412 lines
16 KiB
Python
412 lines
16 KiB
Python
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"""Create lmdb files for [General images (291 images/DIV2K) | Vimeo90K | REDS] training datasets"""
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import sys
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import os.path as osp
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import glob
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import pickle
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from multiprocessing import Pool
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import numpy as np
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import lmdb
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import cv2
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sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
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import data.util as data_util # noqa: E402
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import utils.util as util # noqa: E402
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def main():
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dataset = 'DIV2K_demo' # vimeo90K | REDS | general (e.g., DIV2K, 291) | DIV2K_demo |test
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mode = 'GT' # used for vimeo90k and REDS datasets
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# vimeo90k: GT | LR | flow
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# REDS: train_sharp, train_sharp_bicubic, train_blur_bicubic, train_blur, train_blur_comp
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# train_sharp_flowx4
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if dataset == 'vimeo90k':
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vimeo90k(mode)
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elif dataset == 'REDS':
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REDS(mode)
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elif dataset == 'general':
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opt = {}
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opt['img_folder'] = '../../datasets/DIV2K/DIV2K800_sub'
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opt['lmdb_save_path'] = '../../datasets/DIV2K/DIV2K800_sub.lmdb'
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opt['name'] = 'DIV2K800_sub_GT'
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general_image_folder(opt)
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elif dataset == 'DIV2K_demo':
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opt = {}
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## GT
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opt['img_folder'] = '../../datasets/DIV2K/DIV2K800_sub'
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opt['lmdb_save_path'] = '../../datasets/DIV2K/DIV2K800_sub.lmdb'
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opt['name'] = 'DIV2K800_sub_GT'
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general_image_folder(opt)
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## LR
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opt['img_folder'] = '../../datasets/DIV2K/DIV2K800_sub_bicLRx4'
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opt['lmdb_save_path'] = '../../datasets/DIV2K/DIV2K800_sub_bicLRx4.lmdb'
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opt['name'] = 'DIV2K800_sub_bicLRx4'
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general_image_folder(opt)
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elif dataset == 'test':
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test_lmdb('../../datasets/REDS/train_sharp_wval.lmdb', 'REDS')
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def read_image_worker(path, key):
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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return (key, img)
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def general_image_folder(opt):
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"""Create lmdb for general image folders
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Users should define the keys, such as: '0321_s035' for DIV2K sub-images
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If all the images have the same resolution, it will only store one copy of resolution info.
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Otherwise, it will store every resolution info.
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"""
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#### configurations
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read_all_imgs = False # whether real all images to memory with multiprocessing
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# Set False for use limited memory
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BATCH = 5000 # After BATCH images, lmdb commits, if read_all_imgs = False
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n_thread = 40
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########################################################
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img_folder = opt['img_folder']
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lmdb_save_path = opt['lmdb_save_path']
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meta_info = {'name': opt['name']}
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if not lmdb_save_path.endswith('.lmdb'):
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raise ValueError("lmdb_save_path must end with \'lmdb\'.")
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if osp.exists(lmdb_save_path):
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print('Folder [{:s}] already exists. Exit...'.format(lmdb_save_path))
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sys.exit(1)
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#### read all the image paths to a list
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print('Reading image path list ...')
