修改人脸识别算法
parent
71d85458a4
commit
ee10019aa1
8
pom.xml
8
pom.xml
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@ -46,7 +46,6 @@
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<lowagie.iTextAsian.version>1.0</lowagie.iTextAsian.version>
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<itextpdf.version>5.5.13</itextpdf.version>
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<aspose.words.version>15.8.0</aspose.words.version>
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<opencv.version>4.9.0-0</opencv.version>
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</properties>
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<!-- 依赖声明 -->
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@ -296,13 +295,6 @@
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<version>8.0.31</version>
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</dependency>
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<!-- OpenCV -->
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<dependency>
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<groupId>org.openpnp</groupId>
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<artifactId>opencv</artifactId>
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<version>${opencv.version}</version>
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</dependency>
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</dependencies>
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</dependencyManagement>
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@ -154,11 +154,19 @@
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<artifactId>okhttp</artifactId>
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</dependency>
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<!-- OpenCV -->
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<!-- 百度人脸识别SDK -->
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<dependency>
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<groupId>org.openpnp</groupId>
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<artifactId>opencv</artifactId>
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<groupId>com.baidu.aip</groupId>
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<artifactId>java-sdk</artifactId>
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<version>4.16.14</version>
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</dependency>
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<!-- JSON处理 -->
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<dependency>
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<groupId>org.json</groupId>
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<artifactId>json</artifactId>
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<version>20230227</version>
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</dependency>
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</dependencies>
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</project>
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</project>
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@ -0,0 +1,276 @@
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package com.yanzhu.common.core.utils;
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import com.baidu.aip.face.AipFace;
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import com.baidu.aip.face.MatchRequest;
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import org.json.JSONObject;
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import java.io.IOException;
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import java.net.URL;
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import java.net.URLConnection;
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import java.util.*;
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import javax.imageio.ImageIO;
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import java.awt.image.BufferedImage;
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import java.io.ByteArrayOutputStream;
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/**
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* 基于百度人脸识别API的人脸相似度对比工具类
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*
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* @author yanzhu
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*/
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public class BaiduFaceSimilarityUtils {
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// 百度人脸识别应用的API Key和Secret Key
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private static final String APP_ID = "您的AppID"; // 需要设置有效的AppID
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private static final String API_KEY = "L3PiEzO9dMFJDExMTjOxuTtq";
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private static final String SECRET_KEY = "40LMey6WC1MYIqLU4m5Qe8K4foFUM1bc";
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// 初始化AipFace客户端
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private static final AipFace client = new AipFace(APP_ID, API_KEY, SECRET_KEY);
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// 相似度阈值，大于此值认为是同一个人
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private static final double SIMILARITY_THRESHOLD = 0.8;
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// QPS限制相关常量
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private static final int QPS_LIMIT_DELAY = 1000; // 1秒延迟，避免QPS限制
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private static final int MAX_RETRY_ATTEMPTS = 3; // 最大重试次数
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private static final int RETRY_DELAY = 2000; // 重试延迟（毫秒）
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static {
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// 设置网络连接参数
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client.setConnectionTimeoutInMillis(2000);
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client.setSocketTimeoutInMillis(60000);
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}
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/**
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* 计算两张人脸图片的相似度
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*
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* @param imageUrl1 第一张图片URL
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* @param imageUrl2 第二张图片URL
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* @return 相似度，范围0-1，越大表示越相似
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*/
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public static double calculateFaceSimilarity(String imageUrl1, String imageUrl2) {
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try {
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// 下载图片并转换为Base64编码
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String imageBase64_1 = downloadAndEncodeImage(imageUrl1);
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String imageBase64_2 = downloadAndEncodeImage(imageUrl2);
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if (imageBase64_1 == null || imageBase64_2 == null) {
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return 0.0;
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}
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// 调用百度人脸识别API进行人脸对比
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return calculateImageSimilarityByBaiduAPI(imageBase64_1, imageBase64_2);
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} catch (Exception e) {
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e.printStackTrace();
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return 0.0;
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}
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}
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/**
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* 计算两张Base64编码人脸图片的相似度
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*
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* @param imageBase64_1 第一张图片的Base64编码
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* @param imageBase64_2 第二张图片的Base64编码
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* @return 相似度，范围0-1，越大表示越相似
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*/
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public static double calculateFaceSimilarityByBase64(String imageBase64_1, String imageBase64_2) {
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try {
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// 调用百度人脸识别API进行人脸对比
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return calculateImageSimilarityByBaiduAPI(imageBase64_1, imageBase64_2);
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} catch (Exception e) {
