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This project implements object detection using YOLOv3 with pre-trained weights. It supports live detection from a webcam, image detection, and video detection. The application is built using Python with libraries such as OpenCV, PIL, and Tkinter for the GUI, and runs primarily through a Jupyter Notebook interface.

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Object Detection using YOLOv3

Introduction

This project implements object detection using YOLOv3 with pre-trained weights. It supports live detection from a webcam, image detection, and video detection. The application is built using Python with libraries such as OpenCV, PIL, and Tkinter for the GUI, and runs primarily through a Jupyter Notebook interface.

Features

  • Live Detection: Real-time object detection using a webcam.
  • Image Detection: Detect objects in images.
  • Video Detection: Detect objects in video files.
  • Save Results: Save the processed images and videos with detected objects.

Technologies Used

  • Python: Core programming language.
  • OpenCV: For image and video processing.
  • PIL (Pillow): For image handling.
  • Tkinter: For the graphical user interface.
  • YOLOv3: Object detection model.
  • Jupyter Notebook: For running and displaying the main code.

Installation

  1. Clone the repository:
    git clone https://github.com/yourusername/object-detection-yolov3.git
    cd object-detection-yolov3
  2. Install dependencies:
  • Make sure you have Python installed. Then, install the required libraries:
    pip install numpy opencv-python pillow matplotlib jupyter
  1. Download YOLOv3 weights and configuration:
  • Download the YOLOv3 weights from the official source. (https://pjreddie.com/darknet/yolo/)
  • Download the yolov3.cfg and coco.names files. (already present in repository)
  1. Set up the project:
  • Place yolov3.weights, yolov3.cfg, and coco.names in the project directory.

Usage

  1. Run Jupyter Notebook:
jupyter notebook

Open the object_detection.ipynb notebook.

  1. Live Detection:
  • Click on live detection to start real-time object detection using your webcam.
  1. Image Detection:
  • Click on image detection, select an image file, and the application will detect objects in the image.
  1. Video Detection:
  • Click on video detection and the application will detect objects in the video.

Screenshots

Main Screen

Sample 1

Sample 2

Sample 3

About

This project implements object detection using YOLOv3 with pre-trained weights. It supports live detection from a webcam, image detection, and video detection. The application is built using Python with libraries such as OpenCV, PIL, and Tkinter for the GUI, and runs primarily through a Jupyter Notebook interface.

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