How to use show_match_image method in Airtest

Best Python code snippet using Airtest Github


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...29 PointInImg.update({idx: f})30 Descriptor.update({idx: d})31 # Show match result32 if len(Images) > 1:33 match.show_match_image(Images[0], Images[1])34 # Compute homography35 Transform = {0: np.identity(3)}36 PointInPano = [PointInImg[0][i].pt for i in range(len(PointInImg[0]))]37 DescriptorInPano = Descriptor[0]38 for idx in range(1, len(imgList)):39 print('fitting transformation from ' + str(idx) + ' to pano\t')40 M = SIFTMatcher(DescriptorInPano, Descriptor[idx], THRESHOLD)41 print('matching points:', len(M, ), '\n')42 dst_pts = np.float32([PointInPano[m[0]] for m in M]).reshape(-1, 1, 2)43 src_pts = np.float32([PointInImg[idx][m[1]].pt for m in M]).reshape(-1, 1, 2)44 # H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)45 H = func.findHomographyRANSAC(src_pts.reshape(-1,2), dst_pts.reshape(-1,2))46 # Remove matched keypoints before appending47 indices = [m[1] for m in M]...

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1import cv22import numpy as np3MIN_MATCH_COUNT = 104def show_match_image(img1, img2):5 # Initiate SIFT detector6 sift = cv2.xfeatures2d.SIFT_create()7 # find the keypoints and descriptors with SIFT8 kp1, des1 = sift.detectAndCompute(img1,None)9 kp2, des2 = sift.detectAndCompute(img2,None)10 # BFMatcher with default params11 bf = cv2.BFMatcher()12 matches = bf.knnMatch(des1,des2, k=2)13 # Apply ratio test14 good = []15 for m,n in matches:16 if m.distance < 0.75*n.distance:17 good.append(m)18 # cv2.drawMatchesKnn expects list of lists as matches.19 good_2 = np.expand_dims(good, 1)20 matching = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good_2[:20],None, flags=2)21 if len(good)>MIN_MATCH_COUNT:22 # 獲取關鍵點的坐標23 src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)24 dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)25 H, mask = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC,5.0)26 wrap = cv2.warpPerspective(img2, H, (img2.shape[1]+img2.shape[1] , img2.shape[0]+img2.shape[0]))27 # result = cv2.addWeighted(img1, 0.5, wrap, 0.5, 0)28 wrap[0:img2.shape[0], 0:img2.shape[1]] = img129 # rows, cols = np.where(wrap[:,:,0] !=0)30 # min_row, max_row = min(rows), max(rows) +131 # min_col, max_col = min(cols), max(cols) +132 # result = wrap[min_row:max_row,min_col:max_col,:]#去除黑色無用部分33 result = wrap34 cv2.imshow('Match Result', matching)35 cv2.waitKey(0)36 cv2.destroyAllWindows()37 return matching, result38if __name__ == '__main__':39 img1 = cv2.imread('data/Rainier1.png') # queryImage40 img2 = cv2.imread('data/Rainier3.png') # trainImage...

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