def lowlight_train(lowlight_enhance):
if not os.path.exists(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
lr = args.start_lr * np.ones([args.epoch])
lr[20:] = lr[0] / 10.0
train_low_data = []
train_high_data = []
train_low_data_names = glob('./data/our485/low/*.png') + glob(
'./data/syn/low/*.png')
train_low_data_names.sort()
train_high_data_names = glob('./data/our485/high/*.png') + glob(
'./data/syn/high/*.png')
train_high_data_names.sort()
assert len(train_low_data_names) == len(train_high_data_names)
print('[*] Number of training data: %d' % len(train_low_data_names))
for idx in range(len(train_low_data_names)):
low_im = load_images(train_low_data_names[idx])
train_low_data.append(low_im)
high_im = load_images(train_high_data_names[idx])
train_high_data.append(high_im)
eval_low_data = []
eval_high_data = []
eval_low_data_name = glob('./data/eval/low/*.*')
for idx in range(len(eval_low_data_name)):
eval_low_im = load_images(eval_low_data_name[idx])
eval_low_data.append(eval_low_im)
lowlight_enhance.train(train_low_data,
train_high_data,
eval_low_data,
batch_size=args.batch_size,
patch_size=args.patch_size,
epoch=args.epoch,
lr=lr,
sample_dir=args.sample_dir,
ckpt_dir=os.path.join(args.ckpt_dir, 'Decom'),
eval_every_epoch=args.eval_every_epoch,
train_phase="Decom")
lowlight_enhance.train(train_low_data,
train_high_data,
eval_low_data,
batch_size=args.batch_size,
patch_size=args.patch_size,
epoch=args.epoch,
lr=lr,
sample_dir=args.sample_dir,
ckpt_dir=os.path.join(args.ckpt_dir, 'Relight'),
eval_every_epoch=args.eval_every_epoch,
train_phase="Relight")
def lowlight_train(lowlight_enhance):
if not os.path.exists(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
train_low_data = []
train_high_data = []
train_low_data_names = glob(
'/mnt/hdd/wangwenjing/FGtraining/low/*.png') #./data/train/low/*.png')
train_low_data_names.sort()
train_high_data_names = glob('/mnt/hdd/wangwenjing/FGtraining/normal/*.png'
) #./data/train/normal/*.png')
train_high_data_names.sort()
assert len(train_low_data_names) == len(train_high_data_names)
print('[*] Number of training data: %d' % len(train_low_data_names))
for idx in range(len(train_low_data_names)):
if (idx + 1) % 1000 == 0:
print(idx + 1)
low_im = load_images(train_low_data_names[idx])
train_low_data.append(low_im)
high_im = load_images(train_high_data_names[idx])
train_high_data.append(high_im)
eval_low_data = []
eval_high_data = []
eval_low_data_name = glob('./data/eval/low/*.*')
for idx in range(len(eval_low_data_name)):
eval_low_im = load_images(eval_low_data_name[idx])
eval_low_data.append(eval_low_im)
lowlight_enhance.train(train_low_data,
train_high_data,
eval_low_data,
batch_size=args.batch_size,
patch_size=args.patch_size,
epoch=args.epoch,
sample_dir=args.sample_dir,
ckpt_dir=args.ckpt_dir,
eval_every_epoch=args.eval_every_epoch)
def lowlight_train(lowlight_enhance):
if not os.path.exists(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
global_step = tf.Variable(0, trainable=False)
# lr = args.start_lr * np.ones([args.epoch])
lr_Decom = args.start_lr * np.ones([args.decom_epoch])
lr_Relight = args.start_lr / 10.0 * np.ones([args.relight_epoch])
# lr_Relight = tf.train.exponential_decay(args.start_lr, global_step=global_step, decay_steps=400, decay_rate=0.9)
# 学习率衰减
lr_Decom[40:] = lr_Decom[0] / 10.0
train_low_data = []
train_high_data = []
