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.