def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
# Load the model
facenet.load_model(args.model)
# 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] # For some reason this doesn't work for frozen graphs
image_size = args.image_size
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
batch_size = args.lfw_batch_size
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0 * nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches):
start_index = i * batch_size
end_index = min((i + 1) * batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False,
image_size)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array[start_index:end_index, :] = sess.run(
embeddings, feed_dict=feed_dict)
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(
emb_array, actual_issame, nrof_folds=args.lfw_nrof_folds)
print('Accuracy: %1.3f+-%1.3f' %
(np.mean(accuracy), np.std(accuracy)))
print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' %
(val, val_std, far))
auc = metrics.auc(fpr, tpr)
print('Area Under Curve (AUC): %1.3f' % auc)
eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x),
0., 1.)
print('Equal Error Rate (EER): %1.3f' % eer)
def make_feature(args):
with tf.Graph().as_default():
with tf.Session() as sess:
np.random.seed(seed=args.seed)
dataset = facenet.get_dataset(args.output_dir)
# Check that there are at least one training image per class
for cls in dataset:
assert (
len(cls.image_paths) > 0,
'There must be at least one image for each class in the dataset'
)
paths, labels = facenet.get_image_paths_and_labels(dataset)
print('Number of classes: %d' % len(dataset))
print('Number of images: %d' % len(paths))
# Load the model
print('Loading feature extraction model')
facenet.load_model(args.model)
# 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")
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Calculating features for images')
nrof_images = len(paths)
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_images):
start_index = i * args.batch_size
end_index = min((i + 1) * args.batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False,
args.image_size)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array[start_index:end_index, :] = sess.run(
embeddings, feed_dict=feed_dict)
# Mrege --->
print(str(i + 1) + '/' + str(nrof_images))
class_names = [cls.name.replace('_', ' ') for cls in dataset]
np.savez(args.gallery_filename, emb_array, labels, class_names,
paths)
def main(self, args):
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
print('Loading feature extraction model')
facenet.load_model(args.model)
# 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")
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Calculating features for images')
paths = []
paths.append("289096.png")
images = facenet.load_data(paths, False, False, args.image_size)
feed_dict = {images_placeholder: images, phase_train_placeholder: False}
unknown_array = sess.run(embeddings, feed_dict=feed_dict)
npzfile = np.load('/home/dev/tensormsa_facenet/facenet_tf/pre_model/20170512-110547_gallery_bak.npz')
emb_array = npzfile['arr_0']
labels = npzfile['arr_1']
class_names = npzfile['arr_2']
with tf.Graph().as_default():
with tf.Session() as sess:
# placeholder is used for feeding data.
x = tf.placeholder("float", shape=[None, 128], name='x')
# all the variables are allocated in GPU memory
W1 = tf.Variable(tf.zeros([128, 2]), name='W1')
b1 = tf.Variable(tf.zeros([2]), name='b1')
y = tf.nn.softmax(tf.matmul(x, W1) + b1, name='y')
saver = tf.train.Saver()
saver.restore(sess, "/home/dev/tensormsa_facenet/facenet_tf/pre_model/my_feature_weight/model.ckpt")
results = sess.run(y,feed_dict={x:emb_array*unknown_array})
best_class_indices = [labels[np.argmax(results, axis=0)[0]]]
print(class_names[best_class_indices[0]])
best_class_probabilities = np.amax(results, axis=0)
print(best_class_probabilities)
npzfile = np.load(args.model_dir+'20170512-110547_gallery_bak.npz')
emb_array = npzfile['arr_0']
labels = npzfile['arr_1']
class_names = npzfile['arr_2']
# results is an array of True/False telling if the unknown face matched anyone in the known_faces array
results = face_recognition.compare_faces(emb_array, unknown_array)
print(class_names[labels[results.index(True)]])
