uniface/examples/02_face_alignment.ipynb

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    "# Face Detection and Alignment 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",
    "This notebook demonstrates face detection and alignment using the **UniFace** library.\n",
    "\n",
    "## 1. Install UniFace"
   ]
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  {
   "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 matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import uniface\n",
    "from uniface.detection import RetinaFace\n",
    "from uniface.face_utils import face_alignment\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 Images and Perform Detection + Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_paths = [\n",
    "    '../assets/test_images/image0.jpg',\n",
    "    '../assets/test_images/image1.jpg',\n",
    "    '../assets/test_images/image2.jpg',\n",
    "    '../assets/test_images/image3.jpg',\n",
    "    '../assets/test_images/image4.jpg',\n",
    "]\n",
    "\n",
    "original_images = []\n",
    "detection_images = []\n",
    "aligned_images = []\n",
    "\n",
    "for image_path in image_paths:\n",
    "    # Load image\n",
    "    image = cv2.imread(image_path)\n",
    "    if image is None:\n",
    "        print(f'Error: Could not read {image_path}')\n",
    "        continue\n",
    "\n",
    "    # Detect faces\n",
    "    faces = detector.detect(image)\n",
    "    if not faces:\n",
    "        print(f'No faces detected in {image_path}')\n",
    "        continue\n",
    "\n",
    "    # Draw detections\n",
    "    bbox_image = image.copy()\n",
    "    draw_detections(image=bbox_image, faces=faces, vis_threshold=0.6, corner_bbox=True)\n",
    "\n",
    "    # Align first detected face (returns aligned image and inverse transform matrix)\n",
    "    first_landmarks = faces[0].landmarks\n",
    "    aligned_image, _ = face_alignment(image, first_landmarks, image_size=112)\n",
    "\n",
    "    # Convert BGR to RGB for visualization\n",
    "    original_images.append(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "    detection_images.append(cv2.cvtColor(bbox_image, cv2.COLOR_BGR2RGB))\n",
    "    aligned_images.append(cv2.cvtColor(aligned_image, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "print(f'Processed {len(original_images)} images')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(3, len(original_images), figsize=(15, 10))\n",
    "\n",
    "row_titles = ['Original', 'Detection', 'Aligned']\n",
    "\n",
    "for row, images in enumerate([original_images, detection_images, aligned_images]):\n",
    "    for col, img in enumerate(images):\n",
    "        axes[row, col].imshow(img)\n",
    "        axes[row, col].axis('off')\n",
    "        if col == 0:\n",
    "            axes[row, col].set_title(row_titles[row], fontsize=12, loc='left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes\n",
    "\n",
    "- `detect()` returns a list of `Face` objects with `bbox`, `confidence`, `landmarks` attributes\n",
    "- Access attributes using dot notation: `face.bbox`, `face.landmarks`\n",
    "- `face_alignment()` uses 5-point landmarks to align and crop the face\n",
    "- Default output size is 112x112 (standard for face recognition models)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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