I have a lot of duplicate images. I need to make a selection to reduce the amount of images the Mask RCNN model will perform inference on.
In every collection of duplicates, the images slightly differ. One is brighter, darker, different shadows, different reflection, etc. But all duplicate images are essentially capturing the same object(s) from the same angle with the same background, etc.
What computer vision methods can help me to know before inference what image would produce the most results after inference?
I have tried:
- edge detection (cv2.Canny followed by sum()): Selecting the image with the most edges. In my use case there does not seem to be a strong predictive relation between amount of edges and amount of objects detected.