本文整理汇总了Python中facenet.get_image_paths_and_labels方法的典型用法代码示例。如果您正苦于以下问题：Python facenet.get_image_paths_and_labels方法的具体用法？Python facenet.get_image_paths_and_labels怎么用？Python facenet.get_image_paths_and_labels使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类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个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: import facenet [as 别名]
# 或者: from facenet import get_image_paths_and_labels [as 别名]
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
# 需要导入模块: import facenet [as 别名]
# 或者: from facenet import get_image_paths_and_labels [as 别名]
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)))