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all_img_list = sorted(glob.glob(osp.join(img_folder, '*')))
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keys = []
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for img_path in all_img_list:
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keys.append(osp.splitext(osp.basename(img_path))[0])
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if read_all_imgs:
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#### read all images to memory (multiprocessing)
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dataset = {} # store all image data. list cannot keep the order, use dict
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print('Read images with multiprocessing, #thread: {} ...'.format(n_thread))
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pbar = util.ProgressBar(len(all_img_list))
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def mycallback(arg):
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'''get the image data and update pbar'''
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key = arg[0]
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dataset[key] = arg[1]
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pbar.update('Reading {}'.format(key))
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pool = Pool(n_thread)
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for path, key in zip(all_img_list, keys):
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pool.apply_async(read_image_worker, args=(path, key), callback=mycallback)
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pool.close()
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pool.join()
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print('Finish reading {} images.\nWrite lmdb...'.format(len(all_img_list)))
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#### create lmdb environment
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data_size_per_img = cv2.imread(all_img_list[0], cv2.IMREAD_UNCHANGED).nbytes
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print('data size per image is: ', data_size_per_img)
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data_size = data_size_per_img * len(all_img_list)
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env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
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#### write data to lmdb
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pbar = util.ProgressBar(len(all_img_list))
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txn = env.begin(write=True)
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resolutions = []
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for idx, (path, key) in enumerate(zip(all_img_list, keys)):
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pbar.update('Write {}'.format(key))
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key_byte = key.encode('ascii')
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data = dataset[key] if read_all_imgs else cv2.imread(path, cv2.IMREAD_UNCHANGED)
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if data.ndim == 2:
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H, W = data.shape
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C = 1
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else:
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H, W, C = data.shape
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txn.put(key_byte, data)
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resolutions.append('{:d}_{:d}_{:d}'.format(C, H, W))
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if not read_all_imgs and idx % BATCH == 0:
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txn.commit()
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txn = env.begin(write=True)
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txn.commit()
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env.close()
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print('Finish writing lmdb.')
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#### create meta information
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# check whether all the images are the same size
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assert len(keys) == len(resolutions)
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if len(set(resolutions)) <= 1:
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meta_info['resolution'] = [resolutions[0]]
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meta_info['keys'] = keys
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print('All images have the same resolution. Simplify the meta info.')
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else:
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meta_info['resolution'] = resolutions
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meta_info['keys'] = keys
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print('Not all images have the same resolution. Save meta info for each image.')
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pickle.dump(meta_info, open(osp.join(lmdb_save_path, 'meta_info.pkl'), "wb"))
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print('Finish creating lmdb meta info.')
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def vimeo90k(mode):
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"""Create lmdb for the Vimeo90K dataset, each image with a fixed size
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GT: [3, 256, 448]
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Now only need the 4th frame, e.g., 00001_0001_4
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LR: [3, 64, 112]
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1st - 7th frames, e.g., 00001_0001_1, ..., 00001_0001_7
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key:
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Use the folder and subfolder names, w/o the frame index, e.g., 00001_0001
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flow: downsampled flow: [3, 360, 320], keys: 00001_0001_4_[p3, p2, p1, n1, n2, n3]
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Each flow is calculated with GT images by PWCNet and then downsampled by 1/4
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Flow map is quantized by mmcv and saved in png format
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"""
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#### configurations
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read_all_imgs = False # whether real all images to memory with multiprocessing
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# Set False for use limited memory
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BATCH = 5000 # After BATCH images, lmdb commits, if read_all_imgs = False
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if mode == 'GT':
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img_folder = '../../datasets/vimeo90k/vimeo_septuplet/sequences'
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lmdb_save_path = '../../datasets/vimeo90k/vimeo90k_train_GT.lmdb'
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txt_file = '../../datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt'
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H_dst, W_dst = 256, 448
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elif mode == 'LR':
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img_folder = '../../datasets/vimeo90k/vimeo_septuplet_matlabLRx4/sequences'
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lmdb_save_path = '../../datasets/vimeo90k/vimeo90k_train_LR7frames.lmdb'
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txt_file = '../../datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt'
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H_dst, W_dst = 64, 112
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elif mode == 'flow':
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img_folder = '../../datasets/vimeo90k/vimeo_septuplet/sequences_flowx4'
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lmdb_save_path = '../../datasets/vimeo90k/vimeo90k_train_flowx4.lmdb'
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txt_file = '../../datasets/vimeo90k/vimeo_septuplet/sep_trainlist.txt'
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H_dst, W_dst = 128, 112
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else:
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raise ValueError('Wrong dataset mode: {}'.format(mode))
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n_thread = 40
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########################################################
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if not lmdb_save_path.endswith('.lmdb'):
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raise ValueError("lmdb_save_path must end with \'lmdb\'.")
