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TrainingCodes/dncnn_keras/README.md

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+# DnCNN-keras     
+
+This code is modified from (husqin)[https://github.com/husqin/DnCNN-keras].
+
+### Dependence
+```
+tensorflow
+keras2
+numpy
+opencv
+```
+
+### Train
+```
+main_train.py
+```
+
+### Test
+
+```
+main_test.py
+```
+
+### Results
+
+#### Gaussian Denoising
+
+The average PSNR(dB) results of different methods on the BSD68 dataset.
+
+|  Noise Level | BM3D | DnCNN | DnCNN-keras |
+|:-------:|:-------:|:-------:|:-------:|
+| 25  |  28.57 | 29.23 | 29.23  |
+
+
+
+
+
+
+

TrainingCodes/dncnn_keras/data_generator.py

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+# -*- coding: utf-8 -*-
+
+# =============================================================================
+#  @article{zhang2017beyond,
+#    title={Beyond a {Gaussian} denoiser: Residual learning of deep {CNN} for image denoising},
+#    author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},
+#    journal={IEEE Transactions on Image Processing},
+#    year={2017},
+#    volume={26}, 
+#    number={7}, 
+#    pages={3142-3155}, 
+#  }
+# by Kai Zhang (08/2018)
+# [email protected]
+# https://github.com/cszn
+# modified on the code from https://github.com/husqin/DnCNN-keras
+# =============================================================================
+
+# no need to run this code separately
+
+
+import glob
+#import os
+import cv2
+import numpy as np
+#from multiprocessing import Pool
+
+
+patch_size, stride = 40, 10
+aug_times = 1
+scales = [1, 0.9, 0.8, 0.7]
+batch_size = 128
+
+
+def show(x,title=None,cbar=False,figsize=None):
+    import matplotlib.pyplot as plt
+    plt.figure(figsize=figsize)
+    plt.imshow(x,interpolation='nearest',cmap='gray')
+    if title:
+        plt.title(title)
+    if cbar:
+        plt.colorbar()
+    plt.show()
+
+def data_aug(img, mode=0):
+
+    if mode == 0:
+        return img
+    elif mode == 1:
+        return np.flipud(img)
+    elif mode == 2:
+        return np.rot90(img)
+    elif mode == 3:
+        return np.flipud(np.rot90(img))
+    elif mode == 4:
+        return np.rot90(img, k=2)
+    elif mode == 5:
+        return np.flipud(np.rot90(img, k=2))
+    elif mode == 6:
+        return np.rot90(img, k=3)
+    elif mode == 7:
+        return np.flipud(np.rot90(img, k=3))
+
+def gen_patches(file_name):
+
+    # read image
+    img = cv2.imread(file_name, 0)  # gray scale
+    h, w = img.shape
+    patches = []
+    for s in scales:
+        h_scaled, w_scaled = int(h*s),int(w*s)
+        img_scaled = cv2.resize(img, (h_scaled,w_scaled), interpolation=cv2.INTER_CUBIC)
+        # extract patches
+        for i in range(0, h_scaled-patch_size+1, stride):
+            for j in range(0, w_scaled-patch_size+1, stride):
+                x = img_scaled[i:i+patch_size, j:j+patch_size]
+                #patches.append(x)        
+                # data aug
+                for k in range(0, aug_times):
+                    x_aug = data_aug(x, mode=np.random.randint(0,8))
+                    patches.append(x_aug)
+
+    return patches
+
+def datagenerator(data_dir='data/Train400',verbose=False):
+
+    file_list = glob.glob(data_dir+'/*.png')  # get name list of all .png files
+    # initrialize
+    data = []
+    # generate patches
+    for i in range(len(file_list)):
+        patch = gen_patches(file_list[i])
+        data.append(patch)
+        if verbose:
+            print(str(i+1)+'/'+ str(len(file_list)) + ' is done ^_^')
+    data = np.array(data, dtype='uint8')
+    data = data.reshape((data.shape[0]*data.shape[1],data.shape[2],data.shape[3],1))
+    discard_n = len(data)-len(data)//batch_size*batch_size;
+    data = np.delete(data,range(discard_n),axis = 0)
+    print('^_^-training data finished-^_^')
+    return data
+
+if __name__ == '__main__':   
+
+    data = datagenerator(data_dir='data/Train400')
+
+
+#    print('Shape of result = ' + str(res.shape))
+#    print('Saving data...')
+#    if not os.path.exists(save_dir):
+#            os.mkdir(save_dir)
+#    np.save(save_dir+'clean_patches.npy', res)
+#    print('Done.')       

