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Please can you do a new PR but where these changes are in a new scene detection file e.g. submit your changes as svm_poly3 (or other name of your choice) ? |
angrave
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angrave
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Please see my line-based comments - (basically more comments needed to explain why some choices were made)
Also please can you do a new PR but where these changes are in a new scene detection file e.g. submit your changes as svm_poly3 (or other name of your choice) ?
i.e. I want to be able to be able switch between the current and your new methods.
| M, inliers = cv2.estimateAffinePartial2D(src_pts, dst_pts, method=cv2.RANSAC) | ||
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| inlier_ratio = np.sum(inliers) / len(inliers) | ||
| # Arbitrary threshold |
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Some comments would be useful.
Where did this value come from? Are there any links to web resources that would help me/someone tweak this i the future? What is a reasonable range? What happens if it is too large too small?
The value should at least be in a variable to highlight it
| if m.distance < 0.7 * n.distance: | ||
| good.append(m) | ||
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| if len(good) < 100: |
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Arbtirary constant should be in a variable. Documentation... Valid range? Web resources? When should it be increased? decreased?
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| matches = flann.knnMatch(des1, des2, k=2) | ||
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| # Ratio test as per Lowe's paper |
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Please cite e.g. with DOI or web URL so people can find it
| # print(f"No descriptors found (des1={des1 is None}, des2={des2 is None}) at index {index}") | ||
| return False | ||
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| # FLANN parameters |
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A few notes would be useful here, includingwhat these parameters do
| # Create target | ||
| crop_image = ref[img_y:img_y+img_h, img_x:img_x+img_w] | ||
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| # Initiate SIFT detector |
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A web link would be usesful, plus a comment what this detector does
| return result | ||
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| def find_match(curr, ref, width, height, index): | ||
| # Defining region of search |
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Add function to handle scrolling-related false positives in scene detection