def main(_argv):
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
# define network
model = RetinaFaceModel(cfg, training=False, iou_th=FLAGS.iou_th,
score_th=FLAGS.score_th)
# load checkpoint
checkpoint_dir = FLAGS.weights
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
model.summary()
for i in model.layers:
print(i.output)
model.save(FLAGS.output)
def initialize():
# init
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
# Setup logger
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
# Define network
model = RetinaFaceModel(cfg, training=False,
iou_th=FLAGS.iou_th, score_th=FLAGS.score_th)
# Load checkpoints
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print(
"[*] load ckpt from {}.".format(tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
return model, cfg
def main(args):
ijbc_meta = np.load(args.meta_path)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
set_memory_growth()
#cfg = load_yaml('configs/arc_res50.yaml')
cfg = load_yaml(args.config_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()
img_names = [
os.path.join(args.input_path,
img_name.split('/')[-1])
for img_name in ijbc_meta['img_names']
]
embedding_size = cfg['embd_shape']
batch_size = cfg['batch_size']
img_size = cfg['input_size']
def read_img(filename):
raw = tf.io.read_file(filename)
img = tf.image.decode_jpeg(raw, channels=3)
img = tf.cast(img, tf.float32)
img = img / 255
return img
dataset = tf.data.Dataset.from_tensor_slices(img_names)
dataset = dataset.map(read_img,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
embeddings = model.predict(dataset, batch_size=batch_size, verbose=1)
print('embeddings', embeddings.shape)
np.save(args.output_path, embeddings)
def main(_argv):
# init
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)
# load dataset
test_dataset = load_cifar10_dataset(
cfg['val_batch_size'], split='test', shuffle=False,
drop_remainder=False, using_crop=False, using_flip=False,
using_cutout=False)
# define network
# TODO : change cfg
for num_arch in range(50):
model = CifarModel(cfg, training=False)
model.summary(line_length=80)
print("param size = {:f}MB".format(count_parameters_in_MB(model)))
# load checkpoint
checkpoint_path = './checkpoints/' + cfg['sub_name'] + '/best.ckpt'
try:
model.load_weights('./checkpoints/' + cfg['sub_name'] + '/best.ckpt')
print("[*] load ckpt from {}.".format(checkpoint_path))
except:
print("[*] Cannot find ckpt from {}.".format(checkpoint_path))
exit()
# inference
top1 = AvgrageMeter()
top5 = AvgrageMeter()
for step, (inputs, labels) in enumerate(test_dataset):
# run model
logits = model(inputs)
# cacludate top1, top5 acc
prec1, prec5 = accuracy(logits.numpy(), labels.numpy(), topk=(1, 5))
n = inputs.shape[0]
top1.update(prec1, n)
top5.update(prec5, n)
print(" {:03d}: top1 {:f}, top5 {:f}".format(step, top1.avg, top5.avg))
print("Test Acc: top1 {:.2f}%, top5 {:.2f}%".format(top1.avg, top5.avg))
def main(_):
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)
cfg['num_classes'] = 179721
cfg['num_samples'] = 8621403
cfg['train_dataset'] = 'data/popet/dtat/ms1m_asian.tfrecord'
cfg['sub_name'] = '200324_model'
print(cfg)
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:
str1 = 'this'
str2 = 'this1'
#print(str1 in str2)
file_dir = "newTWICE_id"
output = glob.glob(file_dir + "/*.npy")
for i in range(100):
sampleList = random.sample(output, 2)
embedding1 = np.load(sampleList[0])
embedding2 = np.load(sampleList[1])
dist = distance(embedding1, embedding2, 1)
str1 = sampleList[0].split("\\")[1].split(".")[0]
str2 = sampleList[1].split("\\")[1].split(".")[0]
print(str1)
if dist < 0.4:
print(str1 + " and " + str2 + "is same")
print(dist)
# tp = tp + 1
else:
print(str1 + " and " + str2 + "is diff")
print(dist)
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()
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 = tf.keras.models.load_model(cfg['model_path'])
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 __create_model(self, cfg_path):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
logger = tf.get_logger()
logger.disabled = True
# logger.setLevel(logging.FATAL)
set_memory_growth()
cfg = load_yaml(cfg_path)
model = RetinaFaceModel(cfg, training=False, iou_th=0.4, score_th=0.5)
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
return model
def main(_argv):
# init
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()
# define network
print("[*] Loading weights from {FLAGS.model}")
model = tf.keras.models.load_model(FLAGS.model)
# evaluation
if FLAGS.down_up:
print("[*] Processing on single image {}".format(FLAGS.img_path))
raw_img = cv2.imread(FLAGS.img_path)
lr_img, hr_img = create_lr_hr_pair(raw_img, FLAGS.scale)
sr_img = tensor2img(model(lr_img[np.newaxis, :] / 255))
bic_img = imresize_np(lr_img, FLAGS.scale).astype(np.uint8)
str_format = "[{}] PSNR/SSIM: SR={:.2f}db/{:.2f}"
print(
str_format.format(
os.path.basename(FLAGS.img_path),
calculate_psnr(rgb2ycbcr(sr_img), rgb2ycbcr(hr_img)),
calculate_ssim(rgb2ycbcr(sr_img), rgb2ycbcr(hr_img))))
result_img_path = './Bic_SR_HR_' + os.path.basename(FLAGS.img_path)
print("[*] write the result image {}".format(result_img_path))
results_img = np.concatenate((bic_img, sr_img, hr_img), 1)
cv2.imwrite(result_img_path, results_img)
else:
print("[*] Processing on single image {}".format(FLAGS.img_path))
lr_img = cv2.imread(FLAGS.img_path)
sr_img = tensor2img(model(lr_img[np.newaxis, :] / 255))
result_img_path = './SR_' + os.path.basename(FLAGS.img_path)
print("[*] write the result image {}".format(result_img_path))
cv2.imwrite(result_img_path, sr_img)
import cv2
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from modules.utils import set_memory_growth
from modules.dataset import load_cifar10_dataset
set_memory_growth()
dataset = load_cifar10_dataset(batch_size=1,
split='train',
shuffle=False,
using_normalize=False)
for (img, labels) in dataset:
img = img.numpy()[0]
print(img.shape, labels.shape, labels.numpy())
cv2.imshow('img', cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
if cv2.waitKey(0) == ord('q'):
exit()
def main(_argv):
# init
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)
# define network
model = RetinaFaceModel(cfg,
training=False,
iou_th=FLAGS.iou_th,
score_th=FLAGS.score_th)
# load model from weights.h5
# model.load_weights('./model/mbv2_weights.h5', by_name=True, skip_mismatch=True)
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
if not FLAGS.webcam:
file_path = '/Users/lichaochao/Downloads/images_UMU/'
for file_name in os.listdir(file_path + 'source_images/'):
image_path = file_path + 'source_images/' + file_name
if not os.path.exists(image_path):
print(f"cannot find image path from {image_path}")
continue
img_raw = cv2.imread(image_path)
img = np.float32(img_raw.copy())
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max(cfg['steps']))
img_height, img_width, _ch = img.shape
# run model
outputs = model(img[np.newaxis, ...])
