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  "cells": [
    {
      "id": "2ff65058-f67d-40ad-8ddc-7c1180e615f2",
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      "source": "import cv2\nfrom ultralytics import YOLO\n\n# Load the YOLOv8 model\nmodel = YOLO(\"yolov8s.pt\")  # pre-trained object detection model\n\n# Load the image\nimage_path = \"cars.avif\"\nimage = cv2.imread(image_path)\n\n# Run prediction\nresults = model.predict(source=image, conf=0.5)\n\n# Draw boxes and labels on the image\nannotated = results[0].plot()\n\n# Show image with detected objects\ncv2.imshow(\"Detected Objects\", annotated)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n# Print out object labels and confidence\nfor i, box in enumerate(results[0].boxes):\n    class_id = int(box.cls[0])\n    class_name = model.names[class_id]\n    confidence = float(box.conf[0])\n    print(f\"Object {i+1}: {class_name} ({confidence * 100:.1f}%)\")\n# Add this part to detect objects from a video \ncap = cv2.VideoCapture(\"your_video.mp4\") # change this line to cap = cv2.VideoCapture(0) if you want a live webcam \n\nwhile True:\n    ret, frame = cap.read()\n    if not ret:\n        break\n\n    results = model.predict(source=frame, conf=0.5)\n    annotated = results[0].plot()\n    cv2.imshow(\"YOLOv8 Video Detection\", annotated)\n\n    # Print detected object names\n    for i, box in enumerate(results[0].boxes):\n        class_id = int(box.cls[0])\n        class_name = model.names[class_id]\n        confidence = float(box.conf[0])\n        print(f\"Object {i+1}: {class_name} ({confidence * 100:.1f}%)\")\n\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()",
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      "execution_count": null
    }
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}