TrainingCodes/dncnn_keras/main_test.py

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+# -*- coding: utf-8 -*-
+
+# =============================================================================
+#  @article{zhang2017beyond,
+#    title={Beyond a {Gaussian} denoiser: Residual learning of deep {CNN} for image denoising},
+#    author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},
+#    journal={IEEE Transactions on Image Processing},
+#    year={2017},
+#    volume={26}, 
+#    number={7}, 
+#    pages={3142-3155}, 
+#  }
+# by Kai Zhang (08/2018)
+# [email protected]
+# https://github.com/cszn
+# modified on the code from https://github.com/husqin/DnCNN-keras
+# =============================================================================
+
+# run this to test the model
+
+import argparse
+import os, time, datetime
+#import PIL.Image as Image
+import numpy as np
+from keras.models import load_model, model_from_json
+from skimage.measure import compare_psnr, compare_ssim
+from skimage.io import imread, imsave
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--set_dir', default='data/Test', type=str, help='directory of test dataset')
+    parser.add_argument('--set_names', default=['Set68','Set12'], type=list, help='name of test dataset')
+    parser.add_argument('--sigma', default=25, type=int, help='noise level')
+    parser.add_argument('--model_dir', default=os.path.join('models','DnCNN_sigma25'), type=str, help='directory of the model')
+    parser.add_argument('--model_name', default='model_001.hdf5', type=str, help='the model name')
+    parser.add_argument('--result_dir', default='results', type=str, help='directory of results')
+    parser.add_argument('--save_result', default=0, type=int, help='save the denoised image, 1 or 0')
+    return parser.parse_args()
+
+def to_tensor(img):
+    if img.ndim == 2:
+        return img[np.newaxis,...,np.newaxis]
+    elif img.ndim == 3:
+        return np.moveaxis(img,2,0)[...,np.newaxis]
+
+def from_tensor(img):
+    return np.squeeze(np.moveaxis(img[...,0],0,-1))
+
+def log(*args,**kwargs):
+     print(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S:"),*args,**kwargs)
+
+def save_result(result,path):
+    path = path if path.find('.') != -1 else path+'.png'
+    ext = os.path.splitext(path)[-1]
+    if ext in ('.txt','.dlm'):
+        np.savetxt(path,result,fmt='%2.4f')
+    else:
+        imsave(path,np.clip(result,0,1))
+
+
+def show(x,title=None,cbar=False,figsize=None):
+    import matplotlib.pyplot as plt
+    plt.figure(figsize=figsize)
+    plt.imshow(x,interpolation='nearest',cmap='gray')
+    if title:
+        plt.title(title)
+    if cbar:
+        plt.colorbar()
+    plt.show()
+
+
+if __name__ == '__main__':    
+
+    args = parse_args()
+
+
+    # =============================================================================
+    #     # serialize model to JSON
+    #     model_json = model.to_json()
+    #     with open("model.json", "w") as json_file:
+    #         json_file.write(model_json)
+    #     # serialize weights to HDF5
+    #     model.save_weights("model.h5")
+    #     print("Saved model")
+    # =============================================================================
+
+    if not os.path.exists(os.path.join(args.model_dir, args.model_name)):
+        # load json and create model
+        json_file = open(os.path.join(args.model_dir,'model.json'), 'r')
+        loaded_model_json = json_file.read()
+        json_file.close()
+        model = model_from_json(loaded_model_json)
+        # load weights into new model
+        model.load_weights(os.path.join(args.model_dir,'model.h5'))
+        log('load trained model on Train400 dataset by kai')
+    else:
+        model = load_model(os.path.join(args.model_dir, args.model_name),compile=False)
+        log('load trained model')
+
+    if not os.path.exists(args.result_dir):
+        os.mkdir(args.result_dir)
+
+    for set_cur in args.set_names:  
+
+        if not os.path.exists(os.path.join(args.result_dir,set_cur)):
+            os.mkdir(os.path.join(args.result_dir,set_cur))
+        psnrs = []
+        ssims = [] 
+
+        for im in os.listdir(os.path.join(args.set_dir,set_cur)): 
+            if im.endswith(".jpg") or im.endswith(".bmp") or im.endswith(".png"):
+                #x = np.array(Image.open(os.path.join(args.set_dir,set_cur,im)), dtype='float32') / 255.0
+                x = np.array(imread(os.path.join(args.set_dir,set_cur,im)), dtype=np.float32) / 255.0
+                np.random.seed(seed=0) # for reproducibility
+                y = x + np.random.normal(0, args.sigma/255.0, x.shape) # Add Gaussian noise without clipping
+                y = y.astype(np.float32)
+                y_  = to_tensor(y)
+                start_time = time.time()
+                x_ = model.predict(y_) # inference
+                elapsed_time = time.time() - start_time
+                print('%10s : %10s : %2.4f second'%(set_cur,im,elapsed_time))
+                x_=from_tensor(x_)
+                psnr_x_ = compare_psnr(x, x_)
+                ssim_x_ = compare_ssim(x, x_)
+                if args.save_result:
+                    name, ext = os.path.splitext(im)
+                    show(np.hstack((y,x_))) # show the image
+                    save_result(x_,path=os.path.join(args.result_dir,set_cur,name+'_dncnn'+ext)) # save the denoised image
+                psnrs.append(psnr_x_)
+                ssims.append(ssim_x_)
+
+        psnr_avg = np.mean(psnrs)
+        ssim_avg = np.mean(ssims)
+        psnrs.append(psnr_avg)
+        ssims.append(ssim_avg)
+
+        if args.save_result:
+            save_result(np.hstack((psnrs,ssims)),path=os.path.join(args.result_dir,set_cur,'results.txt'))
+
+        log('Datset: {0:10s} \n  PSNR = {1:2.2f}dB, SSIM = {2:1.4f}'.format(set_cur, psnr_avg, ssim_avg))
+
+
+
+