我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用cv2.SURF。
def Surf(gray): surf = cv2.SURF() kps, des = surf.detectAndCompute(gray, None) return kps, des
def __init__(self, descriptor_type): self.rootsift = False lists = ['sift','rootsift','orb','surf'] if descriptor_type is 'sift': self.lfe = cv2.SIFT() elif descriptor_type is 'surf': self.lfe = cv2.SURF() elif descriptor_type is 'rootsift': self.lfe = cv2.SIFT() elif descriptor_type is 'orb': self.lfe = cv2.ORB() else: assert(descriptor_type in lists)
def _extract_feature(X, feature): """Performs feature extraction :param X: data (rows=images, cols=pixels) :param feature: which feature to extract - None: no feature is extracted - "gray": grayscale features - "rgb": RGB features - "hsv": HSV features - "surf": SURF features - "hog": HOG features :returns: X (rows=samples, cols=features) """ # transform color space if feature == 'gray' or feature == 'surf': X = [cv2.cvtColor(x, cv2.COLOR_BGR2GRAY) for x in X] elif feature == 'hsv': X = [cv2.cvtColor(x, cv2.COLOR_BGR2HSV) for x in X] # operate on smaller image small_size = (32, 32) X = [cv2.resize(x, small_size) for x in X] # extract features if feature == 'surf': surf = cv2.SURF(400) surf.upright = True surf.extended = True num_surf_features = 36 # create dense grid of keypoints dense = cv2.FeatureDetector_create("Dense") kp = dense.detect(np.zeros(small_size).astype(np.uint8)) # compute keypoints and descriptors kp_des = [surf.compute(x, kp) for x in X] # the second element is descriptor: choose first num_surf_features # elements X = [d[1][:num_surf_features, :] for d in kp_des] elif feature == 'hog': # histogram of gradients block_size = (small_size[0] / 2, small_size[1] / 2) block_stride = (small_size[0] / 4, small_size[1] / 4) cell_size = block_stride num_bins = 9 hog = cv2.HOGDescriptor(small_size, block_size, block_stride, cell_size, num_bins) X = [hog.compute(x) for x in X] elif feature is not None: # normalize all intensities to be between 0 and 1 X = np.array(X).astype(np.float32) / 255 # subtract mean X = [x - np.mean(x) for x in X] X = [x.flatten() for x in X] return X
def sift(imageval): file_bytes = np.asarray(bytearray(imageval), dtype=np.uint8) img_data_ndarray = cv2.imdecode(file_bytes, cv2.CV_LOAD_IMAGE_UNCHANGED) gray = cv2.cvtColor(img_data_ndarray, cv2.COLOR_BGR2GRAY) #surf = cv2.SURF(400) sift = cv2.SIFT(40) kp, des = sift.detectAndCompute(gray,None) #kp, des = surf.detectAndCompute(gray,None) #print len(kp)
def surf(imageval): file_bytes = np.asarray(bytearray(imageval), dtype=np.uint8) img_data_ndarray = cv2.imdecode(file_bytes, cv2.CV_LOAD_IMAGE_UNCHANGED) gray = cv2.cvtColor(img_data_ndarray, cv2.COLOR_BGR2GRAY) surf = cv2.SURF(40) #sift = cv2.SIFT(40) #kp, des = sift.detectAndCompute(gray,None) kp, des = surf.detectAndCompute(gray,None) #print len(kp)
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
