forked from mrq/DL-Art-School
142 lines
5.5 KiB
Python
142 lines
5.5 KiB
Python
"""A multi-thread tool to crop large images to sub-images for faster IO."""
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import os
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import os.path as osp
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import sys
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from multiprocessing import Pool
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import numpy as np
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import cv2
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from PIL import Image
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sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
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from utils.util import ProgressBar # noqa: E402
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import data.util as data_util # noqa: E402
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def main():
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mode = 'single' # single (one input folder) | pair (extract corresponding GT and LR pairs)
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opt = {}
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opt['n_thread'] = 20
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opt['compression_level'] = 3 # 3 is the default value in cv2
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# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
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# compression time. If read raw images during training, use 0 for faster IO speed.
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if mode == 'single':
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opt['input_folder'] = 'D:\\vix_cropped'
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opt['save_folder'] = 'D:\\vix_tiled'
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opt['crop_sz'] = 800 # the size of each sub-image
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opt['step'] = 640 # step of the sliding crop window
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opt['thres_sz'] = 200 # size threshold
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extract_signle(opt)
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elif mode == 'pair':
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GT_folder = '../../datasets/div2k/DIV2K_train_HR'
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LR_folder = '../../datasets/div2k/DIV2K_train_LR_bicubic/X4'
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save_GT_folder = '../../datasets/div2k/DIV2K800_sub'
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save_LR_folder = '../../datasets/div2k/DIV2K800_sub_bicLRx4'
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scale_ratio = 4
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crop_sz = 480 # the size of each sub-image (GT)
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step = 240 # step of the sliding crop window (GT)
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thres_sz = 48 # size threshold
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########################################################################
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# check that all the GT and LR images have correct scale ratio
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img_GT_list = data_util._get_paths_from_images(GT_folder)
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img_LR_list = data_util._get_paths_from_images(LR_folder)
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assert len(img_GT_list) == len(img_LR_list), 'different length of GT_folder and LR_folder.'
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for path_GT, path_LR in zip(img_GT_list, img_LR_list):
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img_GT = Image.open(path_GT)
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img_LR = Image.open(path_LR)
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w_GT, h_GT = img_GT.size
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w_LR, h_LR = img_LR.size
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assert w_GT / w_LR == scale_ratio, 'GT width [{:d}] is not {:d}X as LR weight [{:d}] for {:s}.'.format( # noqa: E501
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w_GT, scale_ratio, w_LR, path_GT)
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assert w_GT / w_LR == scale_ratio, 'GT width [{:d}] is not {:d}X as LR weight [{:d}] for {:s}.'.format( # noqa: E501
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w_GT, scale_ratio, w_LR, path_GT)
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# check crop size, step and threshold size
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assert crop_sz % scale_ratio == 0, 'crop size is not {:d}X multiplication.'.format(
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scale_ratio)
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assert step % scale_ratio == 0, 'step is not {:d}X multiplication.'.format(scale_ratio)
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assert thres_sz % scale_ratio == 0, 'thres_sz is not {:d}X multiplication.'.format(
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scale_ratio)
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print('process GT...')
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opt['input_folder'] = GT_folder
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opt['save_folder'] = save_GT_folder
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opt['crop_sz'] = crop_sz
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opt['step'] = step
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opt['thres_sz'] = thres_sz
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extract_signle(opt)
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print('process LR...')
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opt['input_folder'] = LR_folder
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opt['save_folder'] = save_LR_folder
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opt['crop_sz'] = crop_sz // scale_ratio
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opt['step'] = step // scale_ratio
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opt['thres_sz'] = thres_sz // scale_ratio
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extract_signle(opt)
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assert len(data_util._get_paths_from_images(save_GT_folder)) == len(
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data_util._get_paths_from_images(
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save_LR_folder)), 'different length of save_GT_folder and save_LR_folder.'
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else:
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raise ValueError('Wrong mode.')
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def extract_signle(opt):
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input_folder = opt['input_folder']
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save_folder = opt['save_folder']
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if not osp.exists(save_folder):
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os.makedirs(save_folder)
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print('mkdir [{:s}] ...'.format(save_folder))
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else:
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print('Folder [{:s}] already exists. Exit...'.format(save_folder))
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sys.exit(1)
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img_list = data_util._get_paths_from_images(input_folder)
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def update(arg):
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pbar.update(arg)
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pbar = ProgressBar(len(img_list))
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pool = Pool(opt['n_thread'])
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for path in img_list:
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pool.apply_async(worker, args=(path, opt), callback=update)
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pool.close()
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pool.join()
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print('All subprocesses done.')
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def worker(path, opt):
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crop_sz = opt['crop_sz']
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step = opt['step']
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thres_sz = opt['thres_sz']
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img_name = osp.basename(path)
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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n_channels = len(img.shape)
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if n_channels == 2:
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h, w = img.shape
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elif n_channels == 3:
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h, w, c = img.shape
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else:
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raise ValueError('Wrong image shape - {}'.format(n_channels))
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h_space = np.arange(0, h - crop_sz + 1, step)
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if h - (h_space[-1] + crop_sz) > thres_sz:
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h_space = np.append(h_space, h - crop_sz)
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w_space = np.arange(0, w - crop_sz + 1, step)
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if w - (w_space[-1] + crop_sz) > thres_sz:
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w_space = np.append(w_space, w - crop_sz)
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index = 0
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for x in h_space:
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for y in w_space:
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index += 1
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if n_channels == 2:
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crop_img = img[x:x + crop_sz, y:y + crop_sz]
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else:
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crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
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crop_img = np.ascontiguousarray(crop_img)
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cv2.imwrite(
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osp.join(opt['save_folder'],
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img_name.replace('.png', '_s{:03d}.png'.format(index))), crop_img,
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[cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
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return 'Processing {:s} ...'.format(img_name)
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if __name__ == '__main__':
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main()
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