├── LICENSE ├── README.md ├── compute_metrics.py ├── data ├── .DS_Store └── RealEstate10K │ ├── 6f0ec56192a25862.txt │ ├── 9340be220dd1074a.txt │ └── b7c282aff2a43dc3.txt ├── download_realestate10k.py └── figs └── teaser.png /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Yuqian Zhou 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # TransFill-Reference-Inpainting 2 | This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations (Yuqian Zhou, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi) at CVPR'21. According to some confidential reasons, we are not planning to release the training/testing codes and models. Online-demo will be public once we set up the server. However, we release the testing dataset for comparsion, and the scripts to prepare the training dataset. 3 | 4 | [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_TransFill_Reference-Guided_Image_Inpainting_by_Merging_Multiple_Color_and_Spatial_CVPR_2021_paper.html) | [[Project]](https://yzhouas.github.io/projects/TransFill/index.html) | [[Demo Video]](https://www.youtube.com/watch?v=TroIMZmQAZQ) 5 | 6 | ## Introduction 7 | 8 |
9 | 10 | Applications of TransFill: Photo Content Swap, Object Removal, Color Adjustment. 11 |
12 | 13 | Image inpainting is the task of plausibly restoring missing pixels within a hole region that is to be removed from a target image. Most existing technologies exploit patch similarities within the image, or leverage large-scale training data to fill the hole using learned semantic and texture information. However, due to the ill-posed nature of the inpainting task, such methods struggle to complete larger holes containing complicated scenes. In this paper, we propose TransFill, a multi-homography transformed fusion method to fill the hole by referring to another source image that shares scene contents with the target image. We first align the source image to the target image by estimating multiple homographies guided by different depth levels. We then learn to adjust the color and apply a pixel-level warping to each homography-warped source image to make it more consistent with the target. Finally, a pixel-level fusion module is learned to selectively merge the different proposals. Our method achieves state-of-the-art performance on pairs of images across a variety of wide baselines and color differences, and generalizes to user-provided image pairs. 14 | 15 | ## Download and Prepare RealEstate10K 16 | We prepare the script of downloading and extracting paired frames from RealEstate10K. 17 | First, go to the RealEstate10K [official website](https://google.github.io/realestate10k/download.html) to download the .txt files. 18 | Then unzip it and put the folder into the data folder. 19 | 20 | 21 | Run our script to download the video samples and extract paired frames with frame difference (stride) 10, 20 and 30. 22 | ``` 23 | python download_realestate10k.py \ 24 | --txt_dir ./data/RealEstate10K/train \ 25 | --out_dir ./RealEstate10K_frames/train \ 26 | --dataset_dir ./RealEstate10K_pair/train \ 27 | --sample_num 10 28 | ``` 29 | 30 | Choose the sample number to download limited number of samples (say 100 videos). You may need to install [youtube-dl](https://pypi.org/project/youtube_dl/) package or VPNs (in Mainland China) to download YouTube videos. Google also has some limitations of downloading amount, so I did not use multi-thread to increase the downloading speed on purpose. The process is fairly long, so I suggest downloading a subset of videos to extract samples first, and gradually extending it to download the whole dataset. Any other downloading issues, please inquire the original provider of [RealEstate10K](https://google.github.io/realestate10k/download.html). 