使用 Python 和 FaceNet 进行人脸识别

本指南演示了使用facenet-pytorch的人脸相似度检测工具。该工具利用 FaceNet 模型的高质量人脸嵌入，将目标图像与多个候选图像进行比较，以确定最接近的匹配。 让我们来探索一下实现方式。

基本工具和库

1. PyTorch：深度学习操作的基础。
2. FaceNet-PyTorch： 提供用于人脸检测和嵌入生成的预训练模型。
3. Pillow (PIL)： 处理图像处理任务。
4. Matplotlib：用于结果可视化。

采用了两个核心模型：

• MTCNN： 检测图像中的人脸。
• InceptionResnetV1：提取面部嵌入。

初始化

import torch
from facenet_pytorch import MTCNN, InceptionResnetV1
from PIL import Image
import requests
from io import BytesIO
import matplotlib.pyplot as plt

# Initialize face detection (MTCNN) and embedding extraction (InceptionResnetV1) modules.
mtcnn = MTCNN(image_size=160, keep_all=True)
resnet = InceptionResnetV1(pretrained='vggface2').eval()

函数定义

1。图像加载和嵌入提取：

此函数从 URL 检索图像、检测人脸并计算嵌入。

def get_embedding_and_face(image_path):
    """Loads an image, detects faces, and returns the embedding and detected face."""
    try:
        response = requests.get(image_path)
        response.raise_for_status()
        content_type = response.headers.get('Content-Type')
        if 'image' not in content_type:
            raise ValueError(f"Invalid image URL: {content_type}")
        image = Image.open(BytesIO(response.content)).convert("RGB")
    except Exception as e:
        print(f"Image loading error from {image_path}: {e}")
        return None, None

    faces, probs = mtcnn(image, return_prob=True)
    if faces is None or len(faces) == 0:
        return None, None

    embedding = resnet(faces[0].unsqueeze(0))
    return embedding, faces[0]

2。张量到图像的转换：

准备用于显示的张量。

def tensor_to_image(tensor):
    """Converts a normalized tensor to a displayable image array."""
    image = tensor.permute(1, 2, 0).detach().numpy()
    image = (image - image.min()) / (image.max() - image.min())
    image = (image * 255).astype('uint8')
    return image

3。最相似的人脸识别：

将目标图像的嵌入与候选图像的嵌入进行比较。

def find_most_similar(target_image_path, candidate_image_paths):
    """Identifies the most similar image to the target from a list of candidates."""
    target_emb, target_face = get_embedding_and_face(target_image_path)
    if target_emb is None:
        raise ValueError("No face detected in the target image.")

    highest_similarity = float('-inf')
    most_similar_face = None
    most_similar_image_path = None

    candidate_faces = []
    similarities = []

    for candidate_image_path in candidate_image_paths:
        candidate_emb, candidate_face = get_embedding_and_face(candidate_image_path)
        if candidate_emb is None:
            similarities.append(None)
            candidate_faces.append(None)
            continue

        similarity = torch.nn.functional.cosine_similarity(target_emb, candidate_emb).item()
        similarities.append(similarity)
        candidate_faces.append(candidate_face)

        if similarity > highest_similarity:
            highest_similarity = similarity
            most_similar_face = candidate_face
            most_similar_image_path = candidate_image_path

    # Visualization
    plt.figure(figsize=(12, 8))

    # Display target image
    plt.subplot(2, len(candidate_image_paths) + 1, 1)
    plt.imshow(tensor_to_image(target_face))
    plt.title("Target Image")
    plt.axis("off")

    # Display most similar image
    if most_similar_face is not None:
        plt.subplot(2, len(candidate_image_paths) + 1, 2)
        plt.imshow(tensor_to_image(most_similar_face))
        plt.title("Most Similar")
        plt.axis("off")

    # Display all candidates with similarity scores
    for idx, (candidate_face, similarity) in enumerate(zip(candidate_faces, similarities)):
        plt.subplot(2, len(candidate_image_paths) + 1, idx + len(candidate_image_paths) + 2)
        if candidate_face is not None:
            plt.imshow(tensor_to_image(candidate_face))
            plt.title(f"Score: {similarity * 100:.2f}%")
        else:
            plt.title("No Face Detected")
        plt.axis("off")

    plt.tight_layout()
    plt.show()

    if most_similar_image_path is None:
        raise ValueError("No faces detected in candidate images.")

    return most_similar_image_path, highest_similarity

用法

用于比较的图片网址：

image_url_target = 'https://d1mnxluw9mpf9w.cloudfront.net/media/7588/4x3/1200.jpg'
candidate_image_urls = [
    'https://beyondthesinglestory.wordpress.com/wp-content/uploads/2021/04/elon_musk_royal_society_crop1.jpg',
    'https://cdn.britannica.com/56/199056-050-CCC44482/Jeff-Bezos-2017.jpg',
    'https://cdn.britannica.com/45/188745-050-7B822E21/Richard-Branson-2003.jpg'
]

most_similar_image, similarity_score = find_most_similar(image_url_target, candidate_image_urls)
print(f"Most similar image: {most_similar_image}")
print(f"Similarity score: {similarity_score * 100:.2f}%")

结果

结论

这个示例展示了facenet-pytorch的面部识别功能。 人脸检测和嵌入生成的结合可以为各种应用程序创建工具，例如身份验证或内容过滤。

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