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generate_patches_dpdd.py

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+"""
+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))