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e.printStackTrace();
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return 0.0;
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}
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}
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/**
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* 判断两张人脸图片是否匹配（用于考勤等场景）
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*
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* @param imageUrl1 第一张图片URL
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* @param imageUrl2 第二张图片URL
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* @return 是否匹配
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*/
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public static boolean isFaceMatchForAttendance(String imageUrl1, String imageUrl2) {
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double similarity = calculateFaceSimilarity(imageUrl1, imageUrl2);
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return similarity >= SIMILARITY_THRESHOLD;
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}
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/**
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* 判断两张Base64编码人脸图片是否匹配（用于考勤等场景）
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*
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* @param imageBase64_1 第一张图片的Base64编码
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* @param imageBase64_2 第二张图片的Base64编码
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* @return 是否匹配
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*/
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public static boolean isFaceMatchForAttendanceByBase64(String imageBase64_1, String imageBase64_2) {
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double similarity = calculateFaceSimilarityByBase64(imageBase64_1, imageBase64_2);
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return similarity >= SIMILARITY_THRESHOLD;
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}
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/**
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* 下载图片并转换为Base64编码
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*
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* @param imageUrl 图片URL
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* @return Base64编码的图片字符串
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* @throws IOException IO异常
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*/
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private static String downloadAndEncodeImage(String imageUrl) throws IOException {
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try {
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URL url = new URL(imageUrl);
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URLConnection connection = url.openConnection();
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connection.setConnectTimeout(5000);
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connection.setReadTimeout(5000);
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BufferedImage image = ImageIO.read(connection.getInputStream());
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if (image == null) {
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return null;
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}
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// 将图片转换为Base64编码
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ByteArrayOutputStream baos = new ByteArrayOutputStream();
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ImageIO.write(image, "jpg", baos);
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byte[] imageBytes = baos.toByteArray();
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return Base64.getEncoder().encodeToString(imageBytes);
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} catch (Exception e) {
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e.printStackTrace();
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return null;
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}
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}
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/**
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* 调用百度人脸识别API进行人脸对比
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*
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* @param imageBase64_1 第一张图片的Base64编码
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* @param imageBase64_2 第二张图片的Base64编码
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* @return 相似度，范围0-1
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*/
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private static double calculateImageSimilarityByBaiduAPI(String imageBase64_1, String imageBase64_2) {
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return calculateImageSimilarityByBaiduAPIWithRetry(imageBase64_1, imageBase64_2, 0);
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}
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/**
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* 带重试机制的百度人脸识别API调用
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*
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* @param imageBase64_1 第一张图片的Base64编码
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* @param imageBase64_2 第二张图片的Base64编码
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* @param attempt 当前尝试次数
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* @return 相似度，范围0-1
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*/
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private static double calculateImageSimilarityByBaiduAPIWithRetry(String imageBase64_1, String imageBase64_2, int attempt) {
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try {
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// 添加延迟以避免QPS限制
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if (attempt > 0) {
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Thread.sleep(RETRY_DELAY);
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} else {
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Thread.sleep(QPS_LIMIT_DELAY);
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}
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// 创建MatchRequest列表
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List<MatchRequest> requests = new ArrayList<>();
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MatchRequest req1 = new MatchRequest(imageBase64_1, "BASE64");
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req1.setFaceType("LIVE");
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req1.setQualityControl("LOW");
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req1.setLivenessControl("NONE");
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MatchRequest req2 = new MatchRequest(imageBase64_2, "BASE64");
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req2.setFaceType("LIVE");
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req2.setQualityControl("LOW");
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req2.setLivenessControl("NONE");
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requests.add(req1);
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requests.add(req2);
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// 调用百度人脸识别API的人脸对比接口
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JSONObject response = client.match(requests);
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// 解析返回结果
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if (response != null) {
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// 检查是否有QPS限制错误
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if (response.has("error_code") && response.get("error_code") != null) {
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String errorCode = response.get("error_code").toString();
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if ("18".equals(errorCode)) { // 18是QPS限制错误码
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System.err.println("百度API QPS限制: Open api qps request limit reached");
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if (attempt < MAX_RETRY_ATTEMPTS) {
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System.out.println("第" + (attempt + 1) + "次重试...");
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return calculateImageSimilarityByBaiduAPIWithRetry(imageBase64_1, imageBase64_2, attempt + 1);
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} else {
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System.err.println("达到最大重试次数，无法完成请求");
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return 0.0;
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}
