This project demonstrates object detection using the YOLO algorithm with OpenCV in Python. The provided code loads a pre-trained YOLO model and performs object detection on a given image, displaying the detected objects with bounding boxes.

1. Python 3.9
2. OpenCV
3. NumPy

bash Copy code git clone https://github.com/AlirezaHabibi1377/YOLO-Object-Detection cd yolo-object-detection

bash Copy code python -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate

bash Copy code pip install opencv-python numpy

Download the YOLO model configuration, weights, and class names files. Ensure the following files are available in the specified paths:

yolov3.cfg (YOLO configuration file) yolov3.weights (YOLO pre-trained weights) coco.names (COCO class labels) Update the paths in the script to point to these files.

Set the path to the image you want to use for object detection. Update the img_path variable in the script:

python

img_path = r"E:\Perception working\Opencv\yolo_object_detection\sample5.jpg"  # Change this to your image path

Execute the Python script:

python yolo_object_detection.py

My email: mo.alireza77habibi@gmail.com