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https://github.com/xiaoyu258/GeoProj
Blind Geometric Distortion Correction on Images Through Deep Learning
https://github.com/xiaoyu258/GeoProj
deep-learning distortion-correction pytorch
Last synced: 14 days ago
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Blind Geometric Distortion Correction on Images Through Deep Learning
- Host: GitHub
- URL: https://github.com/xiaoyu258/GeoProj
- Owner: xiaoyu258
- License: mit
- Created: 2019-08-28T12:11:11.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-09-28T08:15:08.000Z (about 2 years ago)
- Last Synced: 2024-07-21T08:32:54.864Z (4 months ago)
- Topics: deep-learning, distortion-correction, pytorch
- Language: Python
- Homepage:
- Size: 821 KB
- Stars: 174
- Watchers: 6
- Forks: 51
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-lists - Blind Geometric Distortion Correction on Images Through Deep Learning
README
# GeoProj
### [Paper](https://arxiv.org/abs/1909.03459)
The source code of Blind Geometric Distortion Correction on Images Through Deep Learning by Li et al, CVPR 2019.
## Prerequisites
- Linux or Windows
- Python 3
- CPU or NVIDIA GPU + CUDA CuDNN
## Getting Started
### Dataset Generation
In order to train the model using the provided code, the data needs to be generated in a certain manner.
You can use any distortion-free images to generate the dataset. In this paper, we use [Places365-Standard dataset](http://places2.csail.mit.edu/download.html) at the resolution of 512\*512 as the original non-distorted images to generate the 256\*256 dataset.
Run the following command for dataset generation:
```bash
python data/dataset_generate.py [--sourcedir [PATH]] [--datasetdir [PATH]]
[--trainnum [NUMBER]] [--testnum [NUMBER]]
--sourcedir Path to original non-distorted images
--datasetdir Path to the generated dataset
--trainnum Number of generated training samples
--testnum Number of generated testing samples
```
### Training
Run the following command for help message about optional arguments like learning rate, dataset directory, etc.
```bash
python trainNetS.py --h # if you want to train GeoNetS
python trainNetM.py --h # if you want to train GeoNetM
```
### Use a Pre-trained Model
You can download the pretrained model [here](https://drive.google.com/open?id=1Tdi92IMA-rrX2ozdUMvfiN0jCZY7wIp_).
You can also use `eval.py` and modify the model path, image path and saved result path to your own directory to generate your own results.
### Resampling
Import `resample.resampling.rectification` function to resample the distorted image by the forward flow.
The distorted image should be a Numpy array with the shape of H\*W\*3 for a color image or H\*W for a greyscale image, the forward flow should be an array with the shape of 2\*H\*W.
The function will return the resulting image and a mask to indicate whether each pixel will converge within the maximum iteration.
## Citation
```bash
@inproceedings{li2019blind,
title={Blind Geometric Distortion Correction on Images Through Deep Learning},
author={Li, Xiaoyu and Zhang, Bo and Sander, Pedro V and Liao, Jing},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={4855--4864},
year={2019}
}
```