├── .gitignore ├── LICENSE ├── README.md ├── assets └── teaser.png ├── ckpts └── placeholder.txt ├── datasets └── placehoder.txt ├── download_ckpts.py ├── download_test_set.py ├── install_CUDA11.1.1.sh ├── models ├── DRBNet.py └── __pycache__ │ └── DRBNet.cpython-38.pyc ├── options ├── __pycache__ │ ├── base_options.cpython-38.pyc │ └── test_options.cpython-38.pyc ├── base_options.py └── test_options.py ├── requirements.txt ├── run.py └── util ├── __pycache__ └── util.cpython-38.pyc └── util.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 611 | 612 | If the disclaimer of warranty and limitation of liability provided 613 | above cannot be given local legal effect according to their terms, 614 | reviewing courts shall apply local law that most closely approximates 615 | an absolute waiver of all civil liability in connection with the 616 | Program, unless a warranty or assumption of liability accompanies a 617 | copy of the Program in return for a fee. 618 | 619 | END OF TERMS AND CONDITIONS 620 | 621 | How to Apply These Terms to Your New Programs 622 | 623 | If you develop a new program, and you want it to be of the greatest 624 | possible use to the public, the best way to achieve this is to make it 625 | free software which everyone can redistribute and change under these terms. 626 | 627 | To do so, attach the following notices to the program. It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published 637 | by the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Learning to Deblur using Light Field Generated and Real Defocused Images 2 | 3 | ![License CC BY-NC](https://img.shields.io/badge/License-GNU_AGPv3-yellowgreen.svg?style=flat) 4 | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Jvfbn8HnWAmgTKFpU8fW56wXSbe1S2QI?usp=sharing) 5 | 6 | teaser figure 7 | 8 | This repository contains the official PyTorch implementation of the following paper: 9 | 10 | > **[Learning to Deblur using Light Field Generated and Real Defocused Images](https://arxiv.org/pdf/2204.00367.pdf)**
11 | > Lingyan Ruan\*, Bin Chen\*, Jizhou Li, Miuling Lam （\* equal contribution）
12 | > IEEE Computer Vision and Pattern Recognition (**CVPR Oral**) 2022 13 | 14 | **[PROJECT PAGE](http://lyruan.com/Projects/DRBNet/index.html)** | **[INTERACTIVE WEB APP](https://xi5tau4hrb3hsakw.anvil.app/FJJ5EACSBF63RE7RQL2K6ZDZ)** 15 | 16 | If you find our code useful, please consider citing our paper: 17 | 18 | ``` 19 | @inproceedings{ruan2022learning, 20 | title={Learning to Deblur using Light Field Generated and Real Defocus Images}, 21 | author={Ruan, Lingyan and Chen, Bin and Li, Jizhou and Lam, Miuling}, 22 | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 23 | pages={16304--16313}, 24 | year={2022} 25 | } 26 | 27 | ``` 28 | 29 | ## Code 30 | 31 | ### Prerequisites 32 | 33 | ![Ubuntu](https://img.shields.io/badge/Ubuntu-16.0.4%20&%2018.0.4-blue.svg?style=plastic) 34 | ![Python](https://img.shields.io/badge/Python-3.8.13-yellowgreen.svg?style=plastic) 35 | ![CUDA](https://img.shields.io/badge/CUDA-11.1.1%20-yellowgreen.svg?style=plastic) 36 | ![PyTorch](https://img.shields.io/badge/PyTorch-1.8.0-yellowgreen.svg?style=plastic) 37 | 38 | Notes: the code may also work with other library versions that didn't specify here. 39 | 40 | #### 1. Installation 41 | 42 | Clone this project to your local machine 43 | 44 | ```bash 45 | $ git clone https://github.com/lingyanruan/DRBNet.git 46 | $ cd DRBNet 47 | ``` 48 | #### 2. Environment setup 49 | 50 | ```bash 51 | $ conda create -y --name DRBNet python=3.8.13 && conda activate DRBNet 52 | $ sh install_CUDA11.1.1.sh 53 | # Other version will be checked and updated later. 54 | ``` 55 | 56 | 57 | #### 3. Pre-trained models 58 | 59 | Download and unzip [pretrained weights] under `./ckpts/`: 60 | ```bash 61 | $ python download_ckpts.py 62 | # Weights will be placed in ./ckpts/ 63 | ``` 64 | 65 | 66 | #### 4. Datasets download 67 | 68 | ```bash 69 | $ python download_test_set.py --DPDD --RealDOF --CUHK --PixelDP 70 | # You may skip donwload the specific dataset by removing name, e.g., remove --PixelDP with command python download_test_set.py --DPDD --RealDOF --CUHK 71 | ``` 72 | 73 | The original full datasets could be found here: ([LFDOF](https://sweb.cityu.edu.hk/miullam/AIFNET/), [DPDD](https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel), [CUHK](http://www.cse.cuhk.edu.hk/~leojia/projects/dblurdetect/dataset.html) and [RealDOF](https://www.dropbox.com/s/arox1aixvg67fw5/RealDOF.zip?dl=1)): 74 | 75 | #### 5. Command Line 76 | 77 | ```bash 78 | # Single Image input 79 | $ python run.py --net_mode single --eval_data DPDD --save_images 80 | # eval_data could be RealDOF, CUHK, PixelDP. 