我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用cv2.KMEANS_RANDOM_CENTERS。
def k(screen): Z = screen.reshape((-1,3)) # convert to np.float32 Z = np.float32(Z) # define criteria, number of clusters(K) and apply kmeans() criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) K = 2 ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS) # Now convert back into uint8, and make original image center = np.uint8(center) res = center[label.flatten()] res2 = res.reshape((screen.shape)) return res2
def color_quant(input,K,output): img = cv2.imread(input) Z = img.reshape((-1,3)) # convert to np.float32 Z = np.float32(Z) # define criteria, number of clusters(K) and apply kmeans() criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 15, 1.0) ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS) # Now convert back into uint8, and make original image center = np.uint8(center) res = center[label.flatten()] res2 = res.reshape((img.shape)) cv2.imshow('res2',res2) cv2.waitKey(0) cv2.imwrite(output, res2) cv2.destroyAllWindows()
def k_means(self, a_frame, K=2): """ :param a_frame: :param K: :return: np.ndarray draw the frame use K color's centers """ i = 0 Z = a_frame.reshape((-1, 1)) Z = np.float32(Z) criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) ret, label, center = cv2.kmeans(Z, K, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) center = np.uint8(center) res = center[label.flatten()] res2 = res.reshape((a_frame.shape)) return res2
def cluster(frame_matrix): new_frame_matrix = [] i = 0 for frame in frame_matrix: print "reader {} frame".format(i) i += 1 Z = frame.reshape((-1, 1)) Z = np.float32(Z) criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) K = 2 ret, label, center = cv2.kmeans(Z, K, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) center = np.uint8(center) res = center[label.flatten()] res2 = res.reshape((frame.shape)) new_frame_matrix.append(res2) cv2.imshow('res2', res2) cv2.waitKey(1) cv2.destroyAllWindows()
def gen_codebook(dataset, descriptors, k = 64): """ Generate a k codebook for the dataset. Args: dataset (Dataset object): An object that stores information about the dataset. descriptors (list of integer arrays): The descriptors for every class. k (integer): The number of clusters that are going to be calculated. Returns: list of integer arrays: The k codewords for the dataset. """ iterations = 10 epsilon = 1.0 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, iterations, epsilon) compactness, labels, centers = cv2.kmeans(descriptors, k , criteria, iterations, cv2.KMEANS_RANDOM_CENTERS) return centers
def is_grid(self, grid, image): """ Checks the "gridness" by analyzing the results of a hough transform. :param grid: binary image :return: wheter the object in the image might be a grid or not """ # - Distance resolution = 1 pixel # - Angle resolution = 1° degree for high line density # - Threshold = 144 hough intersections # 8px digit + 3*2px white + 2*1px border = 16px per cell # => 144x144 grid # 144 - minimum number of points on the same line # (but due to imperfections in the binarized image it's highly # improbable to detect a 144x144 grid) lines = cv2.HoughLines(grid, 1, np.pi / 180, 144) if lines is not None and np.size(lines) >= 20: lines = lines.reshape((lines.size / 2), 2) # theta in [0, pi] (theta > pi => rho < 0) # normalise theta in [-pi, pi] and negatives rho lines[lines[:, 0] < 0, 1] -= np.pi lines[lines[:, 0] < 0, 0] *= -1 criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01) # split lines into 2 groups to check whether they're perpendicular if cv2.__version__[0] == '2': density, clmap, centers = cv2.kmeans( lines[:, 1], 2, criteria, 5, cv2.KMEANS_RANDOM_CENTERS) else: density, clmap, centers = cv2.kmeans( lines[:, 1], 2, None, criteria, 5, cv2.KMEANS_RANDOM_CENTERS) if self.debug: self.save_hough(lines, clmap) # Overall variance from respective centers var = density / np.size(clmap) sin = abs(np.sin(centers[0] - centers[1])) # It is probably a grid only if: # - centroids difference is almost a 90° angle (+-15° limit) # - variance is less than 5° (keeping in mind surface distortions) return sin > 0.99 and var <= (5*np.pi / 180) ** 2 else: return False
def is_grid(self, grid, image): """ Checks the "gridness" by analyzing the results of a hough transform. :param grid: binary image :return: wheter the object in the image might be a grid or not """ # - Distance resolution = 1 pixel # - Angle resolution = 1° degree for high line density # - Threshold = 144 hough intersections # 8px digit + 3*2px white + 2*1px border = 16px per cell # => 144x144 grid # 144 - minimum number of points on the same line # (but due to imperfections in the binarized image it's highly # improbable to detect a 144x144 grid) lines = cv2.HoughLines(grid, 1, np.pi / 180, 144) if lines is not None and np.size(lines) >= 20: lines = lines.reshape((lines.size/2), 2) # theta in [0, pi] (theta > pi => rho < 0) # normalise theta in [-pi, pi] and negatives rho lines[lines[:, 0] < 0, 1] -= np.pi lines[lines[:, 0] < 0, 0] *= -1 criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01) # split lines into 2 groups to check whether they're perpendicular if cv2.__version__[0] == '2': density, clmap, centers = cv2.kmeans( lines[:, 1], 2, criteria, 5, cv2.KMEANS_RANDOM_CENTERS) else: density, clmap, centers = cv2.kmeans( lines[:, 1], 2, None, criteria, 5, cv2.KMEANS_RANDOM_CENTERS) # Overall variance from respective centers var = density / np.size(clmap) sin = abs(np.sin(centers[0] - centers[1])) # It is probably a grid only if: # - centroids difference is almost a 90° angle (+-15° limit) # - variance is less than 5° (keeping in mind surface distortions) return sin > 0.99 and var <= (5*np.pi / 180) ** 2 else: return False
