基于Java+SpringBoot实现人脸识别搜索

# 假设已经有了两张输入的人脸图像，分别存储在变量"image1"和"image2"中
# 步骤1：特征提取
feature_vector1 = extract_features(image1)  # 使用某种特征提取方法提取图像1的特征向量
feature_vector2 = extract_features(image2)  # 使用同样的特征提取方法提取图像2的特征向量
# 步骤2：特征表示
normalized_feature1 = normalize(feature_vector1)  # 对特征向量1进行标准化
normalized_feature2 = normalize(feature_vector2)  # 对特征向量2进行标准化
# 步骤3：特征匹配
similarity_score = calculate_similarity(normalized_feature1, normalized_feature2)
# 使用某种相似度计算方法（如欧氏距离、余弦相似度等）来计算两个标准化特征向量之间的相似度得分
# 步骤4：判断是否匹配
threshold = 0.6  # 设置一个阈值，用于确定两个人脸是否匹配
if similarity_score >= threshold:
    print("人脸匹配成功！")
else:
    print("人脸不匹配。")

import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
public class DataCollectionAndPreprocessing {
    public static void main(String[] args) {
        // 步骤1：数据收集 - 读取文件夹中的照片文件
        List<String> imagePaths = collectImagePaths("path/to/image/folder");
        // 步骤2：数据预处理 - 对每张照片进行基本处理
        for (String imagePath : imagePaths) {
            processImage(imagePath);
        }
    }
    // 数据收集：获取文件夹中的照片文件路径
    private static List<String> collectImagePaths(String folderPath) {
        List<String> imagePaths = new ArrayList<>();
        File folder = new File(folderPath);
        if (folder.isDirectory()) {
            File[] files = folder.listFiles();
            if (files != null) {
                for (File file : files) {
                    if (file.isFile() && file.getName().endsWith(".jpg")) {
                        imagePaths.add(file.getAbsolutePath());
                    }
                }
            }
        }
        return imagePaths;
    }
    // 数据预处理：对照片进行基本处理
    private static void processImage(String imagePath) {
        // 在这里可以添加图像处理操作，如缩放、裁剪、转换格式等
        System.out.println("Processing image: " + imagePath);
        // TODO: 图像处理代码
    }
}

public static void main(String[] args) {
        // Load OpenCV library
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        // Load face detection classifier (you need to provide the XML file)
        CascadeClassifier faceCascade = new CascadeClassifier("path/to/haarcascade_frontalface_default.xml");
        // Load face recognition model
        FaceRecognizer faceRecognizer = Face.createLBPHFaceRecognizer();
        // Load input image
        Mat inputImage = Imgcodecs.imread("path/to/input/image.jpg");
        // Convert image to grayscale
        Mat grayImage = new Mat();
        Imgproc.cvtColor(inputImage, grayImage, Imgproc.COLOR_BGR2GRAY);
        // Detect faces in the image
        MatOfRect faces = new MatOfRect();
        faceCascade.detectMultiScale(grayImage, faces);
        // Loop through detected faces
        for (Rect rect : faces.toArray()) {
            // Crop the face region
            Mat faceROI = grayImage.submat(rect);
            // Resize the face image to a fixed size if needed
            Size newSize = new Size(100, 100);
            Imgproc.resize(faceROI, faceROI, newSize);
            // Extract face features using LBPH
            MatOfFloat faceHistogram = new MatOfFloat();
            faceRecognizer.predict_collect(faceROI, faceHistogram);
            // Print the extracted features
            System.out.println("Extracted features for face: " + faceHistogram.dump());
        }
    }

private static final String FACE_CASCADE_CLASSIFIER_PATH = "haarcascade_frontalface_default.xml";
    private static final String FACE_EMBEDDING_MODEL_PATH = "res10_300x300_ssd_iter_140000_fp16.caffemodel";
    private static final String FACE_EMBEDDING_CONFIG_PATH = "deploy.prototxt";
    public static void main(String[] args) {
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
        CascadeClassifier faceCascade = new CascadeClassifier(FACE_CASCADE_CLASSIFIER_PATH);
        Net faceEmbeddingNet = Dnn.readNetFromCaffe(FACE_EMBEDDING_CONFIG_PATH, FACE_EMBEDDING_MODEL_PATH);
        // 读取待查询的图像
        Mat image = Imgcodecs.imread("query_image.jpg");
        // 检测图像中的人脸
        MatOfRect faceRectangles = new MatOfRect();
        faceCascade.detectMultiScale(image, faceRectangles);
        // 遍历每个检测到的人脸
        for (Rect rect : faceRectangles.toArray()) {
            // 提取人脸区域
            Mat faceImage = new Mat(image, rect);
            // 调整人脸图像尺寸为模型要求的大小
            Mat resizedFaceImage = new Mat();
            Imgproc.resize(faceImage, resizedFaceImage, new org.opencv.core.Size(300, 300));
            // 将人脸图像转换为Blob对象
            Mat blob = Dnn.blobFromImage(resizedFaceImage, 1.0, new org.opencv.core.Size(300, 300), new Scalar(104, 177, 123));
            // 使用预训练的人脸嵌入模型计算特征向量
            faceEmbeddingNet.setInput(blob);
            Mat embeddingVector = faceEmbeddingNet.forward();
            // 在控制台打印特征向量，你可以将其与数据库中的嵌入特征进行比对来完成人脸搜索
            System.out.println("特征向量: " + embeddingVector.dump());
        }