Awesome
✨ Instance Segmentation using PixelLib 🙆♂️ 
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 |
|---|---|
 |  |
 |  |
 |  |
 |  |
Videos
| Original Video | Segmented Video |
|---|---|
 |  |
 |  |
 |  |
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!! :smile: ):
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