Python cv2.SURF Examples
The following are 7
code examples of cv2.SURF().
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Example #1
| Source File: keypoint_matching_contrib.py From Airtest with Apache License 2.0 | 8 votes |
|
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, surf is in contrib module, you need to compile it seperately.
try:
self.detector = cv2.xfeatures2d.SURF_create(self.HESSIAN_THRESHOLD, upright=self.UPRIGHT)
except:
import traceback
traceback.print_exc()
raise NoModuleError("There is no %s module in your OpenCV environment, need contribmodule!" % self.METHOD_NAME)
else:
# OpenCV2.x
self.detector = cv2.SURF(self.HESSIAN_THRESHOLD, upright=self.UPRIGHT)
# # create FlnnMatcher object:
self.matcher = cv2.FlannBasedMatcher({'algorithm': self.FLANN_INDEX_KDTREE, 'trees': 5}, dict(checks=50))
Example #2
| Source File: findobj.py From airtest with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
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
Example #3
| Source File: find_obj.py From airtest with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
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
Example #4
| Source File: find_obj.py From ImageAnalysis with MIT License | 6 votes |
|
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
Example #5
| Source File: find_obj.py From PyCV-time with MIT License | 6 votes |
|
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
Example #6
| Source File: servoing_designed_features_quad_panda3d_env.py From citysim3d with MIT License | 4 votes |
|
def __init__(self, action_space, feature_type=None, filter_features=None,
max_time_steps=100, distance_threshold=4.0, **kwargs):
"""
filter_features indicates whether to filter out key points that are not
on the object in the current image. Key points in the target image are
always filtered out.
"""
SimpleQuadPanda3dEnv.__init__(self, action_space, **kwargs)
ServoingEnv.__init__(self, env=self, max_time_steps=max_time_steps, distance_threshold=distance_threshold)
lens = self.camera_node.node().getLens()
self._observation_space.spaces['points'] = BoxSpace(np.array([-np.inf, lens.getNear(), -np.inf]),
np.array([np.inf, lens.getFar(), np.inf]))
film_size = tuple(int(s) for s in lens.getFilmSize())
self.mask_camera_sensor = Panda3dMaskCameraSensor(self.app, (self.skybox_node, self.city_node),
size=film_size,
near_far=(lens.getNear(), lens.getFar()),
hfov=lens.getFov())
for cam in self.mask_camera_sensor.cam:
cam.reparentTo(self.camera_sensor.cam)
self.filter_features = True if filter_features is None else False
self._feature_type = feature_type or 'sift'
if cv2.__version__.split('.')[0] == '3':
from cv2.xfeatures2d import SIFT_create, SURF_create
from cv2 import ORB_create
if self.feature_type == 'orb':
# https://github.com/opencv/opencv/issues/6081
cv2.ocl.setUseOpenCL(False)
else:
SIFT_create = cv2.SIFT
SURF_create = cv2.SURF
ORB_create = cv2.ORB
if self.feature_type == 'sift':
self._feature_extractor = SIFT_create()
elif self.feature_type == 'surf':
self._feature_extractor = SURF_create()
elif self.feature_type == 'orb':
self._feature_extractor = ORB_create()
else:
raise ValueError("Unknown feature extractor %s" % self.feature_type)
if self.feature_type == 'orb':
self._matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
else:
self._matcher = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
self._target_key_points = None
self._target_descriptors = None
Example #7
| Source File: adam_descriptors.py From optimeyes with MIT License | 4 votes |
|
def main():
opencv_haystack =cv2.imread('adam.jpg')
opencv_needle = cv2.imread('adam_rightnostril.jpg')
ngrey = cv2.cvtColor(opencv_needle, cv2.COLOR_BGR2GRAY)
hgrey = cv2.cvtColor(opencv_haystack, cv2.COLOR_BGR2GRAY)
import pdb
pdb.set_trace()
# build feature detector and descriptor extractor
hessian_threshold = 175
detector = cv2.SURF(hessian_threshold)
(hkeypoints, hdescriptors) = detector.detect(hgrey, None, useProvidedKeypoints = False)
(nkeypoints, ndescriptors) = detector.detect(ngrey, None, useProvidedKeypoints = False)
# extract vectors of size 64 from raw descriptors numpy arrays
rowsize = len(hdescriptors) / len(hkeypoints)
if rowsize > 1:
hrows = numpy.array(hdescriptors, dtype = numpy.float32).reshape((-1, rowsize))
nrows = numpy.array(ndescriptors, dtype = numpy.float32).reshape((-1, rowsize))
print "haystack rows shape", hrows.shape
print "needle rows shape", nrows.shape
else:
print '*****************************************************8888'
hrows = numpy.array(hdescriptors, dtype = numpy.float32)
nrows = numpy.array(ndescriptors, dtype = numpy.float32)
rowsize = len(hrows[0])
# kNN training - learn mapping from hrow to hkeypoints index
samples = hrows
responses = numpy.arange(len(hkeypoints), dtype = numpy.float32)
print "sample length", len(samples), "response length", len(responses)
knn = cv2.KNearest()
knn.train(samples,responses)
# retrieve index and value through enumeration
for i, descriptor in enumerate(nrows):
descriptor = numpy.array(descriptor, dtype = numpy.float32).reshape((1, rowsize))
print i, 'descriptor shape', descriptor.shape, 'sample shape', samples[0].shape
retval, results, neigh_resp, dists = knn.find_nearest(descriptor, 1)
res, dist = int(results[0][0]), dists[0][0]
print 'result', res, 'distance', dist
if dist < 0.1:
# draw matched keypoints in red color
color = (0, 0, 255)
else:
# draw unmatched in blue color
color = (255, 0, 0)
# draw matched key points on haystack image
x,y = hkeypoints[res].pt
center = (int(x),int(y))
cv2.circle(opencv_haystack,center,2,color,-1)
# draw matched key points on needle image
x,y = nkeypoints[i].pt
center = (int(x),int(y))
cv2.circle(opencv_needle,center,2,color,-1)
cv2.imshow('haystack',opencv_haystack)
cv2.imshow('needle',opencv_needle)
cv2.waitKey(0)
cv2.destroyAllWindows()