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if osp.exists(lmdb_save_path):
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print('Folder [{:s}] already exists. Exit...'.format(lmdb_save_path))
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sys.exit(1)
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#### read all the image paths to a list
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print('Reading image path list ...')
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with open(txt_file) as f:
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train_l = f.readlines()
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train_l = [v.strip() for v in train_l]
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all_img_list = []
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keys = []
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for line in train_l:
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folder = line.split('/')[0]
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sub_folder = line.split('/')[1]
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all_img_list.extend(glob.glob(osp.join(img_folder, folder, sub_folder, '*')))
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if mode == 'flow':
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for j in range(1, 4):
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keys.append('{}_{}_4_n{}'.format(folder, sub_folder, j))
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keys.append('{}_{}_4_p{}'.format(folder, sub_folder, j))
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else:
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for j in range(7):
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keys.append('{}_{}_{}'.format(folder, sub_folder, j + 1))
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all_img_list = sorted(all_img_list)
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keys = sorted(keys)
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if mode == 'GT': # only read the 4th frame for the GT mode
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print('Only keep the 4th frame.')
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all_img_list = [v for v in all_img_list if v.endswith('im4.png')]
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keys = [v for v in keys if v.endswith('_4')]
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if read_all_imgs:
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#### read all images to memory (multiprocessing)
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dataset = {} # store all image data. list cannot keep the order, use dict
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print('Read images with multiprocessing, #thread: {} ...'.format(n_thread))
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pbar = util.ProgressBar(len(all_img_list))
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def mycallback(arg):
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"""get the image data and update pbar"""
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key = arg[0]
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dataset[key] = arg[1]
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pbar.update('Reading {}'.format(key))
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pool = Pool(n_thread)
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for path, key in zip(all_img_list, keys):
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pool.apply_async(read_image_worker, args=(path, key), callback=mycallback)
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pool.close()
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pool.join()
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print('Finish reading {} images.\nWrite lmdb...'.format(len(all_img_list)))
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#### write data to lmdb
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data_size_per_img = cv2.imread(all_img_list[0], cv2.IMREAD_UNCHANGED).nbytes
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print('data size per image is: ', data_size_per_img)
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data_size = data_size_per_img * len(all_img_list)
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env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
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txn = env.begin(write=True)
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pbar = util.ProgressBar(len(all_img_list))
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for idx, (path, key) in enumerate(zip(all_img_list, keys)):
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pbar.update('Write {}'.format(key))
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key_byte = key.encode('ascii')
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data = dataset[key] if read_all_imgs else cv2.imread(path, cv2.IMREAD_UNCHANGED)
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if 'flow' in mode:
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H, W = data.shape
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assert H == H_dst and W == W_dst, 'different shape.'
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else:
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H, W, C = data.shape
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assert H == H_dst and W == W_dst and C == 3, 'different shape.'
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txn.put(key_byte, data)
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if not read_all_imgs and idx % BATCH == 0:
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txn.commit()
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txn = env.begin(write=True)
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txn.commit()
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env.close()
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print('Finish writing lmdb.')
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#### create meta information
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meta_info = {}
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if mode == 'GT':
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meta_info['name'] = 'Vimeo90K_train_GT'
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elif mode == 'LR':
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meta_info['name'] = 'Vimeo90K_train_LR'
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elif mode == 'flow':
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meta_info['name'] = 'Vimeo90K_train_flowx4'
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channel = 1 if 'flow' in mode else 3
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meta_info['resolution'] = '{}_{}_{}'.format(channel, H_dst, W_dst)
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key_set = set()
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for key in keys:
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if mode == 'flow':
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a, b, _, _ = key.split('_')
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else:
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a, b, _ = key.split('_')
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key_set.add('{}_{}'.format(a, b))
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meta_info['keys'] = list(key_set)
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pickle.dump(meta_info, open(osp.join(lmdb_save_path, 'meta_info.pkl'), "wb"))
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print('Finish creating lmdb meta info.')
