""" Original code: https://github.com/swz30/Restormer/blob/main/Defocus_Deblurring/generate_patches_dpdd.py by Syed Waqas Zamir """ ##### Data preparation file for training Restormer on the DPDD Dataset ######## import cv2 import numpy as np from glob import glob from natsort import natsorted import os from tqdm import tqdm from copy import deepcopy from joblib import Parallel, delayed def shapness_measure(img_temp,kernel_size): conv_x = cv2.Sobel(img_temp,cv2.CV_64F,1,0,ksize=kernel_size) conv_y = cv2.Sobel(img_temp,cv2.CV_64F,0,1,ksize=kernel_size) temp_arr_x=deepcopy(conv_x*conv_x) temp_arr_y=deepcopy(conv_y*conv_y) temp_sum_x_y=temp_arr_x+temp_arr_y temp_sum_x_y=np.sqrt(temp_sum_x_y) return np.sum(temp_sum_x_y) def filter_patch_sharpness(patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp): patches_src_c, patches_trg_c, patches_src_l, patches_src_r = [], [], [], [] fitnessVal_3=[] fitnessVal_7=[] fitnessVal_11=[] fitnessVal_15=[] num_of_img_patches=len(patches_trg_c_temp) for i in range(num_of_img_patches): fitnessVal_3.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),3)) fitnessVal_7.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),7)) fitnessVal_11.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),11)) fitnessVal_15.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),15)) fitnessVal_3=np.asarray(fitnessVal_3) fitnessVal_7=np.asarray(fitnessVal_7) fitnessVal_11=np.asarray(fitnessVal_11) fitnessVal_15=np.asarray(fitnessVal_15) fitnessVal_3=(fitnessVal_3-np.min(fitnessVal_3))/np.max((fitnessVal_3-np.min(fitnessVal_3))) fitnessVal_7=(fitnessVal_7-np.min(fitnessVal_7))/np.max((fitnessVal_7-np.min(fitnessVal_7))) fitnessVal_11=(fitnessVal_11-np.min(fitnessVal_11))/np.max((fitnessVal_11-np.min(fitnessVal_11))) fitnessVal_15=(fitnessVal_15-np.min(fitnessVal_15))/np.max((fitnessVal_15-np.min(fitnessVal_15))) fitnessVal_all=fitnessVal_3*fitnessVal_7*fitnessVal_11*fitnessVal_15 to_remove_patches_number=int(to_remove_ratio*num_of_img_patches) for itr in range(to_remove_patches_number): minArrInd=np.argmin(fitnessVal_all) fitnessVal_all[minArrInd]=2 for itr in range(num_of_img_patches): if fitnessVal_all[itr]!=2: patches_src_c.append(patches_src_c_temp[itr]) patches_trg_c.append(patches_trg_c_temp[itr]) patches_src_l.append(patches_src_l_temp[itr]) patches_src_r.append(patches_src_r_temp[itr]) return patches_src_c, patches_trg_c, patches_src_l, patches_src_r def slice_stride(_img_src_c, _img_trg_c, _img_src_l, _img_src_r): coordinates_list=[] coordinates_list.append([0,0,0,0]) patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp = [], [], [], [] for r in range(0,_img_src_c.shape[0],stride[0]): for c in range(0,_img_src_c.shape[1],stride[1]): if (r+patch_size[0]) <= _img_src_c.shape[0] and (c+patch_size[1]) <= _img_src_c.shape[1]: patches_src_c_temp.append(_img_src_c[r:r+patch_size[0],c:c+patch_size[1]]) patches_trg_c_temp.append(_img_trg_c[r:r+patch_size[0],c:c+patch_size[1]]) patches_src_l_temp.append(_img_src_l[r:r+patch_size[0],c:c+patch_size[1]]) patches_src_r_temp.append(_img_src_r[r:r+patch_size[0],c:c+patch_size[1]]) elif (r+patch_size[0]) <= _img_src_c.shape[0] and not ([r,r+patch_size[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[r:r+patch_size[0],_img_src_c.shape[1]-patch_size[1]:_img_src_c.shape[1]]) patches_trg_c_temp.append(_img_trg_c[r:r+patch_size[0],_img_trg_c.shape[1]-patch_size[1]:_img_trg_c.shape[1]]) patches_src_l_temp.append(_img_src_l[r:r+patch_size[0],_img_src_l.shape[1]-patch_size[1]:_img_src_l.shape[1]]) patches_src_r_temp.append(_img_src_r[r:r+patch_size[0],_img_src_r.shape[1]-patch_size[1]:_img_src_r.shape[1]]) coordinates_list.append([r,r+patch_size[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]]) elif (c+patch_size[1]) <= _img_src_c.shape[1] and not ([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],c,c+patch_size[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[_img_src_c.shape[0]-patch_size[0]:_img_src_c.shape[0],c:c+patch_size[1]]) patches_trg_c_temp.append(_img_trg_c[_img_trg_c.shape[0]-patch_size[0]:_img_trg_c.shape[0],c:c+patch_size[1]]) patches_src_l_temp.append(_img_src_l[_img_src_l.shape[0]-patch_size[0]:_img_src_l.shape[0],c:c+patch_size[1]]) patches_src_r_temp.append(_img_src_r[_img_src_r.shape[0]-patch_size[0]:_img_src_r.shape[0],c:c+patch_size[1]]) coordinates_list.append([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],c,c+patch_size[1]]) elif not ([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[_img_src_c.shape[0]-patch_size[0]:_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1]:_img_src_c.shape[1]]) patches_trg_c_temp.append(_img_trg_c[_img_trg_c.shape[0]-patch_size[0]:_img_trg_c.shape[0],_img_trg_c.shape[1]-patch_size[1]:_img_trg_c.shape[1]]) patches_src_l_temp.append(_img_src_l[_img_src_l.shape[0]-patch_size[0]:_img_src_l.shape[0],_img_src_l.shape[1]-patch_size[1]:_img_src_l.shape[1]]) patches_src_r_temp.append(_img_src_r[_img_src_r.shape[0]-patch_size[0]:_img_src_r.shape[0],_img_src_r.shape[1]-patch_size[1]:_img_src_r.shape[1]]) coordinates_list.append([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]]) return patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp def train_files(file_): lrL_file, lrR_file, lrC_file, hrC_file = file_ filename = os.path.splitext(os.path.split(lrC_file)[-1])[0] lrL_img = cv2.imread(lrL_file, -1) lrR_img = cv2.imread(lrR_file, -1) lrC_img = cv2.imread(lrC_file, -1) hrC_img = cv2.imread(hrC_file, -1) lrC_patches, hrC_patches, lrL_patches, lrR_patches = slice_stride(lrC_img, hrC_img, lrL_img, lrR_img) lrC_patches, hrC_patches, lrL_patches, lrR_patches = filter_patch_sharpness(lrC_patches, hrC_patches, lrL_patches, lrR_patches) num_patch = 0 for lrC_patch, hrC_patch, lrL_patch, lrR_patch in zip(lrC_patches, hrC_patches, lrL_patches, lrR_patches): num_patch += 1 lrL_savename = os.path.join(lrL_tar, filename + '-' + str(num_patch) + '.png') lrR_savename = os.path.join(lrR_tar, filename + '-' + str(num_patch) + '.png') lrC_savename = os.path.join(lrC_tar, filename + '-' + str(num_patch) + '.png') hrC_savename = os.path.join(hrC_tar, filename + '-' + str(num_patch) + '.png') cv2.imwrite(lrL_savename, lrL_patch) cv2.imwrite(lrR_savename, lrR_patch) cv2.imwrite(lrC_savename, lrC_patch) cv2.imwrite(hrC_savename, hrC_patch) def val_files(file_): lrL_file, lrR_file, lrC_file, hrC_file = file_ filename = os.path.splitext(os.path.split(lrC_file)[-1])[0] lrL_savename = os.path.join(lrL_tar, filename + '.png') lrR_savename = os.path.join(lrR_tar, filename + '.png') lrC_savename = os.path.join(lrC_tar, filename + '.png') hrC_savename = os.path.join(hrC_tar, filename + '.png') lrL_img = cv2.imread(lrL_file, -1) lrR_img = cv2.imread(lrR_file, -1) lrC_img = cv2.imread(lrC_file, -1) hrC_img = cv2.imread(hrC_file, -1) w, h = lrC_img.shape[:2] i = (w-val_patch_size)//2 j = (h-val_patch_size)//2 lrL_patch = lrL_img[i:i+val_patch_size, j:j+val_patch_size,:] lrR_patch = lrR_img[i:i+val_patch_size, j:j+val_patch_size,:] lrC_patch = lrC_img[i:i+val_patch_size, j:j+val_patch_size,:] hrC_patch = hrC_img[i:i+val_patch_size, j:j+val_patch_size,:] cv2.imwrite(lrL_savename, lrL_patch) cv2.imwrite(lrR_savename, lrR_patch) cv2.imwrite(lrC_savename, lrC_patch) cv2.imwrite(hrC_savename, hrC_patch) ############ Prepare Training data #################### num_cores = 10 src = 'DPDD/train/' tar = 'train-dpdd' lrL_tar = os.path.join(tar, 'inputL_crops') lrR_tar = os.path.join(tar, 'inputR_crops') lrC_tar = os.path.join(tar, 'inputC_crops') hrC_tar = os.path.join(tar, 'target_crops') os.makedirs(lrL_tar, exist_ok=True) os.makedirs(lrR_tar, exist_ok=True) os.makedirs(lrC_tar, exist_ok=True) os.makedirs(hrC_tar, exist_ok=True) lrL_files = natsorted(glob(os.path.join(src, 'inputL', '*.png'))) lrR_files = natsorted(glob(os.path.join(src, 'inputR', '*.png'))) lrC_files = natsorted(glob(os.path.join(src, 'inputC', '*.png'))) hrC_files = natsorted(glob(os.path.join(src, 'target', '*.png'))) files = [(i, j, k, l) for i, j, k, l in zip(lrL_files, lrR_files, lrC_files, hrC_files)] patch_size = [512, 512] stride = [204, 204] p_max = 0 to_remove_ratio = 0.3 Parallel(n_jobs=num_cores)(delayed(train_files)(file_) for file_ in tqdm(files)) ############ Prepare validation data #################### val_patch_size = 256 src = 'DPDD/test' tar = 'test-dpdd' lrL_tar = os.path.join(tar, 'inputL_crops') lrR_tar = os.path.join(tar, 'inputR_crops') lrC_tar = os.path.join(tar, 'inputC_crops') hrC_tar = os.path.join(tar, 'target_crops') os.makedirs(lrL_tar, exist_ok=True) os.makedirs(lrR_tar, exist_ok=True) os.makedirs(lrC_tar, exist_ok=True) os.makedirs(hrC_tar, exist_ok=True) lrL_files = natsorted(glob(os.path.join(src, 'inputL', '*.png'))) lrR_files = natsorted(glob(os.path.join(src, 'inputR', '*.png'))) lrC_files = natsorted(glob(os.path.join(src, 'inputC', '*.png'))) hrC_files = natsorted(glob(os.path.join(src, 'target', '*.png'))) files = [(i, j, k, l) for i, j, k, l in zip(lrL_files, lrR_files, lrC_files, hrC_files)] Parallel(n_jobs=num_cores)(delayed(val_files)(file_) for file_ in tqdm(files))