def main(argv):
dataset_path = os.path.expanduser(argv.dataset_path)
dataset_path = os.path.abspath(dataset_path)
model_file = os.path.expanduser(argv.model)
model_file = os.path.abspath(model_file)
classifier_filename_exp = os.path.expanduser(argv.output_classifier)
classifier_filename_exp = os.path.abspath(classifier_filename_exp)
assert os.path.exists(dataset_path) is True, 'please correct path...'
np.random.seed(seed=777)
# TF
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
pnet, rnet, onet = facenet.align.detect_face.create_mtcnn(sess, None)
fn.load_model(model_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")
embedding_size = embeddings.get_shape()[1]
dataset = fn.get_dataset(dataset_path)
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 = fn.get_image_paths_and_labels(dataset)
print('Number of classes: %d' % len(dataset))
print('Number of images: %d' % len(paths))
nrof_images = len(paths)
nrof_batches_per_epoch = int(math.ceil(1.0 * nrof_images / 90))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches_per_epoch):
start_index = i * 90
end_index = min((i + 1) * 90, nrof_images)
paths_batch = paths[start_index:end_index]
images = fn.load_data(paths_batch, False, False, 160)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_array[start_index:end_index, :] = sess.run(embeddings,
feed_dict=feed_dict)
model = SVC(kernel='linear', probability=True)
model.fit(emb_array, labels)
class_names = [cls.name.replace('_', ' ') for cls in dataset]
with open(classifier_filename_exp, 'wb') as outfile:
pickle.dump((model, class_names), outfile)
print('Saved classifier model to file "%s"' % classifier_filename_exp)
exit(0)
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 main():
with tf.Graph().as_default() as _:
with tf.Session() as sess:
if get_num_of_files_in_dir(data_dir) == 0:
print('No train data!')
return
dataset = facenet.get_dataset(data_dir)
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(model_path)
# Get input and output tensors
images_placeholder = sess.graph.get_tensor_by_name("input:0")
embeddings = sess.graph.get_tensor_by_name("embeddings:0")
phase_train_placeholder = sess.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 / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches_per_epoch):
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)
classifier_filename_exp = os.path.expanduser(classifier_filename)
x, y = SMOTETomek(random_state=4).fit_sample(emb_array, labels)
print('Training classifier')
model = SVC(kernel='linear', probability=True)
model.fit(x, y)
# Create a list of class names
class_names = [ cls.name.replace('_', ' ') for cls in dataset]
# 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)
def main(args, self):
self.update_state(state='RUNNING')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.25, allow_growth = True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) 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)
classifier_filename_exp = os.path.expanduser(args.classifier_filename)
if (args.mode=='TRAIN'):
# Train classifier
print('Training classifier')
model = SVC(kernel='linear', probability=True)
model.fit(emb_array, labels)
# Create a list of class names
class_names = [ cls.name.replace('_', ' ') for cls in dataset]
# 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)
sess.close()
def classifier(data_dir, model_path, classifier_path):
with tf.Graph().as_default():
with tf.Session() as sess:
if (get_num_of_files_in_dir(data_dir) == 0):
print('There is no train data')
return 0
dataset = facenet.get_dataset(data_dir)
paths, labels = facenet.get_image_paths_and_labels(dataset)
# Load pretrained model's train parameter
facenet.load_model(model_path)
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]
# Preprocess images
nrof_images = len(paths)
nrof_batches_per_epoch = int(
math.ceil(1.0 * nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches_per_epoch):
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)
# Load classifier model, class-label dict
with open(classifier_path, 'rb') as infile:
(model, class_names) = pickle.load(infile)
print(class_names)
# print('Loaded classifier model from file "%s"' % classifier_path)
predictions = model.predict_proba(emb_array)
# print(len(paths))
# print(prediction_time_elapsed/len(paths))
best_class_indices = np.argmax(predictions, axis=1)
best_class_probabilities = predictions[
np.arange(len(best_class_indices)), best_class_indices]
label_to_class_map = create_label_to_class_map(class_names)
print(label_to_class_map)
data_dir_result_label_map = create_data_dir_result_label_map(
paths, best_class_indices, best_class_probabilities)
data_dir_result_class_map = {}
for dir_name in data_dir_result_label_map:
label = data_dir_result_label_map[dir_name]
data_dir_result_class_map[dir_name] = label_to_class_map[label]
print('dir_name : ' + dir_name + '\t recognition : ' +
label_to_class_map[label])
rename_all_files_by_details(best_class_indices,
best_class_probabilities, paths,
label_to_class_map)
return data_dir_result_class_map
def classify_process(sess_name, sess, graph, model, class_names):
start = time.time()
sess_dir = 'tmp/' + sess_name
align_result = align_dataset_mtcnn(sess_name)
if align_result == None:
print('Face not detected!')
shutil.rmtree(sess_dir)
return
print(align_result)
tracking(align_result[0], align_result[1], sess_name)
try:
with graph.as_default():
dataset = facenet.get_dataset(sess_dir + '_t')
paths, labels = facenet.get_image_paths_and_labels(dataset)
# Get input and output tensors
images_placeholder = graph.get_tensor_by_name("input:0")
embeddings = graph.get_tensor_by_name("embeddings:0")
phase_train_placeholder = graph.get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
# Preprocess images
nrof_images = len(paths)
nrof_batches_per_epoch = int(
math.ceil(1.0 * nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches_per_epoch):
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)
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]
label_to_class_map = create_label_to_class_map(class_names)
print(label_to_class_map)
data_dir_result_label_map = create_data_dir_result_label_map(
paths, best_class_indices, best_class_probabilities)
data_dir_result_class_map = {}
for dir_name in data_dir_result_label_map:
label = data_dir_result_label_map[dir_name]
data_dir_result_class_map[dir_name] = label_to_class_map[label]
print(data_dir_result_class_map)
end = time.time()
print('Total time elapsed:', (end - start))
processed_names = []
for name in data_dir_result_class_map.values():
if name == 'unknown' or name in processed_names:
continue
processed_names.append(name)
try:
update_attendance(name)
except Exception as e:
print(e)
sio.emit('attend', name)
for i in range(len(paths)):
filename = paths[i].split('/')[-1]
path = paths[i][:-len(filename)]
acc = round(best_class_probabilities[i] * 50, 4)
acc = pow(acc, 3)
acc = math.sqrt(acc)
acc = round(acc)
if acc >= 200:
for imgname in dataset:
t = str(time.time())
shutil.move(
path + '/' + imgname, 'dataset/{}/{}.jpg'.format(
label_to_class_map[best_class_indices[i]], t))
except Exception as e:
print(e)