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InteractiveImageSegmentation

An interactive image segmentation tool for pixel-wise labeling image dataset in segmentation task, which use GrabCut("“GrabCut”: interactive foreground extraction using iterated graph cuts") and implemented in OpenCV 3 and Python. An OpenCV tutorial can be found here.

Quick Start

Go to this project dir and run 'python interactive_image_segmentation.py resouce/images/ resouce/labels'. You will see a UI window and you can start labelling. If you want to label your own images, collect them into a filefolder, modify the source_dir and save_dir.

How to use

  • CTRL+left mouse button: label certain background pixels
  • SHIFT+left mouse button: label certain foreground pixels
  • CTRL+right mouse button: label possible background pixels
  • SHIFT+right mouse button: label possible foreground pixels
  • 'a'/SPACE: run sengementation again
  • 's'/ENTER: save label and skip to next image
  • 'p': prev image
  • 'n': next image
  • 'q'/ESC: exit

There are some useful py scripts you can use to handle your own data. Usage:

  • 'interactive_image_segmentation.py image_dir save_dir' runs interactive image segmentation
  • 'batch_rename.py dir1 [dir2] ...' renames all images in folder with sequencial numbers.
  • 'img_cvt_format.py image_dir [suffix]' converts all images in folder to the same image format, such as '.png' '.jpg'...
  • 'video2img.py video_file save_dir [stride] [resize] [suffix]' converts a video file to a list of images.

License

This project is released under a MIT license.