README.md

metadata

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

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.

Contents

Table of contents:

• Installation
• Usage
• Example
• Contribution

Installation

With pyenv and poetry

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

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:

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

pip install black

Usage

To format your code:

black .

This setup will help maintain a consistent coding style throughout the project.