def load_model(model):
"""Loads the FaceNet model from its directory path.
Checks if the model is a model directory (containing a metagraph and a checkpoint file)
or if it is a protocol buffer file with a frozen graph.
Note: This is a modified function from the facenet.py load_model() function in the lib directory to return
the graph object.
Args:
model: model path
Returns:
graph: Tensorflow graph object of the model
"""
model_exp = os.path.expanduser(model)
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())
graph = tf.import_graph_def(graph_def, name='')
return graph
else:
print('Model directory: %s' % model_exp)
meta_file, ckpt_file = get_model_filenames(model_exp)
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file))
graph = saver.restore(tf.get_default_session(),
os.path.join(model_exp, ckpt_file))
return graph
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model metagraph and checkpoint
print("Model directory: %s" % args.model_dir)
meta_file, ckpt_file = fc.get_model_filenames(os.path.expanduser(args.model_dir))
print("Metagraph file: %s" % meta_file)
print("Checkpoint file: %s" % ckpt_file)
model_dir_exp = os.path.expanduser(args.model_dir)
saver = tf.train.import_meta_graph(os.path.join(model_dir_exp, meta_file), clear_devices=True)
tf.get_default_session().run(tf.global_variables_initializer())
tf.get_default_session().run(tf.local_variables_initializer())
saver.restore(tf.get_default_session(), os.path.join(model_dir_exp, ckpt_file))
# Retrieve the protobuf graph definition and fix the batch norm nodes
input_graph_def = sess.graph.as_graph_def()
# Freeze the graph def
output_graph_def = freeze_graph_def(sess, input_graph_def, "embeddings,label_batch")
# Serialize and dump the output graph to the filesystem
with tf.gfile.GFile(args.output_file, "wb") as f:
f.write(output_graph_def.SerializeToString())
print("%d ops in the final graph: %s" % (len(output_graph_def.node), args.output_file))
def load_facenet(model_dir):
meta_file, ckpt_file = facenet.get_model_filenames(model_dir)
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
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 = int(images_placeholder.get_shape()[1])
embedding_size = int(embeddings.get_shape()[1])
return (images_placeholder, embeddings,
phase_train_placeholder), (image_size, embedding_size)
def getFacenetFeatures(splits, facenetModelDir, lfwAlignedDir):
print(" + Loading Facenet features.")
video_features = {}
# For every video in the pairs, create list for features
for split in splits:
for pair in split:
if not pair[0] in video_features:
video_features[pair[0]] = []
if not pair[1] in video_features:
video_features[pair[1]] = []
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
meta_file, ckpt_file = facenet.get_model_filenames(facenetModelDir)
facenet.load_model(facenetModelDir, 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")
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
# For every video get the aligned first 100 frames and create features of them
for video in video_features:
repCache = {}
videoDir = os.path.join(lfwAlignedDir, video)
image_paths = os.listdir(videoDir)
images = loadFacenetImages(image_paths, videoDir, image_size)
# Feed single batch of 100 images to network for features
feed_dict = { images_placeholder:images }
emb_array = sess.run(embeddings, feed_dict=feed_dict)
video_features[video] = emb_array
return video_features
reload(sys)
sys.setdefaultencoding("utf-8")
# fileConfig('logger_config.ini')
# logger_error = logging.getLogger('errorhandler')
minsize = 60 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
margin = 44
image_size = 160
detect_model_dir = '/home/liubo-it/FaceRecognization/facenet/data'
recognize_model_dir = '/home/liubo-it/FaceRecognization/facenet/models/casia_facenet/20170208-100636/valid'
sess = tf.Session()
meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(recognize_model_dir))
saver = tf.train.import_meta_graph(os.path.join(recognize_model_dir, meta_file))
saver.restore(sess, os.path.join(recognize_model_dir, 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")
def extract_feature_from_file(pic_path):
# 包含人脸检测和人脸识别
# align_face(pic_path)
images = facenet.load_data([pic_path], False, False, image_size)
if images.shape[-1] != 3:
return None
feed_dict = {images_placeholder: images}
face_feature = sess.run(embeddings, feed_dict=feed_dict)
return face_feature
def facenetExp(lfwAligned, facenetModelDir, cls):
