"""A multi-thread tool to crop large images to sub-images for faster IO.""" import os import os.path as osp import numpy as np import cv2 from PIL import Image import data.util as data_util # noqa: E402 import lmdb import pyarrow import torch.utils.data as data from tqdm import tqdm def main(): mode = 'single' # single (one input folder) | pair (extract corresponding GT and LR pairs) split_img = False opt = {} opt['n_thread'] = 12 opt['compression_level'] = 90 # JPEG compression quality rating. # 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\\ns_images\\imagesets\\images' opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\lmdb_with_ref' opt['crop_sz'] = 512 # the size of each sub-image opt['step'] = 128 # step of the sliding crop window opt['thres_sz'] = 128 # size threshold opt['resize_final_img'] = .5 opt['only_resize'] = False 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.') class LmdbWriter: def __init__(self, lmdb_path, max_mem_size=30*1024*1024*1024, write_freq=5000): self.db = lmdb.open(lmdb_path, subdir=True, map_size=max_mem_size, readonly=False, meminit=False, map_async=True) self.txn = self.db.begin(write=True) self.ref_id = 0 self.tile_ids = {} self.writes = 0 self.write_freq = write_freq self.keys = [] # Writes the given reference image to the db and returns its ID. def write_reference_image(self, ref_img): id = self.ref_id self.ref_id += 1 self.write_image(id, ref_img[0], ref_img[1]) return id # Writes a tile image to the db given a reference image and returns its ID. def write_tile_image(self, ref_id, tile_image): next_tile_id = 0 if ref_id not in self.tile_ids.keys() else self.tile_ids[ref_id] self.tile_ids[ref_id] = next_tile_id+1 full_id = "%i_%i" % (ref_id, next_tile_id) self.write_image(full_id, tile_image[0], tile_image[1]) self.keys.append(full_id) return full_id # Writes an image directly to the db with the given reference image and center point. def write_image(self, id, img, center_point): self.txn.put(u'{}'.format(id).encode('ascii'), pyarrow.serialize(img).to_buffer(), pyarrow.serialize(center_point).to_buffer()) self.writes += 1 if self.writes % self.write_freq == 0: self.txn.commit() self.txn = self.db.begin(write=True) def close(self): self.txn.commit() with self.db.begin(write=True) as txn: txn.put(b'__keys__', pyarrow.serialize(self.keys).to_buffer()) txn.put(b'__len__', pyarrow.serialize(len(self.keys)).to_buffer()) self.db.sync() self.db.close() class TiledDataset(data.Dataset): def __init__(self, opt, split_mode=False): self.split_mode = split_mode 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)) else: return self.get(index, False, False) def get(self, index, split_mode, left_img): path = self.images[index] crop_sz = self.opt['crop_sz'] step = self.opt['step'] thres_sz = self.opt['thres_sz'] only_resize = self.opt['only_resize'] 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. 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 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) dsize = None if only_resize: dsize = (crop_sz, crop_sz) if h < w: h_space = [0] w_space = [(w - h) // 2] crop_sz = h else: h_space = [(h - w) // 2] w_space = [0] crop_sz = w index = 0 resize_factor = self.opt['resize_final_img'] if 'resize_final_img' in self.opt.keys() else 1 dsize = (int(crop_sz * resize_factor), int(crop_sz * resize_factor)) # Reference image should always be first. results = [(cv2.resize(img, dsize, interpolation=cv2.INTER_AREA), (-1,-1))] for x in h_space: for y in w_space: index += 1 crop_img = img[x:x + crop_sz, y:y + crop_sz, :] center_point = (x + crop_sz // 2, y + crop_sz // 2) crop_img = np.ascontiguousarray(crop_img) if 'resize_final_img' in self.opt.keys(): # Resize too. resize_factor = self.opt['resize_final_img'] center_point = (int(center_point[0] * resize_factor), int(center_point[1] * resize_factor)) crop_img = cv2.resize(crop_img, dsize, interpolation=cv2.INTER_AREA) success, buffer = cv2.imencode(".jpg", crop_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']]) assert success results.append((buffer, center_point)) return results def __len__(self): return len(self.images) def identity(x): return x def extract_single(opt, split_img=False): save_folder = opt['save_folder'] if not osp.exists(save_folder): os.makedirs(save_folder) print('mkdir [{:s}] ...'.format(save_folder)) lmdb = LmdbWriter(save_folder) dataset = TiledDataset(opt, split_img) dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity) tq = tqdm(dataloader) for imgs in tq: if imgs is None or len(imgs) <= 1: continue ref_id = lmdb.write_reference_image(imgs[0]) for tile in imgs[1:]: lmdb.write_tile_image(ref_id, tile) lmdb.close() if __name__ == '__main__': main()