uniface/examples/07_face_anonymization.ipynb

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    "# Face Anonymization with UniFace\n",
    "\n",
    "<div style=\"display:flex; flex-wrap:wrap; align-items:center;\">\n",
    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://pepy.tech/projects/uniface\"><img alt=\"PyPI Downloads\" src=\"https://static.pepy.tech/personalized-badge/uniface?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads\"></a>\n",
    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://pypi.org/project/uniface/\"><img alt=\"PyPI Version\" src=\"https://img.shields.io/pypi/v/uniface.svg\"></a>\n",
    "  <a style=\"margin-right:10px; margin-bottom:6px;\" href=\"https://opensource.org/licenses/MIT\"><img alt=\"License\" src=\"https://img.shields.io/badge/License-MIT-blue.svg\"></a>\n",
    "  <a style=\"margin-bottom:6px;\" href=\"https://github.com/yakhyo/uniface\"><img alt=\"GitHub Stars\" src=\"https://img.shields.io/github/stars/yakhyo/uniface.svg?style=social\"></a>\n",
    "</div>\n",
    "\n",
    "**UniFace** is a lightweight, production-ready Python library for face detection, recognition, tracking, landmark analysis, face parsing, gaze estimation, and face attributes.\n",
    "\n",
    "🔗 **GitHub**: [github.com/yakhyo/uniface](https://github.com/yakhyo/uniface) | 📚 **Docs**: [yakhyo.github.io/uniface](https://yakhyo.github.io/uniface)\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "This notebook demonstrates face anonymization using various blur methods for privacy protection.\n",
    "\n",
    "## 1. Install UniFace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q \"uniface[cpu]\"\n",
    "\n",
    "# Clone repo for assets (Colab only)\n",
    "import os\n",
    "if 'COLAB_GPU' in os.environ or 'COLAB_RELEASE_TAG' in os.environ:\n",
    "    if not os.path.exists('uniface'):\n",
    "        !git clone --depth 1 https://github.com/yakhyo/uniface.git\n",
    "    os.chdir('uniface/examples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Import Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import IPython.display as display\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "import uniface\n",
    "from uniface.detection import RetinaFace\n",
    "from uniface.privacy import BlurFace\n",
    "\n",
    "print(f'UniFace version: {uniface.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Load Test Image\n",
    "\n",
    "We'll use a test image to demonstrate face anonymization.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load image\n",
    "image_path = '../assets/test.jpg'\n",
    "image = cv2.imread(image_path)\n",
    "\n",
    "# Display original image\n",
    "pil_image = Image.open(image_path)\n",
    "print(f'Image size: {image.shape[:2]}')\n",
    "pil_image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Quick Start: Anonymization\n",
    "\n",
    "Detect faces and blur them using `BlurFace`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect faces and anonymize\n",
    "detector = RetinaFace()\n",
    "blurrer = BlurFace(method=\"pixelate\")\n",
    "\n",
    "faces = detector.detect(image.copy())\n",
    "anonymized = blurrer.anonymize(image.copy(), faces)\n",
    "\n",
    "# Display result\n",
    "output = cv2.cvtColor(anonymized, cv2.COLOR_BGR2RGB)\n",
    "display.display(Image.fromarray(output))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Compare All Blur Methods\n",
    "\n",
    "UniFace provides 5 different blur methods. Let's compare them:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize detector\n",
    "detector = RetinaFace(conf_thresh=0.5)\n",
    "faces = detector.detect(image)\n",
    "print(f'Detected {len(faces)} faces')\n",
    "\n",
    "# Test all blur methods\n",
    "methods = ['gaussian', 'pixelate', 'blackout', 'elliptical', 'median']\n",
    "\n",
    "for method in methods:\n",
    "    blurrer = BlurFace(method=method)\n",
    "    anonymized = blurrer.anonymize(image.copy(), faces)\n",
    "\n",
    "    output = cv2.cvtColor(anonymized, cv2.COLOR_BGR2RGB)\n",
    "    print(f'\\\\n{method.upper()}:')\n",
    "    display.display(Image.fromarray(output))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Summary\n",
    "\n",
    "This notebook demonstrated:\n",
    "\n",
    "- ✅ Five different blur methods (gaussian, pixelate, blackout, elliptical, median)\n",
    "- ✅ Automatic face detection and blurring\n",
    "\n",
    "### Recommended Methods\n",
    "\n",
    "| Use Case | Method | Parameters |\n",
    "|----------|--------|------------|\n",
    "| News media / Publishing | `pixelate` | `pixel_blocks=10-15` |\n",
    "| Social media | `gaussian` or `elliptical` | `blur_strength=3-5` |\n",
    "| Maximum privacy | `blackout` | `color=(0,0,0)` |\n",
    "| Natural appearance | `elliptical` | `blur_strength=3, margin=20` |\n",
    "\n",
    "### Further Resources\n",
    "\n",
    "- [UniFace Documentation](https://github.com/yakhyo/uniface)\n",
    "- [Other Examples](https://github.com/yakhyo/uniface/tree/main/examples)\n"
   ]
  }
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