preds = tf.concat([
outputs[0][0], outputs[1][0, :, 1][..., tf.newaxis],
outputs[2][0, :, 1][..., tf.newaxis]
], -1)
priors = prior_box_tf((img_height, img_width), cfg['min_sizes'],
cfg['steps'], cfg['clip'])
decode_preds = decode_tf(preds, priors, cfg['variances'])
selected_indices = tf.image.non_max_suppression(
boxes=decode_preds[:, :4],
scores=decode_preds[:, -1],
max_output_size=tf.shape(decode_preds)[0],
iou_threshold=FLAGS.iou_th,
score_threshold=FLAGS.score_th)
outputs = tf.gather(decode_preds, selected_indices).numpy()
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
has_face = False
is_smile = False
for prior_index in range(len(outputs)):
ann = outputs[prior_index]
if ann[-1] >= 0.5:
has_face = True
x1, y1 = int(ann[0] * img_width), int(ann[1] * img_height)
x2, y2 = int(ann[2] * img_width), int(ann[3] * img_height)
text = "face: {:.2f}".format(ann[-1] * 100)
cv2.putText(img, text, (x1 + 5, y1 - 10),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
if ann[-2] >= 0.5:
is_smile = True
smile_text = "smile: {:.2f}".format(ann[-2] * 100)
cv2.putText(img, smile_text, (x1 + 5, y1 + 30),
cv2.FONT_HERSHEY_DUPLEX, 0.5,
(255, 255, 255))
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
else:
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
if is_smile:
dst_file_path = file_path + '/smile_face/' + file_name
elif has_face:
dst_file_path = file_path + '/face/' + file_name
else:
dst_file_path = file_path + '/no_face/' + file_name
cv2.imwrite(dst_file_path, img)
print(dst_file_path)
else:
cam = cv2.VideoCapture('./data/linda_umu.mp4')
# cam.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
# cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
resize = FLAGS.down_scale_factor
frame_height = cam.get(cv2.CAP_PROP_FRAME_HEIGHT) * resize
frame_width = cam.get(cv2.CAP_PROP_FRAME_WIDTH) * resize
max_steps = max(cfg['steps'])
img_pad_h = max_steps - frame_height % max_steps if frame_height % max_steps > 0 else 0
img_pad_w = max_steps - frame_width % max_steps if frame_width % max_steps > 0 else 0
priors = prior_box_tf(
(frame_height + img_pad_h, frame_width + img_pad_w),
cfg['min_sizes'], cfg['steps'], cfg['clip'])
frame_index = 0
outputs = []
start_time = time.time()
while cam.isOpened():
_, frame = cam.read()
if frame is None:
print('no cam')
break
if frame_index < 5:
frame_index += 1
# continue
else:
frame_index = 0
img = np.float32(frame.copy())
if resize < 1:
img = cv2.resize(img, (0, 0),
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max_steps)
# run model
outputs = model(img[np.newaxis, ...])
preds = tf.concat([
outputs[0][0], outputs[1][0, :, 1][..., tf.newaxis],
outputs[2][0, :, 1][..., tf.newaxis]
], -1)
decode_preds = decode_tf(preds, priors, cfg['variances'])
selected_indices = tf.image.non_max_suppression(
boxes=decode_preds[:, :4],
scores=decode_preds[:, -1],
max_output_size=tf.shape(decode_preds)[0],
iou_threshold=FLAGS.iou_th,
score_threshold=FLAGS.score_th)
outputs = tf.gather(decode_preds, selected_indices).numpy()
# recover padding effect
outputs = recover_pad_output(outputs,
pad_params,
resize=resize)
# calculate fps
fps_str = "FPS: %.2f" % (1 / (time.time() - start_time))
start_time = time.time()
cv2.putText(frame, fps_str, (25, 50), cv2.FONT_HERSHEY_DUPLEX,
0.75, (0, 0, 255), 2)
# draw results
for prior_index in range(len(outputs)):
draw_bbox_landm(frame, outputs[prior_index], frame_height,
frame_width)
# calculate fps
# fps_str = "FPS: %.2f" % (1 / (time.time() - start_time))
# start_time = time.time()
# cv2.putText(frame, fps_str, (25, 25),
# cv2.FONT_HERSHEY_DUPLEX, 0.75, (0, 255, 0), 2)
# show frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) == ord('q'):
exit()
def main(_argv):
# init
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)
# define network
model = RetinaFaceModel(cfg,
training=False,
iou_th=FLAGS.iou_th,
score_th=FLAGS.score_th)
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
if not FLAGS.webcam:
if not os.path.exists(FLAGS.img_path):
print(f"cannot find image path from {FLAGS.img_path}")
exit()
print("[*] Processing on single image {}".format(FLAGS.img_path))
img_raw = cv2.imread(FLAGS.img_path)
img_height_raw, img_width_raw, _ = img_raw.shape
img = np.float32(img_raw.copy())
if FLAGS.down_scale_factor < 1.0:
img = cv2.resize(img, (0, 0),
fx=FLAGS.down_scale_factor,
fy=FLAGS.down_scale_factor,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max(cfg['steps']))
# run model
outputs = model(img[np.newaxis, ...]).numpy()
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
# draw and save results
save_img_path = os.path.join('out_' + os.path.basename(FLAGS.img_path))
for prior_index in range(len(outputs)):
draw_bbox_landm(img_raw, outputs[prior_index], img_height_raw,
img_width_raw)
cv2.imwrite(save_img_path, img_raw)
print(f"[*] save result at {save_img_path}")
else:
cam = cv2.VideoCapture("./videos/Bentall_Centra.MP4")
start_time = time.time()
while True:
_, frame = cam.read()
if frame is None:
print("no cam input")
frame_height, frame_width, _ = frame.shape
img = np.float32(frame.copy())
if FLAGS.down_scale_factor < 1.0:
img = cv2.resize(img, (0, 0),
fx=FLAGS.down_scale_factor,
fy=FLAGS.down_scale_factor,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max(cfg['steps']))
# run model
outputs = model(img[np.newaxis, ...]).numpy()
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
# draw results
for prior_index in range(len(outputs)):
draw_bbox_landm(frame, outputs[prior_index], frame_height,
frame_width)
# calculate fps
fps_str = "FPS: %.2f" % (1 / (time.time() - start_time))
start_time = time.time()
cv2.putText(frame, fps_str, (25, 25), cv2.FONT_HERSHEY_DUPLEX,
0.75, (0, 255, 0), 2)
# show frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) == ord('q'):
exit()
def main(_):
# init
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)
# define network
generator = RRDB_Model(cfg['input_size'], cfg['ch_size'], cfg['network_G'])
generator.summary(line_length=80)
discriminator = DiscriminatorVGG128(cfg['gt_size'], cfg['ch_size'])
discriminator.summary(line_length=80)
# load dataset
train_dataset = load_dataset(cfg, 'train_dataset', shuffle=False)
# define optimizer
learning_rate_G = MultiStepLR(cfg['lr_G'], cfg['lr_steps'], cfg['lr_rate'])
learning_rate_D = MultiStepLR(cfg['lr_D'], cfg['lr_steps'], cfg['lr_rate'])
optimizer_G = tf.keras.optimizers.Adam(learning_rate=learning_rate_G,
beta_1=cfg['adam_beta1_G'],
beta_2=cfg['adam_beta2_G'])
optimizer_D = tf.keras.optimizers.Adam(learning_rate=learning_rate_D,
beta_1=cfg['adam_beta1_D'],
beta_2=cfg['adam_beta2_D'])
# define losses function
pixel_loss_fn = PixelLoss(criterion=cfg['pixel_criterion'])
fea_loss_fn = ContentLoss(criterion=cfg['feature_criterion'])
gen_loss_fn = GeneratorLoss(gan_type=cfg['gan_type'])
dis_loss_fn = DiscriminatorLoss(gan_type=cfg['gan_type'])
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(step=tf.Variable(0, name='step'),
optimizer_G=optimizer_G,
optimizer_D=optimizer_D,
model=generator,
discriminator=discriminator)
manager = tf.train.CheckpointManager(checkpoint=checkpoint,
directory=checkpoint_dir,
max_to_keep=3)
if manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
print('[*] load ckpt from {} at step {}.'.format(
manager.latest_checkpoint, checkpoint.step.numpy()))
else:
if cfg['pretrain_name'] is not None:
pretrain_dir = './checkpoints/' + cfg['pretrain_name']
if tf.train.latest_checkpoint(pretrain_dir):
checkpoint.restore(tf.train.latest_checkpoint(pretrain_dir))
checkpoint.step.assign(0)
print("[*] training from pretrain model {}.".format(
pretrain_dir))
else:
print(
"[*] cannot find pretrain model {}.".format(pretrain_dir))
else:
print("[*] training from scratch.")
