| |
|
|
| 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 |
| import multiprocessing |
| from pdb import set_trace as stx |
|
|
| 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) |
|
|
|
|
| |
| num_cores = 10 |
| src = 'Datasets/Downloads/DPDD/train' |
| tar = 'Datasets/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, 'train', 'inputL', '*.png'))) |
| lrR_files = natsorted(glob(os.path.join(src, 'train', 'inputR', '*.png'))) |
| lrC_files = natsorted(glob(os.path.join(src, 'train', 'inputC', '*.png'))) |
| hrC_files = natsorted(glob(os.path.join(src, 'train', '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)) |
|
|
|
|
| |
| val_patch_size = 256 |
| src = 'Datasets/Downloads/DPDD/val' |
| tar = 'Datasets/val/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, 'val', 'inputL', '*.png'))) |
| lrR_files = natsorted(glob(os.path.join(src, 'val', 'inputR', '*.png'))) |
| lrC_files = natsorted(glob(os.path.join(src, 'val', 'inputC', '*.png'))) |
| hrC_files = natsorted(glob(os.path.join(src, 'val', '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)) |
|
|