def test_kmeans(img): ## K???? z = img.reshape((-1, 3)) z = np.float32(z) criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) ret, label, center = cv2.kmeans(z, 20, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) center = np.uint8(center) res = center[label.flatten()] res2 = res.reshape((img.shape)) cv2.imshow('preview', res2) cv2.waitKey()
def find_label_clusters(kitti_base, kittiLabels, shape, num_clusters, descriptors=None): if descriptors is None: progressbar = ProgressBar('Computing descriptors', max=len(kittiLabels)) descriptors = [] for label in kittiLabels: progressbar.next() img = getCroppedSampleFromLabel(kitti_base, label) # img = cv2.resize(img, (shape[1], shape[0]), interpolation=cv2.INTER_AREA) img = resizeSample(img, shape, label) hist = get_hog(img) descriptors.append(hist) progressbar.finish() else: print 'find_label_clusters,', 'Using supplied descriptors.' print len(kittiLabels), len(descriptors) assert(len(kittiLabels) == len(descriptors)) # X = np.random.randint(25,50,(25,2)) # Y = np.random.randint(60,85,(25,2)) # Z = np.vstack((X,Y)) # convert to np.float32 Z = np.float32(descriptors) # define criteria and apply kmeans() K = num_clusters print 'find_label_clusters,', 'kmeans:', K criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) attempts = 10 ret,label,center=cv2.kmeans(Z,K,None,criteria,attempts,cv2.KMEANS_RANDOM_CENTERS) # ret,label,center=cv2.kmeans(Z,2,criteria,attempts,cv2.KMEANS_PP_CENTERS) print 'ret:', ret # print 'label:', label # print 'center:', center # # Now separate the data, Note the flatten() # A = Z[label.ravel()==0] # B = Z[label.ravel()==1] clusters = partition(kittiLabels, label) return clusters # # Plot the data # from matplotlib import pyplot as plt # plt.scatter(A[:,0],A[:,1]) # plt.scatter(B[:,0],B[:,1],c = 'r') # plt.scatter(center[:,0],center[:,1],s = 80,c = 'y', marker = 's') # plt.xlabel('Height'),plt.ylabel('Weight') # plt.show()
def find_sample_clusters(pos_reg_generator, window_dims, hog, num_clusters): regions = list(pos_reg_generator) descriptors = trainhog.compute_hog_descriptors(hog, regions, window_dims, 1) # convert to np.float32 descriptors = [rd.descriptor for rd in descriptors] Z = np.float32(descriptors) # define criteria and apply kmeans() K = num_clusters print 'find_label_clusters,', 'kmeans:', K criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) attempts = 10 ret,label,center=cv2.kmeans(Z,K,None,criteria,attempts,cv2.KMEANS_RANDOM_CENTERS) # ret,label,center=cv2.kmeans(Z,2,criteria,attempts,cv2.KMEANS_PP_CENTERS) print 'ret:', ret # print 'label:', label # print 'center:', center # # Now separate the data, Note the flatten() # A = Z[label.ravel()==0] # B = Z[label.ravel()==1] clusters = partition(regions, label) return clusters
def createTrainingInstances(self, images): instances = [] img_descriptors = [] master_descriptors = [] cv2.ocl.setUseOpenCL(False) orb = cv2.ORB_create() for img, label in images: print img img = read_color_image(img) keypoints = orb.detect(img, None) keypoints, descriptors = orb.compute(img, keypoints) if descriptors is None: descriptors = [] img_descriptors.append(descriptors) for i in descriptors: master_descriptors.append(i) master_descriptors = np.float32(master_descriptors) criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) ret, labels, centers = cv2.kmeans(master_descriptors, self.center_num, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) labels = labels.ravel() count = 0 img_num = 0 for img, label in images: histogram = np.zeros(self.center_num) feature_vector = img_descriptors[img_num] for f in xrange(len(feature_vector)): index = count + f histogram.itemset(labels[index], 1 + histogram.item(labels[index])) count += len(feature_vector) pairing = Instance(histogram, label) instances.append(pairing) self.training_instances = instances self.centers = centers
def local_bow_train(image): instances = [] img_descriptors = [] master_descriptors = [] cv2.ocl.setUseOpenCL(False) orb = cv2.ORB_create() for img, label in images: print img img = read_color_image(img) keypoints = orb.detect(img, None) keypoints, descriptors = orb.compute(img, keypoints) if descriptors is None: descriptors = [] img_descriptors.append(descriptors) for i in descriptors: master_descriptors.append(i) master_descriptors = np.float32(master_descriptors) criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) ret, labels, centers = cv2.kmeans(master_descriptors, self.center_num, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) labels = labels.ravel() count = 0 img_num = 0 for img, label in images: histogram = np.zeros(self.center_num) feature_vector = img_descriptors[img_num] for f in xrange(len(feature_vector)): index = count + f histogram.itemset(labels[index], 1 + histogram.item(labels[index])) count += len(feature_vector) pairing = Instance(histogram, label) instances.append(pairing) self.training_instances = instances self.centers = centers
