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Different picture shapes frames5/29/2023 ![]() Print("Image Similarity: %".format(score * 100)) ![]() (score, diff) = structural_similarity(before_gray, after_gray, full=True) from trics import structural_similarityīefore_gray = cv2.cvtColor(before, cv2.COLOR_BGR2GRAY)Īfter_gray = cv2.cvtColor(after, cv2.COLOR_BGR2GRAY) To visualize the exact differences, we fill the contours onto a mask and on the original image. We iterate through each contour, filter using a minimum threshold area to remove the gray noise, and highlight the differences with a bounding box. Now we filter through the diff image since we only want to find the large differences between the images. The SSIM score after comparing the two images show that they are very similar. We would obtain a cleaner result if we used a lossless compression image format. The gray noisy areas are probably due to. Larger areas of disparity are highlighted in black while smaller differences are in gray.Īll differences -> Significant region differences Specifically, the diff image contains the actual image differences with darker regions having more disparity. But since you're only interested in where the two images differ, the diff image is what we'll focus on. The score represents the structural similarity index between the two input images and can fall between the range with values closer to one representing higher similarity. Using the _similarity function from scikit-image, it returns a score and a difference image, diff. You can install scikit-image with pip install scikit-image. This method is already implemented in the scikit-image library for image processing. To visualize differences between two images, we can take a quantitative approach to determine the exact discrepancies between images using the Structural Similarity Index (SSIM) which was introduced in Image Quality Assessment: From Error Visibility to Structural Similarity. ![]() Method #1: Structural Similarity Index (SSIM) ![]()
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