✨ Instance Segmentation using PixelLib 🙆♂️
![](https://camo.githubusercontent.com/f5ebe61033bfde5600f6a9b509d1f5ee3c75d48c84cc1cdfef82341479c67997/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5072617465656b2d52616c68616e2d627269676874677265656e2e7376673f636f6c6f72423d666630303030)
A streamlit based webapp to perform "State of the Art" instance segmentation on images, videos and live webcam feed using Pixellib.
Installation:
- Simply run the command pip install -r requirements.txt to install the necessary dependencies.
- In case you need to use your GPU for computation, ensure that you have the right CUDA drivers and CUDNN installed.
Usage:
- Simply run the command:
streamlit run app.py
- Navigate to http://localhost:8501 in your web-browser.
- By default, streamlit allows us to upload files of max. 200MB. If you want to have more size for uploading audio files, execute the command :
streamlit run app.py --server.maxUploadSize=1028
Results
Images
Original Image | Segmented Image |
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Videos
Original Video | Segmented Video |
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Live Webcam Feed
Running the Dockerized App
- Ensure you have Docker Installed and Setup in your OS (Windows/Mac/Linux). For detailed Instructions, please refer this.
- Navigate to the folder where you have cloned this repository ( where the Dockerfile is present ).
- Build the Docker Image (don't forget the dot!!
😄 ):
docker build -f Dockerfile -t app:latest .
- Run the docker:
docker run -p 8501:8501 app:latest
This will launch the dockerized app. Navigate to http://localhost:8501/ in your browser to have a look at your application. You can check the status of your all available running dockers by:
docker ps