Python cv2.BRISK_create() Examples
The following are 4
code examples of cv2.BRISK_create().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
cv2
, or try the search function
.
.
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