df = pd.DataFrame(columns=('nPpl', 'nImgs',
'trainTimeSecMean', 'trainTimeSecStd',
'predictTimeSecMean', 'predictTimeSecStd',
'accsMean', 'accsStd'))
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
meta_file, ckpt_file = facenet.get_model_filenames(facenetModelDir)
facenet.load_model(facenetModelDir, 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")
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
repCache = {}
df_i = 0
for nPpl in nPplVals:
print(" + nPpl: {}".format(nPpl))
(X, y) = getFacenetData(lfwAligned, nPpl, nImgs, image_size)
nSampled = X.shape[0]
ss = ShuffleSplit(nSampled, n_iter=10, test_size=0.1, random_state=0)
allTrainTimeSec = []
allPredictTimeSec = []
accs = []
for train, test in ss:
X_train = []
for img in X[train]:
h = hash(str(img.data))
if h in repCache:
rep = repCache[h]
else:
imgs = [img]
imgs = np.array(imgs)
feed_dict = { images_placeholder:imgs }
emb = sess.run(embeddings, feed_dict=feed_dict)
rep = emb[0]
repCache[h] = rep
X_train.append(rep)
start = time.time()
X_train = np.array(X_train)
cls.fit(X_train, y[train])
trainTimeSec = time.time() - start
allTrainTimeSec.append(trainTimeSec)
start = time.time()
X_test = []
for img in X[test]:
imgs = [img]
imgs = np.array(imgs)
feed_dict = { images_placeholder:imgs }
emb = sess.run(embeddings, feed_dict=feed_dict)
X_test.append(emb[0])
y_predict = cls.predict(X_test)
predictTimeSec = time.time() - start
allPredictTimeSec.append(predictTimeSec / len(test))
y_predict = np.array(y_predict)
acc = accuracy_score(y[test], y_predict)
accs.append(acc)
df.loc[df_i] = [nPpl, nImgs,
np.mean(allTrainTimeSec), np.std(allTrainTimeSec),
np.mean(allPredictTimeSec), np.std(allPredictTimeSec),
np.mean(accs), np.std(accs)]
df_i += 1
return df
def facenetExp(lfwAligned, facenetModelDir, cls):
df = pd.DataFrame(columns=('nPpl', 'nImgs', 'trainTimeSecMean',
'trainTimeSecStd', 'predictTimeSecMean',
'predictTimeSecStd', 'accsMean', 'accsStd'))
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
meta_file, ckpt_file = facenet.get_model_filenames(facenetModelDir)
facenet.load_model(facenetModelDir, 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")
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
repCache = {}
df_i = 0
for nPpl in nPplVals:
print(" + nPpl: {}".format(nPpl))
(X, y) = getFacenetData(lfwAligned, nPpl, nImgs, image_size)
nSampled = X.shape[0]
ss = ShuffleSplit(nSampled,
n_iter=10,
test_size=0.1,
random_state=0)
allTrainTimeSec = []
allPredictTimeSec = []
accs = []
for train, test in ss:
X_train = []
for img in X[train]:
h = hash(str(img.data))
if h in repCache:
rep = repCache[h]
else:
imgs = [img]
imgs = np.array(imgs)
feed_dict = {images_placeholder: imgs}
emb = sess.run(embeddings, feed_dict=feed_dict)
rep = emb[0]
repCache[h] = rep
X_train.append(rep)
start = time.time()
X_train = np.array(X_train)
cls.fit(X_train, y[train])
trainTimeSec = time.time() - start
allTrainTimeSec.append(trainTimeSec)
start = time.time()
X_test = []
for img in X[test]:
imgs = [img]
imgs = np.array(imgs)
feed_dict = {images_placeholder: imgs}
emb = sess.run(embeddings, feed_dict=feed_dict)
X_test.append(emb[0])
y_predict = cls.predict(X_test)
predictTimeSec = time.time() - start
allPredictTimeSec.append(predictTimeSec / len(test))
y_predict = np.array(y_predict)
acc = accuracy_score(y[test], y_predict)
accs.append(acc)
df.loc[df_i] = [
nPpl, nImgs,
np.mean(allTrainTimeSec),
np.std(allTrainTimeSec),
np.mean(allPredictTimeSec),
np.std(allPredictTimeSec),
np.mean(accs),
np.std(accs)
]
df_i += 1
return df
reload(sys)
sys.setdefaultencoding("utf-8")
# fileConfig('logger_config.ini')
# logger_error = logging.getLogger('errorhandler')
minsize = 60 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
margin = 44
image_size = 160
detect_model_dir = '/home/liubo-it/FaceRecognization/facenet/data'
recognize_model_dir = '/home/liubo-it/FaceRecognization/facenet/models/casia_facenet/20170208-100636/valid'
sess = tf.Session()
meta_file, ckpt_file = facenet.get_model_filenames(
os.path.expanduser(recognize_model_dir))
saver = tf.train.import_meta_graph(os.path.join(recognize_model_dir,
meta_file))
saver.restore(sess, os.path.join(recognize_model_dir, 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")
def extract_feature_from_file(pic_path):
# 包含人脸检测和人脸识别
# align_face(pic_path)
images = facenet.load_data([pic_path], False, False, image_size)
if images.shape[-1] != 3:
return None
feed_dict = {images_placeholder: images}
