face_encoder = load_model(encoder_model)
encoding_dict = dict()
for person_name in os.listdir(people_dir):
person_dir = os.path.join(people_dir, person_name)
encodes = []
for img_name in os.listdir(person_dir):
img_path = os.path.join(person_dir, img_name)
img = cv2.imread(img_path)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = face_detector.detect_faces(img_rgb)
if results:
res = max(results, key=lambda b: b['box'][2] * b['box'][3])
face, _, _ = get_face(img_rgb, res['box'])
face = normalize(face)
face = cv2.resize(face, required_size)
encode = face_encoder.predict(np.expand_dims(face, axis=0))[0]
encodes.append(encode)
if encodes:
encode = np.sum(encodes, axis=0)
encode = l2_normalizer.transform(np.expand_dims(encode, axis=0))[0]
encoding_dict[person_name] = encode
for key in encoding_dict.keys():
print(key)
with open(encodings_path, 'bw') as file:
pickle.dump(encoding_dict, file)
for person_name in os.listdir(people_dir):
person_dir = os.path.join(people_dir, person_name)
encodes = []
for img_name in os.listdir(person_dir):
img_path = os.path.join(person_dir, img_name)
img = cv2.imread(img_path)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = face_detector.detect_faces(img_rgb)
if results:
box = max(results, key=lambda b: b['box'][2] * b['box'][3])
l_e = box['keypoints']['left_eye']
r_e = box['keypoints']['right_eye']
face = align(img,
l_e,
r_e,
size=required_size,
eye_pos=(0.35, 0.4))
face = normalize(face)
# face, _, _ = get_face(img_rgb, box['box'])
encode = get_encode(face_encoder, face, required_size)
encodes.append(encode)
if encodes:
encode = np.sum(encodes, axis=0)
encode = l2_normalizer.transform(encode.reshape(1, -1))
encoding_dict[person_name] = encode[0]
for key in encoding_dict.keys():
print(key)
with open(encodings_path, 'bw') as file:
pickle.dump(encoding_dict, file)