81 | 82 | 83 | # Dual Image Input - DPDD Dataset 84 | python run.py --net_mode dual --eval_data DPDD --save_images 85 | 86 | ``` 87 | 88 | ## Performance improved on existing works - [DPDNet & KPAC] 89 | 90 | You may go for [DPDNet](https://github.com/lingyanruan/DPDNet) and [KPAC-Net](https://github.com/lingyanruan/KPAC-Net) for their improved version. Details could be found in [Why LFDOF?] section (Table 4 & Figure 8) in the main paper. Their original version could be found [Here: DPDNet-scr](https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel) and [Here: KPAC-Net-scr](https://github.com/lingyanruan/KPAC-Net) 91 | 92 | ## Relevant Resources 93 | 94 | - TCI'20 paper: AIFNet: All-in-focus Image Restoration Network using a Light Field-based Dataset   [[Paper](https://ieeexplore.ieee.org/document/9466450)] [[Project page](https://sweb.cityu.edu.hk/miullam/AIFNET/)] [[LFDOF Dataset](https://sweb.cityu.edu.hk/miullam/AIFNET/)] [[Code](https://github.com/binorchen/AIFNET)] 95 | 96 | ## Contact 97 | 98 | Should you have any questions, please open an issue or contact me [lyruanruan@gmail.com](mailto:lyruanruan@gmail.com) 99 | 100 | Acknowledgment: Some of the codes are based on the [IFAN](https://github.com/codeslake/IFAN) 101 | 102 | ## License 103 | 104 | This software is being made available under the terms in the [LICENSE](LICENSE) file. 105 | 106 | -------------------------------------------------------------------------------- /assets/teaser.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lingyanruan/DRBNet/0fa89a56381130637ca44bf10b023a2d66a71d87/assets/teaser.png -------------------------------------------------------------------------------- /ckpts/placeholder.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lingyanruan/DRBNet/0fa89a56381130637ca44bf10b023a2d66a71d87/ckpts/placeholder.txt -------------------------------------------------------------------------------- /datasets/placehoder.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lingyanruan/DRBNet/0fa89a56381130637ca44bf10b023a2d66a71d87/datasets/placehoder.txt -------------------------------------------------------------------------------- /download_ckpts.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This source code is licensed under the license found in the LICENSE file. 3 | This is the implementation of the "Learning to deblur using light field generated and real defocus images" paper accepted to CVPR 2022. 4 | Project GitHub repository: https://github.com/lingyanruan/DRBNet 5 | Email: lyruanruan@gmail.com 6 | Copyright (c) 2022-present, Lingyan Ruan 7 | ''' 8 | 9 | ## Download weight ############## 10 | import os 11 | import gdown 12 | import shutil 13 | 14 | ### Google drive IDs ###### 15 | ckpt_test = '1vGImev9LdagttXE_nN1gZGVstVTRVQHt' # https://drive.google.com/file/d/1vGImev9LdagttXE_nN1gZGVstVTRVQHt/view?usp=sharing 16 | 17 | # download ckpts 18 | print('ckpt downloading!') 19 | gdown.download(id=ckpt_test, output='ckpts/ckpts.zip', quiet=False) 20 | print('Extracting ckpts ......') 21 | shutil.unpack_archive('ckpts/ckpts.zip') 22 | os.remove('ckpts/ckpts.zip') 23 | print('Successfully download weight!') 24 | 25 | 26 | -------------------------------------------------------------------------------- /download_test_set.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This source code is licensed under the license found in the LICENSE file. 3 | This is the implementation of the "Learning to deblur using light field generated and real defocus images" paper accepted to CVPR 2022. 4 | Project GitHub repository: https://github.com/lingyanruan/DRBNet 5 | Email: lyruanruan@gmail.com 6 | Copyright (c) 2022-present, Lingyan Ruan 7 | ''' 8 | 9 | ## Download DPDD,RealDOF,CUHK,PixelDP test dataset 10 | import os 11 | import gdown 12 | import shutil 13 | 14 | import argparse 15 | 16 | parser = argparse.ArgumentParser() 17 | parser.add_argument('--DPDD', action='store_true', help='download DPDD test set') 18 | parser.add_argument('--RealDOF', action='store_true', help='download RealDOF test set') 19 | parser.add_argument('--CUHK', action='store_true', help='download CUHK test set') 20 | parser.add_argument('--PixelDP', action='store_true', help='download PixelDP test set') 21 | 22 | 23 | args = parser.parse_args() 24 | 25 | ### Google drive IDs ###### 26 | dpdd_test = '1W9HgltHkdQtLjEyhVEl4MTzxmYVGK2-3' # https://drive.google.com/file/d/1W9HgltHkdQtLjEyhVEl4MTzxmYVGK2-3/view?usp=sharing 27 | realdof_test = '18MBe-b4txSMsMtPpPQ40YD4dhtJXCvyf' #https://drive.google.com/file/d/18MBe-b4txSMsMtPpPQ40YD4dhtJXCvyf/view?usp=sharing 28 | cuhk_test = '1HEUE5gIW35VwjLsxukk-fQ2KcvmAMtfC' # https://drive.google.com/file/d/1HEUE5gIW35VwjLsxukk-fQ2KcvmAMtfC/view?usp=sharing 29 | pixeldp_test = '12K038LdCjfjLqR68v09nrmK6pWstibRV' #https://drive.google.com/file/d/12K038LdCjfjLqR68v09nrmK6pWstibRV/view?usp=sharing 30 | 31 | 32 | # download test dataset 33 | if args.DPDD: 34 | print('DPDD Testing Data!') 35 | gdown.download(id=dpdd_test, output='datasets/DPDD.zip', quiet=False) 36 | print('Extracting DPDD test set...') 37 | shutil.unpack_archive('datasets/DPDD.zip', 'datasets') 38 | os.remove('datasets/DPDD.zip') 39 | print('Successfully download DPDD!') 40 | 41 | if args.RealDOF: 42 | print('RealDOF Testing Data!') 43 | gdown.download(id=realdof_test, output='datasets/RealDOF.zip', quiet=False) 44 | print('Extracting RealDOF test set...') 45 | shutil.unpack_archive('datasets/RealDOF.zip', 'datasets') 46 | os.remove('datasets/RealDOF.zip') 47 | print('Successfully download RealDOF!') 