moaaz12-web / Cattle-detection-using-YOLOV8

Cattle detection using YOLOV8 with Tkinter interface

Usage

1. Clone the repository:

   git clone https://github.com/moaaz12-web/Cattle-detection-.git
   cd cattle-image-recognition

2. Install dependencies:

   pip install -r requirements.txt

3. Download weights for Alexnet:

   Before running the application, download the AlexNet model weights from the drive link and place it in the working directory
   https://drive.google.com/file/d/1PnKTyFy9yBtaossFSwrRGsb5S6CPVhEK/view?usp=sharing
   

4. Run the application:

   python tk.py

5. Upload an image through the interface, and witness the advanced image processing workflow:

   • The user-uploaded image undergoes a series of processing functions.
   • The processed image is sent to the AlexNet model, providing a string output indicating the presence of a cow in the image.
   • Subsequently, the image is forwarded to the YOLOv8 model, specifically trained on a cattle dataset.
   • YOLOv8 counts the number of cattle in the image and overlays bounding boxes to visually indicate their locations.
   • The entire process is seamlessly displayed on the Tkinter interface, offering a comprehensive and interactive user experience.