def perform_val(embedding_size,
batch_size,
model,
carray,
issame,
nrof_folds=10,
is_ccrop=False,
is_flip=True):
"""perform val"""
embeddings = np.zeros([len(carray), embedding_size])
for idx in tqdm.tqdm(range(0, len(carray), batch_size)):
batch = carray[idx:idx + batch_size]
batch = np.transpose(batch, [0, 2, 3, 1]) * 0.5 + 0.5
if is_ccrop:
batch = ccrop_batch(batch)
if is_flip:
fliped = hflip_batch(batch)
emb_batch = model(batch) + model(fliped)
embeddings[idx:idx + batch_size] = l2_norm(emb_batch)
else:
batch = ccrop_batch(batch)
emb_batch = model(batch)
embeddings[idx:idx + batch_size] = l2_norm(emb_batch)
tpr, fpr, accuracy, best_thresholds = evaluate(embeddings, issame,
nrof_folds)
return accuracy.mean(), best_thresholds.mean()
def get_embeds(base64_image):
global model
image = base64.b64decode(base64_image)
numpy_image = np.frombuffer(image, dtype=np.uint8)
face = cv2.imdecode(numpy_image, 1)
face = cv2.resize(face, (112, 112))
face = face.astype(np.float32) / 255.
img = np.expand_dims(face, 0)
embeds = l2_norm(model(img))
return embeds
def main(_argv):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
cfg = load_yaml(FLAGS.cfg_path)
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
training=False)
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
if ckpt_path is not None:
print('[*] load ckpt from {}'.format(ckpt_path))
model.load_weights(ckpt_path)
else:
print('[*] Cannot find ckpt.')
exit()
if FLAGS.img_path:
print("[*] Encode {} to ./output_embeds.npy".format(FLAGS.img_path))
img = cv2.imread(FLAGS.img_path)
img = cv2.resize(img, (cfg['input_size'], cfg['input_size']))
img = img.astype(np.float32) / 255.
if len(img.shape) == 3:
img = np.expand_dims(img, 0)
embeds = l2_norm(model(img))
np.save('./output_embeds.npy', embeds)
else:
print("[*] Loading LFW, AgeDB30 and CFP-FP...")
lfw, agedb_30, cfp_fp, lfw_issame, agedb_30_issame, cfp_fp_issame = \
get_val_data(cfg['test_dataset'])
print("[*] Perform Evaluation on LFW...")
acc_lfw, best_th = perform_val(
cfg['embd_shape'], cfg['batch_size'], model, lfw, lfw_issame,
is_ccrop=cfg['is_ccrop'])
print(' acc {:.4f}, th: {:.2f}'.format(acc_lfw, best_th))
print("[*] Perform Evaluation on AgeDB30...")
acc_agedb30, best_th = perform_val(
cfg['embd_shape'], cfg['batch_size'], model, agedb_30,
agedb_30_issame, is_ccrop=cfg['is_ccrop'])
print(' acc {:.4f}, th: {:.2f}'.format(acc_agedb30, best_th))
print("[*] Perform Evaluation on CFP-FP...")
acc_cfp_fp, best_th = perform_val(
cfg['embd_shape'], cfg['batch_size'], model, cfp_fp, cfp_fp_issame,
is_ccrop=cfg['is_ccrop'])
print(' acc {:.4f}, th: {:.2f}'.format(acc_cfp_fp, best_th))
def prepare_facebank(cfg, model):
names = ['Unknown']
embeddings = []
detector = MTCNN()
for name in os.listdir(cfg['face_bank']):
if os.path.isfile(name):
continue
else:
emb = []
for file in tqdm(os.listdir(os.path.join(cfg['face_bank'], name))):
if not os.path.isfile(
os.path.join(cfg['face_bank'], name, file)):
continue
else:
image = cv2.imread(
os.path.join(cfg['face_bank'], name, file))
image = cv2.resize(image,
(cfg['input_size'], cfg['input_size']))
face = detector.detect_faces(image)
if len(face) > 0:
face = face[0]
refrence = get_reference_facial_points(
default_square=True)
landmark = []
for _, points in face['keypoints'].items():
landmark.append(list(points))
warped_face = warp_and_crop_face(
image,
landmark,
reference_pts=refrence,
crop_size=(cfg['input_size'], cfg['input_size']))
image = np.array(warped_face)
image = image.astype(np.float32) / 255.
