Add Docs

README.md

@@ -0,0 +1,154 @@
+ ![Language](https://img.shields.io/badge/language-python--3.7-blue) [![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url] [![LinkedIn][linkedin-shield]][linkedin-url]
+
+<!-- PROJECT LOGO -->
+<br />
+
+<p align="center">
+  <h3 align="center">Video Interpolation using Deep Optical Flow</h3>
+  <p align="center">
+    Intermediate frame interpolation using optical flow with FlowNet2
+    <br />
+    <a href=https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow><strong>Explore the repository»</strong></a>
+    <br />
+    <br />
+    <a href=https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow/blob/master/Problem_Statement.pdf>View Problem Statement</a>
+    <a href=https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow/blob/master/results/report.pdf>View Report</a>
+  </p>
+
+</p>
+
+> tags : frame interpolation, optical flow, flownet2, digital video,deep learning, pytorch
+
+
+
+<!-- ABOUT THE PROJECT -->
+
+## About The Project
+
+This project deals with the task of video frame interpolation with estimated optical flow. In particular, we estimate the forward optical flow (flow from Frame *N* to Frame *N + 2*) and the backward
+flow (flow from Frame *N + 2* to Frame *N*) and use both of them to estimate the intermediate Frame *N*. To estimate the optical flow we use pre-trained [FlowNet2](https://github.com/NVIDIA/flownet2-pytorch) deep learning model and experiment by fine-tuning it. We explore the interpolation performance on [Spheres](./input/sphere) dataset and [Corridor](./input/corridor) dataset.  We observe that the quality of interpolated frames is comparable to original with both the datasets. A detailed description of interpolation algorithms, loss functions, analysis of the results are available in the [Report](./results/report.pdf).
+
+Note: [flownet](./flownet2) folder contains code modified from [NVIDIA FlowNet2](https://github.com/NVIDIA/flownet2-pytorch) Repository and [FlowNet2 PyTorch Wrapper](https://github.com/ErenBalatkan/FlowNet2-PyTorch-EasyToUse-Wrapper). Download the pre-trained models and put it in `./flownet2/pretrained_models` folder.
+
+### Built With
+This project was built with 
+
+* python v3.7
+* pytorch v1.0.0
+* The environment used for developing this project is available at [environment.yml](environment.yml).
+
+
+
+<!-- GETTING STARTED -->
+
+## Getting Started
+
+Clone the repository into a local machine using
+
+```shell
+git clone https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow
+```
+
+### Prerequisites
+
+Create a new conda environment and install all the libraries by running the following command
+
+```shell
+conda env create -f environment.yml
+```
+
+The dataset used in this project is already available in this repository. To test on other datasets, download them and put them in the `input/` folder.
+
+### Instructions to run
+
+We explore with pre-trained [FlowNet2](https://github.com/NVIDIA/flownet2-pytorch) model from NVIDIA and experiment by fine-tuning it. 
+
+##### Pre-trained FlowNet2 model
+
+To interpolate the frame with the pretrained FlowNet2 model, run the following command. This will interpolate the intermediate frames and store it in [this](./results/pretrained/interpolated_frames) folder.
+
+```shell
+python pretrained_interpolation.py
+```
+
+##### FlowNet2 with fine-tuning
+
+To interpolate the frame with the pretrained FlowNet2 model, run the following command. Set the parameters for fine-tuning in the [parameters](./finetuned/parameters.py) file This will interpolate the intermediate frames and store it in [this](./results/finetuned/interpolated_frames) folder.
+
+```shell
+python finetuned_interpolation.py
+```
+
+
+
+<!-- RESULTS -->
+
+## Results
+
+
+
+|             Corridor Dataset             |               Pre-Trained Optical Flow                |               Fine-Tuned Optical Flow                |
+| :--------------------------------------: | :---------------------------------------------------: | :--------------------------------------------------: |
+| ![Corridor](./results/docs/corridor.gif) | ![CorridorPT](./results/docs/corridor_pretrained.gif) | ![CorridorFT](./results/docs/corridor_finetuned.gif) |
+
+|            Sphere Dataset            |             Pre-Trained Optical Flow              |             Fine-Tuned Optical Flow              |
+| :----------------------------------: | :-----------------------------------------------: | :----------------------------------------------: |
+| ![Sphere](./results/docs/sphere.gif) | ![SpherePT](./results/docs/sphere_pretrained.gif) | ![SphereFT](./results/docs/sphere_finetuned.gif) |
+
+
+
+
+
+|                   Corridor Dataset                    |                   Pre-Trained Optical Flow                   |                   Fine-Tuned Optical Flow                    |
+| :---------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
+| ![Corridor](./results/docs/corridor_intermediate.gif) | ![CorridorPT](./results/docs/corridor_pretrained_interpolated.gif) | ![CorridorFT](./results/docs/corridor_finetuned_interpolated.gif) |
+
+|                  Sphere Dataset                   |                   Pre-Trained Optical Flow                   |                   Fine-Tuned Optical Flow                    |
+| :-----------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
+| ![Sphere](./results/docs/sphere_intermediate.gif) | ![SpherePT](./results/docs/sphere_pretrained_interpolated.gif) | ![SphereFT](./results/docs/sphere_finetuned_interpolated.gif) |
+
+
+
+<!-- LICENSE -->
+
+## License
+
+Distributed under the MIT License. See `LICENSE` for more information.
+
+
+
+<!-- CONTACT -->
+## Contact
+
+Vineeth S - [email protected]
+
+Project Link: [https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow](https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow)
+
+
+
+<!-- ACKNOWLEDGEMENTS -->
+## Acknowledgements
+
+* [NVIDIA FlowNet2](https://github.com/NVIDIA/flownet2-pytorch)
+
+  > Fitsum Reda et al. flownet2-pytorch: Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation
+  > with Deep Networks. https://github.com/NVIDIA/flownet2-pytorch . 2017.
+
+
+
+<!-- MARKDOWN LINKS & IMAGES -->
+<!-- https://www.markdownguide.org/basic-syntax/#reference-style-links -->
+
+[contributors-shield]: https://img.shields.io/github/contributors/vineeths96/Video-Interpolation-using-Deep-Optical-Flow.svg?style=flat-square
+[contributors-url]: https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow/graphs/contributors
+[forks-shield]: https://img.shields.io/github/forks/vineeths96/Video-Interpolation-using-Deep-Optical-Flow.svg?style=flat-square
+[forks-url]: https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow/network/members
+[stars-shield]: https://img.shields.io/github/stars/vineeths96/Video-Interpolation-using-Deep-Optical-Flow.svg?style=flat-square
+[stars-url]: https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow/stargazers
+[issues-shield]: https://img.shields.io/github/issues/vineeths96/Video-Interpolation-using-Deep-Optical-Flow.svg?style=flat-square
+[issues-url]: https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow/issues
+[license-shield]: https://img.shields.io/badge/License-MIT-yellow.svg
+[license-url]: https://github.com/vineeths96/Video-Interpolation-using-Deep-Optical-Flow/blob/master/LICENSE
+[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555
+[linkedin-url]: https://linkedin.com/in/vineeths
+