mirror of
https://github.com/yakhyo/uniface.git
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268 lines
8.3 KiB
Plaintext
268 lines
8.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Face Analysis with UniFace\n",
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"\n",
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"<div style=\"display:flex; flex-wrap:wrap; align-items:center;\">\n",
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" <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",
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" <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",
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" <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",
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" <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",
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"</div>\n",
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"\n",
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"**UniFace** is a lightweight, production-ready Python library for face detection, recognition, tracking, landmark analysis, face parsing, gaze estimation, and face attributes.\n",
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"\n",
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"🔗 **GitHub**: [github.com/yakhyo/uniface](https://github.com/yakhyo/uniface) | 📚 **Docs**: [yakhyo.github.io/uniface](https://yakhyo.github.io/uniface)\n",
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"\n",
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"---\n",
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"\n",
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"This notebook demonstrates comprehensive face analysis using the **FaceAnalyzer** class.\n",
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"\n",
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"## 1. Install UniFace"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -q \"uniface[cpu]\"\n",
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"\n",
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"# Clone repo for assets (Colab only)\n",
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"import os\n",
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"if 'COLAB_GPU' in os.environ or 'COLAB_RELEASE_TAG' in os.environ:\n",
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" if not os.path.exists('uniface'):\n",
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" !git clone --depth 1 https://github.com/yakhyo/uniface.git\n",
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" os.chdir('uniface/examples')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Import Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import uniface\n",
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"from uniface.analyzer import FaceAnalyzer\n",
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"from uniface.detection import RetinaFace\n",
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"from uniface.recognition import ArcFace\n",
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"from uniface.attribute import AgeGender\n",
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"from uniface.draw import draw_detections\n",
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"\n",
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"print(uniface.__version__)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Initialize FaceAnalyzer\n",
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"\n",
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"The `FaceAnalyzer` combines detection, recognition, and attribute prediction in one class."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"analyzer = FaceAnalyzer(\n",
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" detector=RetinaFace(confidence_threshold=0.5),\n",
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" recognizer=ArcFace(),\n",
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" attributes=[AgeGender()]\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. Analyze Faces in Images"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"image_paths = [\n",
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" '../assets/test_images/image0.jpg',\n",
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" '../assets/test_images/image1.jpg',\n",
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" '../assets/test_images/image2.jpg',\n",
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"]\n",
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"\n",
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"results = []\n",
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"\n",
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"for image_path in image_paths:\n",
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" # Load image\n",
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" image = cv2.imread(image_path)\n",
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" if image is None:\n",
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" print(f'Error: Could not read {image_path}')\n",
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" continue\n",
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"\n",
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" # Analyze faces - returns list of Face objects\n",
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" faces = analyzer.analyze(image)\n",
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" print(f'\\n{image_path.split(\"/\")[-1]}: Detected {len(faces)} face(s)')\n",
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"\n",
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" # Print face attributes\n",
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" for i, face in enumerate(faces, 1):\n",
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" print(f' Face {i}: {face.sex}, {face.age}y')\n",
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"\n",
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" # Prepare visualization (without text overlay)\n",
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" vis_image = image.copy()\n",
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" draw_detections(image=vis_image, faces=faces, vis_threshold=0.5, corner_bbox=True)\n",
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"\n",
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" results.append((image_path, cv2.cvtColor(vis_image, cv2.COLOR_BGR2RGB), faces))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. Visualize Results\n",
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"\n",
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"Display images with face information shown below each image."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, axes = plt.subplots(2, len(results), figsize=(15, 8),\n",
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" gridspec_kw={'height_ratios': [4, 1]})\n",
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"\n",
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"for idx, (path, vis_image, faces) in enumerate(results):\n",
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" # Display image\n",
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" axes[0, idx].imshow(vis_image)\n",
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" axes[0, idx].axis('off')\n",
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"\n",
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" # Display face information below image\n",
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" axes[1, idx].axis('off')\n",
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" info_text = f'{len(faces)} face(s)\\n'\n",
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" for i, face in enumerate(faces, 1):\n",
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" info_text += f'Face {i}: {face.sex}, {face.age}y\\n'\n",
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"\n",
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" axes[1, idx].text(0.5, 0.5, info_text,\n",
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" ha='center', va='center',\n",
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" fontsize=10, family='monospace')\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 6. Access Face Attributes\n",
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"\n",
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"Each `Face` object contains detection, recognition, and attribute data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get first face from first image\n",
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"_, _, faces = results[0]\n",
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"if faces:\n",
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" face = faces[0]\n",
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"\n",
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" print('Face Attributes:')\n",
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" print(f' - Bounding box: {face.bbox.astype(int).tolist()}')\n",
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" print(f' - Confidence: {face.confidence:.3f}')\n",
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" print(f' - Landmarks shape: {face.landmarks.shape}')\n",
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" print(f' - Age: {face.age} years')\n",
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" print(f' - Gender: {face.sex}')\n",
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" print(f' - Embedding shape: {face.embedding.shape}')\n",
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" print(f' - Embedding dimension: {face.embedding.shape[0]}D')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 7. Compare Face Similarity\n",
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"\n",
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"Use face embeddings to compute similarity between faces."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compare first two faces\n",
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"if len(results) >= 2:\n",
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" face1 = results[0][2][0] # First face from first image\n",
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" face2 = results[1][2][0] # First face from second image\n",
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"\n",
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" similarity = face1.compute_similarity(face2)\n",
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" print(f'Similarity between faces: {similarity:.4f}')\n",
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" print(f'Same person: {\"Yes\" if similarity > 0.6 else \"No\"} (threshold=0.6)')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Notes\n",
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"\n",
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"- `analyzer.analyze()` performs detection, recognition, and attribute prediction in one call\n",
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"- Each `Face` object contains: `bbox`, `confidence`, `landmarks`, `embedding`, `age`, `gender`\n",
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"- Gender is available as both ID (0=Female, 1=Male) and string via `face.sex` property\n",
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"- Face embeddings are L2-normalized (norm ≈ 1.0) for similarity computation\n",
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"- Use `face.compute_similarity(other_face)` to compare faces (returns cosine similarity)\n",
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"- Typical similarity threshold: 0.6 (same person if similarity > 0.6)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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