# define training step function
@tf.function
def train_step(lr, hr):
with tf.GradientTape(persistent=True) as tape:
sr = generator(lr, training=True)
hr_output = discriminator(hr, training=True)
sr_output = discriminator(sr, training=True)
losses_G = {}
losses_D = {}
losses_G['reg'] = tf.reduce_sum(generator.losses)
losses_D['reg'] = tf.reduce_sum(discriminator.losses)
losses_G['pixel'] = cfg['w_pixel'] * pixel_loss_fn(hr, sr)
losses_G['feature'] = cfg['w_feature'] * fea_loss_fn(hr, sr)
losses_G['gan'] = cfg['w_gan'] * gen_loss_fn(hr_output, sr_output)
losses_D['gan'] = dis_loss_fn(hr_output, sr_output)
total_loss_G = tf.add_n([l for l in losses_G.values()])
total_loss_D = tf.add_n([l for l in losses_D.values()])
grads_G = tape.gradient(total_loss_G, generator.trainable_variables)
grads_D = tape.gradient(total_loss_D,
discriminator.trainable_variables)
optimizer_G.apply_gradients(zip(grads_G,
generator.trainable_variables))
optimizer_D.apply_gradients(
zip(grads_D, discriminator.trainable_variables))
return total_loss_G, total_loss_D, losses_G, losses_D
# training loop
summary_writer = tf.summary.create_file_writer('./logs/' + cfg['sub_name'])
prog_bar = ProgressBar(cfg['niter'], checkpoint.step.numpy())
remain_steps = max(cfg['niter'] - checkpoint.step.numpy(), 0)
for lr, hr in train_dataset.take(remain_steps):
checkpoint.step.assign_add(1)
steps = checkpoint.step.numpy()
total_loss_G, total_loss_D, losses_G, losses_D = train_step(lr, hr)
prog_bar.update(
"loss_G={:.4f}, loss_D={:.4f}, lr_G={:.1e}, lr_D={:.1e}".format(
total_loss_G.numpy(), total_loss_D.numpy(),
optimizer_G.lr(steps).numpy(),
optimizer_D.lr(steps).numpy()))
if steps % 10 == 0:
with summary_writer.as_default():
tf.summary.scalar('loss_G/total_loss',
total_loss_G,
step=steps)
tf.summary.scalar('loss_D/total_loss',
total_loss_D,
step=steps)
for k, l in losses_G.items():
tf.summary.scalar('loss_G/{}'.format(k), l, step=steps)
for k, l in losses_D.items():
tf.summary.scalar('loss_D/{}'.format(k), l, step=steps)
tf.summary.scalar('learning_rate_G',
optimizer_G.lr(steps),
step=steps)
tf.summary.scalar('learning_rate_D',
optimizer_D.lr(steps),
step=steps)
if steps % cfg['save_steps'] == 0:
manager.save()
print("\n[*] save ckpt file at {}".format(
manager.latest_checkpoint))
print("\n [*] training done!")
def main(_argv):
# init
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)
aligner = FaceAligner()
# define network
model = RetinaFaceModel(cfg,
training=False,
iou_th=FLAGS.iou_th,
score_th=FLAGS.score_th)
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
if FLAGS.input_stream == '0':
input_stream = 0
elif FLAGS.input_stream == 'rtsp':
input_stream = settings.RTSP_ADDR
else:
input_stream = FLAGS.input_stream
cam = cv2.VideoCapture(input_stream) #("/home/hao/Videos/Webcam/3.webm")
mbv2 = tf.keras.models.load_model(settings.CHECKPOINT_PATH)
anchor_dataset = np.load(settings.ANCHOR_PATH)['arr_0']
label_dataset = np.load(settings.LABEL_PATH)['arr_0']
start_time = time.time()
i = 0
while cam.isOpened():
_, frame = cam.read()
if frame is None:
print("no cam input")
frame_height, frame_width, _ = frame.shape
img = np.float32(frame.copy())
if FLAGS.down_scale_factor < 1.0:
img = cv2.resize(img, (0, 0),
fx=FLAGS.down_scale_factor,
fy=FLAGS.down_scale_factor,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max(cfg['steps']))
# run model
outputs = model(img[np.newaxis, ...]).numpy()
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
# draw results
for prior_index in range(len(outputs)):
ann = outputs[prior_index]
b_box = int(ann[0] * frame_width), int(ann[1] * frame_height), \
int(ann[2] * frame_width), int(ann[3] * frame_height)
if (b_box[0] < 0) or (b_box[1] < 0) or (
b_box[2] >= frame_width) or (b_box[3] >= frame_height):
continue
keypoints = {
'left_eye': (ann[4] * frame_width, ann[5] * frame_height),
'right_eye': (ann[6] * frame_width, ann[7] * frame_height),
'nose': (ann[8], ann[9]),
'left_mouth': (ann[10] * frame_width, ann[11] * frame_height),
'right_mouth': (ann[12] * frame_width, ann[13] * frame_height),
}
out_frame = aligner.align(frame, keypoints, b_box)
scaled = out_frame #cv2.resize(out_frame, (settings.IMAGE_SIZE, settings.IMAGE_SIZE), interpolation=cv2.INTER_CUBIC)
scaled_reshape = scaled.reshape(-1, settings.IMAGE_SIZE,
settings.IMAGE_SIZE, 3)
embed_vector = mbv2(scaled_reshape / 255.0)
label, prob = classify(embed_vector, anchor_dataset, label_dataset)
if prob < 0.5:
label = "Unknown"
cv2.rectangle(frame, (b_box[0], b_box[1]), (b_box[2], b_box[3]),
(0, 255, 0), 2)
cv2.putText(frame, label, (b_box[0], b_box[1]),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
text = "{:.4f}".format(prob)
cv2.putText(frame, text, (b_box[0], b_box[1] + 15),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
# calculate fps
fps_str = "FPS: %.2f" % (1 / (time.time() - start_time))
start_time = time.time()
cv2.putText(frame, fps_str, (25, 25), cv2.FONT_HERSHEY_DUPLEX, 0.75,
(0, 255, 0), 2)
i += 1
# show frame
# cv2.imwrite('UNKNOWN/4/'+str(i)+'.jpeg', frame)
cv2.imshow("frame", frame)
if cv2.waitKey(1) == ord('q'):
exit()
def main(_):
# init
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
set_memory_growth()
logger = tf.get_logger()
logger.disabled = True
logger.setLevel(logging.FATAL)
cfg = load_yaml(FLAGS.cfg_path)
uv_weight_mask = cv2.imread(cfg['uv_weight_mask']) / 255.
# define network
generator = PRNet_Model(cfg['input_size'], cfg['ch_size'])
generator.summary(line_length=80)
# load dataset
train_dataset = load_dataset(cfg,
shuffle=True,
num_workers=cfg['num_workers'])
# define optimizer
learning_rate_G = MultiStepLR(cfg['lr_G'], cfg['lr_steps'], cfg['lr_rate'])
optimizer_G = tf.keras.optimizers.Adam(learning_rate=learning_rate_G,
beta_1=cfg['adam_beta1_G'],
beta_2=cfg['adam_beta2_G'])
# define losses function
loss_fn = WeightedMSE(uv_weight_mask)
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(step=tf.Variable(0, name='step'),
optimizer_G=optimizer_G,
model=generator)
manager = tf.train.CheckpointManager(checkpoint=checkpoint,
directory=checkpoint_dir,
max_to_keep=3)
if manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
print('[*] load ckpt from {} at step {}.'.format(
manager.latest_checkpoint, checkpoint.step.numpy()))
else:
if cfg['pretrain_name'] is not None:
pretrain_dir = './checkpoints/' + cfg['pretrain_name']
if tf.train.latest_checkpoint(pretrain_dir):
checkpoint.restore(tf.train.latest_checkpoint(pretrain_dir))
checkpoint.step.assign(0)
print("[*] training from pretrain model {}.".format(
pretrain_dir))
else:
print(
"[*] cannot find pretrain model {}.".format(pretrain_dir))
else:
print("[*] training from scratch.")
# define training step function
@tf.function
def train_step(img, pos):
with tf.GradientTape() as tape_G:
pre = generator(img, training=True)
losses_G = {}
losses_G['pixel'] = loss_fn(pos, pre)
total_loss_G = tf.add_n([l for l in losses_G.values()])
grads_G = tape_G.gradient(total_loss_G, generator.trainable_variables)
optimizer_G.apply_gradients(zip(grads_G,
generator.trainable_variables))
return total_loss_G, losses_G
# training loop
summary_writer = tf.summary.create_file_writer('./logs/' + cfg['sub_name'])
niter = int(cfg['train_dataset']['num_samples'] * cfg['epoch'] /
cfg['batch_size'])
prog_bar = ProgressBar(niter, checkpoint.step.numpy())
remain_steps = max(niter - checkpoint.step.numpy(), 0)
for sample in take(remain_steps, train_dataset):
checkpoint.step.assign_add(1)
steps = checkpoint.step.numpy()
img, pos = sample['Image'], sample['Posmap']
total_loss_G, losses_G = train_step(img, pos)
prog_bar.update_gan(total_loss_G.numpy(),
optimizer_G.lr(steps).numpy())
if steps % cfg['log_steps'] == 0:
with summary_writer.as_default():
tf.summary.scalar('loss_G/total_loss',
total_loss_G,
step=steps)
for k, l in losses_G.items():
tf.summary.scalar('loss_G/{}'.format(k), l, step=steps)
tf.summary.scalar('learning_rate_G',
optimizer_G.lr(steps),
step=steps)
if steps % cfg['save_steps'] == 0:
manager.save()
print('\n[*] save ckpt file at {}'.format(
manager.latest_checkpoint))
print("\n[*] training done!")