print("Is the unknown face a new person that we've never seen before? {}".format(not True in results))
def main(args):
images = load_and_align_data(args.image_files, args.image_size,
args.margin, args.gpu_memory_fraction)
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
facenet.load_model(args.model)
# 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")
# Run forward pass to calculate embeddings
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb = sess.run(embeddings, feed_dict=feed_dict)
nrof_images = len(args.image_files)
print('Images:')
for i in range(nrof_images):
print('%1d: %s' % (i, args.image_files[i]))
print('')
# Print distance matrix
print('Distance matrix')
print(' ', end='')
for i in range(nrof_images):
print(' %1d ' % i, end='')
print('')
for i in range(nrof_images):
print('%1d ' % i, end='')
for j in range(nrof_images):
dist = np.sqrt(
np.sum(np.square(np.subtract(emb[i, :], emb[j, :]))))
print(' %1.4f ' % dist, end='')
print('')
def main(args):
network = importlib.import_module(args.model_def)
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
# Mrege --->
# log_dir = args.logs_base_dir
if not os.path.isdir(
log_dir): # Create the log directory if it doesn't exist
os.makedirs(log_dir)
model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
# Mrege --->
# model_dir = args.models_base_dir
if not os.path.isdir(
model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
# Write arguments to a text file
facenet.write_arguments_to_file(args, os.path.join(log_dir,
'arguments.txt'))
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
np.random.seed(seed=args.seed)
random.seed(args.seed)
train_set = facenet.get_dataset(args.data_dir)
if args.filter_filename:
train_set = filter_dataset(train_set,
os.path.expanduser(args.filter_filename),
args.filter_percentile,
args.filter_min_nrof_images_per_class)
nrof_classes = len(train_set)
print('Model directory: %s' % model_dir)
print('Log directory: %s' % log_dir)
pretrained_model = None
if args.pretrained_model:
pretrained_model = os.path.expanduser(args.pretrained_model)
print('Pre-trained model: %s' % pretrained_model)
if args.lfw_dir:
print('LFW directory: %s' % args.lfw_dir)
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
lfw_paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
with tf.Graph().as_default():
tf.set_random_seed(args.seed)
global_step = tf.Variable(0, trainable=False)
# Get a list of image paths and their labels
image_list, label_list = facenet.get_image_paths_and_labels(train_set)
assert len(image_list) > 0, 'The dataset should not be empty'
# Create a queue that produces indices into the image_list and label_list
labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
range_size = array_ops.shape(labels)[0]
index_queue = tf.train.range_input_producer(range_size,
num_epochs=None,
shuffle=True,
seed=None,
capacity=32)
index_dequeue_op = index_queue.dequeue_many(
args.batch_size * args.epoch_size, 'index_dequeue')
learning_rate_placeholder = tf.placeholder(tf.float32,
name='learning_rate')
batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
image_paths_placeholder = tf.placeholder(tf.string,
shape=(None, 1),
name='image_paths')
labels_placeholder = tf.placeholder(tf.int64,
shape=(None, 1),
name='labels')
input_queue = data_flow_ops.FIFOQueue(capacity=100000,
dtypes=[tf.string, tf.int64],
shapes=[(1, ), (1, )],
shared_name=None,
name=None)
enqueue_op = input_queue.enqueue_many(
[image_paths_placeholder, labels_placeholder], name='enqueue_op')
nrof_preprocess_threads = 4
images_and_labels = []
for _ in range(nrof_preprocess_threads):
filenames, label = input_queue.dequeue()
images = []
for filename in tf.unstack(filenames):
file_contents = tf.read_file(filename)
image = tf.image.decode_image(file_contents, channels=3)
if args.random_rotate:
image = tf.py_func(facenet.random_rotate_image, [image],
tf.uint8)
if args.random_crop:
image = tf.random_crop(
image, [args.image_size, args.image_size, 3])
else:
image = tf.image.resize_image_with_crop_or_pad(
image, args.image_size, args.image_size)
if args.random_flip:
image = tf.image.random_flip_left_right(image)
#pylint: disable=no-member
image.set_shape((args.image_size, args.image_size, 3))
images.append(tf.image.per_image_standardization(image))
