diff --git a/codes/multi_modal_train.py b/codes/multi_modal_train.py
index fd32ba17..3b4cf1cb 100644
--- a/codes/multi_modal_train.py
+++ b/codes/multi_modal_train.py
@@ -9,8 +9,13 @@
# models are shared. Your best bet is to have all models save state at the same time so that they all load ~ the same
# state when re-started.
import argparse
+
+import yaml
+
import train
import utils.options as option
+from utils.util import OrderedYaml
+
def main(master_opt, launcher):
trainers = []
@@ -40,7 +45,11 @@ if __name__ == '__main__':
#parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_chained_structured_trans_invariance.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
args = parser.parse_args()
- opt = {
- 'trainer_options': ['../options/teco.yml', '../options/exd.yml']
- }
- main(opt, args.launcher)
\ No newline at end of file
+
+ Loader, Dumper = OrderedYaml()
+ with open(args.opt, mode='r') as f:
+ opt = yaml.load(f, Loader=Loader)
+ opt = {
+ 'trainer_options': ['../options/teco.yml', '../options/exd.yml']
+ }
+ main(opt, args.launcher)
\ No newline at end of file
diff --git a/codes/process_video.py b/codes/process_video.py
index 24a6181e..368e1785 100644
--- a/codes/process_video.py
+++ b/codes/process_video.py
@@ -155,6 +155,13 @@ if __name__ == "__main__":
if recurrent_mode and first_frame:
b, c, h, w = data['LQ'].shape
recurrent_entry = torch.zeros((b,c,h*scale,w*scale), device=data['LQ'].device)
+ # Optionally swap out the 'generator' for the first frame to create a better image that the recurrent generator works off of.
+ if 'recurrent_hr_generator' in opt.keys():
+ recurrent_gen = model.env['generators']['generator']
+ model.env['generators']['generator'] = model.env['generators'][opt['recurrent_hr_generator']]
+ else:
+ model.env['generators']['generator'] = recurrent_gen
+
first_frame = False
if recurrent_mode:
data['recurrent'] = recurrent_entry
diff --git a/codes/scripts/extract_subimages_with_ref.py b/codes/scripts/extract_subimages_with_ref.py
index cb6c4465..0e058369 100644
--- a/codes/scripts/extract_subimages_with_ref.py
+++ b/codes/scripts/extract_subimages_with_ref.py
@@ -11,7 +11,6 @@ import torch
def main():
- mode = 'single' # single (one input folder) | pair (extract corresponding GT and LR pairs)
split_img = False
opt = {}
opt['n_thread'] = 2
@@ -19,75 +18,27 @@ def main():
# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
# compression time. If read raw images during training, use 0 for faster IO speed.
- if mode == 'single':
- opt['dest'] = 'file'
- opt['input_folder'] = 'F:\\4k6k\\datasets\\images\\fullvideo\\full_images'
- opt['save_folder'] = 'F:\\4k6k\\datasets\\images\\fullvideo\\256_tiled'
- opt['crop_sz'] = [512, 1024] # the size of each sub-image
- opt['step'] = [512, 1024] # step of the sliding crop window
- opt['thres_sz'] = 128 # size threshold
- opt['resize_final_img'] = [.5, .25]
- opt['only_resize'] = False
+ opt['dest'] = 'file'
+ opt['input_folder'] = 'F:\\4k6k\\datasets\\ns_images\\vr\\images_sized'
+ opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\vr\\paired_images'
+ opt['crop_sz'] = [512, 1024] # the size of each sub-image
+ opt['step'] = [512, 1024] # step of the sliding crop window
+ opt['thres_sz'] = 128 # size threshold
+ opt['resize_final_img'] = [.5, .25]
+ opt['only_resize'] = False
+ opt['vertical_split'] = True
- save_folder = opt['save_folder']
- if not osp.exists(save_folder):
- os.makedirs(save_folder)
- print('mkdir [{:s}] ...'.format(save_folder))
+ save_folder = opt['save_folder']
+ if not osp.exists(save_folder):
+ os.makedirs(save_folder)
+ print('mkdir [{:s}] ...'.format(save_folder))
- if opt['dest'] == 'lmdb':
- writer = LmdbWriter(save_folder)
- else:
- writer = FileWriter(save_folder)
-
- extract_single(opt, writer, split_img)
- elif mode == 'pair':
- GT_folder = '../../datasets/div2k/DIV2K_train_HR'
- LR_folder = '../../datasets/div2k/DIV2K_train_LR_bicubic/X4'
- save_GT_folder = '../../datasets/div2k/DIV2K800_sub'
- save_LR_folder = '../../datasets/div2k/DIV2K800_sub_bicLRx4'
- scale_ratio = 4
- crop_sz = 480 # the size of each sub-image (GT)
- step = 240 # step of the sliding crop window (GT)
- thres_sz = 48 # size threshold
- ########################################################################
- # check that all the GT and LR images have correct scale ratio
- img_GT_list = data_util._get_paths_from_images(GT_folder)
- img_LR_list = data_util._get_paths_from_images(LR_folder)
- assert len(img_GT_list) == len(img_LR_list), 'different length of GT_folder and LR_folder.'
- for path_GT, path_LR in zip(img_GT_list, img_LR_list):
- img_GT = Image.open(path_GT)
- img_LR = Image.open(path_LR)
- w_GT, h_GT = img_GT.size
- w_LR, h_LR = img_LR.size
- assert w_GT / w_LR == scale_ratio, 'GT width [{:d}] is not {:d}X as LR weight [{:d}] for {:s}.'.format( # noqa: E501
- w_GT, scale_ratio, w_LR, path_GT)
- assert w_GT / w_LR == scale_ratio, 'GT width [{:d}] is not {:d}X as LR weight [{:d}] for {:s}.'.format( # noqa: E501
- w_GT, scale_ratio, w_LR, path_GT)
- # check crop size, step and threshold size
- assert crop_sz % scale_ratio == 0, 'crop size is not {:d}X multiplication.'.format(
- scale_ratio)
- assert step % scale_ratio == 0, 'step is not {:d}X multiplication.'.format(scale_ratio)
- assert thres_sz % scale_ratio == 0, 'thres_sz is not {:d}X multiplication.'.format(
- scale_ratio)
- print('process GT...')
- opt['input_folder'] = GT_folder
- opt['save_folder'] = save_GT_folder
- opt['crop_sz'] = crop_sz
- opt['step'] = step
- opt['thres_sz'] = thres_sz
- extract_single(opt)
- print('process LR...')
- opt['input_folder'] = LR_folder
- opt['save_folder'] = save_LR_folder
- opt['crop_sz'] = crop_sz // scale_ratio
- opt['step'] = step // scale_ratio
- opt['thres_sz'] = thres_sz // scale_ratio
- extract_single(opt)
- assert len(data_util._get_paths_from_images(save_GT_folder)) == len(
- data_util._get_paths_from_images(
- save_LR_folder)), 'different length of save_GT_folder and save_LR_folder.'
+ if opt['dest'] == 'lmdb':
+ writer = LmdbWriter(save_folder)
else:
- raise ValueError('Wrong mode.')
+ writer = FileWriter(save_folder)
+
+ extract_single(opt, writer)
class LmdbWriter:
@@ -182,26 +133,22 @@ class FileWriter:
self.flush()
class TiledDataset(data.Dataset):
- def __init__(self, opt, split_mode=False):
- self.split_mode = split_mode
+ def __init__(self, opt):
+ self.split_mode = opt['vertical_split']
self.opt = opt
input_folder = opt['input_folder']
self.images = data_util._get_paths_from_images(input_folder)
def __getitem__(self, index):
if self.split_mode:
- return self.get(index, True, True).extend(self.get(index, True, False))
+ return (self.get(index, True, True), self.get(index, True, False))
else:
- return self.get(index, False, False)
-
- def get_for_scale(self, img, split_mode, left_image, crop_sz, step, resize_factor, ref_resize_factor):
- assert not left_image # Split image not yet supported, False is the default value.
+ # Wrap in a tuple to align with split mode.
+ return (self.get(index, False, False), None)
+ def get_for_scale(self, img, crop_sz, step, resize_factor, ref_resize_factor):
thres_sz = self.opt['thres_sz']
-
h, w, c = img.shape
- if split_mode:
- w = w/2
h_space = np.arange(0, h - crop_sz + 1, step)
if h - (h_space[-1] + crop_sz) > thres_sz:
@@ -231,30 +178,41 @@ class TiledDataset(data.Dataset):
def get(self, index, split_mode, left_img):
path = self.images[index]
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-
- # We must convert the image into a square. Crop the image so that only the center is left, since this is often
- # the most salient part of the image.
- if len(img.shape) == 2: # Greyscale not supported.
- return None
h, w, c = img.shape
- dim = min(h, w)
- img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
- h, w, c = img.shape
# Uncomment to filter any image that doesnt meet a threshold size.
if min(h,w) < 1024:
return None
+ # Greyscale not supported.
+ if len(img.shape) == 2:
+ return None
+
+ # Handle splitting the image if needed.
left = 0
right = w
if split_mode:
if left_img:
left = 0
- right = int(w/2)
+ right = w//2
else:
- left = int(w/2)
+ left = w//2
right = w
img = img[:, left:right]
+ # We must convert the image into a square.
+ dim = min(h, w)
+ if split_mode:
+ # Crop the image towards the center, which makes more sense in split mode.
+ if left_img:
+ img = img[-dim:, -dim:, :]
+ else:
+ img = img[:dim, :dim, :]
+ else:
+ # Crop the image so that only the center is left, since this is often the most salient part of the image.
+ img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
+
+ h, w, c = img.shape
+
tile_dim = int(self.opt['crop_sz'][0] * self.opt['resize_final_img'][0])
dsize = (tile_dim, tile_dim)
ref_resize_factor = h / tile_dim
@@ -266,7 +224,7 @@ class TiledDataset(data.Dataset):
results = [(ref_buffer, (-1,-1), (-1,-1))]
for crop_sz, resize_factor, step in zip(self.opt['crop_sz'], self.opt['resize_final_img'], self.opt['step']):
- results.extend(self.get_for_scale(img, split_mode, left_img, crop_sz, step, resize_factor, ref_resize_factor))
+ results.extend(self.get_for_scale(img, crop_sz, step, resize_factor, ref_resize_factor))
return results, path
def __len__(self):
@@ -276,20 +234,25 @@ class TiledDataset(data.Dataset):
def identity(x):
return x
-def extract_single(opt, writer, split_img=False):
- dataset = TiledDataset(opt, split_img)
+def extract_single(opt, writer):
+ dataset = TiledDataset(opt)
dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
tq = tqdm(dataloader)
- for imgs in tq:
- if imgs is None or imgs[0] is None:
+ for spl_imgs in tq:
+ if spl_imgs is None:
continue
- imgs, path = imgs[0]
- if imgs is None or len(imgs) <= 1:
- continue
- ref_id = writer.write_reference_image(imgs[0], path)
- for tile in imgs[1:]:
- writer.write_tile_image(ref_id, tile)
- writer.flush()
+ spl_imgs = spl_imgs[0]
+ for imgs, lbl in zip(list(spl_imgs), ['left', 'right']):
+ if imgs is None:
+ continue
+ imgs, path = imgs
+ if imgs is None or len(imgs) <= 1:
+ continue
+ path = path + "_" + lbl
+ ref_id = writer.write_reference_image(imgs[0], path)
+ for tile in imgs[1:]:
+ writer.write_tile_image(ref_id, tile)
+ writer.flush()
writer.close()