def _cluster_images(cluster_size, file_path): images = [] positions = [] #print("in cluster images") #print("cluster size: " + str(cluster_size)) for image, lat, lon in images_with_gps(file_path): images.append(image) positions.append([lat, lon]) positions = np.array(positions, np.float32) images = np.array(images).reshape((len(images), 1)) #print('images') #print(images) #print('positions') #print(positions) #print(images.shape) K = float(images.shape[0]) / cluster_size K = int(np.ceil(K)) #print('K') #print(K) #print('labels') labels, centers = tools.kmeans(positions, K)[1:] #print(labels) #print(centers) images = images.ravel() #print(images) labels = labels.ravel() #print(labels) save_clusters(file_path, images, positions, labels, centers)
def _cluster_images(self, meta_data, cluster_size): images = [] positions = [] for image, lat, lon in meta_data.images_with_gps(): images.append(image) positions.append([lat, lon]) positions = np.array(positions, np.float32) images = np.array(images).reshape((len(images), 1)) K = float(images.shape[0]) / cluster_size K = int(np.ceil(K)) labels, centers = tools.kmeans(positions, K)[1:] meta_data.save_clusters(images, positions, labels, centers)
def _cluster_images(self, meta_data, cluster_size): images = [] positions = [] print("in cluster here images") print("cluster size: " + str(cluster_size)) print(meta_data) for image, lat, lon in meta_data.images_with_gps(): images.append(image) positions.append([lat, lon]) positions = np.array(positions, np.float32) images = np.array(images).reshape((len(images), 1)) print('images') print(images) print('positions') print(positions) print(images.shape) K = float(images.shape[0]) / cluster_size K = int(np.ceil(K)) print('K') print(K) print('labels') labels, centers = tools.kmeans(positions, K)[1:] print(labels) print(centers) images = images.ravel() print(images) labels = labels.ravel() print(labels) meta_data.save_clusters(images, positions, labels, centers)