uniface/examples/01_face_detection.ipynb

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    "# Face Detection 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 detection using the **UniFace** library.\n",
    "\n",
    "## 1. Install UniFace"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import IPython.display as display\n",
    "from PIL import Image\n",
    "\n",
    "import uniface\n",
    "from uniface.detection import RetinaFace\n",
    "from uniface.draw import draw_detections\n",
    "\n",
    "print(uniface.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Initialize the Detector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "detector = RetinaFace(\n",
    "    confidence_threshold=0.5,\n",
    "    nms_threshold=0.4,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Load and Display Input Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = '../assets/test.jpg'\n",
    "pil_image = Image.open(image_path)\n",
    "pil_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Detect Faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load image\n",
    "image = cv2.imread(image_path)\n",
    "\n",
    "# Detect faces - returns list of Face objects\n",
    "faces = detector.detect(image)\n",
    "print(f'Detected {len(faces)} face(s)')\n",
    "\n",
    "# Draw detections\n",
    "draw_detections(image=image, faces=faces, vis_threshold=0.6, corner_bbox=True)\n",
    "\n",
    "# Display result\n",
    "output_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "display.display(Image.fromarray(output_image))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Detect Top-K Faces\n",
    "\n",
    "Use `max_num` to limit the number of detected faces.\n",
    "\n",
    "### Top-2 faces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread(image_path)\n",
    "\n",
    "faces = detector.detect(image, max_num=2)\n",
    "print(f'Detected {len(faces)} face(s)')\n",
    "\n",
    "draw_detections(image=image, faces=faces, vis_threshold=0.6, corner_bbox=True)\n",
    "\n",
    "output_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "display.display(Image.fromarray(output_image))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top-5 faces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread(image_path)\n",
    "\n",
    "faces = detector.detect(image, max_num=5)\n",
    "print(f'Detected {len(faces)} face(s)')\n",
    "\n",
    "draw_detections(image=image, faces=faces, vis_threshold=0.6, corner_bbox=True)\n",
    "\n",
    "output_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "display.display(Image.fromarray(output_image))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes\n",
    "\n",
    "- `detect()` returns a list of `Face` objects with attributes: `bbox`, `confidence`, `landmarks`\n",
    "- Access attributes using dot notation: `face.bbox`, `face.confidence`, `face.landmarks`\n",
    "- Adjust `conf_thresh` and `nms_thresh` for your use case\n",
    "- Use `max_num` to limit detected faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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