65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
import cv2 as cv
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import numpy as np
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import os, sys
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key_and_desc_cache = {}
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def getKeyAndDesc(img_path1):
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global key_and_desc_cache
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key = img_path1
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if key in key_and_desc_cache:
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pass
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else:
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detector = cv.SIFT_create()
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img1 = cv.imread(img_path1)
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key_and_desc_cache[key] = detector.detectAndCompute(img1, None)
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return key_and_desc_cache[key]
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def SIFT(img_path1, img_path2):
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try:
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img1 = cv.imread(img_path1)
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img2 = cv.imread(img_path2)
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if img1 is None or img2 is None:
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print("Error loading images!")
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exit(0)
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except:
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print("Error loading images!")
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exit(0)
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# -- Step 1: Detect the keypoints using SIFT Detector, compute the descriptors
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minHessian = 400
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keypoints1, descriptors1 = getKeyAndDesc(img_path1)
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keypoints2, descriptors2 = getKeyAndDesc(img_path2)
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# -- Step 2: Matching descriptor vectors with a brute force matcher
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matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE)
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matches = matcher.match(descriptors1, descriptors2)
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# -- Draw matches
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# img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1], 3), dtype=np.uint8)
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# cv.drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches)
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# # -- Show detected matches
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# cv.imshow("Matches: SIFT (Python)", img_matches)
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# cv.waitKey()
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# draw good matches
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matches = sorted(matches, key=lambda x: x.distance)
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min_dist = matches[0].distance
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good_matches = tuple(filter(lambda x: x.distance <= 1.1 * min_dist, matches))
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# img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1], 3), dtype=np.uint8)
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# cv.drawMatches(
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# img1, keypoints1, img2, keypoints2, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS
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# )
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return len(good_matches)
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# -- Show detected matches
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# cv.imshow("Good Matches: SIFT (Python)", img_matches)
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# cv.waitKey()
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