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photo.","extensions":{"bazaar":{"info":{"input":{"body":{"detector":"sift","image":"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","template":"iVBORw0KGgoAAAANSUhEUgAAAEAAAAAwCAAAAACEICPDAAADAklEQVRIx61UT0gUURz+Znfazdyx0Ex3U1smVqJCKwn6c5FCCaKDoYdOEh7WywQiQh6UisKgS7QgLLSXQrpIdPTPISIiiAqlSHJptbINRTR1E1m3fc0b58/OOjM7rv6YefPmzft9v+/73pvHPIUUftq0ARGYxSjQIHWiysggbRzyyzS2HFJ+twLgzzV9VOlEM/Obo4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detector and descriptor: orb (default, fast, binary descriptors, Hamming distance) or sift (slower, better with scale changes, L2 distance)","enum":["orb","sift"],"type":"string"},"image":{"description":"Base64 of a PNG, JPEG, BMP or WebP file (a data: URI also works). At most 4 million pixels and about 2 MB. Alpha is dropped.","type":"string"},"max_features":{"description":"Most keypoints kept per image, 100-5000 (default 2000)","type":"integer"},"min_inliers":{"description":"Fewest RANSAC inliers for found to be true, 4-200 (default 10)","type":"integer"},"ratio":{"description":"Lowe's ratio test threshold, 0.5-0.95 (default 0.75): a match is kept when its distance is below ratio times the second-best distance","type":"number"},"reproj_threshold":{"description":"RANSAC reprojection error in pixels below which a match is an inlier, 0.5-20 (default 3)","type":"number"},"template":{"description":"Base64 of the pattern to find, same formats. At most 1 million pixels and about 1 MB. 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Use it to see which parts of a pattern are present and where, even when the pattern is scaled or rotated; for the pattern's outline use 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