237 lines
9.5 KiB
Python
237 lines
9.5 KiB
Python
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"""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 numpy as np
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import cv2
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from PIL import Image
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import data.util as data_util # noqa: E402
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import lmdb
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import pyarrow
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import torch.utils.data as data
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from tqdm import tqdm
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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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split_img = False
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opt = {}
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opt['n_thread'] = 12
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opt['compression_level'] = 90 # JPEG compression quality rating.
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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'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\images'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\lmdb_with_ref'
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opt['crop_sz'] = 512 # the size of each sub-image
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opt['step'] = 128 # step of the sliding crop window
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opt['thres_sz'] = 128 # size threshold
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opt['resize_final_img'] = .5
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opt['only_resize'] = False
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extract_single(opt, split_img)
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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_single(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_single(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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class LmdbWriter:
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def __init__(self, lmdb_path, max_mem_size=30*1024*1024*1024, write_freq=5000):
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self.db = lmdb.open(lmdb_path, subdir=True,
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map_size=max_mem_size, readonly=False,
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meminit=False, map_async=True)
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self.txn = self.db.begin(write=True)
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self.ref_id = 0
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self.tile_ids = {}
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self.writes = 0
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self.write_freq = write_freq
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self.keys = []
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# Writes the given reference image to the db and returns its ID.
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def write_reference_image(self, ref_img):
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id = self.ref_id
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self.ref_id += 1
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self.write_image(id, ref_img[0], ref_img[1])
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return id
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# Writes a tile image to the db given a reference image and returns its ID.
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def write_tile_image(self, ref_id, tile_image):
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next_tile_id = 0 if ref_id not in self.tile_ids.keys() else self.tile_ids[ref_id]
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self.tile_ids[ref_id] = next_tile_id+1
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full_id = "%i_%i" % (ref_id, next_tile_id)
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self.write_image(full_id, tile_image[0], tile_image[1])
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self.keys.append(full_id)
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return full_id
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# Writes an image directly to the db with the given reference image and center point.
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def write_image(self, id, img, center_point):
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self.txn.put(u'{}'.format(id).encode('ascii'), pyarrow.serialize(img).to_buffer(), pyarrow.serialize(center_point).to_buffer())
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self.writes += 1
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if self.writes % self.write_freq == 0:
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self.txn.commit()
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self.txn = self.db.begin(write=True)
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def close(self):
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self.txn.commit()
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with self.db.begin(write=True) as txn:
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txn.put(b'__keys__', pyarrow.serialize(self.keys).to_buffer())
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txn.put(b'__len__', pyarrow.serialize(len(self.keys)).to_buffer())
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self.db.sync()
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self.db.close()
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class TiledDataset(data.Dataset):
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def __init__(self, opt, split_mode=False):
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self.split_mode = split_mode
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self.opt = opt
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input_folder = opt['input_folder']
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self.images = data_util._get_paths_from_images(input_folder)
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def __getitem__(self, index):
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if self.split_mode:
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return self.get(index, True, True).extend(self.get(index, True, False))
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else:
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return self.get(index, False, False)
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def get(self, index, split_mode, left_img):
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path = self.images[index]
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crop_sz = self.opt['crop_sz']
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step = self.opt['step']
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thres_sz = self.opt['thres_sz']
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only_resize = self.opt['only_resize']
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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# We must convert the image into a square. Crop the image so that only the center is left, since this is often
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# the most salient part of the image.
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h, w, c = img.shape
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dim = min(h, w)
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img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
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h, w, c = img.shape
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# Uncomment to filter any image that doesnt meet a threshold size.
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if min(h,w) < 1024:
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return
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left = 0
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right = w
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if split_mode:
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if left_img:
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left = 0
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right = int(w/2)
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else:
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left = int(w/2)
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right = w
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w = int(w/2)
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img = img[:, left:right]
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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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dsize = None
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if only_resize:
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dsize = (crop_sz, crop_sz)
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if h < w:
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h_space = [0]
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w_space = [(w - h) // 2]
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crop_sz = h
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else:
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h_space = [(h - w) // 2]
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w_space = [0]
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crop_sz = w
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index = 0
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resize_factor = self.opt['resize_final_img'] if 'resize_final_img' in self.opt.keys() else 1
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dsize = (int(crop_sz * resize_factor), int(crop_sz * resize_factor))
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# Reference image should always be first.
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results = [(cv2.resize(img, dsize, interpolation=cv2.INTER_AREA), (-1,-1))]
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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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crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
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center_point = (x + crop_sz // 2, y + crop_sz // 2)
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crop_img = np.ascontiguousarray(crop_img)
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if 'resize_final_img' in self.opt.keys():
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# Resize too.
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resize_factor = self.opt['resize_final_img']
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center_point = (int(center_point[0] * resize_factor), int(center_point[1] * resize_factor))
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crop_img = cv2.resize(crop_img, dsize, interpolation=cv2.INTER_AREA)
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success, buffer = cv2.imencode(".jpg", crop_img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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assert success
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results.append((buffer, center_point))
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return results
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def __len__(self):
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return len(self.images)
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def identity(x):
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return x
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def extract_single(opt, split_img=False):
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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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lmdb = LmdbWriter(save_folder)
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dataset = TiledDataset(opt, split_img)
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dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
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tq = tqdm(dataloader)
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for imgs in tq:
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if imgs is None or len(imgs) <= 1:
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continue
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ref_id = lmdb.write_reference_image(imgs[0])
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for tile in imgs[1:]:
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lmdb.write_tile_image(ref_id, tile)
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lmdb.close()
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if __name__ == '__main__':
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main()
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