本文整理匯總了Python中facenet.get_image_paths_and_labels方法的典型用法代碼示例。如果您正苦於以下問題：Python facenet.get_image_paths_and_labels方法的具體用法？Python facenet.get_image_paths_and_labels怎麽用？Python facenet.get_image_paths_and_labels使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類facenet的用法示例。
在下文中一共展示了facenet.get_image_paths_and_labels方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: main
# 需要導入模塊: import facenet [as 別名]
# 或者: from facenet import get_image_paths_and_labels [as 別名]
def main(args):
dataset = facenet.get_dataset(args.dir)
paths, _ = facenet.get_image_paths_and_labels(dataset)
t = np.zeros((len(paths)))
x = time.time()
for i, path in enumerate(paths):
start_time = time.time()
with open(path, mode='rb') as f:
_ = f.read()
duration = time.time() - start_time
t[i] = duration
if i % 1000 == 0 or i==len(paths)-1:
print('File %d/%d Total time: %.2f Avg: %.3f Std: %.3f' % (i, len(paths), time.time()-x, np.mean(t[0:i])*1000, np.std(t[0:i])*1000)) 示例2: main
# 需要導入模塊: import facenet [as 別名]
# 或者: from facenet import get_image_paths_and_labels [as 別名]
def main(args):
""" Main
Given a list of images, save out facial encoding data files and copy
images into folders of face clusters.
"""
from os.path import join, basename, exists
from os import makedirs
import numpy as np
import shutil
import sys
if not exists(args.output):
makedirs(args.output)
with tf.Graph().as_default():
with tf.Session() as sess:
image_paths = get_onedir(args.input)
#image_list, label_list = facenet.get_image_paths_and_labels(train_set)
meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.model_dir))
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
load_model(args.model_dir, meta_file, ckpt_file)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
image_size = images_placeholder.get_shape()[1]
print("image_size:",image_size)
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on images')
nrof_images = len(image_paths)
nrof_batches = int(math.ceil(1.0*nrof_images / args.batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
facial_encodings = compute_facial_encodings(sess,images_placeholder,embeddings,phase_train_placeholder,image_size,
embedding_size,nrof_images,nrof_batches,emb_array,args.batch_size,image_paths)
sorted_clusters = cluster_facial_encodings(facial_encodings)
num_cluster = len(sorted_clusters)
# Copy image files to cluster folders
for idx, cluster in enumerate(sorted_clusters):
#save all the cluster
cluster_dir = join(args.output, str(idx))
if not exists(cluster_dir):
makedirs(cluster_dir)
for path in cluster:
shutil.copy(path, join(cluster_dir, basename(path)))