def main(_):
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
num_classes=cfg['num_classes'],
head_type=cfg['head_type'],
embd_shape=cfg['embd_shape'],
w_decay=cfg['w_decay'],
training=True)
learning_rate = tf.constant(cfg['base_lr'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
momentum=0.9,
nesterov=True)
loss_fn = SoftmaxLoss()
ckpt_path = tf.train.latest_checkpoint('./checkpoints/train_' +
cfg['sub_name'])
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] training from scratch.")
model.compile(optimizer=optimizer, loss=loss_fn)
# resnet_model = tf.keras.Model(inputs=model.get_layer('resnet50').input,
# outputs=model.get_layer('resnet50').output)
# resnet_head = tf.keras.Model(inputs=resnet_model.input,
# outputs=resnet_model.get_layer('conv2_block1_add').output)
# resnet_tail = split(resnet_model, 18, 1000) # conv2_block1_out
# output_model = tf.keras.Model(inputs=model.get_layer('OutputLayer').input,
# outputs=model.get_layer('OutputLayer').output)
# archead = tf.keras.Model(inputs=model.get_layer('ArcHead').input,
# outputs=model.get_layer('ArcHead').output)
temp1 = np.ones((62, 112, 3))
temp2 = np.zeros((50, 112, 3))
masked_img = np.concatenate([temp1, temp2], axis=0).reshape(1, 112, 112, 3)
temp1 = np.ones((1, 18, 28, 256))
temp2 = np.zeros((1, 10, 28, 256))
masked = np.concatenate([temp1, temp2], axis=1)
# inputs = Input((112, 112, 3))
# labels = Input([])
# s = resnet_head(inputs)
# s = tf.keras.layers.Multiply()([s, tf.constant(masked, dtype=tf.float32)])
# s = resnet_tail(s)
# s = output_model(s)
# s = archead([s, labels])
# new_model = Model((inputs, labels), s)
# new_model.summary()
# new_model.compile(optimizer=optimizer, loss=loss_fn)
path_to_data = '/home/anhdq23/Desktop/nguyen/data/AR/test2'
anchor_names = get_gallery_pr2(path_to_data) # From 1 to 100
name_dicts = get_probe_pr2(
path_to_data) # Dictionary: {anchor_name:[name_image, ...]}
augment = ImgAugTransform()
import faiss
if FLAGS.mode == 'eager_tf':
top_left_all = [0.012]
best_acc = 0.8
for epochs in range(cfg['epochs']):
logging.info("Shuffle ms1m dataset.")
dataset_len = cfg['num_samples']
steps_per_epoch = dataset_len // cfg['batch_size']
train_dataset = dataset.load_tfrecord_dataset(
cfg['train_dataset'],
cfg['batch_size'],
cfg['binary_img'],
is_ccrop=cfg['is_ccrop'])
sub_train_dataset = dataset.load_tfrecord_dataset(
cfg['train_dataset'],
cfg['batch_size'],
cfg['binary_img'],
is_ccrop=cfg['is_ccrop'])
for batch, ((x, y), (sub_x, sub_y)) in enumerate(
zip(train_dataset, sub_train_dataset)):
x0_new = np.array(x[0], dtype=np.uint8)
x1_new = np.array(x[1], dtype=np.float32)
for i in np.arange(len(x0_new)):
x0_new[i] = augment(x0_new[i])
temp = np.array(x0_new, dtype=np.float32) / 255.0
temp = np.multiply(temp, masked_img)
sub_x0_new = np.array(sub_x[0], dtype=np.uint8)
sub_x1_new = np.array(sub_x[1], dtype=np.float32)
for i in np.arange(len(sub_x0_new)):
sub_x0_new[i] = augment(sub_x0_new[i])
sub_temp = np.array(sub_x0_new, dtype=np.float32) / 255.0
model.train_on_batch(*((sub_temp, sub_x1_new), sub_x1_new))
loss = model.train_on_batch(*((temp, x1_new), x1_new))
if batch % 50 == 0:
verb_str = "Epoch {}/{}: {}/{}, loss={:.6f}, lr={:.6f}"
print(
verb_str.format(
epochs, cfg['epochs'], batch, steps_per_epoch,
loss, cfg['base_lr'] / (1.0 + cfg['w_decay'] *
(epochs * 45489 + batch))))
if batch % cfg['save_steps'] == 0:
resnet_model = tf.keras.Model(
inputs=model.get_layer('resnet50').input,
outputs=model.get_layer('resnet50').output)
output_model = tf.keras.Model(
inputs=model.get_layer('OutputLayer').input,
outputs=model.get_layer('OutputLayer').output)
database_image_names = []
database_feature_list = []
for anchor_name in anchor_names:
img1 = Image.open(
os.path.join(path_to_data, anchor_name))
img1 = img1.resize((112, 112))
img1 = np.array(img1) / 255.0
img1 = np.multiply(img1, masked_img)
fc1 = resnet_model.predict(
img1.reshape((1, 112, 112, 3)))
fc1 = output_model.predict(fc1)
norm_fc1 = preprocessing.normalize(fc1.reshape(
(1, 512)),
norm='l2',
axis=1)
database_image_names.append(anchor_name)
database_feature_list.append(norm_fc1)
index_flat = faiss.IndexFlatL2(512)
gpu_index_flat = index_flat
gpu_index_flat.add(
np.array(database_feature_list).reshape(
(-1, 512))) # add vectors to the index
count = 0
for key in list(name_dicts.keys()):
for name in name_dicts[key]:
img2 = Image.open(
os.path.join(path_to_data, name)).resize(
(112, 112))
img2 = img2.resize((112, 112))
img2 = np.array(img2) / 255.0
img2 = np.multiply(img2, masked_img)
fc2 = resnet_model.predict(
img2.reshape((1, 112, 112, 3)))
fc2 = output_model.predict(fc2)
norm_fc2 = preprocessing.normalize(fc2.reshape(
(1, 512)),
norm='l2',
axis=1)
D, I = gpu_index_flat.search(
norm_fc2, k=1) # actual search
if name[0:5] == database_image_names[I[0]
[0]][0:5]:
count += 1
acc = count / 600.0
if acc > best_acc:
best_acc = acc
print('[*] save ckpt file!')
