Python cv2.NORM_HAMMING Examples

def init_feature(name):
    chunks = name.split('-')
    if chunks[0] == 'sift':
        detector = cv2.xfeatures2d.SIFT_create()
        norm = cv2.NORM_L2
    elif chunks[0] == 'surf':
        detector = cv2.xfeatures2d.SURF_create(800)
        norm = cv2.NORM_L2
    elif chunks[0] == 'orb':
        detector = cv2.ORB_create(400)
        norm = cv2.NORM_HAMMING
    elif chunks[0] == 'akaze':
        detector = cv2.AKAZE_create()
        norm = cv2.NORM_HAMMING
    elif chunks[0] == 'brisk':
        detector = cv2.BRISK_create()
        norm = cv2.NORM_HAMMING
    else:
        return None, None
    if 'flann' in chunks:
        if norm == cv2.NORM_L2:
            flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
        else:
            flann_params= dict(algorithm = FLANN_INDEX_LSH,
                               table_number = 6, # 12
                               key_size = 12,     # 20
                               multi_probe_level = 1) #2
        matcher = cv2.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
    else:
        matcher = cv2.BFMatcher(norm)
    return detector, matcher 

def init_feature(name):
    chunks = name.split('-')
    if chunks[0] == 'sift':
        detector = cv2.SIFT()
        norm = cv2.NORM_L2
    elif chunks[0] == 'surf':
        detector = cv2.SURF(800)
        norm = cv2.NORM_L2
    elif chunks[0] == 'orb':
        detector = cv2.ORB(400)
        norm = cv2.NORM_HAMMING
    else:
        return None, None
    if 'flann' in chunks:
        if norm == cv2.NORM_L2:
            flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
        else:
            flann_params= dict(algorithm = FLANN_INDEX_LSH,
                               table_number = 6, # 12
                               key_size = 12,     # 20
                               multi_probe_level = 1) #2
        matcher = cv2.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
    else:
        matcher = cv2.BFMatcher(norm)
    return detector, matcher 

def init_feature(name):
    chunks = name.split('-')
    if chunks[0] == 'sift':
        detector = cv2.SIFT()
        norm = cv2.NORM_L2
    elif chunks[0] == 'surf':
        detector = cv2.SURF(400)
        norm = cv2.NORM_L2
    elif chunks[0] == 'orb':
        detector = cv2.ORB(400)
        norm = cv2.NORM_HAMMING
    else:
        return None, None
    if 'flann' in chunks:
        if norm == cv2.NORM_L2:
            flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
        else:
            flann_params= dict(algorithm = FLANN_INDEX_LSH,
                               table_number = 6, # 12
                               key_size = 12,     # 20
                               multi_probe_level = 1) #2
        matcher = cv2.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
    else:
        matcher = cv2.BFMatcher(norm)
    return detector, matcher 

def init_detector(self):
        """Init keypoint detector object."""
        # BRIEF is a feature descriptor, recommand CenSurE as a fast detector:
        if check_cv_version_is_new():
            # OpenCV3/4, star/brief is in contrib module, you need to compile it seperately.
            try:
                self.star_detector = cv2.xfeatures2d.StarDetector_create()
                self.brief_extractor = cv2.xfeatures2d.BriefDescriptorExtractor_create()
            except:
                import traceback
                traceback.print_exc()
                print("to use %s, you should build contrib with opencv3.0" % self.METHOD_NAME)
                raise NoModuleError("There is no %s module in your OpenCV environment !" % self.METHOD_NAME)
        else:
            # OpenCV2.x
            self.star_detector = cv2.FeatureDetector_create("STAR")
            self.brief_extractor = cv2.DescriptorExtractor_create("BRIEF")

        # create BFMatcher object:
        self.matcher = cv2.BFMatcher(cv2.NORM_L1)  # cv2.NORM_L1 cv2.NORM_L2 cv2.NORM_HAMMING(not useable) 

def matching(desc1, desc2, do_ratio_test=False, cross_check=True):
    if desc1.dtype == np.bool and desc2.dtype == np.bool:
        desc1, desc2 = np.packbits(desc1, axis=1), np.packbits(desc2, axis=1)
        norm = cv2.NORM_HAMMING
    else:
        desc1, desc2 = np.float32(desc1), np.float32(desc2)
        norm = cv2.NORM_L2

    if do_ratio_test:
        matches = []
        matcher = cv2.BFMatcher(norm)
        for m, n in matcher.knnMatch(desc1, desc2, k=2):
            m.distance = 1.0 if (n.distance == 0) else m.distance / n.distance
            matches.append(m)
    else:
        matcher = cv2.BFMatcher(norm, crossCheck=cross_check)
        matches = matcher.match(desc1, desc2)
    return matches_cv2np(matches) 

def __init__(self, norm_type=cv2.NORM_HAMMING, cross_check = False, ratio_test=kRatioTest, type = FeatureMatcherTypes.FLANN):
        super().__init__(norm_type=norm_type, cross_check=cross_check, ratio_test=ratio_test, type=type)
        if norm_type == cv2.NORM_HAMMING:
            # FLANN parameters for binary descriptors 
            FLANN_INDEX_LSH = 6
            self.index_params= dict(algorithm = FLANN_INDEX_LSH,   # Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search
                        table_number = 6,      # 12
                        key_size = 12,         # 20
                        multi_probe_level = 1) # 2            
        if norm_type == cv2.NORM_L2: 
            # FLANN parameters for float descriptors 
            FLANN_INDEX_KDTREE = 1
            self.index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 4)  
        self.search_params = dict(checks=32)   # or pass empty dictionary                 
        self.matcher = cv2.FlannBasedMatcher(self.index_params, self.search_params)  
        self.matcher_name = 'FlannFeatureMatcher' 

def init_feature(name):
    chunks = name.split('-')
    if chunks[0] == 'sift':
        detector = cv2.SIFT()
        norm = cv2.NORM_L2
    elif chunks[0] == 'surf':
        detector = cv2.SURF(800)
        norm = cv2.NORM_L2
    elif chunks[0] == 'orb':
        detector = cv2.ORB(400)
        norm = cv2.NORM_HAMMING
    else:
        return None, None
    if 'flann' in chunks:
        if norm == cv2.NORM_L2:
            flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
        else:
            flann_params= dict(algorithm = FLANN_INDEX_LSH,
                               table_number = 6, # 12
                               key_size = 12,     # 20
                               multi_probe_level = 1) #2
        matcher = cv2.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
    else:
        matcher = cv2.BFMatcher(norm)
    return detector, matcher 

def init_feature(name):
    chunks = name.split('-')
    if chunks[0] == 'sift':
        detector = cv2.SIFT()
        norm = cv2.NORM_L2
    elif chunks[0] == 'surf':
        detector = cv2.SURF(800)
        norm = cv2.NORM_L2
    elif chunks[0] == 'orb':
        detector = cv2.ORB(400)
        norm = cv2.NORM_HAMMING
    else:
        return None, None
    if 'flann' in chunks:
        if norm == cv2.NORM_L2:
            flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
        else:
            flann_params= dict(algorithm = FLANN_INDEX_LSH,
                               table_number = 6, # 12
                               key_size = 12,     # 20
                               multi_probe_level = 1) #2
        matcher = cv2.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
    else:
        matcher = cv2.BFMatcher(norm)
    return detector, matcher 

def init_feature(name):
    chunks = name.split('-')
    if chunks[0] == 'sift':
        detector = cv2.xfeatures2d.SIFT()
        norm = cv2.NORM_L2
    elif chunks[0] == 'surf':
        detector = cv2.xfeatures2d.SURF(800)
        norm = cv2.NORM_L2
    elif chunks[0] == 'orb':
        detector = cv2.ORB(400)
        norm = cv2.NORM_HAMMING
    elif chunks[0] == 'akaze':
        detector = cv2.AKAZE()
        norm = cv2.NORM_HAMMING
    elif chunks[0] == 'brisk':
        detector = cv2.BRISK()
        norm = cv2.NORM_HAMMING
    else:
        return None, None
    if 'flann' in chunks:
        if norm == cv2.NORM_L2:
            flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
        else:
            flann_params= dict(algorithm = FLANN_INDEX_LSH,
                               table_number = 6, # 12
                               key_size = 12,     # 20
                               multi_probe_level = 1) #2
        matcher = cv2.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
    else:
        matcher = cv2.BFMatcher(norm)
    return detector, matcher 

def __init__(self, norm_type=cv2.NORM_HAMMING, cross_check = False, ratio_test=kRatioTest, type = FeatureMatcherTypes.BF):
        super().__init__(norm_type=norm_type, cross_check=cross_check, ratio_test=ratio_test, type=type)
        self.matcher = cv2.BFMatcher(norm_type, cross_check)     
        self.matcher_name = 'BfFeatureMatcher'   


# Flann Matcher 

def __init__(self, norm_type=cv2.NORM_HAMMING, cross_check = False, ratio_test=kRatioTest, type = FeatureMatcherTypes.BF):
        self.type = type 
        self.norm_type = norm_type 
        self.cross_check = cross_check   # apply cross check 
        self.matches = []
        self.ratio_test = ratio_test 
        self.matcher = None 
        self.matcher_name = ''
        
        
    # input: des1 = queryDescriptors, des2= trainDescriptors
    # output: idx1, idx2  (vectors of corresponding indexes in des1 and des2, respectively) 

def feature_matcher_factory(norm_type=cv2.NORM_HAMMING, cross_check=False, ratio_test=kRatioTest, type=FeatureMatcherTypes.FLANN):
    if type == FeatureMatcherTypes.BF:
        return BfFeatureMatcher(norm_type=norm_type, cross_check=cross_check, ratio_test=ratio_test, type=type)
    if type == FeatureMatcherTypes.FLANN:
        return FlannFeatureMatcher(norm_type=norm_type, cross_check=cross_check, ratio_test=ratio_test, type=type)
    return None 

def init_detector(self):
        """Init keypoint detector object."""
        self.detector = cv2.ORB_create()
        # create BFMatcher object:
        self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING)  # cv2.NORM_L1 cv2.NORM_L2 cv2.NORM_HAMMING(not useable) 

def init_detector(self):
        """Init keypoint detector object."""
        self.detector = cv2.AKAZE_create()
        # create BFMatcher object:
        self.matcher = cv2.BFMatcher(cv2.NORM_L1)  # cv2.NORM_L1 cv2.NORM_L2 cv2.NORM_HAMMING(not useable) 

def init_detector(self):
        """Init keypoint detector object."""
        self.detector = cv2.BRISK_create()
        # create BFMatcher object:
        self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING)  # cv2.NORM_L1 cv2.NORM_L2 cv2.NORM_HAMMING(not useable) 

def init_detector(self):
        """Init keypoint detector object."""
        self.detector = cv2.KAZE_create()
        # create BFMatcher object:
        self.matcher = cv2.BFMatcher(cv2.NORM_L1)  # cv2.NORM_L1 cv2.NORM_L2 cv2.NORM_HAMMING(not useable) 

def _get_matches(descriptors1, descriptors2, matcher, norm, verbose):
    ''' Match keypoints using BFMatcher with cv2.NORM_HAMMING '''
    t0 = time.time()
    bf = matcher(norm)
    matches = bf.knnMatch(descriptors1, descriptors2, k=2)
    t1 = time.time()
    if verbose:
        print('Keypoints matched', t1 - t0)
    return matches 

