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# Visualize dataset analysis
`tools/analysis_tools/dataset_analysis.py` help users get the renderings of the four functions, 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/90811472/200314770-4fb21626-72f2-4a4c-be5d-bf860ad830ec.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/90811472/200315007-96e8e795-992a-4c72-90fa-f6bc00b3f2c7.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/90811472/200315044-4bdedcf6-087a-418e-8fe8-c2d3240ceba8.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/90811472/200315075-71680fe2-db6f-4981-963e-a035c1281fc1.jpg"/>
Print List: Generated by the sub function `show_class_list` and `show_data_list`.
<img src="https://user-images.githubusercontent.com/90811472/200315152-9d6df91c-f2d2-4bba-9f95-b790fac37b62.jpg"/>
```shell
python tools/analysis_tools/dataset_analysis.py ${CONFIG} \
[--type ${TYPE}] \
[--class-name ${CLASS_NAME}] \
[--area-rule ${AREA_RULE}] \
[--func ${FUNC}] \
[--out-dir ${OUT_DIR}]
```
E,g：
1.Use `config` file `configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py` analyze the dataset, By default,the data loading 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:
```shell
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py
```
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:
```shell
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
--val-dataset
```
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:
```shell
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
--class-name person
```
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]`：
```shell
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
--area-rule 30 70 125
```
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:
```shell
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
--func show_bbox_num
```
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_dirs/dataset_analysis`:
```shell
python tools/analysis_tools/dataset_analysis.py configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py \
--out-dir work_dirs/dataset_analysis
```
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