model.save_weights(
'checkpoints/{}/e_{}_b_{}.ckpt'.format(
cfg['sub_name'], epochs,
batch % steps_per_epoch))
print("The current acc: {:.6f} ".format(acc))
print("The best acc: {:.6f} ".format(best_acc))
model.save_weights('checkpoints/train_{}/{}.ckpt'.format(
cfg['sub_name'], cfg['sub_name']))
def main(_argv):
# init
face_aligner = FaceAligner()
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)
# define network
model = RetinaFaceModel(cfg,
training=False,
iou_th=FLAGS.iou_th,
score_th=FLAGS.score_th)
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
if not FLAGS.webcam:
save_count = 0
for path, subdirs, files in os.walk(FLAGS.img_path):
for name in files:
if name.endswith('.jpg'):
img_path = os.path.join(path, name)
if not os.path.exists(img_path):
print(f"cannot find image path from {img_path}")
exit()
if save_count < FLAGS.img_num:
print("[*] Processing on single image {}".format(
img_path))
img_raw = cv2.imread(img_path)
img_height_raw, img_width_raw, _ = img_raw.shape
img = np.float32(img_raw.copy())
if FLAGS.down_scale_factor < 1.0:
img = cv2.resize(img, (0, 0),
fx=FLAGS.down_scale_factor,
fy=FLAGS.down_scale_factor,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img,
max_steps=max(
cfg['steps']))
# run model
outputs = model(img[np.newaxis, ...]).numpy()
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
# draw and save results
save_img_path = os.path.join(
'out_' + os.path.basename(img_path))
for prior_index in range(len(outputs)):
draw_bbox_landm(img_raw, outputs[prior_index],
img_height_raw, img_width_raw)
cv2.imwrite(save_img_path, img_raw)
print(f"[*] save result at {save_img_path}")
save_count += 1
else:
cam = cv2.VideoCapture(0)
start_time = time.time()
while True:
_, frame = cam.read()
if frame is None:
print("no cam input")
frame_height, frame_width, _ = frame.shape
orig_frame = frame.copy()
face = None
img = cv2.resize(frame, (512, 512))
img = np.float32(frame.copy())
if FLAGS.down_scale_factor < 1.0:
img = cv2.resize(img, (0, 0),
fx=FLAGS.down_scale_factor,
fy=FLAGS.down_scale_factor,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max(cfg['steps']))
# run model
start_time = time.time()
outputs = model(img[np.newaxis, ...]).numpy()
inference_time = f"Inf: {time.time() - start_time}"
cv2.putText(frame, inference_time, (25, 50),
cv2.FONT_HERSHEY_DUPLEX, 0.75, (0, 255, 0), 2)
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
# draw results
for prior_index in range(len(outputs)):
preds = decode_predictions((frame_width, frame_height),
outputs)
for key, value in preds.items():
bbox = value[0]['bbox']
left_eye = value[0]['left_eye']
right_eye = value[0]['right_eye']
# Our face ROI
face = orig_frame[bbox[1]:bbox[3], bbox[0]:bbox[2]]
# Eyes
x1_le = left_eye[0] - 25
y1_le = left_eye[1] - 25
x2_le = left_eye[0] + 25
y2_le = left_eye[1] + 25
x1_re = right_eye[0] - 25
y1_re = right_eye[1] - 25
x2_re = right_eye[0] + 25
y2_re = right_eye[1] + 25
if left_eye[1] > right_eye[1]:
A = (right_eye[0], left_eye[1])
else:
A = (left_eye[0], right_eye[1])
# Calc our rotating degree
delta_x = right_eye[0] - left_eye[0]
delta_y = right_eye[1] - left_eye[1]
angle = np.arctan(
delta_y / (delta_x + 1e-17)) # avoid devision by zero
angle = (angle * 180) / np.pi
# compute the desired right eye x-coordinate based on the
# desired x-coordinate of the left eye
desiredRightEyeX = 1.0 - 0.35
# determine the scale of the new resulting image by taking
# the ratio of the distance between eyes in the *current*
# image to the ratio of distance between eyes in the
# *desired* image
dist = np.sqrt((delta_x**2) + (delta_y**2))
desiredDist = (desiredRightEyeX - 0.35)
desiredDist *= 256
scale = desiredDist / dist
eyesCenter = ((left_eye[0] + right_eye[0]) // 2,
(left_eye[1] + right_eye[1]) // 2)
cv2.circle(frame, A, 5, (255, 0, 0), -1)
cv2.putText(frame, str(int(angle)), (x1_le - 15, y1_le),
cv2.FONT_HERSHEY_DUPLEX, 0.75, (0, 255, 0), 2)
cv2.line(frame, right_eye, left_eye, (0, 200, 200), 3)
cv2.line(frame, left_eye, A, (0, 200, 200), 3)
cv2.line(frame, right_eye, A, (0, 200, 200), 3)
cv2.line(frame, (left_eye[0], left_eye[1]),
(right_eye[0], right_eye[1]), (0, 200, 200), 3)
rotated = face_aligner.align(orig_frame, left_eye,
right_eye)
draw_bbox_landm(frame, outputs[prior_index], frame_height,
frame_width)
# calculate fps
fps_str = "FPS: %.2f" % (1 / (time.time() - start_time))
start_time = time.time()
cv2.putText(frame, fps_str, (25, 25), cv2.FONT_HERSHEY_DUPLEX,
0.75, (0, 255, 0), 2)
# show frame
cv2.imshow('frame', frame)
if face is not None:
cv2.imshow('face aligned', rotated)
if cv2.waitKey(1) == ord('q'):
exit()
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'])
print(ckpt_path)
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))
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))
print(embeds.shape)
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()
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(_):
'''
Train for one epoch to get supernet , then random sample 50 architectures for finetuning.
This structure is basically the same as train_search.py
TODO: Add PGD here and calculate FSP
'''
# init
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)
# define network
sna = SearchNetArch(cfg)
sna.model.summary(line_length=80)
print("param size = {:f}MB".format(count_parameters_in_MB(sna.model)))
# load dataset
t_split = f"train[0%:{int(cfg['train_portion'] * 100)}%]"
v_split = f"train[{int(cfg['train_portion'] * 100)}%:100%]"
train_dataset = load_cifar10_dataset(
cfg['batch_size'],
split=t_split,
shuffle=True,
drop_remainder=True,
using_normalize=cfg['using_normalize'],
using_crop=cfg['using_crop'],
using_flip=cfg['using_flip'],
using_cutout=cfg['using_cutout'],
cutout_length=cfg['cutout_length'])
val_dataset = load_cifar10_dataset(cfg['batch_size'],
split=v_split,
shuffle=True,
drop_remainder=True,
using_normalize=cfg['using_normalize'],
using_crop=cfg['using_crop'],
using_flip=cfg['using_flip'],
using_cutout=cfg['using_cutout'],
cutout_length=cfg['cutout_length'])
# define optimizer
steps_per_epoch = int(cfg['dataset_len'] * cfg['train_portion'] //
cfg['batch_size'])
learning_rate = CosineAnnealingLR(initial_learning_rate=cfg['init_lr'],
t_period=cfg['epoch'] * steps_per_epoch,
lr_min=cfg['lr_min'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
momentum=cfg['momentum'])
optimizer_arch = tf.keras.optimizers.Adam(
learning_rate=cfg['arch_learning_rate'], beta_1=0.5, beta_2=0.999)
# define losses function
criterion = CrossEntropyLoss()
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(step=tf.Variable(0, name='step'),
optimizer=optimizer,
optimizer_arch=optimizer_arch,
model=sna.model,
alphas_normal=sna.alphas_normal,
alphas_reduce=sna.alphas_reduce,
betas_normal=sna.betas_normal,
betas_reduce=sna.betas_reduce)
manager = tf.train.CheckpointManager(checkpoint=checkpoint,
directory=checkpoint_dir,
max_to_keep=3)
if manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
print('[*] load ckpt from {} at step {}.'.format(
manager.latest_checkpoint, checkpoint.step.numpy()))
else:
print("[*] training from scratch.")
print(f"[*] searching model after {cfg['start_search_epoch']} epochs.")
# define training step function for model
@tf.function
def train_step(inputs, labels):
with tf.GradientTape() as tape:
logits = sna.model((inputs, *sna.arch_parameters), training=True)
losses = {}
losses['reg'] = tf.reduce_sum(sna.model.losses)
losses['ce'] = criterion(labels, logits)
total_loss = tf.add_n([l for l in losses.values()])
grads = tape.gradient(total_loss, sna.model.trainable_variables)
grads = [(tf.clip_by_norm(grad, cfg['grad_clip'])) for grad in grads]
optimizer.apply_gradients(zip(grads, sna.model.trainable_variables))
return logits, total_loss, losses
# define training step function for arch_parameters
@tf.function
def train_step_arch(inputs, labels):
with tf.GradientTape() as tape:
logits = sna.model((inputs, *sna.arch_parameters), training=True)
losses = {}
losses['reg'] = cfg['arch_weight_decay'] * tf.add_n(
[tf.reduce_sum(p**2) for p in sna.arch_parameters])
losses['ce'] = criterion(labels, logits)
total_loss = tf.add_n([l for l in losses.values()])
grads = tape.gradient(total_loss, sna.arch_parameters)
optimizer_arch.apply_gradients(zip(grads, sna.arch_parameters))
return losses
summary_writer = tf.summary.create_file_writer('./logs/' + cfg['sub_name'])
print("[*] finished searching for one epoch")
print("[*] Start sampling architetures")
prog_bar = ProgressBar(50, 0)
# Start sampling for 50 archs
for geno_num in range(50):
genotype = sna.get_genotype(random_search_flag=True)
prog_bar.update(f"\n Sampled{geno_num}th arch: {genotype}")
# print(f"\n Sampled {geno_num}th arch: {genotype}")
f = open(
os.path.join('./logs', cfg['sub_name'],
'search_random_arch_genotype.py'), 'a')
f.write(f"\n{cfg['sub_name']}_{geno_num} = {genotype}\n")
f.close()
print("Sampling done!")
debugpy.wait_for_client()
def main(_):
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'],
num_classes=cfg['num_classes'],
head_type=cfg['head_type'],
embd_shape=cfg['embd_shape'],
w_decay=cfg['w_decay'],
training=True)
model.summary(line_length=80)
if cfg['train_dataset']:
logging.info("load ms1m dataset.")
dataset_len = cfg['num_samples']
steps_per_epoch = dataset_len // cfg['batch_size']
train_dataset = dataset.load_tfrecord_dataset(cfg['train_dataset'],
cfg['batch_size'],
cfg['binary_img'],
is_ccrop=cfg['is_ccrop'])
else:
logging.info("load fake dataset.")