images_and_labels.append([images, label])
image_batch, label_batch = tf.train.batch_join(
images_and_labels,
batch_size=batch_size_placeholder,
shapes=[(args.image_size, args.image_size, 3), ()],
enqueue_many=True,
capacity=4 * nrof_preprocess_threads * args.batch_size,
allow_smaller_final_batch=True)
image_batch = tf.identity(image_batch, 'image_batch')
image_batch = tf.identity(image_batch, 'input')
label_batch = tf.identity(label_batch, 'label_batch')
print('Total number of classes: %d' % nrof_classes)
print('Total number of examples: %d' % len(image_list))
print('Building training graph')
# Build the inference graph
prelogits, _ = network.inference(
image_batch,
args.keep_probability,
phase_train=phase_train_placeholder,
bottleneck_layer_size=args.embedding_size,
weight_decay=args.weight_decay)
logits = slim.fully_connected(
prelogits,
len(train_set),
activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(args.weight_decay),
scope='Logits',
reuse=False)
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
# Add center loss
if args.center_loss_factor > 0.0:
prelogits_center_loss, _ = facenet.center_loss(
prelogits, label_batch, args.center_loss_alfa, nrof_classes)
tf.add_to_collection(
tf.GraphKeys.REGULARIZATION_LOSSES,
prelogits_center_loss * args.center_loss_factor)
learning_rate = tf.train.exponential_decay(
learning_rate_placeholder,
global_step,
args.learning_rate_decay_epochs * args.epoch_size,
args.learning_rate_decay_factor,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
# Calculate the average cross entropy loss across the batch
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=label_batch,
logits=logits,
name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy,
name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# Calculate the total losses
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([cross_entropy_mean] + regularization_losses,
name='total_loss')
# Build a Graph that trains the model with one batch of examples and updates the model parameters
train_op = facenet.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay,
tf.global_variables(), args.log_histograms)
# Create a saver
saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# Start running operations on the Graph.
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord, sess=sess)
with sess.as_default():
if pretrained_model:
print('Restoring pretrained model: %s' % pretrained_model)
facenet.load_model(pretrained_model)
#saver.restore(sess, pretrained_model)
# Training and validation loop
print('Running training')
epoch = 0
while epoch < args.max_nrof_epochs:
step = sess.run(global_step, feed_dict=None)
epoch = step // args.epoch_size
# Train for one epoch
train(args, sess, epoch, image_list, label_list,
index_dequeue_op, enqueue_op, image_paths_placeholder,
labels_placeholder, learning_rate_placeholder,
phase_train_placeholder, batch_size_placeholder,
global_step, total_loss, train_op, summary_op,
summary_writer, regularization_losses,
args.learning_rate_schedule_file)
# Save variables and the metagraph if it doesn't exist already
save_variables_and_metagraph(sess, saver, summary_writer,
model_dir, subdir, step)
# Evaluate on LFW
if args.lfw_dir:
evaluate(sess, enqueue_op, image_paths_placeholder,
labels_placeholder, phase_train_placeholder,
batch_size_placeholder, embeddings, label_batch,
lfw_paths, actual_issame, args.lfw_batch_size,
args.lfw_nrof_folds, log_dir, step,
summary_writer)
return model_dir
def realtime_run_sess(self, sess):
self.pnet, self.rnet, self.onet = detect_face.create_mtcnn(sess, None)
# Pre Train Load the model
print('Loading feature extraction model')
facenet.load_model(self.feature_model)
# Get input and output tensors
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")
# Model Load
if self.pair_type.count('distance') == 0:
classifier_filename_exp = os.path.expanduser(
self.classifier_filename)
with open(classifier_filename_exp, 'rb') as infile:
(self.model, self.class_names) = pickle.load(infile)
print('load classifier file-> %s' % classifier_filename_exp)
print('')
self.logger = logging.getLogger('myapp')
hdlr = logging.FileHandler(self.log_dir + '/myface.log')
formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
hdlr.setFormatter(formatter)