48 | 49 | if args.CUHK: 50 | print('CUHK Testing Data!') 51 | gdown.download(id=cuhk_test, output='datasets/CUHK.zip', quiet=False) 52 | print('Extracting CUHK test set...') 53 | shutil.unpack_archive('datasets/CUHK.zip', 'datasets') 54 | os.remove('datasets/CUHK.zip') 55 | print('Successfully download CUHK!') 56 | 57 | if args.PixelDP: 58 | print('PixelDP Testing Data!') 59 | gdown.download(id=pixeldp_test, output='datasets/PixelDP.zip', quiet=False) 60 | print('Extracting PixelDP test set...') 61 | shutil.unpack_archive('datasets/PixelDP.zip', 'datasets') 62 | os.remove('datasets/PixelDP.zip') 63 | print('Successfully download PixelDP!') 64 | 65 | -------------------------------------------------------------------------------- /install_CUDA11.1.1.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1.1 -c pytorch -c conda-forge 3 | pip install --no-cache -r requirements.txt 4 | -------------------------------------------------------------------------------- /models/DRBNet.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This source code is licensed under the license found in the LICENSE file. 3 | This is the implementation of the "Learning to deblur using light field generated and real defocus images" paper accepted to CVPR 2022. 4 | Project GitHub repository: https://github.com/lingyanruan/DRBNet 5 | Email: lyruanruan@gmail.com 6 | Copyright (c) 2022-present, Lingyan Ruan 7 | ''' 8 | 9 | 10 | import os 11 | import numpy as np 12 | import torch 13 | import torch.nn as nn 14 | import torchvision.utils as vutils 15 | from pathlib import Path 16 | import cv2 17 | import torch.nn.functional as F 18 | 19 | 20 | def conv(in_channels, out_channels, kernel_size=3, stride=1,dilation=1, bias=True, act='LeakyReLU'): 21 | if act is not None: 22 | if act == 'LeakyReLU': 23 | act_ = nn.LeakyReLU(0.1,inplace=True) 24 | elif act == 'Sigmoid': 25 | act_ = nn.Sigmoid() 26 | elif act == 'Tanh': 27 | act_ = nn.Tanh() 28 | 29 | return nn.Sequential( 30 | nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=((kernel_size-1)//2)*dilation, bias=bias), 31 | act_ 32 | ) 33 | else: 34 | return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, padding=((kernel_size-1)//2)*dilation, bias=bias) 35 | 36 | def upconv(in_channels, out_channels): 37 | return nn.Sequential( 38 | nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, bias=True), 39 | nn.LeakyReLU(0.1,inplace=True) 40 | ) 41 | 42 | def resnet_block(in_channels, kernel_size=3, dilation=[1,1], bias=True, res_num=1): 43 | return ResnetBlock(in_channels, kernel_size, dilation, bias=bias, res_num=res_num) 44 | 45 | class ResnetBlock(nn.Module): 46 | def __init__(self, in_channels, kernel_size, dilation, bias, res_num): 47 | super(ResnetBlock, self).__init__() 48 | self.res_num = res_num 49 | self.stem = nn.ModuleList([ 50 | nn.Sequential( 51 | nn.Conv2d(in_channels, in_channels, kernel_size=kernel_size, stride=1, dilation=dilation[0], padding=((kernel_size-1)//2)*dilation[0], bias=bias), 52 | nn.LeakyReLU(0.1, inplace=True), 53 | nn.Conv2d(in_channels, in_channels, kernel_size=kernel_size, stride=1, dilation=dilation[1], padding=((kernel_size-1)//2)*dilation[1], bias=bias), 54 | ) for i in range(res_num) 55 | ]) 56 | def forward(self, x): 57 | 58 | if self.res_num > 1: 59 | temp = x 60 | 61 | for i in range(self.res_num): 62 | xx = self.stem[i](x) 63 | x = x + xx 64 | if self.res_num > 1: 65 | x = x + temp 66 | 67 | return x 68 | 69 | def FAC(feat_in, kernel, ksize): 70 | """ 71 | customized FAC 72 | """ 73 | channels = feat_in.size(1) 74 | N, kernels, H, W = kernel.size() 75 | pad = (ksize - 1) // 2 76 | 77 | feat_in = F.pad(feat_in, (pad, pad, pad, pad), mode="replicate") 78 | feat_in = feat_in.unfold(2, ksize, 1).unfold(3, ksize, 1) 79 | feat_in = feat_in.permute(0, 2, 3, 1, 5, 4).contiguous() 80 | feat_in = feat_in.reshape(N, H, W, channels, -1) 81 | 82 | if channels ==3 and kernels == ksize*ksize: 83 | #### 84 | kernel = kernel.permute(0, 2, 3, 1).reshape(N, H, W, 1, ksize, ksize) 85 | kernel = torch.cat([kernel,kernel,kernel],channels) 86 | kernel = kernel.permute(0, 1, 2, 3, 5, 4).reshape(N, H, W, channels, -1) 87 | 88 | else: 89 | kernel = kernel.permute(0, 2, 3, 1).reshape(N, H, W, channels, ksize, ksize) 90 | kernel = kernel.permute(0, 1, 2, 3, 5, 4).reshape(N, H, W, channels, -1) 91 | 92 | feat_out = torch.sum(feat_in * kernel, -1) 93 | feat_out = feat_out.permute(0, 3, 1, 2).contiguous() 94 | 95 | return feat_out 96 | 97 | class DRBNet_single(nn.Module): 98 | def __init__(self, ): 99 | super(DRBNet_single, self).__init__() 100 | 101 | 102 | ks = 3 103 | 104 | ch1 = 32 105 | ch2 = ch1 * 2 106 | ch3 = ch1 * 4 107 | ch4 = ch1 * 8 108 | self.ch4 = ch4 109 | self.kernel_width = 7 110 | self.kernel_dim = self.kernel_width*self.kernel_width 111 | 112 | 113 | # feature extractor 114 | self.conv1_1 = conv(3, ch1, kernel_size=ks, stride=1) 115 | self.conv1_2 = conv(ch1, ch1, kernel_size=ks, stride=1) 116 | self.conv1_3 = conv(ch1, ch1, kernel_size=ks, stride=1) 117 | 118 | self.conv2_1 = conv(ch1, ch2, kernel_size=ks, stride=2) 119 | self.conv2_2 = conv(ch2, ch2, kernel_size=ks, stride=1) 120 | self.conv2_3 = conv(ch2, ch2, kernel_size=ks, stride=1) 121 | 122 | self.conv3_1 = conv(ch2, ch3, kernel_size=ks, stride=2) 123 | self.conv3_2 = conv(ch3, ch3, kernel_size=ks, stride=1) 124 | self.conv3_3 = conv(ch3, ch3, kernel_size=ks, stride=1) 