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Deep_ISP

Pre-requisites

The code was written with Python 3.6.8 with the following dependencies:

  • cuda release 9.0, V9.0.176
  • tensorflow 1.12.0
  • keras 2.2.4
  • numpy 1.16.4
  • scipy 1.2.1
  • imageio 2.5.0
  • skimage 0.15.0
  • matplotlib 3.1.0
  • cuDNN 7.4.1

This code has been tested in Ubuntu 16.04.6 LTS with 4 NVIDIA GeForce GTX 1080 Ti GPUs (each with 11 GB RAM).

How to Use

Clone the repository:

git clone https://github.com/puneesh00/deep_isp.git

Training

To train the network, run the following command:

python main.py -exp isp -dataset (full path to dataset directory) -save (full path to the repository) -

Testing

Download weights

  Download weights for the model, and place them in the cloned git repository. They can be found here.

To infer full resolution images, run the following command:

python infer_full.py -path (give full path to the repository) -w weights2_0191.h5 -dataset (path to full resolution raw images)

  This will generate the output images in a folder results (default name) in the git repository.

To infer cropped frames, run the following command:

python infer.py -path (give full path to the repository) -w weights2_0191.h5 -dataset (path to cropped raw images) -res results_cropped

  This will generate the output images in a folder results_cropped in the git repository.