def api_register():
if not request.files:
print("nofile")
return jsonify({'status': 'failed', 'error': 'no_file'}), 400
if not 'username' in request.form:
print('no name')
return jsonify({'status': 'failed', 'error': 'no_name'}), 400
file = request.files['file']
name = request.form['username']
file.save(path.join(app.config['UPLOAD_FOLDER'], name + ".png"))
encoding = None
try:
encoding = api.train(file)
except api.NoFaceDetectedError:
print('no face detected')
if not encoding is None:
encoded = encoding.tostring()
user = database.create_user(name, encoded)
if not user:
return jsonify({'state': False, 'error': 'cannot insert new user'})
encodings[name] = encoding
with open('encodings.pickle', 'wb') as f:
pickle.dump(encodings, f)
return jsonify({'state': True})
else:
return jsonify({
'state': False,
'error': 'cannot find a face in the picture'
})
def main(args):
batch_size = 32 # Default
k = 10 # Default
if len(args) >= 2:
batch_size = int(args[1])
if len(args) >= 3:
k = int(args[2])
if args[0] == "train":
print("Training from scratch ...")
print("Batch size is ", batch_size)
api.train(batch_size, True, True, True, 100)
elif args[0] == "finetune":
print("Finetune checkpoint ...")
print("Batch size is ", batch_size)
api.train(batch_size, False, True, True, 100)
elif args[0] == "validate":
print("Validate The System ...")
print("Batch size is ", batch_size)
api.validate_system(batch_size)
elif args[0] == "preprocess_questions":
print("Preprocess Questions ...")
api.prepare_data()
elif args[0] == "extract_all_features":
print("Extract Images Features ...")
print("Batch size is ", batch_size)
api.extract_features(batch_size)
elif args[0] == "evaluate":
print("Evaluating Example ...")
print("Image Path: ", args[1])
print("Question words: ", args[1:])
api.evaluate_example_url(args[1], args[1:])
elif args[0] == "trace":
print("Trace Statistics of a Batch of Validation Set ...")
print("Batch size is ", batch_size)
print("K is ", k)
api.trace(batch_size, k)
print('%d frames extracted' % len(faces))
print('PersonId: %s' % person_id)
print('FaceIds')
print('=======')
for face in faces:
print(add_face(face, person_id))
if args.list:
list_ids = list_people_ids()
if len(list_ids) == 0:
print_error('No persons found')
print('Persons IDs:')
for id in list_ids:
print(id)
if args.delete:
id = args.delete[0]
delete_person(id)
print('Person deleted')
if args.train:
train()
print('Training successfully started')
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
if args.log_path:
file_handler = logging.FileHandler(args.log_path)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
else:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
logger.info('Running with args : {}'.format(args))
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if args.gpu is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
random.seed(args.seed)
np.random.seed(args.seed)
tf.set_random_seed(args.seed)
if args.prepare:
prepare(args)
if args.train:
train(args)
if args.evaluate:
evaluate(args)
if args.segment:
segment(args)