122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
# ------------------------------------------------------------------------
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# Copyright (c) 2022 megvii-model. All Rights Reserved.
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# ------------------------------------------------------------------------
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# Modified from BasicSR (https://github.com/xinntao/BasicSR)
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# Copyright 2018-2020 BasicSR Authors
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# ------------------------------------------------------------------------
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import cv2
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import numpy as np
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import os
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import sys
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from multiprocessing import Pool
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from os import path as osp
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from tqdm import tqdm
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from basicsr.utils import scandir_SIDD
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from basicsr.utils.create_lmdb import create_lmdb_for_SIDD
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def main():
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opt = {}
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opt['n_thread'] = 20
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opt['compression_level'] = 3
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opt['input_folder'] = './datasets/SIDD/Data'
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opt['save_folder'] = './datasets/SIDD/train/input_crops'
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opt['crop_size'] = 512
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opt['step'] = 384
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opt['thresh_size'] = 0
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opt['keywords'] = '_NOISY'
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extract_subimages(opt)
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opt['save_folder'] = './datasets/SIDD/train/gt_crops'
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opt['keywords'] = '_GT'
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extract_subimages(opt)
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create_lmdb_for_SIDD()
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def extract_subimages(opt):
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"""Crop images to subimages.
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Args:
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opt (dict): Configuration dict. It contains:
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input_folder (str): Path to the input folder.
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save_folder (str): Path to save folder.
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n_thread (int): Thread number.
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"""
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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(f'mkdir {save_folder} ...')
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else:
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print(f'Folder {save_folder} already exists. Exit.')
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# sys.exit(1)
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img_list = list(scandir_SIDD(input_folder, keywords=opt['keywords'], recursive=True, full_path=True))
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pbar = tqdm(total=len(img_list), unit='image', desc='Extract')
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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(
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worker, args=(path, opt), callback=lambda arg: pbar.update(1))
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pool.close()
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pool.join()
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pbar.close()
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print('All processes done.')
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def worker(path, opt):
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"""Worker for each process.
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Args:
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path (str): Image path.
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opt (dict): Configuration dict. It contains:
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crop_size (int): Crop size.
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step (int): Step for overlapped sliding window.
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thresh_size (int): Threshold size. Patches whose size is lower
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than thresh_size will be dropped.
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save_folder (str): Path to save folder.
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compression_level (int): for cv2.IMWRITE_PNG_COMPRESSION.
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Returns:
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process_info (str): Process information displayed in progress bar.
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"""
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crop_size = opt['crop_size']
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step = opt['step']
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thresh_size = opt['thresh_size']
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img_name, extension = osp.splitext(osp.basename(path))
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img_name = img_name.replace(opt['keywords'], '')
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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if img.ndim == 2:
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h, w = img.shape
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elif img.ndim == 3:
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h, w, c = img.shape
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else:
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raise ValueError(f'Image ndim should be 2 or 3, but got {img.ndim}')
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h_space = np.arange(0, h - crop_size + 1, step)
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if h - (h_space[-1] + crop_size) > thresh_size:
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h_space = np.append(h_space, h - crop_size)
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w_space = np.arange(0, w - crop_size + 1, step)
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if w - (w_space[-1] + crop_size) > thresh_size:
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w_space = np.append(w_space, w - crop_size)
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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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cropped_img = img[x:x + crop_size, y:y + crop_size, ...]
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cropped_img = np.ascontiguousarray(cropped_img)
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cv2.imwrite(
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osp.join(opt['save_folder'],
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f'{img_name}_s{index:03d}{extension}'), cropped_img,
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[cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
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process_info = f'Processing {img_name} ...'
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return process_info
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if __name__ == '__main__':
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main()
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# ... make sidd to lmdb |