dataset_len = 1
steps_per_epoch = 1
train_dataset = dataset.load_fake_dataset(cfg['input_size'])
learning_rate = tf.constant(cfg['base_lr'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
momentum=0.9,
nesterov=True)
loss_fn = SoftmaxLoss()
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)
epochs, steps = get_ckpt_inf(ckpt_path, steps_per_epoch)
else:
print("[*] training from scratch.")
epochs, steps = 1, 1
if FLAGS.mode == 'eager_tf':
# Eager mode is great for debugging
# Non eager graph mode is recommended for real training
summary_writer = tf.summary.create_file_writer('./logs/' +
cfg['sub_name'])
train_dataset = iter(train_dataset)
while epochs <= cfg['epochs']:
inputs, labels = next(train_dataset)
with tf.GradientTape() as tape:
logist = model(inputs, training=True)
reg_loss = tf.reduce_sum(model.losses)
pred_loss = loss_fn(labels, logist)
total_loss = pred_loss + reg_loss
grads = tape.gradient(total_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if steps % 5 == 0:
verb_str = "Epoch {}/{}: {}/{}, loss={:.2f}, lr={:.4f}"
print(
verb_str.format(epochs, cfg['epochs'],
steps % steps_per_epoch, steps_per_epoch,
total_loss.numpy(), learning_rate.numpy()))
with summary_writer.as_default():
tf.summary.scalar('loss/total loss',
total_loss,
step=steps)
tf.summary.scalar('loss/pred loss', pred_loss, step=steps)
tf.summary.scalar('loss/reg loss', reg_loss, step=steps)
tf.summary.scalar('learning rate',
optimizer.lr,
step=steps)
if steps % cfg['save_steps'] == 0:
print('[*] save ckpt file!')
model.save_weights('checkpoints/{}/e_{}_b_{}.ckpt'.format(
cfg['sub_name'], epochs, steps % steps_per_epoch))
steps += 1
epochs = steps // steps_per_epoch + 1
else:
model.compile(optimizer=optimizer,
loss=loss_fn,
run_eagerly=(FLAGS.mode == 'eager_fit'))
mc_callback = ModelCheckpoint(
'checkpoints/' + cfg['sub_name'] + '/e_{epoch}_b_{batch}.ckpt',
save_freq=cfg['save_steps'] * cfg['batch_size'],
verbose=1,
save_weights_only=True)
tb_callback = TensorBoard(log_dir='logs/',
update_freq=cfg['batch_size'] * 5,
profile_batch=0)
tb_callback._total_batches_seen = steps
tb_callback._samples_seen = steps * cfg['batch_size']
callbacks = [mc_callback, tb_callback]
history = model.fit(train_dataset,
epochs=cfg['epochs'],
steps_per_epoch=steps_per_epoch,
callbacks=callbacks,
initial_epoch=epochs - 1)
print("[*] training done!")
def main(_argv):
# init
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)
# define network
model = RetinaFaceModel(cfg,
training=False,
iou_th=FLAGS.iou_th,
score_th=FLAGS.score_th)
# load model from weights.h5
# model.load_weights('./model/mbv2_weights.h5', by_name=True, skip_mismatch=True)
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
if not FLAGS.webcam:
if not os.path.exists(FLAGS.img_path):
print(f"cannot find image path from {FLAGS.img_path}")
exit()
print("[*] Processing on single image {}".format(FLAGS.img_path))
img_raw = cv2.imread(FLAGS.img_path)
img = np.float32(img_raw.copy())
# testing scale
target_size = 320
img_size_max = np.max(img.shape[0:2])
resize = float(target_size) / float(img_size_max)
img = cv2.resize(img,
None,
None,
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max(cfg['steps']))
# run model
outputs = model(img[np.newaxis, ...]).numpy()
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# draw and save results
save_img_path = os.path.join('out_' + os.path.basename(FLAGS.img_path))
for prior_index in range(len(outputs)):
draw_bbox_landm(img, outputs[prior_index], target_size,
target_size)
cv2.imwrite(save_img_path, img)
print(f"[*] save result at {save_img_path}")
else:
cam = cv2.VideoCapture('./data/lichaochao.mp4')
# cam = cv2.VideoCapture(0)
frame_height = int(cam.get(cv2.CAP_PROP_FRAME_HEIGHT))
frame_width = int(cam.get(cv2.CAP_PROP_FRAME_WIDTH))
fourcc = cv2.VideoWriter_fourcc(*'XVID')
fps = cam.get(cv2.CAP_PROP_FPS)
out = cv2.VideoWriter('chaochao1.mp4',
fourcc,
fps=fps,
frameSize=(frame_height, frame_width))
resize = FLAGS.down_scale_factor
frame_height *= resize
frame_width *= resize
max_steps = max(cfg['steps'])
img_pad_h = max_steps - frame_height % max_steps if frame_height % max_steps > 0 else 0
img_pad_w = max_steps - frame_width % max_steps if frame_width % max_steps > 0 else 0
priors = prior_box_tf(
(frame_height + img_pad_h, frame_width + img_pad_w),
cfg['min_sizes'], cfg['steps'], cfg['clip'])
frame_index = 0
outputs = []
start_time = time.time()
while cam.isOpened():
_, frame = cam.read()
if frame is None:
print('no cam')
break
if frame_index < 5:
frame_index += 1
# continue
else:
frame_index = 0
img = np.float32(frame.copy())
if resize < 1:
img = cv2.resize(img, (0, 0),
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max_steps)
# run model
outputs = model(img[np.newaxis, ...])
preds = tf.concat([
outputs[0][0], outputs[1][0, :, 1][..., tf.newaxis],
outputs[2][0, :, 1][..., tf.newaxis]
], -1)
decode_preds = decode_tf(preds, priors, cfg['variances'])
selected_indices = tf.image.non_max_suppression(
boxes=decode_preds[:, :4],
scores=decode_preds[:, -1],
max_output_size=tf.shape(decode_preds)[0],
iou_threshold=FLAGS.iou_th,
score_threshold=FLAGS.score_th)
outputs = tf.gather(decode_preds, selected_indices).numpy()
# recover padding effect
outputs = recover_pad_output(outputs,
pad_params,
resize=resize)
# calculate fps
# fps_str = "FPS: %.2f" % (1 / (time.time() - start_time))
# start_time = time.time()
# cv2.putText(frame, fps_str, (25, 50),
# cv2.FONT_HERSHEY_DUPLEX, 0.75, (0, 0, 255), 2)
# draw results
for prior_index in range(len(outputs)):
draw_bbox_landm(frame, outputs[prior_index], frame_height,
frame_width)
# calculate fps
# fps_str = "FPS: %.2f" % (1 / (time.time() - start_time))
# start_time = time.time()
# cv2.putText(frame, fps_str, (25, 25),
# cv2.FONT_HERSHEY_DUPLEX, 0.75, (0, 255, 0), 2)
# show frame
out.write(frame)
cv2.imshow('frame', frame)
if cv2.waitKey(1) == ord('q'):
exit()
def main(_):
# init
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)
# define network
model = RetinaFaceModel(cfg, training=True)
model.summary(line_length=80)
# define prior box
priors = prior_box((cfg['input_size'], cfg['input_size']),
cfg['min_sizes'], cfg['steps'], cfg['clip'])
# load dataset
train_dataset = load_dataset(cfg, priors, shuffle=True)
# define optimizer
steps_per_epoch = cfg['dataset_len'] // cfg['batch_size']
learning_rate = MultiStepWarmUpLR(
initial_learning_rate=cfg['init_lr'],
lr_steps=[e * steps_per_epoch for e in cfg['lr_decay_epoch']],
lr_rate=cfg['lr_rate'],
warmup_steps=cfg['warmup_epoch'] * steps_per_epoch,
min_lr=cfg['min_lr'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
momentum=0.9,
nesterov=True)
# define losses function
multi_box_loss = MultiBoxLoss()
# load checkpoint
checkpoint_dir = '/content/drive/My Drive/Colab/checkpoints/' + cfg[
'sub_name']
checkpoint = tf.train.Checkpoint(step=tf.Variable(0, name='step'),
optimizer=optimizer,
model=model)
manager = tf.train.CheckpointManager(checkpoint=checkpoint,
directory=checkpoint_dir,
max_to_keep=3)
if manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
print('[*] load ckpt from {} at step {}.'.format(
manager.latest_checkpoint, checkpoint.step.numpy()))
else:
print("[*] training from scratch.")