31 | 32 | 33 | ## Download Testing Data 34 | We shared the testing images in the paper, including the 'Small Set' containing 300 pairs of images from RealEstate10K, and a 'Real Set' containing 100+ challenging paired images from users. The data can be downloaded from the [Google Drive](https://drive.google.com/file/d/15tjUa0WA5K6kyee3Juodzvr_zKzGJvUG/view?usp=sharing). 35 | 36 | To reproduce the results in the Table 1 of the paper, download and unzip the 'Small Set' into data folder, and run 37 | ``` 38 | python compute_metrics.py 39 | ``` 40 | The script will compare the images generated by TransFill with the ground truth images in the target folder, and return PSNR, SSIM and LPIPS score. 41 | 42 | In the 'Real Set', ProFill and TransFill results are shared for the researchers to compare. Note that there are some failure cases within the folder, which shows the room for future works to improve TransFill. 43 | 44 | ## Test on Your Own Data 45 | We plan to set up the online demo server in the near future. But before we finish that, if you are really eager for a comparsion of the results for research purpose, feel free to send the testing data in the format of 'target', 'source', 'hole' folders to zhouyuqian133@gmail.com (preferred) or yuqzhou@adobe.com. The resolution has better be smaller than 1K x 1K, otherwise we have to resize the image to avoid memory issues. To make fully use of the advantages of TransFill, we suggest the hole to be large enough by including more background contents of the target image. 46 | 47 | We won't keep your data and will return the testing results to you within 2 working days. 48 | 49 | ## Citation 50 | If you think this repo and the manuscript helpful, please consider citing us. 51 | ``` 52 | @inproceedings{zhou2021transfill, 53 | title={TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations}, 54 | author={Zhou, Yuqian and Barnes, Connelly and Shechtman, Eli and Amirghodsi, Sohrab}, 55 | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 56 | pages={2266--2276}, 57 | year={2021} 58 | } 59 | ``` 60 | 61 | ## Acknowledgements 62 | This project is conducted when the author interned at Adobe Photoshop and Adobe Research. 63 | 64 | 65 | -------------------------------------------------------------------------------- /compute_metrics.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from skimage.metrics import structural_similarity as comp_ssim 3 | import lpips 4 | import glob 5 | import os 6 | import imageio 7 | import torch 8 | import math 9 | 10 | def calc_ssim( hr, sr, align=False): 11 | sr = np.transpose(sr[0].cpu().numpy(), (1,2,0)) 12 | hr = np.transpose(hr[0].cpu().numpy(), (1,2,0)) 13 | return comp_ssim(sr/255., hr/255., multichannel=True) 14 | 15 | def calc_psnr(hr, sr, scale=1, rgb_range=255, align=False, dataset=None): 16 | if hr.nelement() == 1: return 0 17 | diff = (sr - hr) / rgb_range 18 | mse = diff.pow(2).mean() 19 | return -10 * math.log10(mse) 20 | 21 | def calc_lpips(hr, sr, loss_fn): 22 | sr = (sr / 255.) * 2- 1 23 | hr = (hr / 255.) * 2- 1 24 | d = loss_fn(sr, hr)[0,0,0,0] 25 | return d 26 | 27 | def np2Tensor(*args): 28 | def _np2Tensor(img): 29 | np_transpose = np.ascontiguousarray(img.transpose((2, 0, 1))) 30 | np_transpose = np.expand_dims(np_transpose, 0) 31 | tensor = torch.from_numpy(np_transpose).float() 32 | return tensor 33 | 34 | return [_np2Tensor(a) for a in args] 35 | 36 | def Mean(lst): 37 | return sum(lst) / len(lst) 38 | 39 | def compute_all(root): 40 | all_file = glob.glob(os.path.join(root, 'target', '*GT.png')) 41 | psnr = [] 42 | ssim = [] 43 | lpips_score = [] 44 | loss_fn_alex = lpips.LPIPS(net='alex') 45 | for file_name