Note: This branch contains all the restoration results, including 512×512 face region and the final result by putting the enhanced face to the origial input. The former version that can only generate the face result is put in master branch
Downloading from the following url and put them into ./.
The folder structure should be:
.
├── checkpoints
│ ├── facefh_dictionary
│ │ └── latest_net_G.pth
├── weights
│ └── vgg19.pth
├── DictionaryCenter512
│ ├── right_eye_256_center.npy
│ ├── right_eye_128_center.npy
│ ├── right_eye_64_center.npy
│ ├── right_eye_32_center.npy
│ └── ...
└── ...
(Video Installation Tutorial. Thanks for bycloudump's tremendous help.)
- Pytorch (≥1.1 is recommended)
- dlib
- dominate
- cv2
- tqdm
- face-alignment
cd ./FaceLandmarkDetection python setup.py install cd ..
*pip install cmake
*Install dlib with conda "conda install -c conda-forge dlib"
conda install nomkl --channel conda-forge
python test_FaceDict.py --test_path ./TestData/TestWhole --results_dir ./Results/TestWholeResults --upscale_factor 4 --gpu_ids 0 --test_path # test image path
--results_dir # save the results path
--upscale_factor # the upsample factor for the final result
--gpu_ids # gpu id. if use cpu, set gpu_ids=-1
Note: our DFDNet can only generate 512×512 face result for any given face image.
- Step0_Input:
# Save the input image. - Step1_AffineParam:
# Save the crop and align parameters for copying the face result to the original input. - Step1_CropImg:
# Save the cropped face images and resize them to 512×512. - Step2_Landmarks:
# Save the facial landmarks for RoIAlign. - Step3_RestoreCropFace:
# Save the face restoration result (512×512). - Step4_FinalResults:
# Save the final restoration result by putting the enhanced face to the original input.
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- Enhance all the faces in one image.
- Enhance the background.
@InProceedings{Li_2020_ECCV,
author = {Li, Xiaoming and Chen, Chaofeng and Zhou, Shangchen and Lin, Xianhui and Zuo, Wangmeng and Zhang, Lei},
title = {Blind Face Restoration via Deep Multi-scale Component Dictionaries},
booktitle = {ECCV},
year = {2020}
}
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
