def update_lfw():
socketio_app.emit('log-lfw', {'message': 'Start downloading...'})
archive = absolute(app.config['FACE_NET_DATA_DIR']) + '.tar.gz'
data = absolute(app.config['FACE_NET_DATA_DIR'])
urllib.request.urlretrieve(app.config['FACE_NET_LWF_URL'], archive)
socketio_app.emit('log-lfw', {'message': 'Extracting...'})
os.makedirs(data, exist_ok=True)
tar = tarfile.open(archive, "r:gz")
tar.extractall(data)
tar.close()
os.remove(archive)
socketio_app.emit('log-lfw', {'message': 'Done'})
socketio_app.emit('finish-lfw', get_lfw_info())
def verify_by_image(email, image):
crop_dim = 180
user = User.query.filter_by(email=email).first()
if not user: return False
images = []
images.append(image)
try:
user_wights = np.load(user.get_profile_images_weights() + '.npy')
except FileNotFoundError:
return False
user_wights = user_wights.reshape(1)
for i in user_wights[0]:
images.append(
face_net_instance.process_image(user_wights[0][i], crop_dim))
model_path = absolute(app.config['FACE_NET_WEIGHTS_FILE'])
embs = get_emmbedings(images=images, model_path=model_path)
image_emb = embs[0]
for emb in embs[1:]:
print('Compare Embedings', np.linalg.norm(image_emb - emb))
return False
def get_output_info():
dir = absolute(app.config['FACE_NET_OUTPUT_DIR'])
size = get_size(dir)
if size == 0:
return {'error': 'Images are not preprocessed'}
return {'path': dir, 'size': sizeof_fmt(size)}
def update_landmark():
socketio_app.emit('log-landmark', {'message': 'Start downloading...'})
file = absolute(app.config['FACE_NET_LANDMARKS_FILE'])
archive = file + '.bz2'
urllib.request.urlretrieve(app.config['FACE_NET_LANDMARKS_URL'], archive)
socketio_app.emit('log-landmark', {'message': 'Extracting...'})
with open(file, 'wb') as new_file, bz2.BZ2File(archive, 'rb') as bz2_file:
for bytes in iter(lambda: bz2_file.read(100 * 1024), b''):
#for bytes in file.read():
new_file.write(bytes)
os.remove(archive)
socketio_app.emit('log-landmark', {'message': 'Done'})
socketio_app.emit('finish-landmark', get_landmark_info())
def make_tests():
crop_dim = 180
print('make_tests')
socketio_app.emit('log-prediction', {'message': 'Start making tests...'})
im1 = '/home/srivoknovski/Python/flask/acme/Networks/FaceNet/data/lfw/Aaron_Peirsol/Aaron_Peirsol_0002.jpg'
im2 = '/home/srivoknovski/Python/flask/acme/Networks/FaceNet/data/lfw/Aaron_Peirsol/Aaron_Peirsol_0004.jpg'
im3 = '/home/srivoknovski/Python/flask/acme/Networks/FaceNet/data/lfw/Aaron_Tippin/Aaron_Tippin_0001.jpg'
with open(im1, "rb") as image_file:
socketio_app.emit('log-prediction', {'image': (image_file.read())})
with open(im2, "rb") as image_file:
socketio_app.emit('log-prediction', {'image': (image_file.read())})
with open(im3, "rb") as image_file:
socketio_app.emit('log-prediction', {'image': (image_file.read())})
socketio_app.emit(
'log-prediction', {
'message':
'preprocessing test images..., Crop dimension {}'.format(crop_dim)
})
images = []
images.append(face_net_instance.process_image(im1, crop_dim))
images.append(face_net_instance.process_image(im2, crop_dim))
images.append(face_net_instance.process_image(im3, crop_dim))
socketio_app.emit('log-prediction', {'message': 'loading model...'})
model_path = absolute(app.config['FACE_NET_WEIGHTS_FILE'])
embs = get_emmbedings(images=images, model_path=model_path)
socketio_app.emit(
'log-prediction', {
'message':
'Model path {} {}'.format(model_path, os.path.getsize(model_path))
})
diff1 = np.linalg.norm(embs[0] - embs[1])
diff2 = np.linalg.norm(embs[0] - embs[2])
print(im1, im2, np.linalg.norm(embs[0] - embs[1]))
print(im1, im3, np.linalg.norm(embs[0] - embs[2]))
socketio_app.emit('log-prediction', {'message': 'Done'})
socketio_app.emit('finish-prediction', {
'The same persons': str(diff1),
'The different persons': str(diff2)
})
def get_landmark_info():
file = absolute(app.config['FACE_NET_LANDMARKS_FILE'])
if not os.path.isfile(file): return {'error': 'File does not exists'}
return {'path': file, 'size': sizeof_fmt(os.path.getsize(file))}
def get_model_info():
dir = absolute(app.config['FACE_NET_WEIGHTS_DIR'])
if not os.path.isdir(dir): return {'error': 'Folder does not exists'}
return {'path': dir, 'size': sizeof_fmt(get_size(dir))}
def update_model():
socketio_app.emit('log-model', {'message': 'Start updating...'})
download_and_extract_model('20170511-185253',
absolute(app.config['FACE_NET_WEIGHTS_DIR']))
socketio_app.emit('log-model', {'message': 'Done'})
socketio_app.emit('finish-model', get_model_info())
def update_output():
socketio_app.emit('log-output', {'message': 'Start updating...'})
output_dir = absolute(app.config['FACE_NET_OUTPUT_DIR'])
preprocess(absolute(app.config['FACE_NET_DATA_DIR']), output_dir, 180)
socketio_app.emit('log-output', {'message': 'Done'})
socketio_app.emit('finish-output', get_output_info())
def get_lfw_info():
data = absolute(app.config['FACE_NET_DATA_DIR'])
return {'path': data, 'size': sizeof_fmt(get_size(data))}