face_feature = sess.run(embeddings, feed_dict=feed_dict)
def facenetExp(ytfAligned, lfwAligned, facenetModelDir, cls):
df = pd.DataFrame(columns=('nPpl', 'nImgs', 'trainTimeSecMean',
'trainTimeSecStd', 'predictTimeSecMean',
'predictTimeSecStd', 'accsMean', 'accsStd'))
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
meta_file, ckpt_file = facenet.get_model_filenames(facenetModelDir)
facenet.load_model(facenetModelDir, 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")
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
repCache = {}
df_i = 0
for nPpl in nPplVals:
print(" + nPpl: {}".format(nPpl))
(X, y) = getFacenetData(ytfAligned, lfwAligned, nPpl, nImgs,
image_size)
#nSampled = X.shape[0]
#print 'nSampled:', nSampled
#ss = ShuffleSplit(nSampled, n_iter=10, test_size=0.1, random_state=0)
allTrainTimeSec = []
allPredictTimeSec = []
accs = []
X_train = []
Y_train = []
X_test = []
Y_test = []
for index, (train, test) in enumerate(X):
#print 'split:', train, test
imgs = np.array(train) # use vid as batch and one forward
feed_dict = {images_placeholder: imgs}
emb = sess.run(embeddings, feed_dict=feed_dict)
X_train.extend(rep)
Y_train.extend([y[index] for img in train])
imgs = np.array(test) # use vid as batch and one forward
feed_dict = {images_placeholder: imgs}
emb = sess.run(embeddings, feed_dict=feed_dict)
X_test.extend(rep)
Y_test.extend([y[index] for img in test])
start = time.time()
X_train = np.array(X_train)
Y_train = np.array(Y_train)
cls.fit(X_train, Y_train)
trainTimeSec = time.time() - start
allTrainTimeSec.append(trainTimeSec)
start = time.time()
#for vid in test:
# for img in vid:
# imgs = [img]
# imgs = np.array(imgs)
# feed_dict = { images_placeholder:imgs }
# emb = sess.run(embeddings, feed_dict=feed_dict)
# X_test.append(emb[0])
# Y_test.append(y[index])
y_predict = cls.predict(X_test)
predictTimeSec = time.time() - start
allPredictTimeSec.append(predictTimeSec / len(test))
y_predict = np.array(y_predict)
Y_test = np.array(Y_test)
acc = accuracy_score(Y_test, y_predict)
accs.append(acc)
print 'accs:', accs
df.loc[df_i] = [
nPpl, nImgs,
np.mean(allTrainTimeSec),
np.std(allTrainTimeSec),
np.mean(allPredictTimeSec),
np.std(allPredictTimeSec),
np.mean(accs),
np.std(accs)
]
df_i += 1
return df
def facenetExp(lfwAligned, facenetModelDir, cls):
df = pd.DataFrame(columns=('nPpl', 'nImgs',
'trainTimeSecMean', 'trainTimeSecStd',
'predictTimeSecMean', 'predictTimeSecStd',
'accsMean', 'accsStd'))
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
meta_file, ckpt_file = facenet.get_model_filenames(facenetModelDir)
facenet.load_model(facenetModelDir, 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")
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
repCache = {}
df_i = 0
for nPpl in nPplVals:
print(" + nPpl: {}".format(nPpl))
(X, y) = getFacenetData(lfwAligned, nPpl, nImgs, image_size)
nSampled = X.shape[0]
print 'nSampled:', nSampled
ss = ShuffleSplit(nSampled, n_iter=10, test_size=0.1, random_state=0)
allTrainTimeSec = []
allPredictTimeSec = []
accs = []
for train, test in ss:
#print 'split:', train, test
X_train = []
Y_train = []
for index, vid in zip(train, X[train]):
imgs = vid # use vid as batch and one forward
imgs = np.array(imgs)
feed_dict = { images_placeholder:imgs }
rep_array = sess.run(embeddings, feed_dict=feed_dict)
rep_array = np.array(rep_array)
print 'train', rep_array.shape, rep_array.mean(axis=0).shape
X_train.append(rep_array.mean(axis=0))
Y_train.append(y[index])
start = time.time()
X_train = np.array(X_train)
Y_train = np.array(Y_train)
cls.fit(X_train, Y_train)
trainTimeSec = time.time() - start
allTrainTimeSec.append(trainTimeSec)
start = time.time()
X_test = []
Y_test = []
for index, vid in zip(test, X[test]):
imgs = vid
imgs = np.array(imgs)
feed_dict = { images_placeholder:imgs }
rep_array = sess.run(embeddings, feed_dict=feed_dict)
rep_array = np.array(rep_array)
print 'test', rep_array.shape, rep_array.mean(axis=0).shape
X_test.append(rep_array.mean(axis=0))
Y_test.append(y[index])
y_predict = cls.predict(X_test)
predictTimeSec = time.time() - start
allPredictTimeSec.append(predictTimeSec / len(test))
y_predict = np.array(y_predict)
Y_test = np.array(Y_test)
acc = accuracy_score(Y_test, y_predict)
accs.append(acc)
print 'accs:', accs
df.loc[df_i] = [nPpl, nImgs,
np.mean(allTrainTimeSec), np.std(allTrainTimeSec),
np.mean(allPredictTimeSec), np.std(allPredictTimeSec),
np.mean(accs), np.std(accs)]
df_i += 1
return df