self.logger.addHandler(hdlr)
self.logger.setLevel(logging.WARNING)
# dlib rotation
self.predictor = dlib.shape_predictor(
self.pre_model_dir +
self.predictor_68_face_landmarks.replace('.bz2', ''))
self.detector = dlib.get_frontal_face_detector()
self.fa = FaceAligner(self.predictor,
desiredFaceWidth=self.image_size +
self.cropped_size)
self.gallery_load_flag = True
if self.runtype == 'test':
self.debug = True
predict.getpredict_test(self, sess)
else:
self.debug = True
self.pretime = '99' # 1 second save
video_capture = cv2.VideoCapture(0)
self.predict_flag = True
self.stand_box_flag = True
while True:
ret, frame = video_capture.read()
frame = cv2.flip(frame, 1)
self.saveframe = frame
frame = cv2.resize(frame, (0, 0),
fx=self.readImageSizeX,
fy=self.readImageSizeY)
if self.stand_box_flag:
self.stand_box = [self.stand_box[0], self.stand_box[1]]
self.stand_box.append(frame.shape[1] - self.stand_box[0])
self.stand_box.append(frame.shape[0] - self.stand_box[1])
frame = predict.getpredict(self, sess, frame)
frame = cv2.resize(frame, (0, 0),
fx=self.viewImageSizeX,
fy=self.viewImageSizeY)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
np.random.seed(seed=args.seed)
if args.use_split_dataset:
dataset_tmp = facenet.get_dataset(args.data_dir)
train_set, test_set = split_dataset(
dataset_tmp, args.min_nrof_images_per_class,
args.nrof_train_images_per_class)
if (args.mode == 'TRAIN'):
dataset = train_set
elif (args.mode == 'CLASSIFY'):
dataset = test_set
else:
dataset = facenet.get_dataset(args.data_dir)
# Check that there are at least one training image per class
for cls in dataset:
assert (
len(cls.image_paths) > 0,
'There must be at least one image for each class in the dataset'
)
paths, labels = facenet.get_image_paths_and_labels(dataset)
print('Number of classes: %d' % len(dataset))
print('Number of images: %d' % len(paths))
# Load the model
print('Loading feature extraction model')
facenet.load_model(args.model)
# 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")
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Calculating features for images')
nrof_images = len(paths)
nrof_batches_per_epoch = int(
math.ceil(1.0 * nrof_images / args.batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches_per_epoch):
start_index = i * args.batch_size
end_index = min((i + 1) * args.batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False,
args.image_size)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array[start_index:end_index, :] = sess.run(
embeddings, feed_dict=feed_dict)
# Mrege --->
print('Feature Extraction:' + str(i + 1) + '/' +
str(nrof_batches_per_epoch))
classifier_filename_exp = os.path.expanduser(
args.classifier_filename)
if (args.mode == 'TRAIN'):
# Train classifier
print('Training classifier')
if args.pair_type == 'cnn_pair':
emb_array, labels = utils.get_images_labels_pair(
emb_array, labels)
utils.train_weight(emb_array, labels)
elif args.pair_type == 'svm' or args.pair_type == 'svm_pair':
model = SVC(kernel='linear', probability=True)
class_names = [
cls.name.replace('_', ' ') for cls in dataset
]
print('Model fit Total Labels:' + str(len(labels)))
if args.pair_type == 'svm_pair':
emb_array, labels = utils.get_images_labels_pair_same(
emb_array, labels, dataset, class_names)
class_names = ['same', 'diff']
print('Model fit start... True count(' +
str(labels.count(0)) + '), False count(' +
str(labels.count(1)) + ')')
model = model.fit(emb_array, labels)
# Saving classifier model
with open(classifier_filename_exp, 'wb') as outfile:
pickle.dump((model, class_names), outfile)
print('Saved classifier model to file "%s"' %
classifier_filename_exp)
elif (args.mode == 'CLASSIFY'):
# Classify images
print('Testing classifier')
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
print('Loaded classifier model from file "%s"' %
classifier_filename_exp)
predictions = model.predict_proba(emb_array)
best_class_indices = np.argmax(predictions, axis=1)
best_class_probabilities = predictions[
np.arange(len(best_class_indices)), best_class_indices]
for i in range(len(best_class_indices)):
print('%4d %s: %.3f' %
(i, class_names[best_class_indices[i]],
best_class_probabilities[i]))
accuracy = np.mean(np.equal(best_class_indices, labels))
print('Accuracy: %.3f' % accuracy)