{out_name}_bbox_wh.jpg',
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bbox_inches='tight',
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pad_inches=0.1) # Save Image
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plt.close()
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print(f'End and save in {out_dir}/{out_name}_bbox_wh.jpg')
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@ -176,7 +183,7 @@ def show_bbox_wh_ratio(args, fig_set, class_name, class_bbox_ratio):
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print('\n\nDrawing bbox_wh_ratio figure:')
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# Draw designs
|
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fig, ax = plt.subplots(
|
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figsize=(fig_set['figsize'][0], fig_set['figsize'][1]), dpi=600)
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figsize=(fig_set['figsize'][0], fig_set['figsize'][1]), dpi=300)
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||||
|
||||
# Set the position of the map and label on the x-axis
|
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positions = list(range(0, 6 * len(class_name), 6))
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|
|
@ -222,7 +229,10 @@ def show_bbox_wh_ratio(args, fig_set, class_name, class_bbox_ratio):
|
|||
if not os.path.exists(out_dir):
|
||||
os.makedirs(out_dir)
|
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out_name = fig_set['out_name']
|
||||
fig.savefig(f'{out_dir}/{out_name}_bbox_ratio.jpg') # Save Image
|
||||
fig.savefig(
|
||||
f'{out_dir}/{out_name}_bbox_ratio.jpg',
|
||||
bbox_inches='tight',
|
||||
pad_inches=0.1) # Save Image
|
||||
plt.close()
|
||||
print(f'End and save in {out_dir}/{out_name}_bbox_ratio.jpg')
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||||
|
||||
|
|
@ -240,7 +250,7 @@ def show_bbox_area(args, fig_set, area_rule, class_name, bbox_area_num):
|
|||
|
||||
# Draw designs
|
||||
fig = plt.figure(
|
||||
figsize=(fig_set['figsize'][0], fig_set['figsize'][1]), dpi=600)
|
||||
figsize=(fig_set['figsize'][0], fig_set['figsize'][1]), dpi=300)
|
||||
for i in range(len(area_rule) - 1):
|
||||
area_num = [bbox_area_num[idx][i] for idx in range(len(class_name))]
|
||||
plt.bar(
|
||||
|
|
@ -251,7 +261,11 @@ def show_bbox_area(args, fig_set, area_rule, class_name, bbox_area_num):
|
|||
color=colors[i])
|
||||
for idx, (x, y) in enumerate(zip(positions.tolist(), area_num)):
|
||||
plt.text(
|
||||
x + width * i, y, y, ha='center', fontsize=fig_set['fontsize'])
|
||||
x + width * i,
|
||||
y,
|
||||
y,
|
||||
ha='center',
|
||||
fontsize=fig_set['fontsize'] - 1)
|
||||
|
||||
# Draw titles, labels and so on
|
||||
plt.xticks(rotation=fig_set['xticks_angle'])
|
||||
|
|
@ -276,7 +290,10 @@ def show_bbox_area(args, fig_set, area_rule, class_name, bbox_area_num):
|
|||
if not os.path.exists(out_dir):
|
||||
os.makedirs(out_dir)
|
||||
out_name = fig_set['out_name']
|
||||
fig.savefig(f'{out_dir}/{out_name}_bbox_area.jpg') # Save Image
|
||||
fig.savefig(
|
||||
f'{out_dir}/{out_name}_bbox_area.jpg',
|
||||
bbox_inches='tight',
|
||||
pad_inches=0.1) # Save Image
|
||||
plt.close()
|
||||
print(f'End and save in {out_dir}/{out_name}_bbox_area.jpg')
|
||||
|
||||
|
|
@ -331,19 +348,19 @@ def show_data_list(args, area_rule):
|
|||
print(data_info)
|
||||
|
||||
|
||||
def show_dataset_classes(dataset_classes):
|
||||
def show_data_classes(data_classes):
|
||||
"""When printing an error, all class names of the dataset."""
|
||||
print('\n\nThe name of the class contained in the dataset:')
|
||||
data_classes_info = PrettyTable()
|
||||
data_classes_info.title = 'Information of dataset class'
|
||||
# List Print Settings
|
||||
# If the quantity is too large, 25 rows will be displayed in each column
|
||||
if len(dataset_classes) < 25:
|
||||
data_classes_info.add_column('Class name', dataset_classes)
|
||||
elif len(dataset_classes) % 25 != 0 and len(dataset_classes) > 25:
|
||||
col_num = int(len(dataset_classes) / 25) + 1
|
||||
data_name_list = list(dataset_classes)
|
||||
for i in range(0, (col_num * 25) - len(dataset_classes)):
|
||||
if len(data_classes) < 25:
|
||||
data_classes_info.add_column('Class name', data_classes)
|
||||
elif len(data_classes) % 25 != 0 and len(data_classes) > 25:
|
||||
col_num = int(len(data_classes) / 25) + 1
|
||||
data_name_list = list(data_classes)
|
||||
for i in range(0, (col_num * 25) - len(data_classes)):
|
||||
data_name_list.append('')
|
||||
for i in range(0, len(data_name_list), 25):
|
||||
data_classes_info.add_column('Class name',
|
||||
|
|
@ -367,12 +384,21 @@ def main():
|
|||
elif args.val_dataset is True:
|
||||
dataset = DATASETS.build(cfg.val_dataloader.dataset)
|
||||
|
||||
data_list = dataset.load_data_list()
|
||||
# Determine whether the dataset is ConcatDataset
|
||||
if isinstance(dataset, ConcatDataset):
|
||||
datasets = dataset.datasets
|
||||
data_list = []
|
||||
for idx in range(len(datasets)):
|
||||
datasets_list = datasets[idx].load_data_list()
|
||||
data_list += datasets_list
|
||||
else:
|
||||
data_list = dataset.load_data_list()
|
||||
|
||||
# 2.Prepare data
|
||||
# Drawing settings
|
||||
fig_all_set = {
|
||||
'figsize': [45, 18],
|
||||
'fontsize': 4,
|
||||
'figsize': [35, 18],
|
||||
'fontsize': int(10 - 0.08 * len(dataset.metainfo['CLASSES'])),
|
||||
'xticks_angle': 70,
|
||||
'out_name': cfg.dataset_type
|
||||
}
|
||||
|
|
@ -393,8 +419,8 @@ def main():
|
|||
classes_idx = [dataset.metainfo['CLASSES'].index(args.class_name)]
|
||||
fig_set = fig_one_set
|
||||
else:
|
||||
dataset_classes = dataset.metainfo['CLASSES']
|
||||
show_dataset_classes(dataset_classes)
|
||||
data_classes = dataset.metainfo['CLASSES']
|
||||
show_data_classes(data_classes)
|
||||
raise RuntimeError(f'Expected args.class_name to be one of the list,'
|
||||
f'but got "{args.class_name}"')
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue