Python cv2.BRISK_create() Examples
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code examples of cv2.BRISK_create().
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Example #1
Source File: find_obj.py From OpenCV-Python-Tutorial with MIT License | 10 votes |
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
Example #2
Source File: coverdescriptor.py From PracticalPythonAndOpenCV_CaseStudies with GNU General Public License v3.0 | 5 votes |
def describe(self, image): # Initialize the BRISK detector and feature extractor descriptor = cv2.BRISK_create() # Check if SIFT should be utilized to detect and extract features if self.use_sift: descriptor = cv2.xfeatures2d.SIFT_create() # Detect keypoints in the image, describing the region surrounding each keypoint, then convert the keypoints # to a NumPy array (keypoints, descriptors) = descriptor.detectAndCompute(image, None) keypoints = np.float32([keypoint.pt for keypoint in keypoints]) # Return a tuple of keypoints and descriptors return (keypoints, descriptors)
Example #3
Source File: keypoint_matching.py From Airtest with Apache License 2.0 | 5 votes |
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)
Example #4
Source File: params.py From stereo_ptam with GNU General Public License v3.0 | 4 votes |
def __init__(self, config='GFTT-BRIEF'): super().__init__() if config == 'GFTT-BRIEF': self.feature_detector = cv2.GFTTDetector_create( maxCorners=1000, minDistance=12.0, qualityLevel=0.001, useHarrisDetector=False) self.descriptor_extractor = cv2.xfeatures2d.BriefDescriptorExtractor_create( bytes=32, use_orientation=False) elif config == 'GFTT-BRISK': self.feature_detector = cv2.GFTTDetector_create( maxCorners=2000, minDistance=15.0, qualityLevel=0.01, useHarrisDetector=False) self.descriptor_extractor = cv2.BRISK_create() elif config == 'ORB-ORB': self.feature_detector = cv2.ORB_create( nfeatures=1000, scaleFactor=1.2, nlevels=1, edgeThreshold=31) self.descriptor_extractor = self.feature_detector else: raise NotImplementedError self.descriptor_matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=False) self.matching_cell_size = 15 # pixels self.matching_neighborhood = 3 self.matching_distance = 30 self.frustum_near = 0.1 # meters self.frustum_far = 1000.0 self.ground = True self.lc_max_inbetween_distance = 50 self.lc_distance_threshold = 15 self.lc_embedding_distance = 20.0 self.view_image_width = 400 self.view_image_height = 130 self.view_camera_width = 0.75 self.view_viewpoint_x = 0 self.view_viewpoint_y = -500 # -10 self.view_viewpoint_z = -100 # -0.1 self.view_viewpoint_f = 2000