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

3. 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)

4. 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.

2. Live Detection:

• Click on live detection to start real-time object detection using your webcam.

3. Image Detection:

• Click on image detection, select an image file, and the application will detect objects in the image.

4. Video Detection:

• Click on video detection and the application will detect objects in the video.

Screenshots