Features:
User-friendly interface with navigation bar, image upload form, and image display section. Built using React.js for a responsive and smooth user experience. Back-end powered by Streamlit for image processing tasks. Utilizes OpenCV and PyTorch for object detection (e.g., vehicle recognition and counting). Packaged as a Docker application for easy deployment. Prerequisites:
Node.js and npm (or yarn) installed on your system. Docker Desktop installed on your system. Setup:
Clone the repository:
Bash git clone https://your_github_repo_url.git cd your_project_name Use code with caution. Install dependencies:
Bash npm install # or yarn install Use code with caution. Running the Application:
Build the Docker images:
Bash docker-compose build Use code with caution. Run the application:
Bash docker-compose up Use code with caution. This will start the application. The frontend will typically be accessible on http://localhost:80.
Testing:
Access the application in your web browser. Use the upload form to select an image from your local machine. The application will process the image and display both the original and the processed image with detected objects highlighted. Additional Notes:
You can stop the containers using docker-compose down. To detach from the running containers and keep them running in the background, use docker-compose up -d. Dockerfile and docker-compose.yml:
The project includes separate Dockerfiles for the frontend and backend, along with a docker-compose.yml file that specifies the environment and services. These files define how the application is packaged and run within Docker containers.
Further Development:
This project provides a foundation for building a web application with object detection capabilities.