in all_file: 46 | print(file_name) 47 | hr = imageio.imread(file_name) 48 | sr = imageio.imread(file_name.replace('/target/','/transfill/').replace('GT','Final')) 49 | hr, sr = np2Tensor(hr, sr) 50 | psnr.append(calc_psnr(hr, sr)) 51 | ssim.append(calc_ssim(hr, sr)) 52 | lpips_score.append(calc_lpips(hr, sr, loss_fn_alex)) 53 | return Mean(psnr), Mean(ssim), Mean(lpips_score) 54 | 55 | root = 'data/Small_Set' 56 | print(compute_all(root)) 57 | -------------------------------------------------------------------------------- /data/.DS_Store: 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0.493232836 0.876858400 0.500000000 0.500000000 0.000000000 0.000000000 0.999835372 -0.002103517 -0.018021965 0.007480068 0.002089019 0.999997497 -0.000823243 -0.020129357 0.018023653 0.000785459 0.999837279 -0.585093093 81 | 184384200 0.493232836 0.876858400 0.500000000 0.500000000 0.000000000 0.000000000 0.999825776 -0.001999632 -0.018558502 0.007883266 0.001998850 0.999997973 -0.000060674 -0.021473511 0.018558586 0.000023568 0.999827802 -0.588022689 82 | 184417567 0.493232836 0.876858400 0.500000000 0.500000000 0.000000000 0.000000000 0.999818563 -0.002064789 -0.018936092 0.007941920 0.002079087 0.999997556 0.000735416 -0.022757271 0.018934527 -0.000774653 0.999820411 -0.591238512 83 | 184450933 0.493232836 0.876858400 0.500000000 0.500000000 0.000000000 0.000000000 0.999808908 -0.002011325 -0.019445073 0.008441726 0.002038874 0.999996960 0.001397072 -0.023400319 0.019442203 -0.001436451 0.999809980 -0.594026619 84 | 184484300 0.493232836 0.876858400 0.500000000 0.500000000 0.000000000 0.000000000 0.999802709 -0.002132687 -0.019746944 0.008455320 0.002173479 0.999995530 0.002044499 -0.023936479 0.019742494 -0.002087016 0.999802947 -0.597215096 85 | 184517667 0.493232836 0.876858400 0.500000000 0.500000000 0.000000000 0.000000000 0.999794722 -0.002306315 -0.020129379 0.008531376 0.002371212 0.999992073 0.003200741 -0.025435108 0.020121837 -0.003247816 0.999792278 -0.600408480 86 | -------------------------------------------------------------------------------- /download_realestate10k.py: -------------------------------------------------------------------------------- 1 | from __future__ import unicode_literals 2 | from pytube import YouTube 3 | import os 4 | import glob 5 | import cv2 6 | import youtube_dl 7 | import numpy as np 8 | 9 | def download_dataset(txt_dir, out_dir, sample_num=100, stride=1, remove_video=True): 10 | all_files = sorted(glob.glob(os.path.join(txt_dir, '*.txt'))) 11 | for i in range(0, min(sample_num, len(all_files))): 12 | f = all_files[i] 13 | print(f) 14 | file_name = f.split('/')[-1].split('.')[0] #the file name and remark 15 | out_f = os.path.join(out_dir,file_name) 16 | 17 | if os.path.exists(out_f): 18 | print('the file exists. skip....') 19 | continue 20 | video_txt = open(f) 21 | content = video_txt.readlines() 22 | url = content[0] #the url file 23 | try: 24 | ydl_opts = {'outtmpl': '%(id)s.%(ext)s'} 25 | with youtube_dl.YoutubeDL(ydl_opts) as ydl: 26 | info = ydl.extract_info(url, download=True) 27 | output_file = ydl.prepare_filename(info) 28 | except: 29 | print("An exception occurred, maybe because of the downloading limits of youtube.") 