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 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()
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.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))
backbone_type=cfg['backbone_type'],
training=False)
if which_datatest == 'lfw':
dataset_test_path = 'lfw_align_112/lfw'
elif which_datatest == 'agedb':
dataset_test_path = 'agedb_align_112/agedb_30'
elif which_datatest == 'cfp':
dataset_test_path = 'cfp_align_112/cfp_fp'
else:
dataset_test_path = which_datatest
print("[*] Loading dataset test {}...".format(which_datatest))
dataset_test, dataset_test_issame = \
get_val_data(cfg['test_dataset'],dataset_test_path)
test_summary_writer = tf.summary.create_file_writer(cfg['logs_dir'] +
cfg['sub_name'] +
'/test_' +
which_datatest)
checkpoint_dir = os.path.join(cfg['checkpoints_dir'], cfg['sub_name'])
ckpt_path = None
steps_per_epoch = cfg['num_samples'] // cfg['batch_size']
while True:
new_ckpt_path = wait_for_new_checkpoint(checkpoint_dir, ckpt_path)
ckpt_path = new_ckpt_path
