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How to use show_match_image method in Airtest

Best Python code snippet using Airtest

main.py

Source:main.py

...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]...

match.py

Source:match.py

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        # ç²åééµé»çåæ¨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,:]#å»é¤é»è²ç¡ç¨é¨å33        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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