if len(image.shape) == 3:
image = np.expand_dims(image, 0)
emb.append(l2_norm(model(image)).numpy())
if len(emb) == 0:
continue
emb = np.array(emb)
mean = np.mean(emb, axis=0)
embeddings.append(mean)
names.append(name)
embeddings = np.array(embeddings)
names = np.array(names)
np.save(os.path.join('data', 'facebank.npy'), embeddings)
np.save(os.path.join('data', 'names.npy'), names)
return embeddings, names
def get_vect_face_img(align_face_img): # 벡터화된 이미지 값을 직접 넣도록 했습니다.
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = '0' # 기본값 0
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
img = align_face_img
# img = cv2.imread(img_path) # opencv 로 이미지 읽어옴
# img = cv2.resize(img, (cfg['input_size'], cfg['input_size'])) # 이미지 크기 조정
img = img.astype(np.float32) / 255. # 0. ~ 255. 사이 값 변환
if len(img.shape) == 3: # 차원 조절
img = np.expand_dims(img, 0)
embeds = l2_norm(model(img)) # l2 normalization?
return embeds
def main(_argv):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
training=False)
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] Cannot find ckpt from {}.".format(ckpt_path))
exit()
if FLAGS.img_path:
print("Check Start!!!")
file_dir = "C:/Users/smgg/Desktop/dataset/superjunior/all3.jpeg"
npy_dir = "/SCLab/newTWICE_id/*,npy"
img_list = glob.glob(file_dir)
npy_list = glob.glob(npy_dir)
for img_name in img_list:
img = cv2.cvtColor(cv2.imread(img_name), cv2.COLOR_BGR2RGB)
detector = MTCNN()
data_list = detector.detect_faces(img)
for data in data_list:
xmin, ymin, width, height = data['box']
xmax = xmin + width
ymax = ymin + height
face_image = img[ymin:ymax, xmin: xmax, :]
face_image = cv2.cvtColor(face_image, cv2.COLOR_RGB2BGR)
# cv2.imshow('', face_image)
# cv2.waitKey(0)
img_resize = cv2.resize(face_image, (cfg['input_size'], cfg['input_size']))
img_resize = img_resize.astype(np.float32) / 255.
if len(img_resize.shape) == 3:
img_resize = np.expand_dims(img_resize, 0)
embeds = l2_norm(model(img_resize))
i = 0
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 1)
for npy_name in npy_list:
name_embeds = np.load(npy_name)
# print(1)
if distance(embeds, name_embeds,1) < 0.37:
i = i + 1
name = npy_name.split('/')[5].split('\\')[1].split('.npy')[0]
cv2.putText(img, name, (xmin, ymin - 15*(i)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1,cv2.LINE_AA)
# else:
# cv2.putText(img, "Unknown", (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA)
# cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cv2.imshow('', img)
cv2.waitKey(0)
def main(_argv):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
training=False)
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] Cannot find ckpt from {}.".format(ckpt_path))
exit()
if FLAGS.img_path:
print("[*] Encode {} to ./output_embeds.npy".format(FLAGS.img_path))
# file_dir = "C:/Users/chaehyun/PycharmProjects/ArcFace37_TF2x/data/AFDB_masked_face_dataset/*"
file_dir = "C:/Users/smgg/Desktop/dataset/superjunior/*.jpg"
# detector = MTCNN()
i = 0
img_list = glob.glob(file_dir)
for i_list in img_list:
img = cv2.cvtColor(cv2.imread(i_list), cv2.COLOR_BGR2RGB)
detector = MTCNN()
data_list = detector.detect_faces(img)
for data in data_list:
xmin, ymin, width, height = data['box']
xmax = xmin + width
ymax = ymin + height
face_image = img[ymin:ymax, xmin:xmax, :]
face_image = cv2.cvtColor(face_image, cv2.COLOR_RGB2BGR)
cv2.imshow('', face_image)
print(i_list)
img_resize = cv2.resize(face_image,
(cfg['input_size'], cfg['input_size']))
cv2.imwrite("./{}.jpg".format(i), img_resize)
cv2.imshow('', img_resize)
cv2.waitKey(0)
i = i + 1
cv2.putText(img, "0", (xmin, ymin - 10),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1,
cv2.LINE_AA)
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 1)
img_resize = img_resize.astype(np.float32) / 255.
