# Object Detection with YOLO (Ultralytics)

This guide demonstrates how to perform object detection using the YOLO (You Only Look Once) model via the `ultralytics` library. YOLO is known for its speed and accuracy in real-time object detection.

**Modules Used:**
*   `ultralytics`: The official Python package for YOLOv8 (and newer).
*   [[programming/python/modules/argparse-module|argparse]]: To handle command-line arguments.

## Installation

```bash
pip install ultralytics
```

## The Code

Save this as `object_detect.py`.

```python
from ultralytics import YOLO
import argparse
import sys
import os

def detect_objects(image_path, model_name='yolov8n.pt', show=False, save=True):
    if not os.path.exists(image_path):
        print(f"Error: Image '{image_path}' not found.")
        return

    print(f"Loading model '{model_name}'...")
    try:
        # Load a pretrained YOLO model
        # yolov8n.pt is the "nano" version (fastest, least accurate)
        # Other options: yolov8s.pt, yolov8m.pt, yolov8l.pt, yolov8x.pt
        # The weights will be downloaded automatically if not found locally.
        model = YOLO(model_name)
    except Exception as e:
        print(f"Error loading model: {e}")
        return

    print(f"Processing '{image_path}'...")
    
    # Perform inference
    # save=True saves the annotated image to 'runs/detect/predict/'
    # show=True displays the image in a window
    results = model(image_path, save=save, show=show)

    # Results is a list (one for each image passed)
    for result in results:
        boxes = result.boxes
        print(f"\nDetected {len(boxes)} objects:")
        
        # Summary of detections
        for box in boxes:
            # Class ID
            cls_id = int(box.cls[0])
            # Class Name
            cls_name = model.names[cls_id]
            # Confidence Score
            conf = float(box.conf[0])
            
            print(f" - {cls_name} ({conf:.2f})")

    if save:
        print(f"\nResults saved to directory: {results[0].save_dir}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="YOLO Object Detection")
    parser.add_argument("image", help="Path to the input image")
    parser.add_argument("--model", default="yolov8n.pt", help="YOLO model to use (default: yolov8n.pt)")
    parser.add_argument("--show", action="store_true", help="Display the result in a window")
    parser.add_argument("--no-save", action="store_true", help="Do not save the annotated image")
    
    args = parser.parse_args()
    
    detect_objects(args.image, args.model, args.show, not args.no_save)
```

## Usage

```bash
# Detect objects in an image using the default Nano model
python object_detect.py street.jpg

# Use a larger model (Medium) and display the result
python object_detect.py street.jpg --model yolov8m.pt --show
```

[[programming/python/python]]