Object Detection Mini Project

from ultralytics import YOLO
import cv2
from collections import Counter

def count_objects_in_image(image_path, model_path="yolov8n.pt",
                            confidence=0.5):
    """
    Detect, count, and report all objects in an image.
    """
    model = YOLO(model_path)
    results = model(image_path, conf=confidence)

    image = cv2.imread(image_path)
    all_detections = []

    for result in results:
        for box in result.boxes:
            class_name = model.names[int(box.cls[0])]
            conf = float(box.conf[0])
            x1, y1, x2, y2 = [int(v) for v in box.xyxy[0].tolist()]

            all_detections.append({
                "class": class_name,
                "confidence": conf,
                "bbox": (x1, y1, x2-x1, y2-y1)
            })

    # Count objects by class
    class_counts = Counter(d["class"] for d in all_detections)

    print(f"=== Object Detection Report: {image_path} ===")
    print(f"Total objects detected: {len(all_detections)}")
    print()
    print("Object counts:")
    for cls, count in sorted(class_counts.items(), key=lambda x: -x[1]):
        avg_conf = sum(d["confidence"] for d in all_detections if d["class"] == cls) / count
        print(f"  {cls:15s}: {count} (avg confidence: {avg_conf:.1%})")

    print()
    print("Individual detections:")
    for d in sorted(all_detections, key=lambda x: -x["confidence"]):
        print(f"  {d['class']:15s}: {d['confidence']:.1%}")

    return all_detections, class_counts

count_objects_in_image("street_photo.jpg")