https://github.com/nitin-bommi/image-enhancement

https://github.com/nitin-bommi/image-enhancement

Last synced: 12 days ago
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• Host: GitHub
• URL: https://github.com/nitin-bommi/image-enhancement
• Owner: nitin-bommi
• Created: 2022-02-13T07:01:26.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2022-02-15T12:07:19.000Z (almost 3 years ago)
• Last Synced: 2024-11-18T07:40:37.256Z (about 1 month ago)
• Language: Python
• Size: 12.7 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Detecting Objects Under Extreme Illumination Conditions

With the spirit of reproducible research, this repository contains all the codes required to produce the results in the manuscript: 

> N. S. Bommi, A. L. Costuchen, and S. Dev, Detecting Objects Under Extreme Illumination Conditions, *under review*, 2022.

Please cite the above paper if you intend to use whole/part of the code. This code is only for academic and research purposes.

## Code Organization

All codes are written in `python`. 

### Code 

The script to reproduce all the figures, tables in the paper are as follows:

+ `brightness_enhancement.py`: Loads the pretrained RetinaNet model and performs brighntess enhancement on a set of images. 

+ `contrast_enhancement.py`: Performs contrast enhancement on the images specified in the path.

+ `custom_model.py`: To train the model with custom weights and images. The model can be trained from a checkpoint or from scratch.

+ `preprocess.py`: Clean the dataset by identifying images whose annotations are not valid and removing them while saving the rest in separate folders.  

+ `pretrained_model.py`: Performs object detection on a single image by using a pretrained model. 

+ `results.py`: Code used to generate various plots for comparison. 

+ `sharpness_enhancement.py`: Performs sharpness enhancement on the images specified in the path. A pretrained or a custom trained model can be used. 

### Pretrained RetinaNet model 

The model used for the experiments can be found [here](https://drive.google.com/file/d/1L4UZv-_VtWP2yWkTQZo9OIP5c4T8vl5F/view?usp=sharing).

### Dataset 

We also share the dataset used in our paper, and can be found [here](https://drive.google.com/drive/folders/1rkix-gDmcGn4f0BBbs7WQvxs27Dcbql1?usp=sharing).