Python cv2.RANSAC屬性代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def findHomography(image_1_kp, image_2_kp, matches):
    image_1_points = np.zeros((len(matches), 1, 2), dtype=np.float32)
    image_2_points = np.zeros((len(matches), 1, 2), dtype=np.float32)

    for i in range(0,len(matches)):
        image_1_points[i] = image_1_kp[matches[i].queryIdx].pt
        image_2_points[i] = image_2_kp[matches[i].trainIdx].pt


    homography, mask = cv2.findHomography(image_1_points, image_2_points, cv2.RANSAC, ransacReprojThreshold=2.0)

    return homography


#
#   Align the images so they overlap properly...
#
# 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def removeOutliersByMask(self, mask): 
        if mask is not None:    
            n = self.kpn_cur.shape[0]     
            mask_index = [ i for i,v in enumerate(mask) if v > 0]    
            self.kpn_cur = self.kpn_cur[mask_index]           
            self.kpn_ref = self.kpn_ref[mask_index]           
            if self.des_cur is not None: 
                self.des_cur = self.des_cur[mask_index]        
            if self.des_ref is not None: 
                self.des_ref = self.des_ref[mask_index]  
            if kVerbose:
                print('removed ', n-self.kpn_cur.shape[0],' outliers')                

    # fit essential matrix E with RANSAC such that:  p2.T * E * p1 = 0  where  E = [t21]x * R21
    # out: [Rrc, trc]   (with respect to 'ref' frame) 
    # N.B.1: trc is estimated up to scale (i.e. the algorithm always returns ||trc||=1, we need a scale in order to recover a translation which is coherent with previous estimated poses)
    # N.B.2: this function has problems in the following cases: [see Hartley/Zisserman Book]
    # - 'geometrical degenerate correspondences', e.g. all the observed features lie on a plane (the correct model for the correspondences is an homography) or lie on a ruled quadric 
    # - degenerate motions such a pure rotation (a sufficient parallax is required) or an infinitesimal viewpoint change (where the translation is almost zero)
    # N.B.3: the five-point algorithm (used for estimating the Essential Matrix) seems to work well in the degenerate planar cases [Five-Point Motion Estimation Made Easy, Hartley]
    # N.B.4: as reported above, in case of pure rotation, this algorithm will compute a useless fundamental matrix which cannot be decomposed to return the rotation 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def frame_homography(totalPts, homTh):
    """
    Filter foreground points i.e. the outlier points found by fitting
    homography using RANSAC
    Input:
        totalPts: (numAllPoints, 4): x0, y0, x1, y1
        fgPts: (numAllPoints, 4): x0, y0, x1, y1
    """
    if totalPts.ndim != 2 or totalPts.shape[0] < 8 or homTh < 0:
        return totalPts

    import cv2
    p1 = totalPts[:, :2].astype('float')
    p2 = totalPts[:, 2:4].astype('float')
    _, status = cv2.findHomography(
        p1, p2, cv2.RANSAC, ransacReprojThreshold=homTh)
    fgPts = totalPts[status[:, 0] == 0, :]
    return fgPts 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def getHomography(self, rightKps, rightDescriptor):
        rawMatches = self.matcher.knnMatch(self.leftDescriptor, rightDescriptor, 2)
        matches = []

        for m in rawMatches:
            if(len(m)==2 and m[0].distance < m[1].distance*self.ratio):
                matches.append((m[0].trainIdx, m[0].queryIdx))

        if(len(matches) >=4):
            # print(matches)
            ptsB = np.float32([self.leftKps[i] for (_, i) in matches])
            ptsA = np.float32([rightKps[i] for (i, _) in matches])

            # ptsB = H*ptsA
            H, status = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, self.reprojThresh)
            return H

        return None 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def compute_homography_error(kpts1, kpts2, matches, shape2, H_gt):
    if matches.shape[0] == 0:
        return False, None
    kpts1 = kpts1[matches[:, 0]]
    kpts2 = kpts2[matches[:, 1]]
    H, _ = cv2.findHomography(kpts2, kpts1, cv2.RANSAC, 3.0)
    if H is None:
        return None

