Add DnCNN3 for JPEG image deblocking

Author: cszn

main_test_dncnn3_deblocking.py

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+import os.path
+import logging
+
+import numpy as np
+from datetime import datetime
+from collections import OrderedDict
+
+import torch
+
+from utils import utils_logger
+from utils import utils_model
+from utils import utils_image as util
+#import os
+#os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+
+'''
+Spyder (Python 3.6)
+PyTorch 1.1.0
+Windows 10 or Linux
+
+Kai Zhang ([email protected])
+github: https://github.com/cszn/KAIR
+        https://github.com/cszn/DnCNN
+
+@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},
+  volume={26},
+  number={7},
+  pages={3142--3155},
+  year={2017},
+  publisher={IEEE}
+}
+
+% If you have any question, please feel free to contact with me.
+% Kai Zhang (e-mail: [email protected]; github: https://github.com/cszn)
+
+by Kai Zhang (12/Dec./2019)
+'''
+
+"""
+# --------------------------------------------
+|--model_zoo          # model_zoo
+   |--dncnn3          # model_name
+|--testset            # testsets
+   |--set12           # testset_name
+   |--bsd68
+|--results            # results
+   |--set12_dncnn3    # result_name = testset_name + '_' + model_name
+# --------------------------------------------
+"""
+
+
+def main():
+
+    # ----------------------------------------
+    # Preparation
+    # ----------------------------------------
+
+    model_name = 'dncnn3'     # 'dncnn3'- can be used for blind Gaussian denoising, JPEG deblocking (quality factor 5-100) and super-resolution (x234)
+
+    # important!
+    testset_name = 'bsd68'    # test set, low-quality grayscale/color JPEG images
+    n_channels = 1            # set 1 for grayscale image, set 3 for color image
+
+
+    x8 = False                       # default: False, x8 to boost performance
+    testsets = 'testsets'     # fixed
+    results = 'results'       # fixed
+    result_name = testset_name + '_' + model_name # fixed
+    L_path = os.path.join(testsets, testset_name) # L_path, for Low-quality grayscale/Y-channel JPEG images
+    E_path = os.path.join(results, result_name)   # E_path, for Estimated images
+    util.mkdir(E_path)
+
+    model_pool = 'model_zoo'  # fixed
+    model_path = os.path.join(model_pool, model_name+'.pth')
+    logger_name = result_name
+    utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
+    logger = logging.getLogger(logger_name)
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    # ----------------------------------------
+    # load model
+    # ----------------------------------------
+
+    from models.network_dncnn import DnCNN as net
+    model = net(in_nc=1, out_nc=1, nc=64, nb=20, act_mode='R')
+    model.load_state_dict(torch.load(model_path), strict=True)
+    model.eval()
+    for k, v in model.named_parameters():
+        v.requires_grad = False
+    model = model.to(device)
+    logger.info('Model path: {:s}'.format(model_path))
+    number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
+    logger.info('Params number: {}'.format(number_parameters))
+
+    logger.info(L_path)
+    L_paths = util.get_image_paths(L_path)
+
+    for idx, img in enumerate(L_paths):
+
+        # ------------------------------------
+        # (1) img_L
+        # ------------------------------------
+        img_name, ext = os.path.splitext(os.path.basename(img))
+        logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext))
+        img_L = util.imread_uint(img, n_channels=n_channels)
+        img_L = util.uint2single(img_L)
+        if n_channels == 3:
+            ycbcr = util.rgb2ycbcr(img_L, False)
+            img_L = ycbcr[..., 0:1]
+        img_L = util.single2tensor4(img_L)
+        img_L = img_L.to(device)
+
+        # ------------------------------------
+        # (2) img_E
+        # ------------------------------------
+        if not x8:
+            img_E = model(img_L)
+        else:
+            img_E = utils_model.test_mode(model, img_L, mode=3)
+
+        img_E = util.tensor2single(img_E)
+        if n_channels == 3:
+            ycbcr[..., 0] = img_E
+            img_E = util.ycbcr2rgb(ycbcr)
+        img_E = util.single2uint(img_E)
+
+        # ------------------------------------
+        # save results
+        # ------------------------------------
+        util.imsave(img_E, os.path.join(E_path, img_name+'.png'))
+
+
+if __name__ == '__main__':
+
+    main()