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 = []
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)