def index_faces(self,paths, paths_to_pk, output_dir, video_pk):
with tf.Graph().as_default():
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.15
with tf.Session(config=config) as sess:
output_dir = os.path.expanduser(output_dir)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
logging.info("Loading trained model...\n")
meta_file, ckpt_file, model_dir = facenet.get_model_filenames()
facenet.load_model(model_dir, meta_file, ckpt_file)
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]
embedding_size = embeddings.get_shape()[1]
logging.info('Generating embeddings from images...\n')
start_time = time.time()
batch_size = 25
nrof_images = len(paths)
nrof_batches = int(np.ceil(1.0 * nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
count = 0
path_count = {}
entries = []
for i in xrange(nrof_batches):
start_index = i * batch_size
end_index = min((i + 1) * batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
for eindex, fname in enumerate(paths_batch):
count += 1
entry = {
'path': fname,
'detection_primary_key': paths_to_pk[fname],
'index': eindex,
'type': 'detection',
'video_primary_key': video_pk
}
entries.append(entry)
images = facenet.load_data(paths_batch, do_random_crop=False, do_random_flip=False,
image_size=image_size, do_prewhiten=True)
feed_dict = {images_placeholder: images, phase_train_placeholder: False}
emb_array[start_index:end_index, :] = sess.run(embeddings, feed_dict=feed_dict)
if nrof_images:
time_avg_forward_pass = (time.time() - start_time) / float(nrof_images)
logging.info("Forward pass took avg of %.3f[seconds/image] for %d images\n" % (
time_avg_forward_pass, nrof_images))
logging.info("Finally saving embeddings and gallery to: %s" % (output_dir))
feat_fname = os.path.join(output_dir, "facenet.npy")
entries_fname = os.path.join(output_dir, "facenet.json")
np.save(feat_fname, emb_array)
fh = open(entries_fname, 'w')
json.dump(entries, fh)
fh.close()
return path_count, emb_array, entries, feat_fname, entries_fname
def load(self):
if self.session is None:
logging.warning("Loading the network {}".format(self.name))
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.15
self.session = tf.InteractiveSession(config=config)
self.graph_def = tf.Graph().as_default()
meta_file, ckpt_file, model_dir = facenet.get_model_filenames()
self.saver = tf.train.import_meta_graph(os.path.join(model_dir, meta_file))
self.saver.restore(self.session, os.path.join(model_dir, ckpt_file))
self.images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
self.embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
self.phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
self.image_size = self.images_placeholder.get_shape()[1]
self.embedding_size = self.embeddings.get_shape()[1]
# 前方高能
# 该段代码占用过多CPU资源，放在全局供其他函数调用
with tf.Graph().as_default():
gpu_memory_fraction = 1.0
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = create_mtcnn(sess, None)
with tf.Graph().as_default():
sess = tf.Session()
# 加载模型
meta_file, ckpt_file = get_model_filenames(MODELPATH)
saver = tf.train.import_meta_graph(os.path.join(MODELPATH, meta_file))
saver.restore(sess, os.path.join(MODELPATH, ckpt_file))
# 获得输入输出张量
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")
# 进行人脸识别，加载
print('Creating networks and loading parameters')
class MemberUploadFace(View):
"""
确认收款界面相关功能，包括人脸识别以及生成订单
"""
print('Loaded classifier model from file "%s"' % classifier_filename_exp)
graph = tf.Graph()
model_exp = os.path.expanduser(model_path)
if os.path.isfile(model_exp):
print('Model filename: %s' % model_exp)
with gfile.FastGFile(model_exp, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def, name='')
sess = tf.Session(graph=graph)
else:
print('Model directory: %s' % model_exp)
meta_file, ckpt_file = facenet.get_model_filenames(model_exp)
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
sess = tf.Session(graph=graph)
saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file))
saver.restore(sess, os.path.join(model_exp, ckpt_file))
def background_task():
while True:
files = list(
map(lambda x: os.path.join(UPLOAD_FOLDER, x),
sorted(os.listdir(UPLOAD_FOLDER))))
if len(files) >= 5:
