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Extract subimages mod

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This commit is contained in:
James Betker 2020-05-23 21:07:41 -06:00
parent 90073fc761
commit 445e7e7053
1 changed files with 35 additions and 12 deletions
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45
codes/data_scripts/extract_subimages.py
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@ -13,18 +13,19 @@ import data.util as data_util # noqa: E402
def main():
mode = 'single' # single (one input folder) | pair (extract corresponding GT and LR pairs)
split_img = False
opt = {}
opt['n_thread'] = 20
opt['compression_level'] = 3 # 3 is the default value in cv2
# 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['input_folder'] = 'D:\\vix_cropped'
opt['save_folder'] = 'D:\\vix_tiled'
opt['crop_sz'] = 800 # the size of each sub-image
opt['step'] = 640 # step of the sliding crop window
opt['thres_sz'] = 200 # size threshold
extract_signle(opt)
opt['input_folder'] = 'Z:\\4k6k\\datasets\\adrianna\\adrianna_vids\\images'
opt['save_folder'] = 'Z:\\4k6k\\datasets\\adrianna\\adrianna_vids\\tiled'
opt['crop_sz'] = 64 # the size of each sub-image
opt['step'] = 48 # step of the sliding crop window
opt['thres_sz'] = 20 # size threshold
extract_single(opt, split_img)
elif mode == 'pair':
GT_folder = '../../datasets/div2k/DIV2K_train_HR'
LR_folder = '../../datasets/div2k/DIV2K_train_LR_bicubic/X4'
@ -60,14 +61,14 @@ def main():
opt['crop_sz'] = crop_sz
opt['step'] = step
opt['thres_sz'] = thres_sz
extract_signle(opt)
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_signle(opt)
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.'
@ -75,7 +76,7 @@ def main():
raise ValueError('Wrong mode.')
def extract_signle(opt):
def extract_single(opt, split_img=False):
input_folder = opt['input_folder']
save_folder = opt['save_folder']
if not osp.exists(save_folder):
@ -93,13 +94,17 @@ def extract_signle(opt):
pool = Pool(opt['n_thread'])
for path in img_list:
if split_img:
pool.apply_async(worker, args=(path, opt, True, False), callback=update)
pool.apply_async(worker, args=(path, opt, True, True), callback=update)
else:
pool.apply_async(worker, args=(path, opt), callback=update)
pool.close()
pool.join()
print('All subprocesses done.')
def worker(path, opt):
def worker(path, opt, split_mode=False, left_img=True):
crop_sz = opt['crop_sz']
step = opt['step']
thres_sz = opt['thres_sz']
@ -114,6 +119,22 @@ def worker(path, opt):
else:
raise ValueError('Wrong image shape - {}'.format(n_channels))
# Uncomment to filter any image that doesnt meet a threshold size.
#if w < 3000:
# return
left = 0
right = w
if split_mode:
if left_img:
left = 0
right = int(w/2)
else:
left = int(w/2)
right = w
w = int(w/2)
img = img[:, left:right]
h_space = np.arange(0, h - crop_sz + 1, step)
if h - (h_space[-1] + crop_sz) > thres_sz:
h_space = np.append(h_space, h - crop_sz)
@ -130,9 +151,11 @@ def worker(path, opt):
else:
crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
crop_img = np.ascontiguousarray(crop_img)
# If this fails, change it and the imwrite below to the write extension.
assert img_name.contains(".png")
cv2.imwrite(
osp.join(opt['save_folder'],
img_name.replace('.png', '_s{:03d}.png'.format(index))), crop_img,
img_name.replace('.png', '_l{:05d}_s{:03d}.png'.format(left, index))), crop_img,
[cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
return 'Processing {:s} ...'.format(img_name)