def __init__(self, model_path):
self._model_path = model_path
self._model_files = {
'shape_predictor':
os.path.join(model_path, 'shape_predictor_68_face_landmarks.dat'),
'face_template':
os.path.join(model_path, 'face_template.npy'),
'mean':
os.path.join(model_path, 'mean.npy'),
'stddev':
os.path.join(model_path, 'stddev.npy'),
'cnn_weights':
os.path.join(model_path, 'weights_cnn.h5'),
'tpe_weights':
os.path.join(model_path, 'weights_tpe.h5'),
}
for model_file in self._model_files.values():
if not os.path.exists(model_file):
raise FileNotFoundError(model_file)
self._mean = np.load(self._model_files['mean'])
self._stddev = np.load(self._model_files['stddev'])
self._fd = FaceDetector()
self._fa = FaceAligner(self._model_files['shape_predictor'],
self._model_files['face_template'])
cnn = build_cnn(227, 266)
cnn.load_weights(self._model_files['cnn_weights'])
self._cnn = Bottleneck(cnn, ~1)
_, tpe = build_tpe(256, 256)
tpe.load_weights(self._model_files['tpe_weights'])
self._tpe = tpe
def initialize_model(self):
self._mean = np.load(self._model_files['mean'])
self._stddev = np.load(self._model_files['stddev'])
self._fd = FaceDetector()
self._fa = FaceAligner(self._model_files['shape_predictor'],
self._model_files['face_template'])
cnn = build_cnn(227, 266)
cnn.load_weights(self._model_files['cnn_weights'])
self._cnn = Bottleneck(cnn, ~1)
_, tpe = build_tpe(256, 256)
tpe.load_weights(self._model_files['tpe_weights'])
self._tpe = tpe
class FaceVerificator:
def __init__(self, model_dir):
self._model_dir = model_dir
self._model_files = {
'shape_predictor': os.path.join(model_dir, 'shape_predictor_68_face_landmarks.dat'),
'face_template': os.path.join(model_dir, 'face_template.npy'),
'mean': os.path.join(model_dir, 'mean.npy'),
'stddev': os.path.join(model_dir, 'stddev.npy'),
'cnn_weights': os.path.join(model_dir, 'weights_cnn.h5'),
'tpe_weights': os.path.join(model_dir, 'weights_tpe.h5'),
}
def initialize_model(self):
self._mean = np.load(self._model_files['mean'])
self._stddev = np.load(self._model_files['stddev'])
self._fd = FaceDetector()
self._fa = FaceAligner(self._model_files['shape_predictor'],
self._model_files['face_template'])
cnn = build_my_cnn(227, 24)
cnn.load_weights(self._model_files['cnn_weights'])
self._cnn = Bottleneck(cnn, ~1)
_, tpe = build_tpe(24, 24)
tpe.load_weights(self._model_files['tpe_weights'])
self._tpe = tpe
def normalize(self, img):
img = clip_to_range(img)
return (img - self._mean) / self._stddev
def process_image(self, img):
face_rects = self._fd.detect_faces(img, upscale_factor=2, greater_than=GREATER_THAN)
if not face_rects:
return []
faces = self._fa.align_faces(img, face_rects, dim=IMSIZE, border=IMBORDER)
faces = list(map(self.normalize, faces))
faces_y = self._cnn.predict(faces, batch_size=BATCH_SIZE)
faces_y = self._tpe.predict(faces_y, batch_size=BATCH_SIZE)
return list(zip(face_rects, faces_y))
def compare_many(self, dist, xs, ys):
xs = np.array(xs)
ys = np.array(ys)
scores = xs @ ys.T
return scores, scores > dist
import os
import os.path
import numpy as np
from skimage import io
from preprocessing import FaceDetector, FaceAligner, clip_to_range
from tqdm import tqdm
from itertools import repeat
fd = FaceDetector()
fa = FaceAligner('../model/shape_predictor_68_face_landmarks.dat',
'../model/face_template.npy')
IMAGE_FORMATS = {'.jpg', '.jpeg', '.png'}
def get_images(path):
return list(
filter(
lambda s: os.path.isfile(os.path.join(path, s)) and os.path.
splitext(s)[1] in IMAGE_FORMATS, os.listdir(path)))
def get_folders(path):
return list(
filter(lambda s: os.path.isdir(os.path.join(path, s)),
os.listdir(path)))
def list_data(data_path):
#string = 'test{}.jpg'.format(counter)
#path = os.path.join(string)
#image = cv2.imread(path, 0)
if counter == 0:
currentImg = images[0]
else:
currentImg = images[counter * timestep - 1]
if window_counter == 0:
firstImg = currentImg.copy()
image_copy = currentImg.copy()
fa = FaceAligner(predictor, desiredFaceWidth=800)
rects2 = detector(firstImg, 2)
# loop over the face detections
for rect in rects2:
# extract the ROI of the *original* face, then align the face
# using facial landmarks
(x, y, w, h) = rect_to_bb(rect)
faceOrig = imutils.resize(currentImg[y:y + h, x:x + w], width=250)
faceAligned = fa.align(currentImg, currentImg, rect)
#shape2 = predictor(image_copy, rect)
# display the output images
#cv2.imshow("Original", faceOrig)
#cv2.imwrite('original3.png', faceOrig)
# cv2.imshow("Aligned", faceAligned)