def get_match_coords(keyPoints1, descriptors1,
                                    keyPoints2, descriptors2,
                                    matcher=cv2.BFMatcher,
                                    norm=cv2.NORM_HAMMING,
                                    ratio_test=0.7,
                                    verbose=True,
                                    **kwargs):
    ''' Filter matching keypoints and convert to X,Y coordinates
    Parameters
    ----------
        keyPoints1 : list - keypoints on img1 from find_key_points()
        descriptors1 : list - descriptors on img1 from find_key_points()
        keyPoints2 : list - keypoints on img2 from find_key_points()
        descriptors2 : list - descriptors on img2 from find_key_points()
        matcher : matcher from CV2
        norm : int - type of distance
        ratio_test : float - Lowe ratio
        verbose : bool - print some output ?
    Returns
    -------
        x1, y1, x2, y2 : coordinates of start and end of displacement [pixels]
    '''
    matches = _get_matches(descriptors1,
                           descriptors2, matcher, norm, verbose)
    x1, y1, x2, y2 = _filter_matches(matches, ratio_test,
                                     keyPoints1, keyPoints2, verbose)
    return x1, y1, x2, y2 

def __init__(self):

        if hasattr(self, 'trained') and self.trained:
            return

        super(WarpFilterModel, self).__init__()

        for lang in Localization.get_game_languages():
            model_filename = IkaUtils.get_path(
                'data', 'webcam_calibration.%s.model' % lang)
            if os.path.exists(model_filename):
                break

        self.detector = cv2.AKAZE_create()
        self.norm = cv2.NORM_HAMMING
        self.matcher = cv2.BFMatcher(self.norm)

        try:
            self.loadModelFromFile(model_filename)
            num_keypoints = len(self.calibration_image_keypoints)
            IkaUtils.dprint('%s: Loaded model data\n  %s (%d keypoints)' % (self, model_filename, num_keypoints))
        except:
            IkaUtils.dprint('%s: Could not load model data. Trying to rebuild...' % self)

            calibration_image = cv2.imread('camera/ika_usbcam/Pause.png', 0)
            self.calibration_image_size = calibration_image.shape[:2]
            calibration_image_hight, calibration_image_width = \
                calibration_image.shape[ :2]
            self.calibration_image_keypoints, self.calibration_image_descriptors = \
                self.detector.detectAndCompute( calibration_image, None)

            print(self.calibration_image_keypoints)
            print(self.calibration_image_descriptors)

            model_filename = IkaUtils.get_path(
                'data',
                'webcam_calibration.%s.model' % Localization.get_game_languages()[0]
            )
            self.saveModelToFile(model_filename)
            IkaUtils.dprint('%s: Created model %s' % (self, model_filename))

        self.trained = True 

def configure():
    global detect_scale
    global the_matcher
    global max_distance
    global min_pairs

    detect_scale = detector_node.getFloat('scale')
    detector_str = detector_node.getString('detector')
    if detector_str == 'SIFT' or detector_str == 'SURF':
        norm = cv2.NORM_L2
        max_distance = 270.0
    elif detector_str == 'ORB' or detector_str == 'Star':
        norm = cv2.NORM_HAMMING
        max_distance = 64
    else:
        log("Detector not specified or not known:", detector_str)
        quit()

    # work around a feature/bug: flann enums don't exist
    FLANN_INDEX_KDTREE = 1
    FLANN_INDEX_LSH    = 6
    if norm == cv2.NORM_L2:
        flann_params = {
            'algorithm': FLANN_INDEX_KDTREE,
            'trees': 5
        }
    else:
        flann_params = {
            'algorithm': FLANN_INDEX_LSH,
            'table_number': 6,     # 12
            'key_size': 12,        # 20
            'multi_probe_level': 1 #2
        }
    search_params = {
        'checks': 100
    }
    the_matcher = cv2.FlannBasedMatcher(flann_params, search_params)
    min_pairs = matcher_node.getFloat('min_pairs')

# Iterate through all the matches for the specified image and
# delete keypoints that don't satisfy the homography (or
# fundamental) relationship.  Returns true if match set is clean, false
# if keypoints were removed.
#
# Notice: this tends to eliminate matches that aren't all on the
# same plane, so if the scene has a lot of depth, this could knock
# out a lot of good matches.