Python+OpenCV：Feature Matching + Homography to find Objects

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# 图像特征匹配(Feature Matching with Homography)
def lmc_cv_image_feature_matching_homography():"""函数功能: 图像特征匹配(Feature Matching with FLANN)。"""# 读取图像image1 = lmc_cv.imread('D:/99-Research/Python/Image/Brochure01.jpg', lmc_cv.IMREAD_GRAYSCALE)image2 = lmc_cv.imread('D:/99-Research/Python/Image/Brochure02.jpg', lmc_cv.IMREAD_GRAYSCALE)# Initiate SIFT detectormin_match_count = 10sift = lmc_cv.SIFT_create()# find the keypoints and descriptors with SIFTkeypoints1, descriptors1 = sift.detectAndCompute(image1, None)keypoints2, descriptors2 = sift.detectAndCompute(image2, None)flann_index_kdtree = 1index_params = dict(algorithm=flann_index_kdtree, trees=5)search_params = dict(checks=50)flann = lmc_cv.FlannBasedMatcher(index_params, search_params)matches = flann.knnMatch(descriptors1, descriptors2, k=2)# store all the good matches as per Lowe's ratio test.good = []for m, n in matches:if m.distance < 0.7 * n.distance:good.append(m)if len(good) > min_match_count:src_pts = np.float32([keypoints1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)m, mask = lmc_cv.findHomography(src_pts, dst_pts, lmc_cv.RANSAC, 5.0)matches_mask = mask.ravel().tolist()h, w = image1.shapepts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)dst = lmc_cv.perspectiveTransform(pts, m)img2 = lmc_cv.polylines(image2, [np.int32(dst)], True, 255, 3, lmc_cv.LINE_AA)else:print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))matches_mask = Nonedraw_params = dict(matchColor=(0, 255, 0),  # draw matches in green colorsinglePointColor=None,matchesMask=matches_mask,  # draw only inliersflags=2)# cv.drawMatchesKnn expects list of lists as matches.result_image = lmc_cv.drawMatches(image1, keypoints1, image2, keypoints2, good, None, **draw_params)# 显示图像pyplot.figure('Feature Matching with FLANN and Homography')pyplot.subplot(1, 1, 1)pyplot.imshow(result_image, 'gray')pyplot.title('Feature Matching with FLANN and Homography')pyplot.xticks([])pyplot.yticks([])# 根据用户输入保存图像if ord("q") == (lmc_cv.waitKey(0) & 0xFF):# 销毁窗口pyplot.close('all')return

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Python+OpenCV：Feature Matching + Homography to find Objects

Python+OpenCV：Feature Matching + Homography to find Objects

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