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def REDS(mode):
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"""Create lmdb for the REDS dataset, each image with a fixed size
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GT: [3, 720, 1280], key: 000_00000000
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LR: [3, 180, 320], key: 000_00000000
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key: 000_00000000
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flow: downsampled flow: [3, 360, 320], keys: 000_00000005_[p2, p1, n1, n2]
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Each flow is calculated with the GT images by PWCNet and then downsampled by 1/4
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Flow map is quantized by mmcv and saved in png format
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"""
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#### configurations
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read_all_imgs = False # whether real all images to memory with multiprocessing
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# Set False for use limited memory
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BATCH = 5000 # After BATCH images, lmdb commits, if read_all_imgs = False
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if mode == 'train_sharp':
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img_folder = '../../datasets/REDS/train_sharp'
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lmdb_save_path = '../../datasets/REDS/train_sharp_wval.lmdb'
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H_dst, W_dst = 720, 1280
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elif mode == 'train_sharp_bicubic':
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img_folder = '../../datasets/REDS/train_sharp_bicubic'
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lmdb_save_path = '../../datasets/REDS/train_sharp_bicubic_wval.lmdb'
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H_dst, W_dst = 180, 320
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elif mode == 'train_blur_bicubic':
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img_folder = '../../datasets/REDS/train_blur_bicubic'
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lmdb_save_path = '../../datasets/REDS/train_blur_bicubic_wval.lmdb'
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H_dst, W_dst = 180, 320
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elif mode == 'train_blur':
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img_folder = '../../datasets/REDS/train_blur'
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lmdb_save_path = '../../datasets/REDS/train_blur_wval.lmdb'
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H_dst, W_dst = 720, 1280
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elif mode == 'train_blur_comp':
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img_folder = '../../datasets/REDS/train_blur_comp'
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lmdb_save_path = '../../datasets/REDS/train_blur_comp_wval.lmdb'
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H_dst, W_dst = 720, 1280
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elif mode == 'train_sharp_flowx4':
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img_folder = '../../datasets/REDS/train_sharp_flowx4'
|
||
|
|
lmdb_save_path = '../../datasets/REDS/train_sharp_flowx4.lmdb'
|
||
|
|
H_dst, W_dst = 360, 320
|
||
|
|
n_thread = 40
|
||
|
|
########################################################
|
||
|
|
if not lmdb_save_path.endswith('.lmdb'):
|
||
|
|
raise ValueError("lmdb_save_path must end with \'lmdb\'.")
|
||
|
|
if osp.exists(lmdb_save_path):
|
||
|
|
print('Folder [{:s}] already exists. Exit...'.format(lmdb_save_path))
|
||
|
|
sys.exit(1)
|
||
|
|
|
||
|
|
#### read all the image paths to a list
|
||
|
|
print('Reading image path list ...')
