| # [FILM](https://github.com/google-research/frame-interpolation): Windows Installation Instructions | |
| ## Anaconda Python 3.9 (Optional) | |
| #### Install Anaconda3 Python3.9 | |
| * Go to [https://www.anaconda.com/products/individual](https://www.anaconda.com/products/individual) and click the "Download" button. | |
| * Download the Windows [64-Bit](https://repo.anaconda.com/archive/Anaconda3-2021.11-Windows-x86_64.exe) or [32-bit](https://repo.anaconda.com/archive/Anaconda3-2021.11-Windows-x86.exe) Graphical Installer, depending on your system needs. | |
| * Run the downloaded (`.exe`) file to begin the installation. | |
| * (Optional) Check the "Add Anaconda3 to my PATH environment variable". You may get a 'red text' warning of its implications, you may ignore it for this setup. | |
| #### Create a new Anaconda virtual environment | |
| * Open a new Terminal | |
| * Type the following command: | |
| ``` | |
| conda create -n frame_interpolation pip python=3.9 | |
| ``` | |
| * The above command will create a new virtual environment with the name `frame_interpolation` | |
| #### Activate the Anaconda virtual environment | |
| * Activate the newly created virtual environment by typing in your terminal (Command Prompt or PowerShell) | |
| ``` | |
| conda activate frame_interpolation | |
| ``` | |
| * Once activated, your terminal should look like: | |
| ``` | |
| (frame_interpolation) <present working directory> > | |
| ``` | |
| ## NVIDIA GPU Support | |
| #### Install CUDA Toolkit | |
| * Go to [https://developer.nvidia.com/cuda-11.2.1-download-archive](https://developer.nvidia.com/cuda-11.2.1-download-archive) and select your `Windows`. | |
| * Download and install `CUDA Tookit 11.2.1`. | |
| * Additional CUDA installation information available [here](https://docs.nvidia.com/cuda/archive/11.2.2/cuda-installation-guide-microsoft-windows/index.html). | |
| #### Install cuDNN | |
| * Go to [https://developer.nvidia.com/rdp/cudnn-download](https://developer.nvidia.com/rdp/cudnn-download). | |
| * Create a user profile (if needed) and login. | |
| * Select `cuDNN v8.1.0 (January 26th, 2021), for CUDA 11.0,11.1 and 11.2`. | |
| * Download [cuDNN Library for Widnows (x86)](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.1.0.77/11.2_20210127/cudnn-11.2-windows-x64-v8.1.0.77.zip). | |
| * Extract the contents of the zipped folder (it contains a folder named `cuda`) into `<INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\`. `<INSTALL_PATH>` points to the installation directory specified during CUDA Toolkit installation. By default, `<INSTAL_PATH> = C:\Program Files`. | |
| #### Environment Setup | |
| * Add the following paths to your 'Advanced System Settings' > 'Environment Variables ...' > Edit 'Path', and add: | |
| * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin | |
| * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp | |
| * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include | |
| * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64 | |
| * <INSTALL_PATH>\NVIDIA GPU Computing Toolkit\CUDA\v11.2\cuda\bin | |
| #### Verify Installation | |
| * Open a **new** terminal and type `conda activate frame_interpolation`. | |
| * Install (temporarily) tensorflow and run a simple operation, by typing: | |
| ``` | |
| pip install --ignore-installed --upgrade tensorflow==2.6.0 | |
| python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))" | |
| ``` | |
| * You should see success messages: 'Created device /job:localhost/replica:0/task:0/device:GPU:0'. | |
| ## FILM Installation | |
| * Get Frame Interpolation source codes | |
| ``` | |
| git clone https://github.com/google-research/frame-interpolation | |
| cd frame-interpolation | |
| ``` | |
| * Install dependencies | |
| ``` | |
| pip install -r requirements.txt | |
| conda install -c conda-forge ffmpeg | |
| ``` | |
| * Download pre-traned models, detailed [here](https://github.com/google-research/frame-interpolation#pre-trained-models). | |
| ## Running the Codes | |
| * One mid-frame interpolation. Note: `python3` may not be recognized in Windows, so simply drop `3` as below. | |
| ``` | |
| python -m eval.interpolator_test --frame1 photos\one.png --frame2 photos\two.png --model_path <pretrained_models>\film_net\Style\saved_model --output_frame photos\output_middle.png | |
| ``` | |
| * Large resolution mid-frame interpolation: Set `block_height` and `--block_width` to subdivide along the height and width to create patches, where the interpolator will be run iteratively, and the resulting interpolated mid-patches will be reconstructed into a final mid-frame. In the example below, will create and run on 4 patches (2*2). | |
| ``` | |
| python -m eval.interpolator_test --frame1 photos\one.png --frame2 photos\two.png --block_height 2 --block_wdith 2 --model_path <pretrained_models>\film_net\Style\saved_model --output_frame photos\output_middle.png | |
| ``` | |
| * Many in-between frames interpolation | |
| ``` | |
| python -m eval.interpolator_cli --pattern "photos" --model_path <pretrained_models>\film_net\Style\saved_model --times_to_interpolate 6 --output_video | |
| ``` | |
| ## Acknowledgments | |
| This windows installation guide is heavily based on [tensorflow-object-detection-api-tutorial](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/install.html) . | |