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✨ Instance Segmentation using PixelLib 🙆‍♂️ Project Status: Active

A streamlit based webapp to perform "State of the Art" instance segmentation on images, videos and live webcam feed using Pixellib.

image

Installation:

Usage:

  1. Simply run the command:
streamlit run app.py
  1. Navigate to http://localhost:8501 in your web-browser.
  2. 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 ImageSegmented Image
pic1pic1
pic2pic2
pic3pic3
pic4pic4

Videos

Original VideoSegmented Video
pic1pic1
pic2pic2
pic3pic3

Live Webcam Feed

live_feed1

livefeed2

Running the Dockerized App

  1. Ensure you have Docker Installed and Setup in your OS (Windows/Mac/Linux). For detailed Instructions, please refer this.
  2. Navigate to the folder where you have cloned this repository ( where the Dockerfile is present ).
  3. Build the Docker Image (don't forget the dot!! :smile: ):
docker build -f Dockerfile -t app:latest .
  1. 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