roomba.py 文件源码

def match_outlines(self, orig_image, skewed_image):
        orig_image = np.array(orig_image)
        skewed_image = np.array(skewed_image)
        try:
            surf = cv2.xfeatures2d.SURF_create(400)
        except Exception:
            surf = cv2.SIFT(400)
        kp1, des1 = surf.detectAndCompute(orig_image, None)
        kp2, des2 = surf.detectAndCompute(skewed_image, None)

        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)
        matches = flann.knnMatch(des1, des2, 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)

        MIN_MATCH_COUNT = 10
        if len(good) > MIN_MATCH_COUNT:
            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)

            # see https://ch.mathworks.com/help/images/examples/find-image-rotation-and-scale-using-automated-feature-matching.html for details
            ss = M[0, 1]
            sc = M[0, 0]
            scaleRecovered = math.sqrt(ss * ss + sc * sc)
            thetaRecovered = math.atan2(ss, sc) * 180 / math.pi
            self.log.info("MAP: Calculated scale difference: %.2f, "
                          "Calculated rotation difference: %.2f" %
                          (scaleRecovered, thetaRecovered))

            #deskew image
            im_out = cv2.warpPerspective(skewed_image, np.linalg.inv(M),
                (orig_image.shape[1], orig_image.shape[0]))
            return im_out

        else:
            self.log.warn("MAP: Not  enough  matches are found   -   %d/%d"
                          % (len(good), MIN_MATCH_COUNT))
            return skewed_image