if len(img_resize.shape) == 3:
img_resize = np.expand_dims(img_resize, 0)
embeds = l2_norm(model(img_resize))
#print('./newTWICE_id/{}.npy'.format(i_list.split('/')[6].split('\\')[1].split('.jpg')[0]))
np.save(
'./newTWICE_id/{}.npy'.format(
i_list.split('/')[5].split('\\')[1].split('.jpg')[0]),
embeds)
else:
print("[*] Loading LFW, AgeDB30 and CFP-FP...")
lfw, agedb_30, cfp_fp, lfw_issame, agedb_30_issame, cfp_fp_issame = \
get_val_data(cfg['test_dataset'])
print("[*] Perform Evaluation on LFW...")
acc_lfw, best_th = perform_val(cfg['embd_shape'],
cfg['batch_size'],
model,
lfw,
lfw_issame,
is_ccrop=cfg['is_ccrop'])
print(" acc {:.4f}, th: {:.2f}".format(acc_lfw, best_th))
print("[*] Perform Evaluation on AgeDB30...")
acc_agedb30, best_th = perform_val(cfg['embd_shape'],
cfg['batch_size'],
model,
agedb_30,
agedb_30_issame,
is_ccrop=cfg['is_ccrop'])
print(" acc {:.4f}, th: {:.2f}".format(acc_agedb30, best_th))
print("[*] Perform Evaluation on CFP-FP...")
acc_cfp_fp, best_th = perform_val(cfg['embd_shape'],
cfg['batch_size'],
model,
cfp_fp,
cfp_fp_issame,
is_ccrop=cfg['is_ccrop'])
print(" acc {:.4f}, th: {:.2f}".format(acc_cfp_fp, best_th))
def main(_argv):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
training=False)
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] Cannot find ckpt from {}.".format(ckpt_path))
exit()
if FLAGS.update:
print('Face bank updating...')
targets, names = prepare_facebank(cfg, model)
print('Face bank updated')
else:
targets, names = load_facebank(cfg)
print('Face bank loaded')
if FLAGS.video is None:
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture(str(FLAGS.video))
if FLAGS.save:
video_writer = cv2.VideoWriter('./recording.avi',
cv2.VideoWriter_fourcc(*'XVID'), 10,
(640, 480))
# frame rate 6 due to my laptop is quite slow...
while cap.isOpened():
is_success, frame = cap.read()
if is_success:
img = frame
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
bboxes, landmarks, faces = align_multi(
cfg, img, min_confidence=FLAGS.min_confidence, limits=3)
bboxes = bboxes.astype(int)
embs = []
for face in faces:
if len(face.shape) == 3:
face = np.expand_dims(face, 0)
face = face.astype(np.float32) / 255.
embs.append(l2_norm(model(face)).numpy())
list_min_idx = []
list_score = []
for emb in embs:
dist = [euclidean(emb, target) for target in targets]
min_idx = np.argmin(dist)
list_min_idx.append(min_idx)
list_score.append(dist[int(min_idx)])
list_min_idx = np.array(list_min_idx)
list_score = np.array(list_score)
list_min_idx[list_score > FLAGS.threshold] = -1
for idx, box in enumerate(bboxes):
frame = utils.draw_box_name(box, landmarks[idx],
names[list_min_idx[idx] + 1],
frame)
frame = cv2.resize(frame, (640, 480))
cv2.imshow('face Capture', frame)
key = cv2.waitKey(1) & 0xFF
if FLAGS.save:
video_writer.write(frame)
if key == ord('q'):
break
cap.release()
if FLAGS.save:
video_writer.release()
cv2.destroyAllWindows()