train_low_data_names = glob('../DATA/uw_ll/syn/low/*.jpg')
train_low_data_names.sort()
train_high_data_names = glob('../DATA/uw_ll/syn/normal/*.jpg')
train_high_data_names.sort()
assert len(train_low_data_names) == len(train_high_data_names)
print('[*] Number of training data: %d' % len(train_low_data_names))
for idx in range(len(train_low_data_names)):
low_im = load_images(train_low_data_names[idx])
train_low_data.append(low_im)
high_im = load_images(train_high_data_names[idx])
train_high_data.append(high_im)
eval_low_data = []
eval_high_data = []
eval_low_data_name = glob('../DATA/uw_ll/val/low/*.*')
for idx in range(len(eval_low_data_name)):
eval_low_im = load_images(eval_low_data_name[idx])
eval_low_data.append(eval_low_im)
lowlight_enhance.train(train_low_data,
train_high_data,
eval_low_data,
batch_size=args.batch_size,
patch_size=args.patch_size,
epoch=int(args.decom_epoch),
lr=lr_Decom,
sample_dir=args.sample_dir,
ckpt_dir=os.path.join(args.ckpt_dir, 'Decom'),
eval_every_epoch=int(args.eval_every_epoch),
train_phase="Decom")
lowlight_enhance.train(train_low_data,
train_high_data,
eval_low_data,
batch_size=args.batch_size,
patch_size=args.patch_size,
epoch=int(args.relight_epoch),
lr=lr_Relight,
sample_dir=args.sample_dir,
ckpt_dir=os.path.join(args.ckpt_dir, 'Relight'),
eval_every_epoch=int(args.eval_every_epoch),
train_phase="Relight")
def lowlight_train(lowlight_enhance):
if not os.path.exists(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
lr = args.start_lr * np.ones([args.epoch])
lr[40:] = lr[0] / 10.0
train_low_data = []
train_high_data = []
train_low_data_eq = []
train_low_data_clahe = []
train_high_data_eq = []
train_low_data_eq_guide = []
train_high_data_eq_guide = []
train_low_data_eq_guide_weight = []
train_low_data_eq_clahe_weight = []
train_high_data_eq_guide_weight = []
train_low_data_names = glob('./data/our485/low/*.png')
train_low_data_names.sort()
train_high_data_names = glob('./data/our485/low/*.png')
train_high_data_names.sort()
assert len(train_low_data_names) == len(train_high_data_names)
print('[*] Number of training data: %d' % len(train_low_data_names))
for idx in range(len(train_low_data_names)):
low_im = load_images(train_low_data_names[idx])
#low_im = white_world(low_im)
train_low_data.append(low_im)
high_im = load_images(train_high_data_names[idx])
# high_im = white_world(high_im)
train_high_data.append(high_im)
# train_low_data_max_chan = np.max(meanFilter(low_im,winSize=(5,5)),axis=2,keepdims=True)
train_low_data_max_chan = np.max(high_im, axis=2, keepdims=True)
weight_eq_clahe = 0 #sigmoid(5*(meanFilter(train_low_data_max_chan,(20,20))-0.5))
train_low_data_max_channel = (1 - weight_eq_clahe) * histeq(
train_low_data_max_chan) + weight_eq_clahe * adapthisteq(
train_low_data_max_chan)
# train_low_data_max_channel = histeq(low_im[:,:,1])
train_low_data_eq.append(train_low_data_max_channel[:, :, :])
eval_low_data = []
eval_high_data = []
eval_low_data_name = glob('./data/eval15/low/*.*')
for idx in range(len(eval_low_data_name)):
eval_low_im = load_images(eval_low_data_name[idx])
eval_low_data.append(eval_low_im)
lowlight_enhance.train(train_low_data,
train_low_data_eq,
eval_low_data,
train_high_data,
batch_size=args.batch_size,
patch_size=args.patch_size,
epoch=args.epoch,
lr=lr,
sample_dir=args.sample_dir,
ckpt_dir=os.path.join(args.ckpt_dir, 'Decom'),
eval_every_epoch=args.eval_every_epoch,
train_phase="Decom")