125 | 126 | self.conv4_1 = conv(ch3, ch4, kernel_size=ks, stride=2) 127 | self.conv4_2 = conv(ch4, ch4, kernel_size=ks, stride=1) 128 | self.conv4_3 = conv(ch4, ch4, kernel_size=ks, stride=1) 129 | 130 | self.conv4_4 = nn.Sequential( 131 | conv(ch4, ch4, kernel_size=ks), 132 | resnet_block(ch4, kernel_size=ks, res_num=1), 133 | resnet_block(ch4, kernel_size=ks, res_num=1), 134 | conv(ch4, ch4, kernel_size=ks)) 135 | 136 | self.upconv3_u = upconv(ch4, ch3) 137 | self.upconv3_1 = resnet_block(ch3, kernel_size=ks, res_num=1) 138 | self.upconv3_2 = resnet_block(ch3, kernel_size=ks, res_num=1) 139 | # here has a dynamic filter and res 140 | 141 | self.img_d8_feature = nn.Sequential( 142 | conv(3, ch2, kernel_size=ks, stride=1), 143 | conv(ch2, ch3, kernel_size=ks, stride=1), 144 | conv(ch3, ch4, kernel_size=ks, stride=1) 145 | ) 146 | 147 | self.upconv3_kernel = nn.Sequential( 148 | conv(ch4*2, ch4, kernel_size=ks, stride=1), 149 | conv(ch4, ch3, kernel_size=ks, stride=1), 150 | conv(ch3, self.kernel_dim, kernel_size=1, stride=1,act=None) 151 | ) 152 | 153 | self.upconv3_res = nn.Sequential( 154 | conv(ch4*2, ch4, kernel_size=ks, stride=1), 155 | conv(ch4, ch2, kernel_size=ks, stride=1), 156 | conv(ch2, 3, kernel_size=1, stride=1) 157 | ) 158 | 159 | self.upconv2_u = upconv(ch3, ch2) 160 | self.upconv2_1 = resnet_block(ch2, kernel_size=ks, res_num=1) 161 | self.upconv2_2 = resnet_block(ch2, kernel_size=ks, res_num=1) 162 | 163 | 164 | self.img_d4_feature = nn.Sequential( 165 | conv(3, ch2, kernel_size=ks, stride=1), 166 | conv(ch2, ch3, kernel_size=ks, stride=1), 167 | conv(ch3, ch3, kernel_size=ks, stride=1), 168 | ) 169 | 170 | 171 | self.upconv2_kernel = nn.Sequential( 172 | conv(ch3*2, ch3, kernel_size=ks, stride=1), 173 | conv(ch3, ch3, kernel_size=ks, stride=1), 174 | conv(ch3, self.kernel_dim, kernel_size=1, stride=1,act=None) 175 | ) 176 | 177 | self.upconv2_res = nn.Sequential( 178 | conv(ch3*2, ch3, kernel_size=ks, stride=1), 179 | conv(ch3, ch2, kernel_size=ks, stride=1), 180 | conv(ch2, 3, kernel_size=1, stride=1) 181 | ) 182 | self.img_d2_feature = nn.Sequential( 183 | conv(3, ch2, kernel_size=ks, stride=1), 184 | conv(ch2, ch2, kernel_size=ks, stride=1), 185 | conv(ch2, ch2, kernel_size=ks, stride=1) 186 | ) 187 | 188 | self.upconv1_u = upconv(ch2, ch1) 189 | self.upconv1_1 = resnet_block(ch1, kernel_size=ks, res_num=1) 190 | self.upconv1_2 = resnet_block(ch1, kernel_size=ks, res_num=1) 191 | 192 | 193 | self.img_d1_feature = nn.Sequential( 194 | conv(3, ch2, kernel_size=ks, stride=1), 195 | conv(ch2, ch2, kernel_size=ks, stride=1), 196 | conv(ch2, ch1, kernel_size=ks, stride=1), 197 | ) 198 | 199 | 200 | self.upconv1_kernel = nn.Sequential( 201 | conv(ch2*2, ch2, kernel_size=ks, stride=1), 202 | conv(ch2, ch2, kernel_size=ks, stride=1), 203 | conv(ch2, self.kernel_dim, kernel_size=1, stride=1,act=None) 204 | ) 205 | 206 | self.upconv1_res = nn.Sequential( 207 | conv(ch2*2, ch2, kernel_size=ks, stride=1), 208 | conv(ch2, ch2, kernel_size=ks, stride=1), 209 | conv(ch2, 3, kernel_size=1, stride=1) 210 | ) 211 | 212 | 213 | self.upconv0_kernel = nn.Sequential( 214 | conv(ch1*2, ch2, kernel_size=ks, stride=1), 215 | conv(ch2, ch2, kernel_size=ks, stride=1), 216 | conv(ch2, self.kernel_dim, kernel_size=1, stride=1,act=None) 217 | ) 218 | 219 | self.upconv0_res = nn.Sequential( 220 | conv(ch1*2, ch2, kernel_size=ks, stride=1), 221 | conv(ch2, ch2, kernel_size=ks, stride=1), 222 | conv(ch2, 3, kernel_size=1, stride=1) 223 | ) 224 | 225 | ########################################################################## 226 | def forward(self, C): 227 | # feature extractor 228 | f1 = self.conv1_3(self.conv1_2(self.conv1_1(C))) 229 | f2 = self.conv2_3(self.conv2_2(self.conv2_1(f1))) 230 | f3 = self.conv3_3(self.conv3_2(self.conv3_1(f2))) 231 | f_C = self.conv4_3(self.conv4_2(self.conv4_1(f3))) 232 | 233 | f = self.conv4_4(f_C) 234 | 235 | img_d8 = F.interpolate(C, scale_factor=1/8, mode='area') 236 | img_d8_feature = self.img_d8_feature(img_d8) 237 | feature_d8 = torch.cat([f,img_d8_feature],1) #ch4*2 238 | kernel_d8 = self.upconv3_kernel(feature_d8) 239 | 240 | res_f8 = self.upconv3_res(feature_d8) 241 | 242 | est_img_d8 = img_d8 + FAC(img_d8, kernel_d8, self.kernel_width) + res_f8 243 | 244 | f = self.upconv3_u(f) + f3 245 | f = self.upconv3_2(self.upconv3_1(f)) 246 | 247 | est_img_d4_interpolate =F.interpolate(est_img_d8, scale_factor=2, mode='area') 248 | 249 | 250 | img_d4_feature = self.img_d4_feature(est_img_d4_interpolate) 251 | feature_d4 = torch.cat([f,img_d4_feature],1) 252 | kernel_d4 = self.upconv2_kernel(feature_d4) 253 | 254 | res_f4 = self.upconv2_res(feature_d4) 255 | 256 | est_img_d4 = est_img_d4_interpolate + FAC(est_img_d4_interpolate, kernel_d4, self.kernel_width) + res_f4 257 | 258 | f = self.upconv2_u(f) + f2 259 | f = self.upconv2_2(self.upconv2_1(f)) 260 | 261 | 262 | est_img_d2_interpolate =F.interpolate(est_img_d4, scale_factor=2, mode='area') 263 | 264 | img_d2_feature = self.img_d2_feature(est_img_d2_interpolate) 265 | feature_d2 = torch.cat([f,img_d2_feature],1) 266 | 267 | kernel_d2 = self.upconv1_kernel(feature_d2) 268 | res_f2 = self.upconv1_res(feature_d2) 269 | 270 | est_img_d2 = est_img_d2_interpolate + FAC(est_img_d2_interpolate, kernel_d2, self.kernel_width) + res_f2 271 | 272 | 273 | f = self.upconv1_u(f) + f1 274 | f = self.upconv1_2(self.upconv1_1(f)) 275 | 276 | est_img_d1_interploate =F.interpolate(est_img_d2, scale_factor=2, mode='area') 277 | 278 | img_d1_feature = self.img_d1_feature(est_img_d1_interploate) 279 | feature_d1 = torch.cat([f,img_d1_feature],1) 280 | kernel_d1 = self.upconv0_kernel(feature_d1) 281 | 282 | res_f1 = self.upconv0_res(feature_d1) 283 | 284 | est_img_d1 = est_img_d1_interploate + FAC(est_img_d1_interploate, kernel_d1,self.kernel_width) + res_f1 285 | 286 | est_img_d1_ = torch.clip(est_img_d1,-1.0,1.0) 287 | 288 | return est_img_d1_ 289 | 290 | 291 | 292 | ########################################################################################## 293 | ## dual views net 294 | 295 | 296 | class DeblurNet_dual(nn.Module): 297 | def __init__(self,): 298 | super(DeblurNet_dual, self).__init__() 299 | 300 | 301 | 302 | ks = 3 303 | 304 | ch1 = 32 305 | ch2 = ch1 * 2 306 | ch3 = ch1 * 4 307 | ch4 = ch1 * 8 308 | self.ch4 = ch4 309 | self.kernel_width = 7 310 | self.kernel_dim = self.kernel_width*self.kernel_width 311 | 312 | # feature extractor 313 | self.conv1_1 = conv(6, ch1, kernel_size=ks, stride=1) 314 | self.conv1_2 = conv(ch1, ch1, kernel_size=ks, stride=1) 315 | self.conv1_3 = conv(ch1, ch1, kernel_size=ks, stride=1) 316 | 317 | self.conv2_1 = conv(ch1, ch2, kernel_size=ks, stride=2) 318 | self.conv2_2 = conv(ch2, ch2, kernel_size=ks, stride=1) 319 | self.conv2_3 = conv(ch2, ch2, kernel_size=ks, stride=1) 320 | 321 | self.conv3_1 = conv(ch2, ch3, kernel_size=ks, stride=2) 322 | self.conv3_2 = conv(ch3, ch3, kernel_size=ks, stride=1) 323 | self.conv3_3 = conv(ch3, ch3, kernel_size=ks, stride=1) 324 | 325 | self.conv4_1 = conv(ch3, ch4, kernel_size=ks, stride=2) 326 | self.conv4_2 = conv(ch4, ch4, kernel_size=ks, stride=1) 327 | self.conv4_3 = conv(ch4, ch4, kernel_size=ks, stride=1) 328 | 329 | self.conv4_4 = nn.Sequential( 330 | conv(ch4, ch4, kernel_size=ks), 331 | resnet_block(ch4, kernel_size=ks, res_num=1), 332 | resnet_block(ch4, kernel_size=ks, res_num=1), 333 | conv(ch4, ch4, kernel_size=ks)) 334 | 335 | 336 | self.upconv3_u = upconv(ch4, ch3) 337 | self.upconv3_1 = resnet_block(ch3, kernel_size=ks, res_num=1) 338 | self.upconv3_2 = resnet_block(ch3, kernel_size=ks, res_num=1) 339 | 340 | 341 | self.img_d8_feature = nn.Sequential( 342 | conv(3, ch2, kernel_size=ks, stride=1), 343 | conv(ch2, ch3, kernel_size=ks, stride=1), 344 | conv(ch3, ch4, kernel_size=ks, stride=1) 345 | ) 346 | 347 | self.upconv3_kernel = nn.Sequential( 348 | conv(ch4*2, ch4, kernel_size=ks, stride=1), 349 | conv(ch4, ch3, kernel_size=ks, stride=1), 350 | conv(ch3, self.kernel_dim, kernel_size=1, stride=1,act=None) 351 | ) 352 | 353 | self.upconv3_res = nn.Sequential( 354 | conv(ch4*2, ch4, kernel_size=ks, stride=1), 355 | conv(ch4, ch2, kernel_size=ks, stride=1), 356 | conv(ch2, 3, kernel_size=1, stride=1) 357 | ) 358 | 359 | self.upconv2_u = upconv(ch3, ch2) 360 | self.upconv2_1 = resnet_block(ch2, kernel_size=ks, res_num=1) 361 | self.upconv2_2 = resnet_block(ch2, kernel_size=ks, res_num=1) 362 | 363 | 364 | self.img_d4_feature = nn.Sequential( 365 | conv(3, ch2, kernel_size=ks, stride=1), 366 | conv(ch2, ch3, kernel_size=ks, stride=1), 367 | conv(ch3, ch3, kernel_size=ks, stride=1), 368 | ) 369 | 370 | 371 | self.upconv2_kernel = nn.Sequential( 372 | conv(ch3*2, ch3, kernel_size=ks, stride=1), 373 | conv(ch3, ch3, kernel_size=ks, stride=1), 374 | conv(ch3, self.kernel_dim, kernel_size=1, stride=1,act=None) 375 | ) 376 | 377 | self.upconv2_res = nn.Sequential( 378 | conv(ch3*2, ch3, kernel_size=ks, stride=1), 379 | conv(ch3, ch2, kernel_size=ks, stride=1), 380 | conv(ch2, 3, kernel_size=1, stride=1) 381 | ) 382 | self.img_d2_feature = nn.Sequential( 383 | conv(3, ch2, kernel_size=ks, stride=1), 384 | conv(ch2, ch2, kernel_size=ks, stride=1), 385 | conv(ch2, ch2, kernel_size=ks, stride=1) 386 | ) 387 | 388 | self.upconv1_u = upconv(ch2, ch1) 389 | self.upconv1_1 = resnet_block(ch1, kernel_size=ks, res_num=1) 390 | self.upconv1_2 = resnet_block(ch1, kernel_size=ks, res_num=1) 391 | 392 | 393 | self.img_d1_feature = nn.Sequential( 394 | conv(3, ch2, kernel_size=ks, stride=1), 395 | conv(ch2, ch2, kernel_size=ks, stride=1), 396 | conv(ch2, ch1, kernel_size=ks, stride=1), 397 | ) 398 | 399 | 400 | self.upconv1_kernel = nn.Sequential( 401 | conv(ch2*2, ch2, kernel_size=ks, stride=1), 402 | conv(ch2, ch2, kernel_size=ks, stride=1), 403 | conv(ch2, self.kernel_dim, kernel_size=1, stride=1,act=None) 404 | ) # 5*5 kernel 405 | 406 | self.upconv1_res = nn.Sequential( 407 | conv(ch2*2, ch2, kernel_size=ks, stride=1), 408 | conv(ch2, ch2, kernel_size=ks, stride=1), 409 | conv(ch2, 3, kernel_size=1, stride=1) 410 | ) 411 | 412 | 413 | self.upconv0_kernel = nn.Sequential( 414 | conv(ch1*2, ch2, kernel_size=ks, stride=1), 415 | conv(ch2, ch2, kernel_size=ks, stride=1), 416 | conv(ch2, self.kernel_dim, kernel_size=1, stride=1,act=None) 417 | ) # 5*5 kernel 418 | 419 | self.upconv0_res = nn.Sequential( 420 | conv(ch1*2, ch2, kernel_size=ks, stride=1), 421 | conv(ch2, ch2, kernel_size=ks, stride=1), 