# define training step function
@tf.function
def train_step(inputs, labels):
with tf.GradientTape() as tape:
predictions = model(inputs, training=True)
losses = {}
losses['reg'] = tf.reduce_sum(model.losses)
losses['loc'], losses['landm'], losses['class'] = \
multi_box_loss(labels, predictions)
total_loss = tf.add_n([l for l in losses.values()])
grads = tape.gradient(total_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
return total_loss, losses
# training loop
summary_writer = tf.summary.create_file_writer('./logs/' + cfg['sub_name'])
remain_steps = max(
steps_per_epoch * cfg['epoch'] - checkpoint.step.numpy(), 0)
prog_bar = ProgressBar(steps_per_epoch,
checkpoint.step.numpy() % steps_per_epoch)
for inputs, labels in train_dataset.take(remain_steps):
checkpoint.step.assign_add(1)
steps = checkpoint.step.numpy()
total_loss, losses = train_step(inputs, labels)
prog_bar.update("epoch={}/{}, loss={:.4f}, lr={:.1e}".format(
((steps - 1) // steps_per_epoch) + 1, cfg['epoch'],
total_loss.numpy(),
optimizer.lr(steps).numpy()))
if steps % 10 == 0:
with summary_writer.as_default():
tf.summary.scalar('loss/total_loss', total_loss, step=steps)
for k, l in losses.items():
tf.summary.scalar('loss/{}'.format(k), l, step=steps)
tf.summary.scalar('learning_rate',
optimizer.lr(steps),
step=steps)
if steps % cfg['save_steps'] == 0:
manager.save()
print("\n[*] save ckpt file at {}".format(
manager.latest_checkpoint))
manager.save()
print("\n[*] training done! save ckpt file at {}".format(
manager.latest_checkpoint))
def main(_argv):
mkdir(FLAGS.destination_dir)
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)
aligner = FaceAligner(desiredFaceSize=128)
# define network
model = RetinaFaceModel(cfg, training=False, iou_th=FLAGS.iou_th,
score_th=FLAGS.score_th)
# load checkpoint
checkpoint_dir = './checkpoints/' + cfg['sub_name']
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
total = 0
processed_total = 0
CLASS_NAMES = np.array(os.listdir(FLAGS.folder_path))
temp = os.listdir(FLAGS.destination_dir)
temp.sort()
CLASS_NAMES.sort()
for f in CLASS_NAMES:
processed_image = 0
######################################
# Need modified for using
######################################
if os.path.isfile(FLAGS.folder_path+f):
continue
if f in temp and f != temp[-1]:
continue
items = os.listdir(FLAGS.folder_path+f)
mkdir(FLAGS.destination_dir+f)
for path in items:
frame = cv2.imread(FLAGS.folder_path + f +'/'+ path)
if frame is None:
continue
frame_height, frame_width, _ = frame.shape
img = np.float32(frame.copy())
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image(img, max_steps=max(cfg['steps']))
# run model
outputs = model(img[np.newaxis, ...]).numpy()
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
if len(outputs) < 1:
continue
ann = max(outputs, key=lambda x: (x[2]-x[0])*(x[3]-x[1]))
b_box = int(ann[0] * frame_width), int(ann[1] * frame_height), \
int(ann[2] * frame_width), int(ann[3] * frame_height)
if (b_box[0]<0) or (b_box[1]<0) or (b_box[2]>=frame_width) or (b_box[3]>=frame_height):
continue
keypoints = {
'left_eye': (ann[4] * frame_width,ann[5] * frame_height),
'right_eye': (ann[6] * frame_width,ann[7] * frame_height),
'nose': (ann[8], ann[9]),
'left_mouth': (ann[10] * frame_width, ann[11] * frame_height),
'right_mouth': (ann[12] * frame_width,ann[13] * frame_height),
}
# croped_image = frame[b_box[1]:b_box[3],b_box[0]:b_box[2], :]
# out_frame = cv2.resize(croped_image, (112,112), interpolation=cv2.INTER_CUBIC)
out_frame = aligner.align(frame, keypoints, b_box)
# for i in range(4,14):
# if i%2 == 0:
# ann[i] = int(ann[i]*frame_width)
# else:
# ann[i] = int(ann[i]*frame_height)
# out_frame = norm_crop(frame, np.array([ann[4:6],ann[6:8],ann[8:10],ann[10:12],ann[12:14]]))
try:
cv2.imwrite(FLAGS.destination_dir + f +'/'+ path, out_frame)
processed_image += 1
except FileExistsError as e:
pass
print(f + " Done")
total += len(items)
processed_total += processed_image
def main(_):
# init
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
set_memory_growth()
# load PRNet model
cfg = load_yaml(FLAGS.cfg_path)
model = PRN(cfg, is_dlib=True)
# evaluation
if not FLAGS.use_cam: # on test-img
print(
"[*] Processing on images in {}. Press 's' to save result.".format(
FLAGS.img_path))
img_paths = glob.glob(os.path.join(FLAGS.img_path, '*'))
for img_path in img_paths:
img = cv2.imread(img_path)
pos = model.process(img_path)
if pos is None:
continue
vertices = model.get_vertices(pos)
kpt = model.get_landmarks(pos)
camera_matrix, _ = estimate_pose(vertices)
cv2.imshow('Input', img)
cv2.imshow('Sparse alignment', plot_kpt(img, kpt))
cv2.imshow('Dense alignment', plot_vertices(img, vertices))
cv2.imshow('Pose', plot_pose_box(img, camera_matrix, kpt))
cv2.moveWindow('Input', 0, 0)
cv2.moveWindow('Sparse alignment', 500, 0)
cv2.moveWindow('Dense alignment', 1000, 0)
cv2.moveWindow('Pose', 1500, 0)
key = cv2.waitKey(0)
if key == ord('q'):
exit()
elif key == ord('s'):
cv2.imwrite(
os.path.join(FLAGS.save_path, os.path.basename(img_path)),
plot_kpt(img, kpt))
print("Result saved in {}".format(FLAGS.save_path))
else: # webcam demo
cap = cv2.VideoCapture(0)
start_time = time.time()
count = 1
while (True):
_, image = cap.read()
pos = model.process(image)
fps_str = 'FPS: %.2f' % (1 / (time.time() - start_time))
start_time = time.time()
cv2.putText(image, fps_str, (25, 25), cv2.FONT_HERSHEY_DUPLEX,
0.75, (0, 255, 0), 2)
cv2.imshow('Input', image)
cv2.moveWindow('Input', 0, 0)
key = cv2.waitKey(1)
if pos is None:
cv2.waitKey(1)
cv2.destroyWindow('Sparse alignment')
cv2.destroyWindow('Dense alignment')
cv2.destroyWindow('Pose')
if key & 0xFF == ord('q'):
break
continue
else:
vertices = model.get_vertices(pos)
kpt = model.get_landmarks(pos)
camera_matrix, _ = estimate_pose(vertices)
result_list = [
plot_kpt(image, kpt),
plot_vertices(image, vertices),
plot_pose_box(image, camera_matrix, kpt)
]
cv2.imshow('Sparse alignment', result_list[0])
cv2.imshow('Dense alignment', result_list[1])
cv2.imshow('Pose', result_list[2])
cv2.moveWindow('Sparse alignment', 500, 0)
cv2.moveWindow('Dense alignment', 1000, 0)
cv2.moveWindow('Pose', 1500, 0)
if key & 0xFF == ord('s'):
image_name = 'prnet_cam_' + str(count)
save_path = FLAGS.save_path
cv2.imwrite(
os.path.join(save_path, image_name + '_result.jpg'),
np.concatenate(result_list, axis=1))
cv2.imwrite(
os.path.join(save_path, image_name + '_image.jpg'),
image)
count += 1
print("Result saved in {}".format(FLAGS.save_path))
if key & 0xFF == ord('q'):
break
def main(_):
# init
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)
# define training step function
@tf.function
def train_step(inputs, labels, drop_path_prob):
with tf.GradientTape() as tape:
logits, logits_aux = model((inputs, drop_path_prob), training=True)
losses = {}
losses['reg'] = tf.reduce_sum(model.losses)
losses['ce'] = criterion(labels, logits)
losses['ce_auxiliary'] = \
cfg['auxiliary_weight'] * criterion(labels, logits_aux)
total_loss = tf.add_n([l for l in losses.values()])
grads = tape.gradient(total_loss, model.trainable_variables)
grads = [(tf.clip_by_norm(grad, cfg['grad_clip'])) for grad in grads]
optimizer.apply_gradients(zip(grads, model.trainable_variables))
return logits, total_loss, losses
# Used to store the final accuracy for every arch
final_acc = pd.DataFrame(data=None, columns=['arch_name', 'acc'])
loop_num = 50
if Debug:
# debugpy.wait_for_client()
loop_num = 1
# define network
for arch_num in range(loop_num):
# read the arch
arch = str(f"{cfg['sub_name']}_{arch_num}")
cfg['arch'] = arch
model = CifarModel(cfg, training=True, file_name=FLAGS.file_name)
if Debug:
model.summary(line_length=80)
print("param size = {:f}MB".format(count_parameters_in_MB(model)))
# load dataset
train_dataset = load_cifar10_dataset(
cfg['batch_size'],
split='train',
shuffle=True,
drop_remainder=True,
using_normalize=cfg['using_normalize'],
using_crop=cfg['using_crop'],
using_flip=cfg['using_flip'],
using_cutout=cfg['using_cutout'],
cutout_length=cfg['cutout_length'])
val_dataset = load_cifar10_dataset(
cfg['val_batch_size'],
split='test',
shuffle=False,
drop_remainder=False,
using_normalize=cfg['using_normalize'],
using_crop=False,
using_flip=False,
using_cutout=False)
# define optimizer
steps_per_epoch = cfg['dataset_len'] // cfg['batch_size']
learning_rate = CosineAnnealingLR(initial_learning_rate=cfg['init_lr'],
t_period=cfg['epoch'] *
steps_per_epoch,
lr_min=cfg['lr_min'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
momentum=cfg['momentum'])
# define losses function
criterion = CrossEntropyLoss()
# load checkpoint
checkpoint_dir = './checkpoints/' + arch
checkpoint = tf.train.Checkpoint(step=tf.Variable(0, name='step'),
optimizer=optimizer,
model=model)
manager = tf.train.CheckpointManager(checkpoint=checkpoint,
directory=checkpoint_dir,
max_to_keep=3)
if manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
print('[*] load ckpt from {} at step {}.'.format(
manager.latest_checkpoint, checkpoint.step.numpy()))
else:
print("[*] training from scratch.")
