ultralytics/examples/heatmaps.ipynb

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    "<div align=\"center\">\n",
    "\n",
    "  <a href=\"https://ultralytics.com/yolo\" target=\"_blank\">\n",
    "    <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
    "\n",
    "  [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
    "\n",
    "  <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
    "  <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n",
    "  <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
    "  <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
    "  <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
    "\n",
    "Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
    "\n",
    "YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
    "\n",
    "We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/guides/heatmaps\">Heatmap Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Setup\n",
    "\n",
    "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware.\n",
    "\n",
    "[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)"
   ]
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  {
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   },
   "outputs": [
    {
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     "text": [
      "Ultralytics YOLOv8.2.17 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.8/78.2 GB disk)\n"
     ]
    }
   ],
   "source": [
    "%pip install ultralytics\n",
    "import ultralytics\n",
    "\n",
    "ultralytics.checks()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m7VkxQ2aeg7k"
   },
   "source": [
    "# Introduction to Heatmaps\n",
    "\n",
    "A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.\n",
    "\n",
    "## Real World Applications\n",
    "\n",
    "|                                                                 Transportation                                                                  |                                                                 Retail                                                                  |\n",
    "|:-----------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:|\n",
    "| ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/288d7053-622b-4452-b4e4-1f41aeb764aa) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/edef75ad-50a7-4c0a-be4a-a66cdfc12802) |\n",
    "|                                                    Ultralytics YOLOv8 Transportation Heatmap                                                    |                                                    Ultralytics YOLOv8 Retail Heatmap                                                    |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Cx-u59HQdu2o"
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   "source": [
    "import cv2\n",
    "\n",
    "from ultralytics import YOLO, solutions\n",
    "\n",
    "# Load YOLO model\n",
    "model = YOLO(\"yolov8n.pt\")\n",
    "\n",
    "# Open video file\n",
    "cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
    "assert cap.isOpened(), \"Error reading video file\"\n",
    "\n",
    "# Get video properties\n",
    "w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))\n",
    "\n",
    "# Initialize video writer\n",
    "video_writer = cv2.VideoWriter(\"heatmap_output.avi\", cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (w, h))\n",
    "\n",
    "# Initialize heatmap object\n",
    "heatmap_obj = solutions.Heatmap(\n",
    "    colormap=cv2.COLORMAP_PARULA,\n",
    "    view_img=True,\n",
    "    shape=\"circle\",\n",
    "    classes_names=model.names,\n",
    ")\n",
    "\n",
    "while cap.isOpened():\n",
    "    success, im0 = cap.read()\n",
    "    if not success:\n",
    "        print(\"Video frame is empty or video processing has been successfully completed.\")\n",
    "        break\n",
    "\n",
    "    # Perform tracking on the current frame\n",
    "    tracks = model.track(im0, persist=True, show=False)\n",
    "\n",
    "    # Generate heatmap on the frame\n",
    "    im0 = heatmap_obj.generate_heatmap(im0, tracks)\n",
    "\n",
    "    # Write the frame to the output video\n",
    "    video_writer.write(im0)\n",
    "\n",
    "# Release resources\n",
    "cap.release()\n",
    "video_writer.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
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   "source": [
    "# Additional Resources\n",
    "\n",
    "## Community Support\n",
    "\n",
    "For more information on using heatmaps with Ultralytics, you can explore the comprehensive [Ultralytics Heatmaps Docs](https://docs.ultralytics.com/guides/heatmaps/). This guide covers everything from basic concepts to advanced techniques, ensuring you get the most out of your heatmap visualizations.\n",
    "\n",
    "## Ultralytics ⚡ Resources\n",
    "\n",
    "At Ultralytics, we are committed to providing cutting-edge AI solutions. Here are some key resources to learn more about our company and get involved with our community:\n",
    "\n",
    "- [Ultralytics HUB](https://ultralytics.com/hub): Simplify your AI projects with Ultralytics HUB, our no-code tool for effortless YOLO training and deployment.\n",
    "- [Ultralytics Licensing](https://ultralytics.com/license): Review our licensing terms to understand how you can use our software in your projects.\n",
    "- [About Us](https://ultralytics.com/about): Discover our mission, vision, and the story behind Ultralytics.\n",
    "- [Join Our Team](https://ultralytics.com/work): Explore career opportunities and join our team of talented professionals.\n",
    "\n",
    "## YOLOv8 🚀 Resources\n",
    "\n",
    "YOLOv8 is the latest evolution in the YOLO series, offering state-of-the-art performance in object detection and image segmentation. Here are some essential resources to help you get started with YOLOv8:\n",
    "\n",
    "- [GitHub](https://github.com/ultralytics/ultralytics): Access the YOLOv8 repository on GitHub, where you can find the source code, contribute to the project, and report issues.\n",
    "- [Docs](https://docs.ultralytics.com/): Explore the official documentation for YOLOv8, including installation guides, tutorials, and detailed API references.\n",
    "- [Discord](https://ultralytics.com/discord): Join our Discord community to connect with other users, share your projects, and get help from the Ultralytics team.\n",
    "\n",
    "These resources are designed to help you leverage the full potential of Ultralytics' offerings and YOLOv8. Whether you're a beginner or an experienced developer, you'll find the information and support you need to succeed."
   ]
  }
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