    w, h = shape2
    corners2 = to_homogeneous(
        np.array([[0, 0], [0, h-1], [w-1, h-1], [w-1, 0]]))
    corners1_gt = np.dot(corners2, np.transpose(H_gt))
    corners1_gt = corners1_gt[:, :2] / corners1_gt[:, 2:]
    corners1 = np.dot(corners2, np.transpose(H))
    corners1 = corners1[:, :2] / corners1[:, 2:]
    mean_dist = np.mean(np.linalg.norm(corners1 - corners1_gt, axis=1))
    return mean_dist 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def findHomography(self, i1, i2, pairs):
        src = []
        dst = []
        for pair in pairs:
            c1 = i1.coord_list[pair[0]]
            c2 = i2.coord_list[pair[1]]
            src.append( c1 )
            dst.append( c2 )
        #H, status = cv2.findHomography(np.array([src]).astype(np.float32),
        #                               np.array([dst]).astype(np.float32),
        #                               cv2.RANSAC, 5.0)
        H, status = cv2.findHomography(np.array([src]).astype(np.float32),
                                       np.array([dst]).astype(np.float32))
        #print str(affine)
        return H

    # compare against best 'placed' image (averaging transform
    # matrices together directly doesn't do what we want) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def match(self, key_a, fea_a, key_b, fea_b):
		# Compute the raw matches and initialize the list of actual matches
		matcher = cv2.DescriptorMatcher_create(self.distance_method)
		raw_matches = matcher.knnMatch(fea_b, fea_a, 2)
		matches = []

		# Loop over the raw matches
		for match in raw_matches:
			# Ensure the distance is within a certain ratio of each other
			if len(match) == 2 and match[0].distance < match[1].distance * self.ratio:
				matches.append((match[0].trainIdx, match[0].queryIdx))

		# Check to see if there are enough matches to process
		if len(matches) > self.min_matches:
			# Construct the two sets of points
			poi_a = np.float32([key_a[i] for (i, _) in matches])
			poi_b = np.float32([key_b[j] for (_, j) in matches])

			# Compute the homography between the two sets of points and compute the ratio of matched points
			(_, status) = cv2.findHomography(poi_a, poi_b, cv2.RANSAC, 4.0)

			# Return the ratio of the number of matched keypoints to the total number of keypoints
			return float(status.sum()) / status.size

		# No matches were found
		return -1.0 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def track(self, frame):
        '''Returns a list of detected TrackedTarget objects'''
        self.frame_points, frame_descrs = self.detect_features(frame)
        if len(self.frame_points) < MIN_MATCH_COUNT:
            return []
        matches = self.matcher.knnMatch(frame_descrs, k = 2)
        matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
        if len(matches) < MIN_MATCH_COUNT:
            return []
        matches_by_id = [[] for _ in xrange(len(self.targets))]
        for m in matches:
            matches_by_id[m.imgIdx].append(m)
        tracked = []
        for imgIdx, matches in enumerate(matches_by_id):
            if len(matches) < MIN_MATCH_COUNT:
                continue
            target = self.targets[imgIdx]
            p0 = [target.keypoints[m.trainIdx].pt for m in matches]
            p1 = [self.frame_points[m.queryIdx].pt for m in matches]
            p0, p1 = np.float32((p0, p1))
            H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)
            status = status.ravel() != 0
            if status.sum() < MIN_MATCH_COUNT:
                continue
            p0, p1 = p0[status], p1[status]

            x0, y0, x1, y1 = target.rect
            quad = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
            quad = cv2.perspectiveTransform(quad.reshape(1, -1, 2), H).reshape(-1, 2)

            track = TrackedTarget(target=target, p0=p0, p1=p1, H=H, quad=quad)
            tracked.append(track)
        tracked.sort(key = lambda t: len(t.p0), reverse=True)
        return tracked 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def match_and_draw(win):
        with Timer('matching'):
            raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
        p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
        if len(p1) >= 4:
            H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
            print('%d / %d  inliers/matched' % (np.sum(status), len(status)))
            # do not draw outliers (there will be a lot of them)
            kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag]
        else:
            H, status = None, None
            print('%d matches found, not enough for homography estimation' % len(p1))

        vis = explore_match(win, img1, img2, kp_pairs, None, H) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def match_and_draw(win):
        print('matching...')
        raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
        p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
        if len(p1) >= 4:
            H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
            print('%d / %d  inliers/matched' % (np.sum(status), len(status)))
        else:
            H, status = None, None
            print('%d matches found, not enough for homography estimation' % len(p1))

