from skimage.feature import local_binary_pattern
from numpy import linalg as la
import base64
from src import detect_faces
from PIL import Image
import csv
_lfw_landmarks = 'data/LFW.csv'
_lfw_images = 'data/peopleDevTest.txt'
_lfw_root = '/home/aaron/Datasets/database/'
_lbpfaces_path = 'data/lbpfaces.npy'
meanface_path = 'data/meanImage.npy'
eigenVec_path = 'data/eigenVectors_new.npy'
weightVec_path = 'data/weightVectors_updated.npy'
args = test_args.get_args()
PEOPLE_FOLDER = os.path.join('static', 'people_photo')
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
# initialize flask application
app = Flask(__name__, template_folder='templates')
app.config['UPLOAD_FOLDER'] = PEOPLE_FOLDER
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0
frameCount = 0
fileName = ""
filePath = ""
def get_landmarks(image):
from arguments.test_args import get_args
import torch
from models import net_resolution
if __name__ == '__main__':
args = get_args()
weights = torch.load(args.srnet_pth)
model = net_resolution.get_model()
model.load_state_dict(weights['net'])
print(weights)