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Object Detection Application

This repository contains an object detection application built using OpenCV and TensorFlow's pre-trained SSD MobileNet model. The app allows users to detect objects in real-time using a webcam feed or to upload an image for detection. It also includes a streamlit app that does the same.

Features

  • Real-time Object Detection: Uses a webcam feed for live object detection.
  • Image Upload Functionality: Detects objects in uploaded images.
  • Customizable Confidence Threshold: Set the confidence level for object detection.
  • Dark Mode GUI: Built with a dark-themed GUI using customtkinter.
  • Streamlit UI: Built a deployable web based GUI using streamlit.

File Overview

  • main.py: Contains the source code for the local GUI-based object detection application.
  • app.py: Contains the code for the Streamlit UI object detection application.
  • coco.names: Contains the list of object class labels used by the model.
  • ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt: Configuration file for the SSD MobileNet model.
  • frozen_inference_graph.pb: Pre-trained model file for SSD MobileNet.
  • requirements.txt: Lists Python dependencies for the application.
  • LICENSE: License information for the project.

Prerequisites

  • Python 3.8 or above.
  • A working webcam for real-time detection (optional if using the image upload feature).

Installation

  1. Clone the Repository:

    git clone https://github.com/VanshajR/Object-Detection
cd Object-Detection

  2. Install Dependencies:

    pip install -r requirements.txt

Running the Application

  1. Run Locally:

    python main.py

    Or, to run the streamlit app:

    streamlit run app.py

  2. Usage:

    • Click "Start Detection" to begin real-time object detection.
    • Use "Upload Image" to upload an image and detect objects in it.
    • Click "Stop Detection" to stop the live webcam feed.

Notes

  • Ensure the coco.names, ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt, and frozen_inference_graph.pb files are in the same directory as app.py.
  • For real-time detection, a webcam should be connected and accessible.

License

This project is licensed under the MIT License.