getPMatrix.py 文件源码

def getMatches(self, sceneImage):
        """
        sceneImage: ?????array??

        return dst: ????????

        """
        # Initiate SIFT detector
        sift = cv2.xfeatures2d.SIFT_create()

        # find the keypoints and descriptors with SIFT
        kp1, des1 = sift.detectAndCompute(self.MarkImage[:,:,0],None)
        kp2, des2 = sift.detectAndCompute(sceneImage[:,:,0],None)

        # create BFMatcher object
        FLANN_INDEX_KDTREE = 0
        index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
        search_params = dict(checks = 50)

        flann = cv2.FlannBasedMatcher(index_params, search_params)

        # Match descriptors.
        matches = flann.knnMatch(des1,des2,k=2)

        # Sort them in the order of their distance.
        good = []
        for m,n in matches:
            if m.distance < 0.7*n.distance:
                good.append(m)
        if len(good) < self.MIN_MATCH_COUNT:
            return None

        src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)

        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
        matchesMask = mask.ravel().tolist()

        h,w = self.MarkImage.shape[:2]
        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
        dst = cv2.perspectiveTransform(pts,M)

        draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                           singlePointColor = None,
                           matchesMask = matchesMask, # draw only inliers
                           flags = 2)

        self.SceneImage = sceneImage
        self.DrawParams = draw_params
        self.KP1 = kp1
        self.KP2 = kp2
        self.GoodMatches = good
        return dst