OD_dataset_conversion_scripts

Object detection dataset conversion scripts

1. PASCAL VOC => YOLO: voc2yolo.py

2. YOLO => PASCAL VOC: yolo2voc.py

3. PASCAL VOC => COCO: voc2coco.py

4. COCO => PASCAL VOC

   Use utils_cv.detection.data.coco2voc to complete this conversion. The process is listed below:

   • Install Microsoft utils_cv package: pip install git+https://github.com/microsoft/ComputerVision.git@master#egg=utils_cv
   • Import fumction: from utils_cv.detection.data import coco2voc
   • Function Signature:

   Signature:
   coco2voc(
       anno_path: str,
       output_dir: str,
       anno_type: str = 'instance',
       download_images: bool = False,
   ) -> None
   Docstring:
   Convert COCO annotation (single .json file) to Pascal VOC annotations
       (multiple .xml files).
   
   Args:
       anno_path: path to coco-formated .json annotation file
       output_dir: root output directory
       anno_type: "instance" for rectangle annotation, or "keypoint" for keypoint annotation.
       download_images: if true then download images from their urls.
   

5. PASCAL VOC => CSV: voc2csv.py

6. PASCAL VOC => TXT: voc2txt.py

7. PASCAL VOC dataset information: voc_dataset_information.py

8. PASCAL VOC Augmentation: voc_augument.py

9. YOLO Augmentation: yolo_augument.py

10. Rename file names: rename_files.py

11. Generate VOC/ImageSets/Main/trainval.txt(train.txt,val.txt,test.txt): voc_gen_trainval_test.py

12. Cluster anchors used in YOLO series: anchor-cluster.py