        vis = explore_match(win, img1, img2, kp_pairs, status, H) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def _find_homography(self, sch_pts, src_pts):
        """多組特征點對時，求取單向性矩陣."""
        try:
            M, mask = cv2.findHomography(sch_pts, src_pts, cv2.RANSAC, 5.0)
        except Exception:
            import traceback
            traceback.print_exc()
            raise HomographyError("OpenCV error in _find_homography()...")
        else:
            if mask is None:
                raise HomographyError("In _find_homography(), find no transfomation matrix...")
            else:
                return M, mask 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def _find_homography(sch_pts, src_pts):
    """多組特征點對時，求取單向性矩陣."""
    try:
        M, mask = cv2.findHomography(sch_pts, src_pts, cv2.RANSAC, 5.0)
    except Exception:
        import traceback
        traceback.print_exc()
        raise HomographyError("OpenCV error in _find_homography()...")
    else:
        if mask is None:
            raise HomographyError("In _find_homography(), find no mask...")
        else:
            return M, mask 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def ransac_orb_distance(self, pic_package_from: Dict, pic_package_to: Dict) -> Dict[str, sd.AlgoMatch]:
        """
        Distance between two provided pictures (dicts) with RANSAC-ORB methods
        :param pic_package_from: first picture dict
        :param pic_package_to: second picture dict
        :return: A dictionary of algo name to the match detail (distance, decision ..)
        """

        answer = {}
        self.logger.info("RANSAC-Orb distance computation ... ")

        # Verify if what is needed to compute it is present
        if pic_package_from.get("ORB_DESCRIPTORS", None) is None \
                or pic_package_to.get("ORB_DESCRIPTORS", None) is None:
            self.logger.warning(f"RANSAC-ORB descriptors are NOT presents in the results.")
            raise AlgoFeatureNotPresentError("None RANSAC-ORB descriptors in orb distance.")

        # Verify if what is needed to compute it is present
        if pic_package_from.get("ORB_KEYPOINTS", None) is None \
                or pic_package_to.get("ORB_KEYPOINTS", None) is None:
            self.logger.warning(f"RANSAC-ORB keypoints are NOT presents in the results.")
            raise AlgoFeatureNotPresentError("None RANSAC-ORB keypoints in orb distance.")

        # Add result for enabled algorithms
        try:
            if self.fe_conf.RANSAC_ORB.get("is_enabled", False):
                answer = self.add_results(self.fe_conf.RANSAC_ORB, pic_package_from, pic_package_to, answer)

        except Exception as e:
            self.logger.error(traceback.print_tb(e.__traceback__))
            self.logger.error("Error during RANSAC-orb distance calculation : " + str(e))

        return answer 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def filter_matches(matches: List, matches_threshold_to_accelerate: float) -> List:
        # Output a list of filtered matches, according to distance
        # Do remove the farthest matches to greatly accelerate RANSAC
        # From : http://answers.opencv.org/question/984/performance-of-findhomography/

        diminished_matches = []
        for m in matches:
            if m.distance < matches_threshold_to_accelerate:
                diminished_matches.append(m)

        return diminished_matches 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import RANSAC [as 別名]
def find_homography(keypoints_pic1, keypoints_pic2, matches) -> (List, np.float32, np.float32):
        # Find an Homography matrix between two pictures
        # From two list of keypoints and a list of matches, extrat
        # A list of good matches found by RANSAC and two transformation matrix (an homography and a rigid homography/affine)

        # Instanciate outputs
        good = []

        # Transforming keypoints to list of points
        src_pts = np.float32([keypoints_pic1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
        dst_pts = np.float32([keypoints_pic2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)

        # Find the transformation between points
        transformation_matrix, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

        # Compute a rigid transformation (without depth, only scale + rotation + translation)
        transformation_rigid_matrix, rigid_mask = cv2.estimateAffinePartial2D(src_pts, dst_pts)

        # Get a mask list for matches = A list that says "This match is an in/out-lier"
        matchesMask = mask.ravel().tolist()

        # Filter the matches list thanks to the mask
        for i, element in enumerate(matchesMask):
            if element == 1:
                good.append(matches[i])

        return good, transformation_matrix, transformation_rigid_matrix