# training loop
summary_writer = tf.summary.create_file_writer('./logs/' +
cfg['sub_name'])
total_steps = steps_per_epoch * cfg['epoch']
remain_steps = max(total_steps - checkpoint.step.numpy(), 0)
prog_bar = ProgressBar(steps_per_epoch,
checkpoint.step.numpy() % steps_per_epoch)
train_acc = AvgrageMeter()
val_acc = AvgrageMeter()
best_acc = 0.
for inputs, labels in train_dataset.take(remain_steps):
checkpoint.step.assign_add(1)
drop_path_prob = cfg['drop_path_prob'] * (
tf.cast(checkpoint.step, tf.float32) / total_steps)
steps = checkpoint.step.numpy()
epochs = ((steps - 1) // steps_per_epoch) + 1
logits, total_loss, losses = train_step(inputs, labels,
drop_path_prob)
train_acc.update(
accuracy(logits.numpy(), labels.numpy())[0], cfg['batch_size'])
prog_bar.update(
"epoch={}/{}, loss={:.4f}, acc={:.2f}, lr={:.2e}".format(
epochs, cfg['epoch'], total_loss.numpy(), train_acc.avg,
optimizer.lr(steps).numpy()))
if steps % cfg['val_steps'] == 0 and steps > 1:
print("\n[*] validate...", end='')
val_acc.reset()
for inputs_val, labels_val in val_dataset:
logits_val, _ = model((inputs_val, tf.constant([0.])))
val_acc.update(
accuracy(logits_val.numpy(), labels_val.numpy())[0],
inputs_val.shape[0])
if val_acc.avg > best_acc:
best_acc = val_acc.avg
model.save_weights(
f"checkpoints/{cfg['sub_name']}/best.ckpt")
val_str = " val acc {:.2f}%, best acc {:.2f}%"
print(val_str.format(val_acc.avg, best_acc), end='')
if steps % 10 == 0:
with summary_writer.as_default():
tf.summary.scalar('acc/train', train_acc.avg, step=steps)
tf.summary.scalar('acc/val', val_acc.avg, step=steps)
tf.summary.scalar('loss/total_loss',
total_loss,
step=steps)
for k, l in losses.items():
tf.summary.scalar('loss/{}'.format(k), l, step=steps)
tf.summary.scalar('learning_rate',
optimizer.lr(steps),
step=steps)
if steps % cfg['save_steps'] == 0:
manager.save()
print("\n[*] save ckpt file at {}".format(
manager.latest_checkpoint))
if steps % steps_per_epoch == 0:
train_acc.reset()
manager.save()
print("\n[*] training one arch done! save ckpt file at {}".format(
manager.latest_checkpoint))
final_acc.loc[arch_num] = list([arch, best_acc])
print("Whole training ended, the best result is :")
print("\t", final_acc.iloc[final_acc['acc'].idxmax()])
def main(_argv):
# init
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)
# define network
model = RetinaFaceModel(cfg,
training=False,
iou_th=FLAGS.iou_th,
score_th=FLAGS.score_th)
# load checkpoint
checkpoint_dir = "./checkpoints/" + cfg["sub_name"]
checkpoint = tf.train.Checkpoint(model=model)
if tf.train.latest_checkpoint(checkpoint_dir):
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
print("[*] load ckpt from {}.".format(
tf.train.latest_checkpoint(checkpoint_dir)))
else:
print("[*] Cannot find ckpt from {}.".format(checkpoint_dir))
exit()
# evaluation on testing dataset
testset_folder = cfg["testing_dataset_path"]
testset_list = os.path.join(testset_folder, "label.txt")
img_paths, _ = load_info(testset_list)
for img_index, img_path in enumerate(img_paths):
print(" [{} / {}] det {}".format(img_index + 1, len(img_paths),
img_path))
img_raw = cv2.imread(img_path, cv2.IMREAD_COLOR)
img_height_raw, img_width_raw, _ = img_raw.shape
img = np.float32(img_raw.copy())
# testing scale
target_size = 1600
max_size = 2150
img_shape = img.shape
img_size_min = np.min(img_shape[0:2])
img_size_max = np.max(img_shape[0:2])
resize = float(target_size) / float(img_size_min)
# prevent bigger axis from being more than max_size:
if np.round(resize * img_size_max) > max_size:
resize = float(max_size) / float(img_size_max)
if FLAGS.origin_size:
if os.path.basename(img_path) == "6_Funeral_Funeral_6_618.jpg":
resize = 0.5 # this image is too big to avoid OOM problem
else:
resize = 1
img = cv2.resize(img,
None,
None,
fx=resize,
fy=resize,
interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# pad input image to avoid unmatched shape problem
img, pad_params = pad_input_image_(img, max_steps=max(cfg["steps"]))
# run model
outputs = model(img[np.newaxis, ...]).numpy()
# recover padding effect
outputs = recover_pad_output(outputs, pad_params)
# write results
img_name = os.path.basename(img_path)
sub_dir = os.path.basename(os.path.dirname(img_path))
save_name = os.path.join(FLAGS.save_folder, sub_dir,
img_name.replace(".jpg", ".txt"))
pathlib.Path(os.path.join(FLAGS.save_folder,
sub_dir)).mkdir(parents=True, exist_ok=True)
with open(save_name, "w") as file:
bboxs = outputs[:, :4]
confs = outputs[:, -1]
file_name = img_name + "\n"
bboxs_num = str(len(bboxs)) + "\n"
file.write(file_name)
file.write(bboxs_num)
for box, conf in zip(bboxs, confs):
x = int(box[0] * img_width_raw)
y = int(box[1] * img_height_raw)
w = int(box[2] * img_width_raw) - int(box[0] * img_width_raw)
h = int(box[3] * img_height_raw) - int(box[1] * img_height_raw)
confidence = str(conf)
line = str(x) + " " + str(y) + " " + str(w) + " " + str(
h) + " " + confidence + " \n"
file.write(line)
# save images
pathlib.Path(os.path.join("./results", cfg["sub_name"],
sub_dir)).mkdir(parents=True, exist_ok=True)
if FLAGS.save_image:
for prior_index in range(len(outputs)):
if outputs[prior_index][15] >= FLAGS.vis_th:
draw_bbox_landm(img_raw, outputs[prior_index],
img_height_raw, img_width_raw)
cv2.imwrite(
os.path.join("./results", cfg["sub_name"], sub_dir, img_name),
img_raw)
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))