422 | conv(ch2, 3, kernel_size=1, stride=1) 423 | ) 424 | 425 | 426 | 427 | ########################################################################## 428 | def forward(self, C,R,L): 429 | 430 | # feature extractor 431 | 432 | input = torch.cat([R,L],1) 433 | f1 = self.conv1_3(self.conv1_2(self.conv1_1(input))) 434 | f2 = self.conv2_3(self.conv2_2(self.conv2_1(f1))) 435 | f3 = self.conv3_3(self.conv3_2(self.conv3_1(f2))) 436 | f_C = self.conv4_3(self.conv4_2(self.conv4_1(f3))) 437 | 438 | f = self.conv4_4(f_C) 439 | 440 | img_d8 = F.interpolate(C, scale_factor=1/8, mode='area') 441 | img_d8_feature = self.img_d8_feature(img_d8) 442 | feature_d8 = torch.cat([f,img_d8_feature],1) 443 | kernel_d8 = self.upconv3_kernel(feature_d8) 444 | 445 | res_f8 = self.upconv3_res(feature_d8) 446 | 447 | est_img_d8 = img_d8 + FAC(img_d8, kernel_d8, self.kernel_width) + res_f8 448 | 449 | 450 | f = self.upconv3_u(f) + f3 451 | f = self.upconv3_2(self.upconv3_1(f)) 452 | 453 | est_img_d4_interpolate =F.interpolate(est_img_d8, scale_factor=2, mode='area') 454 | 455 | 456 | img_d4_feature = self.img_d4_feature(est_img_d4_interpolate) 457 | feature_d4 = torch.cat([f,img_d4_feature],1) 458 | kernel_d4 = self.upconv2_kernel(feature_d4) 459 | 460 | res_f4 = self.upconv2_res(feature_d4) 461 | 462 | est_img_d4 = est_img_d4_interpolate + FAC(est_img_d4_interpolate, kernel_d4, self.kernel_width) + res_f4 463 | 464 | 465 | f = self.upconv2_u(f) + f2 466 | f = self.upconv2_2(self.upconv2_1(f)) 467 | 468 | 469 | est_img_d2_interpolate =F.interpolate(est_img_d4, scale_factor=2, mode='area') 470 | 471 | img_d2_feature = self.img_d2_feature(est_img_d2_interpolate) 472 | feature_d2 = torch.cat([f,img_d2_feature],1) 473 | 474 | kernel_d2 = self.upconv1_kernel(feature_d2) 475 | res_f2 = self.upconv1_res(feature_d2) 476 | 477 | est_img_d2 = est_img_d2_interpolate + FAC(est_img_d2_interpolate, kernel_d2, self.kernel_width) + res_f2 478 | 479 | 480 | f = self.upconv1_u(f) + f1 481 | f = self.upconv1_2(self.upconv1_1(f)) 482 | 483 | est_img_d1_interploate =F.interpolate(est_img_d2, scale_factor=2, mode='area') 484 | 485 | img_d1_feature = self.img_d1_feature(est_img_d1_interploate) 486 | feature_d1 = torch.cat([f,img_d1_feature],1) 487 | kernel_d1 = self.upconv0_kernel(feature_d1) 488 | 489 | res_f1 = self.upconv0_res(feature_d1) 490 | 491 | est_img_d1 = est_img_d1_interploate + FAC(est_img_d1_interploate, kernel_d1,self.kernel_width) + res_f1 492 | est_img_d1_ =torch.clip(est_img_d1,-1.0,1.0) 493 | 494 | return est_img_d1_ 495 | 496 | 497 | 498 | 499 | 500 | 501 | 502 | 503 | 504 | 505 | -------------------------------------------------------------------------------- /models/__pycache__/DRBNet.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lingyanruan/DRBNet/0fa89a56381130637ca44bf10b023a2d66a71d87/models/__pycache__/DRBNet.cpython-38.pyc -------------------------------------------------------------------------------- /options/__pycache__/base_options.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lingyanruan/DRBNet/0fa89a56381130637ca44bf10b023a2d66a71d87/options/__pycache__/base_options.cpython-38.pyc -------------------------------------------------------------------------------- /options/__pycache__/test_options.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lingyanruan/DRBNet/0fa89a56381130637ca44bf10b023a2d66a71d87/options/__pycache__/test_options.cpython-38.pyc -------------------------------------------------------------------------------- /options/base_options.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | from util import util 4 | 5 | 6 | class BaseOptions(): 7 | def __init__(self): 8 | self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) 9 | self.initialized = False 10 | 11 | def initialize(self): 12 | 13 | self.parser.add_argument('--dataroot_rf', default='./datasets/RealDOF', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)') 14 | self.parser.add_argument('--dataroot_pixeldp', default='./datasets/PixelDP', help='PixelDP dataset path') 15 | self.parser.add_argument('--dataroot_lf', default='./datasets/LFDOF/test_data', help='LFDOF dataset path') 16 | self.parser.add_argument('--dataroot_dpdd', default='./datasets/DPDD', help='DPDD dataset path') 17 | self.parser.add_argument('--dataroot_cuhk', default='./datasets/CUHK', help='CUHK dataset path') 18 | self.parser.add_argument('--name', type=str, default='defocus_deblur', help='name of the experiment. It decides where to store samples and models') 19 | self.initialized = True 20 | 21 | 22 | def parse(self): 23 | if not self.initialized: 24 | self.initialize() 25 | self.opt = self.parser.parse_args() 26 | return self.opt 27 | -------------------------------------------------------------------------------- /options/test_options.py: -------------------------------------------------------------------------------- 1 | from .base_options import BaseOptions 2 | 3 | 4 | class TestOptions(BaseOptions): 5 | def initialize(self): 6 | BaseOptions.initialize(self) 7 | self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') 8 | self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') 9 | self.parser.add_argument('--eval_data', type=str, default='DPD', help='DPD|LF|RealDOF|PixelDP') 10 | self.parser.add_argument('--save_images', action='store_true', help='save images') 11 | self.parser.add_argument('--net_mode', type=str, default='single', help='single | dual') 12 | self.parser.add_argument('--ckpt_path', type=str, default='./ckpts/', help='single | dual') 13 | self.isTrain = False -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | ptflops==0.6.4 2 | scikit-image==0.18.1 3 | opencv-python==4.5.1.48 4 | natsort==7.1.1 5 | gdown==4.5.1 6 | lpips==0.1.4 -------------------------------------------------------------------------------- /run.