Image segmentation using Python and OpenCV

Question

I have been trying to segment the tread from images. The expected segmentation(manually segmented) is 1

The methods I have tried so far,

• Found out the edges using the Line segment detector in OpenCV. Then grouped line segments based on nearness and orientation. From the groups, sampled the points to fit a two-degree polynomial. Using the curve equations, I found the mask for the tread.
• Treated the problem as a binary segmentation problem and trained a CNN to get the mask. Since I don't have enough samples to train, I cannot use this method.
• Tried out the watershed method. Failed due to poor marker construction.

Please note that I cannot use color thresholding as the background color changes.

Below is the image I want to segment.

Any idea on how I should approach this problem or which techniques will be useful will be of great help. I'm constrained to use Python and OpenCV.

Thanks.

Answer 1

Bunch of links for you to check out:

• https://www.pyimagesearch.com/2018/09/03/semantic-segmentation-with-opencv-and-deep-learning/
• https://www.learnopencv.com/image-segmentation/
• https://www.pyimagesearch.com/2018/11/26/instance-segmentation-with-opencv/
• https://divamgupta.com/image-segmentation/2019/06/06/deep-learning-semantic-segmentation-keras.html
• https://www.analyticsvidhya.com/blog/2019/04/introduction-image-segmentation-techniques-python/
• https://heartbeat.fritz.ai/deep-learning-for-image-segmentation-u-net-architecture-ff17f6e4c1cf

Does your camera angle and framing change as well? If it's not, there's nothing bad in abusing it and fitting pre-defined curves.