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| title: Video Background Removal | |
| emoji: 👁 | |
| colorFrom: green | |
| colorTo: yellow | |
| sdk: streamlit | |
| sdk_version: 1.31.1 | |
| app_file: app.py | |
| pinned: false | |
| # AI Powered Video Background Removal Tool | |
| [](https://github.com/killian31/VideoBackgroundRemoval/stargazers) | |
| [](https://github.com/killian31/VideoBackgroundRemoval/actions/workflows/github-actions-black-formatting.yml) | |
| [](https://wakatime.com/badge/github/killian31/VideoBackgroundRemoval) | |
| The Video Background Removal Tool is designed to enable users to effortlessly remove backgrounds from videos by selecting a subject in a single frame. This powerful tool is optimized to run on CPUs and boasts a user-friendly interface, making it ideal for a wide range of users, especially online content creators like YouTubers. | |
| <p align="center"> | |
| <table> | |
| <tr> | |
| <td> | |
| <img src="assets/example.gif" width="385" height="216" /> | |
| </td> | |
| <td> | |
| <img src="assets/output_example.gif" width="385" height="216" /> | |
| </td> | |
| </tr> | |
| </table> | |
| </p> | |
| ## Contents | |
| Table of contents: | |
| - [Installation](#installation) | |
| - [Usage](#usage) | |
| - [Example](#example) | |
| - [Contribution](#how-to-contribute) | |
| ## Installation | |
| ### With pyenv and poetry | |
| ```bash | |
| git clone https://github.com/killian31/VideoBackgroundRemoval.git | |
| cd VideoBackgroundRemoval | |
| pyenv virtualenv 3.11.9 vbr | |
| pyenv activate vbr | |
| pip install poetry | |
| poetry install | |
| ``` | |
| ## Usage | |
| ### Using the Streamlit app (locally) | |
| Run `streamlit run app.py` to launch the Streamlit app. Then, upload a video, draw a | |
| bounding box around what you want to remove the background from, using the sliders, | |
| and click on Segment Video. | |
| ### Command line | |
| ```bash | |
| usage: main.py [-h] [--video_filename VIDEO_FILENAME] [--dir_frames DIR_FRAMES] [--image_start IMAGE_START] [--image_end IMAGE_END] [--bbox_file BBOX_FILE] [--skip_vid2im] | |
| [--mobile_sam_weights MOBILE_SAM_WEIGHTS] [--tracker_name {yolov7,yoloS}] [--output_dir OUTPUT_DIR] [--output_video OUTPUT_VIDEO] [--auto_detect] | |
| [--background_color BACKGROUND_COLOR] | |
| options: | |
| -h, --help show this help message and exit | |
| --video_filename VIDEO_FILENAME | |
| path to the video | |
| --dir_frames DIR_FRAMES | |
| path to the directory in which all input frames will be stored | |
| --image_start IMAGE_START | |
| first image to be stored | |
| --image_end IMAGE_END | |
| last image to be stored, last one if 0 | |
| --bbox_file BBOX_FILE | |
| path to the bounding box text file | |
| --skip_vid2im whether to write the video frames as images | |
| --mobile_sam_weights MOBILE_SAM_WEIGHTS | |
| path to MobileSAM weights | |
| --tracker_name {yolov7,yoloS} | |
| tracker name | |
| --output_dir OUTPUT_DIR | |
| directory to store the output frames | |
| --output_video OUTPUT_VIDEO | |
| path to store the output video | |
| --auto_detect whether to use a bounding box to force the model to segment the object | |
| --background_color BACKGROUND_COLOR | |
| background color for the output (hex) | |
| ``` | |
| ## Example | |
| The following command line is a working example from a video stored in the repo: | |
| ```bash | |
| python3 main.py --video_filename assets/example.mp4 --dir_frames ./frames --bbox_file bbox.txt --mobile_sam_weights models/mobile_sam.pt --output_dir output_frames --output_video output.mp4 | |
| ``` | |
| ## How to Contribute | |
| We welcome contributions from the community! To ensure a consistent code style, we ask contributors to follow these guidelines: | |
| ### Code Format | |
| Please format your code using the `black` code formatter. | |
| #### Installation | |
| ```bash | |
| pip install black | |
| ``` | |
| #### Usage | |
| To format your code: | |
| ```bash | |
| black . | |
| ``` | |
| This setup will help maintain a consistent coding style throughout the project. | |