"""A multi-thread tool to crop large images to sub-images for faster IO.""" import os import os.path as osp import sys from multiprocessing import Pool import numpy as np import cv2 from PIL import Image sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__)))) from utils.util import ProgressBar # noqa: E402 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 = True 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'] = 'F:\\4k6k\\datasets\\vrp\\images_sized' opt['save_folder'] = 'F:\\4k6k\\datasets\\vrp\\images_tiled' opt['crop_sz'] = 320 # the size of each sub-image opt['step'] = 280 # step of the sliding crop window opt['thres_sz'] = 200 # 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' 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.' else: raise ValueError('Wrong mode.') def extract_single(opt, split_img=False): input_folder = opt['input_folder'] save_folder = opt['save_folder'] if not osp.exists(save_folder): os.makedirs(save_folder) print('mkdir [{:s}] ...'.format(save_folder)) else: print('Folder [{:s}] already exists. Exit...'.format(save_folder)) sys.exit(1) img_list = data_util._get_paths_from_images(input_folder) def update(arg): pbar.update(arg) pbar = ProgressBar(len(img_list)) 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, split_mode=False, left_img=True): crop_sz = opt['crop_sz'] step = opt['step'] thres_sz = opt['thres_sz'] img_name = osp.basename(path) img = cv2.imread(path, cv2.IMREAD_UNCHANGED) n_channels = len(img.shape) if n_channels == 2: h, w = img.shape elif n_channels == 3: h, w, c = img.shape 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) w_space = np.arange(0, w - crop_sz + 1, step) if w - (w_space[-1] + crop_sz) > thres_sz: w_space = np.append(w_space, w - crop_sz) index = 0 for x in h_space: for y in w_space: index += 1 if n_channels == 2: crop_img = img[x:x + crop_sz, y:y + crop_sz] 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 ".jpg" in img_name cv2.imwrite( osp.join(opt['save_folder'], img_name.replace('.jpg', '_l{:05d}_s{:03d}.png'.format(left, index))), crop_img, [cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']]) return 'Processing {:s} ...'.format(img_name) if __name__ == '__main__': main()