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extract_yolo_v3_retraining_data_from_video

Decompose video into individual frames with bounding box output formatted for Yolo v3 retraining.

TODO: ### DOI

Use object tracking software (OpenCV2) to generate yolov3 suitable training images from videos for new object types

  • Obtain videos of the objects of interest
  • test each video
    • Run python object_track_extract_yolov3.py test <video_path>
    • The first video frame is loaded
    • Draw a bounding box around the object of interest
    • Press the spacebar and verify that the bounding box properly follows the object without errors
      • If errors occur, change the tracker_types in the script to another type and try again
  • capture data from each video
    • Run python object_track_extract_yolov3.py capture <video_path> <output_path>
    • Follow the same process as above.
    • With the 'capture' argument, data will be output to the <output path>

Environment Requirements

  • Python 3.7
  • OpenCV
    • Install cv2 in python3 via pip install opencv-contrib-python
    • The "contrib" installation expands the install to include all the object tracking packages

References

Object Tracking Video code modified from Satya Mallick's article on LearnOpenCV here: https://www.learnopencv.com/object-tracking-using-opencv-cpp-python/