Deep-Image-Analogy

Unofficial,PyTorch version of Deep Image Analogy.https://arxiv.org/abs/1705.01088. This project focuses on documentation of the project , and simplifying the structure. A blog post on it is coming soon.

Some Outputs (The two images in the middle are generated by the Algorithm)

This Project uses python3.6 and Cuda

To Install Dependencies:

be in the root directory and run pip install -r requirements.txt

To See Step by Step working of Project :

Run the Deep Image Analogy.ipynb file in the notebooks folder (using jupyter)

To run project:

cd into src , and run python Deep-Img-Analogy.py INPUT_IMG_A INPUT_IMG_BB OUTPUT_IMG

Note

This project uses Adam as optimizer instead of LBFGS. LBFGS was giving really poor results.

Project Organization

├── data
│   ├── outputs <-- folder to store outputs
│   └── raw <-- folder to store inputs
├── LICENSE.md
├── notebooks
│   ├── Deep Image Analogy.ipynb Full Pipeline in a step by step manner
│   ├── PatchMatch-Demo.ipynb Raw Patchmatch demo
│   └── WLS.ipynb Weighted Least Squares Implementation Demo (currently not being used by this project)
├── README.md 
├── requirements.txt <-- Project requirements. 
└── src
    ├── Deep-Img-Analogy.py <-- End to end executable with command line interface.
    ├── models
    │   └── VGG19.py <-- modified VGG19 with support for deconvolution, and other things. 
    ├── PatchMatch
    │   └── PatchMatchOrig.py <-- CPU version of PatchMatch. GPU version may come in the future.
    ├── Utils.py <-- Helper Utilities
    └── WLS.py <-- Weighted Least Squares.

Project based on the cookiecutter data science project template. #cookiecutterdatascience