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| # -*- coding: utf-8 -*- | |
| import argparse | |
| import cv2 | |
| import numpy as np | |
| import os, shutil | |
| import sys | |
| from multiprocessing import Pool | |
| from os import path as osp | |
| from tqdm import tqdm | |
| import random | |
| from collections import namedtuple | |
| # Import files from the local folder | |
| root_path = os.path.abspath('.') | |
| sys.path.append(root_path) | |
| from degradation.ESR.usm_sharp import USMSharp | |
| class worker: | |
| def __init__(self, start_index=1): | |
| # The index you want to start with | |
| self.output_index = start_index | |
| def process(self, path, opt, usm_sharper): | |
| ''' crop the image here (also do usm here) | |
| Args: | |
| path (str): path of the image | |
| opt (dict): all setting in a dictionary | |
| usm_sharper (class): usm sharpener | |
| Returns: | |
| cropped_num (int): how many cropped images you have for this path | |
| ''' | |
| crop_size = opt['crop_size'] # usually 400 | |
| # read image | |
| img = cv2.imread(path) | |
| height, width = img.shape[0:2] | |
| res_store = [] | |
| crop_num = (height//crop_size)*(width//crop_size) | |
| random_num = opt['crop_num_per_img'] | |
| # Use shift offset to make image more cover origional image size | |
| shift_offset_h, shift_offset_w = 0, 0 | |
| if random_num == -1: | |
| # We should select all sub-frames order by order (not randomly select here) | |
| choices = [i for i in range(crop_num)] | |
| shift_offset_h = 0 #random.randint(0, height - crop_size * (height//crop_size)) | |
| shift_offset_w = 0 #random.randint(0, width - crop_size * (width//crop_size)) | |
| else: | |
| # Divide imgs by crop_size x crop_size and choose opt['crop_num_per_img'] num of them to avoid overlap | |
| num = min(random_num, crop_num) | |
| choices = random.sample(range(crop_num), num) | |
| for choice in choices: | |
| row_num = (width//crop_size) | |
| x, y = crop_size * (choice // row_num), crop_size * (choice % row_num) | |
| # add offset | |
| res_store.append((x, y)) | |
| # Sharp the image before selection | |
| if opt['usm_save_folder'] != None: | |
| sharpened_img = usm_sharper(img) | |
| for (h, w) in res_store: | |
| cropped_img = img[h+shift_offset_h : h+crop_size+shift_offset_h, w+shift_offset_w : w+crop_size+shift_offset_w, ...] | |
| cropped_img = np.ascontiguousarray(cropped_img) | |
| cv2.imwrite(osp.join(opt['save_folder'], f'img_{self.output_index:06d}.png'), cropped_img, [cv2.IMWRITE_PNG_COMPRESSION, 0]) # Save in lossless mode | |
| # store the sharpened cropped image | |
| if opt['usm_save_folder'] != None: | |
| cropped_sharpened_img = sharpened_img[h+shift_offset_h : h+crop_size+shift_offset_h, w+shift_offset_w : w+crop_size+shift_offset_w, ...] | |
| cropped_sharpened_img = np.ascontiguousarray(cropped_sharpened_img) | |
| cv2.imwrite(osp.join(opt['usm_save_folder'], f'img_{self.output_index:06d}.png'), cropped_sharpened_img, [cv2.IMWRITE_PNG_COMPRESSION, 0]) | |
| self.output_index += 1 | |
| cropped_num = len(res_store) | |
| return cropped_num | |
| def extract_subimages(opt): | |
| # Input | |
| input_folders = opt['input_folders'] | |
| # Make folders | |
| save_folder = opt['save_folder'] | |
| usm_save_folder = opt['usm_save_folder'] | |
| if osp.exists(save_folder): | |
| print(f'Folder {save_folder} already exists. Program will delete this folder!') | |
| shutil.rmtree(save_folder) | |
| os.makedirs(save_folder) | |
| if usm_save_folder != None: | |
| if osp.exists(usm_save_folder): | |
| print(f'Folder {usm_save_folder} already exists. Program will delete this folder!') | |
| shutil.rmtree(usm_save_folder) | |
| print("Use usm sharp") | |
| os.makedirs(usm_save_folder) | |
| # USM | |
| usm_sharper = USMSharp(type="cv2") | |
| # Iterate all datasets' folders | |
| start_index = 1 | |
| for input_folder in input_folders: | |
| print(input_folder, start_index) | |
| # Scan all images | |
| img_list = [] | |
| for file in sorted(os.listdir(input_folder)): | |
| if file.split(".")[-1] in ["png", "jpg"]: | |
| img_list.append(osp.join(input_folder, file)) | |
| # Iterate can crop | |
| obj = worker(start_index=start_index) # The start_index determines where you will start your naming your image (usually start from 0) | |
| for path in img_list: | |
| if random.random() < opt['select_rate']: | |
| cropped_num = obj.process(path, opt, usm_sharper) | |
| start_index += cropped_num | |
| print(start_index, path) | |
| else: | |
| print("SKIP") | |
| print('All processes done.') | |
| def main(args): | |
| opt = {} | |
| input_folders = [] | |
| if type(args.input_folder) == str: | |
| input_folders.append(args.input_folder) | |
| else: | |
| for input_folder in args.input_folder: | |
| input_folders.append(input_folder) | |
| print("input folders have ", input_folders) | |
| opt['input_folders'] = input_folders | |
| opt['save_folder'] = args.save_folder | |
| opt['usm_save_folder'] = args.output_usm | |
| opt['crop_size'] = args.crop_size | |
| opt['crop_num_per_img'] = args.crop_num_per_img | |
| opt['select_rate'] = args.select_rate | |
| # Extract subimages | |
| extract_subimages(opt) | |
| if __name__ == '__main__': | |
| random.seed(777) # We setup a random seed such that all program get the same cropped images | |
| parser = argparse.ArgumentParser() | |
| # Try to split image after default | |
| parser.add_argument('-i', '--input_folder', nargs='+', type=str, default='datasets/all_Anime_hq_frames_resize', help='Input folder') # TODO: support multiple image input | |
| parser.add_argument('-o', '--save_folder', type=str, default='datasets/train_hr', help='Output folder') | |
| parser.add_argument('--output_usm', type=str, help='usm sharpened hr folder') | |
| parser.add_argument('--crop_size', type=int, default=360, help='Crop size') | |
| parser.add_argument('--select_rate', type=float, default=1, help='(0-1): Proportion to keep; 1 means to keep them all') | |
| parser.add_argument('--crop_num_per_img', type=int, default=-1, help='Crop size (int); -1 means use all possible sub-frames') | |
| args = parser.parse_args() | |
| main(args) |