30 | continue 31 | 32 | #if video is already downloaded, start extracting frames 33 | os.makedirs(out_f, exist_ok=True) 34 | if not os.path.exists(output_file): output_file = output_file.replace('.mp4','.mkv') 35 | os.rename(output_file, os.path.join(out_f, file_name + '.mp4')) 36 | line = url 37 | vidcap = cv2.VideoCapture(os.path.join(out_f, file_name + '.mp4')) 38 | frame_ind = 1 39 | frame_file = open(out_f + '/pos.txt','w') 40 | for num in range(1, len(content), stride): 41 | line = content[num] 42 | frame_file.write(line) 43 | if line == '\n': break 44 | #line = video_txt.readline() 45 | ts = line.split(' ')[0][:-3] #extract the time stamp 46 | if ts == '': break 47 | vidcap.set(cv2.CAP_PROP_POS_MSEC,int(ts)) # just cue to 20 sec. position 48 | success,image = vidcap.read() 49 | if success: 50 | cv2.imwrite(out_f + '/' + str(frame_ind) + '.jpg', image) # save frame as JPEG file 51 | frame_ind += stride 52 | frame_file.close() 53 | video_txt.close() 54 | 55 | if remove_video: 56 | os.remove(os.path.join(out_f, file_name + '.mp4')) 57 | 58 | 59 | def make_dataset_pairs(all_frame_dir, out_dir): 60 | ''' 61 | #Randomly Extracting Frames with Stride and Resizing (s = 0.5) 62 | ''' 63 | stride = [10, 20, 30] 64 | output1 = os.path.join(out_dir, 'target') #target images 65 | output2 = os.path.join(out_dir, 'source') #source images 66 | os.makedirs(output1, exist_ok=True) 67 | os.makedirs(output2, exist_ok=True) 68 | 69 | folder_name = sorted(glob.glob(os.path.join(all_frame_dir, '*'))) 70 | for i in range(len(folder_name)): #processing the training dataset folder in which contains all the images 71 | print(i) 72 | video = folder_name[i] 73 | file_name = glob.glob(os.path.join(video, '*.jpg')) 74 | def getint(name): 75 | num = name.split('/')[-1].split('.')[0] 76 | return int(num) 77 | file_name = sorted(file_name, key=getint) 78 | number_frame = len(file_name) #how many frames in total 79 | try: 80 | for stride_n in stride: #randomly select three types of strides 81 | target = np.random.choice(number_frame - stride_n, 1)[0] 82 | source = target + stride_n 83 | target_frame = file_name[target] 84 | source_frame = file_name[source] 85 | target_frame_image = cv2.imread(target_frame) 86 | source_frame_image = cv2.imread(source_frame) 87 | if target_frame_image.shape[0] == 720: #if smaller size: no need to resize 88 | target_frame_image = cv2.resize(target_frame_image, (0,0), fx=0.5, fy=0.5) 89 | source_frame_image = cv2.resize(source_frame_image, (0,0), fx=0.5, fy=0.5) 90 | basename = video.split('/')[-1] + '_' + str(stride_n) 91 | cv2.imwrite(os.path.join(output1, basename + '_target.png'), target_frame_image) 92 | cv2.imwrite(os.path.join(output2, basename + '_source.png'), source_frame_image) 93 | except: 94 | print('Something wrong with' + video) 95 | 96 | if __name__ == "__main__": 97 | #using the script to prepare the dataset 98 | import argparse 99 | 100 | parser = argparse.ArgumentParser(description='Download RealEstate10K Dataset') 101 | parser.add_argument('--txt_dir', metavar='path', default = './RealEstate10K', required=False, 102 | help='path to the original dataset txt files downloaded online') 103 | parser.add_argument('--frame_dir', metavar='path', default = './RealEstate10K_frames', required=False, 104 | help='extract all the frames of videos') 105 | parser.add_argument('--dataset_dir', metavar='path', default = './RealEstate10K_pair', required=False, 106 | help='output paired dataset dir: stride is 10, 20, 30') 107 | parser.add_argument('--sample_num', type=int, default=1, help='numer of video to download and smple, default 1') 108 | args = parser.parse_args() 109 | 110 | txt_dir = args.txt_dir 111 | out_dir = args.frame_dir 112 | dataset_dir = args.dataset_dir 113 | download_dataset(txt_dir, out_dir, sample_num=args.sample_num) 114 | make_dataset_pairs(out_dir, dataset_dir) 115 | 116 | -------------------------------------------------------------------------------- /figs/teaser.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yzhouas/TransFill-Reference-Inpainting/a397862dd6837e4eb38ec650fe015f8754c8fb9e/figs/teaser.png --------------------------------------------------------------------------------