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import os
import sys
from multiprocessing import Pool
from os import path as osp
import cv2
import numpy as np
from tqdm import tqdm
def scandir(dir_path, suffix=None, recursive=False, full_path=False):
"""Scan a directory to find the interested files.
Args:
dir_path (str): Path of the directory.
suffix (str | tuple(str), optional): File suffix that we are
interested in. Default: None.
recursive (bool, optional): If set to True, recursively scan the
directory. Default: False.
full_path (bool, optional): If set to True, include the dir_path.
Default: False.
Returns:
A generator for all the interested files with relative pathes.
"""
if (suffix is not None) and not isinstance(suffix, (str, tuple)):
raise TypeError('"suffix" must be a string or tuple of strings')
root = dir_path
def _scandir(dir_path, suffix, recursive):
for entry in os.scandir(dir_path):
if not entry.name.startswith('.') and entry.is_file():
if full_path:
return_path = entry.path
else:
return_path = osp.relpath(entry.path, root)
if suffix is None:
yield return_path
elif return_path.endswith(suffix):
yield return_path
else:
if recursive:
yield from _scandir(
entry.path, suffix=suffix, recursive=recursive)
else:
continue
return _scandir(dir_path, suffix=suffix, recursive=recursive)
def main():
"""A multi-thread tool to crop large images to sub-images for faster IO.
It is used for DIV2K dataset.
opt (dict): Configuration dict. It contains:
n_thread (int): Thread number.
compression_level (int): CV_IMWRITE_PNG_COMPRESSION from 0 to 9.
A higher value means a smaller size and longer compression time.
Use 0 for faster CPU decompression. Default: 3, same in cv2.
input_folder (str): Path to the input folder.
save_folder (str): Path to save folder.
crop_size (int): Crop size.
step (int): Step for overlapped sliding window.
thresh_size (int): Threshold size. Patches whose size is lower
than thresh_size will be dropped.
Usage:
For each folder, run this script.
Typically, there are four folders to be processed for DIV2K dataset.
DIV2K_train_HR
DIV2K_train_LR_bicubic/X2
DIV2K_train_LR_bicubic/X3
DIV2K_train_LR_bicubic/X4
After process, each sub_folder should have the same number of
subimages.
Remember to modify opt configurations according to your settings.
"""
opt = {}
opt['n_thread'] = 40
opt['compression_level'] = 3
# HR images
opt['input_folder'] = './GT'
opt['save_folder'] = './GT_sub'
opt['crop_size'] = 480
opt['step'] = 240
opt['thresh_size'] = 0
extract_subimages(opt)
def extract_subimages(opt):
"""Crop images to subimages.
Args:
opt (dict): Configuration dict. It contains:
input_folder (str): Path to the input folder.
save_folder (str): Path to save folder.
n_thread (int): Thread number.
"""
input_folder = opt['input_folder']
save_folder = opt['save_folder']
if not osp.exists(save_folder):
os.makedirs(save_folder)
print(f'mkdir {save_folder} ...', exist_okay=True)
else:
pass
# print(f'Folder {save_folder} already exists. Exit.')
# sys.exit(1)
img_list = list(scandir(input_folder, full_path=True))
pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
pool = Pool(opt['n_thread'])
img_list = sorted(img_list)[-3:-2]
for path in img_list:
worker(path, opt)
raise NotImplementedError
pool.apply_async(
worker, args=(path, opt), callback=lambda arg: pbar.update(1))
pool.close()
pool.join()
pbar.close()
print('All processes done.')
def worker(path, opt):
"""Worker for each process.
Args:
path (str): Image path.
opt (dict): Configuration dict. It contains:
crop_size (int): Crop size.
step (int): Step for overlapped sliding window.
thresh_size (int): Threshold size. Patches whose size is lower
than thresh_size will be dropped.
save_folder (str): Path to save folder.
compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.
Returns:
process_info (str): Process information displayed in progress bar.
"""
crop_size = opt['crop_size']
step = opt['step']
thresh_size = opt['thresh_size']
img_name, extension = osp.splitext(osp.basename(path))
# remove the x2, x3, x4 and x8 in the filename for DIV2K
img_name = img_name.replace('x2',
'').replace('x3',
'').replace('x4',
'').replace('x8', '')
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
if img.ndim == 2:
h, w = img.shape
elif img.ndim == 3:
h, w, c = img.shape
else:
raise ValueError(f'Image ndim should be 2 or 3, but got {img.ndim}')
h_space = np.arange(0, h - crop_size + 1, step)
if h - (h_space[-1] + crop_size) > thresh_size:
h_space = np.append(h_space, h - crop_size)
w_space = np.arange(0, w - crop_size + 1, step)
if w - (w_space[-1] + crop_size) > thresh_size:
w_space = np.append(w_space, w - crop_size)
index = 0
for x in h_space:
for y in w_space:
index += 1
cropped_img = img[x:x + crop_size, y:y + crop_size, ...]
cropped_img = np.ascontiguousarray(cropped_img)
cv2.imwrite(
osp.join(opt['save_folder'],
f'{img_name}_s{index:03d}{extension}'), cropped_img,
[cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
process_info = f'Processing {img_name} ...'
return process_info
if __name__ == '__main__':
main()
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