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This source code is licensed under the license found in the LICENSE file. 3 | This is the implementation of the "Learning to deblur using light field generated and real defocus images" paper accepted to CVPR 2022. 4 | Project GitHub repository: https://github.com/lingyanruan/DRBNet 5 | Email: lyruanruan@gmail.com 6 | Copyright (c) 2022-present, Lingyan Ruan 7 | ''' 8 | 9 | import os 10 | from options.test_options import TestOptions 11 | from datetime import datetime 12 | import torch 13 | import torchvision.utils as vutils 14 | from ptflops import get_model_complexity_info 15 | from util.util import * 16 | from pathlib import Path 17 | import time 18 | import sys 19 | import lpips 20 | from glob import glob 21 | from natsort import natsorted 22 | from skimage.metrics import peak_signal_noise_ratio as compute_psnr 23 | from skimage.metrics import structural_similarity as compute_ssim 24 | from models.DRBNet import * 25 | 26 | #### metrics ################################# 27 | compute_lpips = lpips.LPIPS(net='alex').cuda() 28 | 29 | opt = TestOptions().parse() 30 | 31 | #### define time 32 | folder_time = datetime.now().strftime('%Y-%m-%d_%H%M') 33 | 34 | # results save position 35 | opt.results_dir = opt.results_dir + '/' + opt.name + '/' + opt.eval_data + '/' + opt.net_mode +'/'+ folder_time 36 | 37 | #### make directory ################################ 38 | Path(os.path.join(opt.results_dir, 'input' )).mkdir(parents=True, exist_ok=True) 39 | Path(os.path.join(opt.results_dir, 'output')).mkdir(parents=True, exist_ok=True) 40 | 41 | ## evaluation values 42 | PSNR_total,SSIM_total,LPIPS_total = 0,0,0 43 | PSNR_score, SSIM_score, LPIPS_score , total_time= 0,0,0,0 44 | 45 | 46 | ######################################### Dataset List ################################################# 47 | input_c_file_path_list = [] 48 | 49 | if opt.eval_data == 'DPDD': 50 | input_c_file_path_list = natsorted(glob(os.path.join(opt.dataroot_dpdd, 'test_c','source', '*.png'))) 51 | input_r_file_path_list = natsorted(glob(os.path.join(opt.dataroot_dpdd, 'test_r', 'source', '*.png'))) 52 | input_l_file_path_list = natsorted(glob(os.path.join(opt.dataroot_dpdd, 'test_l', 'source','*.png'))) 53 | gt_file_path_list = natsorted(glob(os.path.join(opt.dataroot_dpdd, 'test_c', 'target', '*.png'))) 54 | 55 | elif opt.eval_data == 'RealDOF': 56 | input_c_file_path_list = natsorted(glob(os.path.join(opt.dataroot_rf, 'source', '*.png'))) 57 | gt_file_path_list = natsorted(glob(os.path.join(opt.dataroot_rf, 'target', '*.png'))) 58 | 59 | elif opt.eval_data == 'PixelDP': 60 | input_c_file_path_list = natsorted(glob(os.path.join(opt.dataroot_pixeldp, 'test_c','source', '*.png'))) 61 | gt_file_path_list = None 62 | 63 | elif opt.eval_data == 'CUHK': 64 | input_c_file_path_list = natsorted(glob(os.path.join(opt.dataroot_cuhk,'*'))) 65 | gt_file_path_list = None 66 | 67 | total_files = len(input_c_file_path_list) 68 | 69 | assert total_files > 0, 'Wrong Dataset Name or No Dataset Exist, Please Check!!' 70 | 71 | print('\n\n================================= EVALUATION START ==================================================') 72 | 73 | for i, filename in enumerate(input_c_file_path_list): 74 | # Read Image 75 | C = crop_image(read_image(input_c_file_path_list[i], 255.0))*2-1 76 | C = torch.FloatTensor(C.transpose(0, 3, 1, 2).copy()).cuda() 77 | filename = os.path.split(filename)[-1] 78 | 79 | if opt.net_mode == 'dual': 80 | R,L = crop_image(read_image(input_r_file_path_list[i], 255.0))*2-1, crop_image(read_image(input_l_file_path_list[i], 255.0))*2-1 81 | R,L = torch.FloatTensor(R.transpose(0, 3, 1, 2).copy()).cuda(), torch.FloatTensor(L.transpose(0, 3, 1, 2).copy()).cuda() 82 | if gt_file_path_list is not None: 83 | GT = crop_image(read_image(gt_file_path_list[i], 255.0)) # here to [0,1] 84 | GT = torch.FloatTensor(GT.transpose(0, 3, 1, 2).copy()).cuda() 85 | 86 | ##test resut 87 | with torch.no_grad(): 88 | 89 | if opt.net_mode == 'single': 90 | network = DRBNet_single().cuda() 91 | opt.ckpt_path = './ckpts/single/single_image_defocus_deblurring.pth' #final one 92 | network.load_state_dict(torch.load(opt.ckpt_path)) 93 | start_time = time.time() 94 | output = network(C) 95 | time_per = time.time() - start_time 96 | else: 97 | network = DeblurNet_dual().cuda() 98 | opt.ckpt_path = './ckpts/dual/dual_images_defocus_deblurring.pth' 99 | network.load_state_dict(torch.load(opt.ckpt_path)) 100 | start_time = time.time() 101 | output = network(C,R,L) 102 | time_per = time.time() - start_time 103 | 104 | 105 | total_time = total_time + time_per 106 | 107 | output_cpu = (output.cpu().numpy()[0].transpose(1, 2, 0) +1.0 )/2.0 # to [0,1] for psnr and ssim evaluation 108 | 109 | if gt_file_path_list is not None: 110 | GT_cpu = GT.cpu().numpy()[0].transpose(1, 2, 0) 111 | PSNR_score = compute_psnr(output_cpu, GT_cpu,data_range=1.0) 112 | SSIM_score = compute_ssim(output_cpu, GT_cpu,data_range=1.0,multichannel=True) 113 | LPIPS_score = compute_lpips(output, GT * 2. - 1.).item() 114 | 115 | if opt.save_images: 116 | save_file_path_deblur_input = os.path.join(opt.results_dir, 'input', '{}'.format(filename)) 117 | save_file_path_deblur = os.path.join(opt.results_dir, 'output', '{}'.format(filename)) 118 | vutils.save_image((C+1.0)/2.0, '{}'.format(save_file_path_deblur_input), nrow=1, padding = 0, normalize = False) 119 | vutils.save_image((output+1.0)/2.0, '{}'.format(save_file_path_deblur), nrow=1, padding = 0, normalize = False) 120 | 121 | # Log 122 | print('[EVAL on {}][{:02}/{}] {} PSNR: {:.5f}, SSIM: {:.5f}, LPIPS: {:.5f}, Time: {:.5f}sec'.format( opt.eval_data, i + 1, total_files, filename, PSNR_score, SSIM_score, LPIPS_score, time_per)) 123 | with open(os.path.join(opt.results_dir, 'score_{}.txt'.format(opt.eval_data)), 'w' if i == 0 else 'a') as file: 124 | file.write('[EVAL][{:02}/{}] {} PSNR: {:.5f}, SSIM: {:.5f}, LPIPS: {:.5f}, Time: {:.5f}sec \n'.format( i + 1, total_files, filename, PSNR_score, SSIM_score, LPIPS_score, time_per)) 125 | file.close() 126 | 127 | PSNR_total += PSNR_score 128 | SSIM_total += SSIM_score 129 | LPIPS_total += LPIPS_score 130 | 131 | ###=============================== network parameters info =======================================####### 132 | PSNR_mean,SSIM_mean,LPIPS_mean,time_mean = PSNR_total / total_files,SSIM_total / total_files, LPIPS_total/total_files, total_time/total_files 133 | 134 | def prepare_input(resolution): 135 | input_blur_C = torch.FloatTensor(1, 3, 720, 1280).cuda() 136 | input_blur_L = torch.FloatTensor(1, 3, 720, 1280).cuda() 137 | input_blur_R = torch.FloatTensor(1, 3, 720, 1280).cuda() 138 | return dict(C = input_blur_C, R=input_blur_L, L=input_blur_R) 139 | 140 | 141 | ### add network parameters info####### 142 | if opt.net_mode == 'single': 143 | Macs,params = get_model_complexity_info(network, (3, 720, 1280), as_strings=False) 144 | print('\t{:<30} {:<8} B'.format('Computational complexity (Macs): ', Macs / 1000 ** 3 )) 145 | print('\t{:<30} {:<8} M'.format('Number of parameters: ',params / 1000 ** 2, '\n')) 146 | 147 | else: 148 | Macs,params = get_model_complexity_info(network, (1,3, 720, 1280),input_constructor=prepare_input, as_strings=False,print_per_layer_stat=False) 149 | print('\t{:<30} {:<8} B'.format('Computational complexity (Macs): ', Macs / 1000 ** 3 )) 150 | print('\t{:<30} {:<8} M'.format('Number of parameters: ',params / 1000 ** 2, '\n')) 151 | 152 | 153 | sys.stdout.write('\n[TOTAL |{}] PSNR: {:.5f} SSIM: {:.5f} LPIPS: {:.5f} ({:.5f}sec)'.format(opt.eval_data, PSNR_mean, SSIM_mean, LPIPS_mean, time_mean)) 154 | with open(os.path.join(opt.results_dir, 'score_{}.txt'.format(opt.eval_data)), 'a') as file: 155 | file.write('\n[TOTAL ] PSNR: {:.5f} SSIM: {:.5f} LPIPS: {:.5f} ({:.5f}sec)'.format( PSNR_mean, SSIM_mean, LPIPS_mean, time_mean)) 156 | file.write('\n{:<30} {:<8} B'.format('Computational complexity (Macs): ', Macs / 1000 ** 3 )) 157 | file.write('\n{:<30} {:<8} M'.format('Number of parameters: ', params / 1000 ** 2, '\n')) 158 | file.close() 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | -------------------------------------------------------------------------------- /util/__pycache__/util.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lingyanruan/DRBNet/0fa89a56381130637ca44bf10b023a2d66a71d87/util/__pycache__/util.cpython-38.pyc -------------------------------------------------------------------------------- /util/util.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | import cv2 4 | import os 5 | 6 | def read_image(path, norm_val = None): 7 | 8 | if norm_val == (2**16-1): 9 | frame = cv2.imread(path, -1) 10 | frame = frame / norm_val 11 | frame = frame[...,::-1] 12 | else: 13 | frame = cv2.cvtColor(cv2.imread(path, cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB) 14 | frame = frame / 255. 15 | return np.expand_dims(frame, axis = 0) 16 | 17 | 18 | def crop_image(img, val = 16): 19 | shape = img.shape 20 | if len(shape) == 4: 21 | _, h, w, _ = shape[:] 22 | return img[:, 0 : h - h % val, 0 : w - w % val, :] 23 | elif len(shape) == 3: 24 | h, w = shape[:2] 25 | return img[0 : h - h % val, 0 : w - w % val, :] 26 | elif len(shape) == 2: 27 | h, w = shape[:2] 28 | return img[0 : h - h % val, 0 : w - w % val] 29 | 30 | 31 | def make_lf_aif_gt_dataset(img_list,dir): 32 | aif_gt_files = [] 33 | assert os.path.isdir(dir), '%s is not a valid directory' % dir 34 | for f in img_list: 35 | aif_file = os.path.split(f)[-1].split('_ap')[0] 36 | aif_file_name_tmp = aif_file + '.png' 37 | aif_file_name = os.path.join(dir, aif_file_name_tmp) 38 | if os.path.exists(aif_file_name): 39 | aif_gt_files.append(aif_file_name) 40 | return aif_gt_files 41 | --------------------------------------------------------------------------------