|
||
|
|
all_img_list = data_util._get_paths_from_images(img_folder)
|
||
|
|
keys = []
|
||
|
|
for img_path in all_img_list:
|
||
|
|
split_rlt = img_path.split('/')
|
||
|
|
folder = split_rlt[-2]
|
||
|
|
img_name = split_rlt[-1].split('.png')[0]
|
||
|
|
keys.append(folder + '_' + img_name)
|
||
|
|
|
||
|
|
if read_all_imgs:
|
||
|
|
#### read all images to memory (multiprocessing)
|
||
|
|
dataset = {} # store all image data. list cannot keep the order, use dict
|
||
|
|
print('Read images with multiprocessing, #thread: {} ...'.format(n_thread))
|
||
|
|
pbar = util.ProgressBar(len(all_img_list))
|
||
|
|
|
||
|
|
def mycallback(arg):
|
||
|
|
'''get the image data and update pbar'''
|
||
|
|
key = arg[0]
|
||
|
|
dataset[key] = arg[1]
|
||
|
|
pbar.update('Reading {}'.format(key))
|
||
|
|
|
||
|
|
pool = Pool(n_thread)
|
||
|
|
for path, key in zip(all_img_list, keys):
|
||
|
|
pool.apply_async(read_image_worker, args=(path, key), callback=mycallback)
|
||
|
|
pool.close()
|
||
|
|
pool.join()
|
||
|
|
print('Finish reading {} images.\nWrite lmdb...'.format(len(all_img_list)))
|
||
|
|
|
||
|
|
#### create lmdb environment
|
||
|
|
data_size_per_img = cv2.imread(all_img_list[0], cv2.IMREAD_UNCHANGED).nbytes
|
||
|
|
print('data size per image is: ', data_size_per_img)
|
||
|
|
data_size = data_size_per_img * len(all_img_list)
|
||
|
|
env = lmdb.open(lmdb_save_path, map_size=data_size * 10)
|
||
|
|
|
||
|
|
#### write data to lmdb
|
||
|
|
pbar = util.ProgressBar(len(all_img_list))
|
||
|
|
txn = env.begin(write=True)
|
||
|
|
for idx, (path, key) in enumerate(zip(all_img_list, keys)):
|
||
|
|
pbar.update('Write {}'.format(key))
|
||
|
|
key_byte = key.encode('ascii')
|
||
|
|
data = dataset[key] if read_all_imgs else cv2.imread(path, cv2.IMREAD_UNCHANGED)
|
||
|
|
if 'flow' in mode:
|
||
|
|
H, W = data.shape
|
||
|
|
assert H == H_dst and W == W_dst, 'different shape.'
|
||
|
|
else:
|
||
|
|
H, W, C = data.shape
|
||
|
|
assert H == H_dst and W == W_dst and C == 3, 'different shape.'
|
||
|
|
txn.put(key_byte, data)
|
||
|
|
if not read_all_imgs and idx % BATCH == 0:
|
||
|
|
txn.commit()
|
||
|
|
txn = env.begin(write=True)
|
||
|
|
txn.commit()
|
||
|
|
env.close()
|
||
|
|
print('Finish writing lmdb.')
|
||
|
|
|
||
|
|
#### create meta information
|
||
|
|
meta_info = {}
|
||
|
|
meta_info['name'] = 'REDS_{}_wval'.format(mode)
|
||
|
|
channel = 1 if 'flow' in mode else 3
|
||
|
|
meta_info['resolution'] = '{}_{}_{}'.format(channel, H_dst, W_dst)
|
||
|
|
meta_info['keys'] = keys
|
||
|
|
pickle.dump(meta_info, open(osp.join(lmdb_save_path, 'meta_info.pkl'), "wb"))
|
||
|
|
print('Finish creating lmdb meta info.')
|
||
|
|
|
||
|
|
|
||
|
|
def test_lmdb(dataroot, dataset='REDS'):
|
||
|
|
env = lmdb.open(dataroot, readonly=True, lock=False, readahead=False, meminit=False)
|
||
|
|
meta_info = pickle.load(open(osp.join(dataroot, 'meta_info.pkl'), "rb"))
|
||
|
|
print('Name: ', meta_info['name'])
|
||
|
|
print('Resolution: ', meta_info['resolution'])
|
||
|
|
print('# keys: ', len(meta_info['keys']))
|
||
|
|
# read one image
|
||
|
|
if dataset == 'vimeo90k':
|
||
|
|
key = '00001_0001_4'
|
||
|
|
else:
|
||
|
|
key = '000_00000000'
|
||
|
|
print('Reading {} for test.'.format(key))
|
||
|
|
with env.begin(write=False) as txn:
|
||
|
|
buf = txn.get(key.encode('ascii'))
|
||
|
|
img_flat = np.frombuffer(buf, dtype=np.uint8)
|
||
|
|
C, H, W = [int(s) for s in meta_info['resolution'].split('_')]
|
||
|
|
img = img_flat.reshape(H, W, C)
|
||
|
|
cv2.imwrite('test.png', img)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|