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- cv2.xfeatures2d.SIFT_create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> retval
- | . @param nfeatures The number of best features to retain. The features are ranked by their scores
- | . (measured in SIFT algorithm as the local contrast)
- | .
- | . @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper.
- | . The number of octaves is computed automatically from the image resolution.
- | .
- | . @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
- | . (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
- | .
- | . @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
- | . is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
- | . filtered out (more features are retained).
- | .
- | . @param sigma The sigma of the Gaussian applied to the input image at the octave #0. If your image
- | . is captured with a weak camera with soft lenses, you might want to reduce the number.
- |
- sift = cv2.xfeatures2d.SIFT_create(nfeatures = 50, nOctaveLayers=3)
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