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} else if (!errorCode.equals("0")) {
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System.err.println("百度API错误: " + response.optString("error_msg", "未知错误"));
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return 0.0;
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}
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}
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// 检查result字段是否存在且不为null
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if (response.has("result") && !response.isNull("result")) {
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JSONObject result = response.getJSONObject("result");
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if (result != null && result.has("score")) {
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// 百度API返回的相似度是百分比，需要转换为0-1范围
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double score = result.getDouble("score");
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return score / 100.0;
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}
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} else {
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// 如果result为null，输出详细信息用于调试
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System.err.println("百度API返回结果为空或无效: " + response.toString());
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return 0.0;
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}
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}
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return 0.0;
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} catch (Exception e) {
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e.printStackTrace();
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return 0.0;
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}
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}
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/**
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* 设置API Key和Secret Key
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*
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* @param appId App ID
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* @param apiKey API Key
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* @param secretKey Secret Key
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*/
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public static void setCredentials(String appId, String apiKey, String secretKey) {
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// 重新初始化AipFace客户端
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// 注意：在实际应用中，应该通过配置文件或环境变量来设置这些密钥
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}
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public static void main(String[] args) {
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System.out.println("开始测试人脸相似度算法...");
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// 测试1：同一个人的不同照片（应该匹配）
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String userPicture1 = "https://xiangguan.sxyanzhu.com/statics/2025/09/03/836e5c21f83604894486069394dcd22e_20250903170015A004.jpg";
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String userPicture2 = "http://62.234.3.186/statics/2025/06/11/a28457e2847333886c8e0f4fd9cd24bc_20250611101313A331.jpg";
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String userPicture3 = "https://xiangguan.sxyanzhu.com/statics/2025/09/03/c4e2c8fb9d9f66e03902f9e3fea17f99_20250903165717A003.jpg";
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String userPicture4 = "https://xiangguan.sxyanzhu.com/statics/2025/09/03/8c7dd922ad47494fc02c388e12c00eac_20250903132353A852.png";
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String userPicture5 = "http://62.234.3.186/statics/2025/06/11/87052f8fa3eaa8840bc2e4fe556a825e_20250611101011A328.jpg";
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String userPicture6 = "https://xiangguan.sxyanzhu.com/statics/2025/09/05/a851473a6f5f6373b1ea73487d36dfba_20250905095748A594.jpg";
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// 打印每张图片的人脸信息
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Map<String,String[]> map = new HashMap<>();
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map.put("1-2",new String[]{userPicture1,userPicture2});
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map.put("1-3",new String[]{userPicture1,userPicture3});
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map.put("1-4",new String[]{userPicture1,userPicture4});
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map.put("1-5",new String[]{userPicture1,userPicture5});
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map.put("2-3",new String[]{userPicture2,userPicture3});
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map.put("2-4",new String[]{userPicture2,userPicture4});
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map.put("2-5",new String[]{userPicture2,userPicture5});
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map.put("3-4",new String[]{userPicture3,userPicture4});
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map.put("3-5",new String[]{userPicture3,userPicture5});
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map.put("4-5",new String[]{userPicture4,userPicture5});
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map.put("3-6",new String[]{userPicture3,userPicture6});
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System.out.println("相似度测试结果:");
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for (String key : map.keySet()) {
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String[] strings = map.get(key);
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String img1 = strings[0];
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String img2 = strings[1];
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double similarity1 = calculateFaceSimilarity(img1, img2);
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System.out.printf(key+":%f%n",similarity1);
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}
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System.out.println("\n测试完成。");
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}
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}
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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@ -190,12 +190,6 @@
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<artifactId>hasor-dataway</artifactId>
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<version>4.2.5</version>
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</dependency>
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<!-- OpenCV -->
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<dependency>
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<groupId>org.openpnp</groupId>
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<artifactId>opencv</artifactId>
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</dependency>
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</dependencies>
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<build>
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@ -4,7 +4,7 @@ import java.util.List;
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import java.io.IOException;
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import javax.servlet.http.HttpServletResponse;
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import com.yanzhu.common.core.utils.ImageSimilarityUtils;
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import com.yanzhu.common.core.utils.BaiduFaceSimilarityUtils;
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import com.yanzhu.common.core.utils.poi.ExcelUtil;
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import com.yanzhu.common.core.web.controller.BaseController;
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import com.yanzhu.common.core.web.domain.AjaxResult;
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@ -133,11 +133,10 @@ public class ProMobileAttendanceConfigController extends BaseController
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String attImg=attData.getBasePath()+attData.getAttImg();
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// 使用专门为人脸识别考勤优化的相似度计算
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double similarity = ImageSimilarityUtils.calculateFaceSimilarity(userPicture, attImg);
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double similarity = BaiduFaceSimilarityUtils.calculateFaceSimilarity(userPicture, attImg);
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System.out.println("相似度:"+similarity);
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// 根据是否使用OpenCV调整阈值
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// OpenCV场景下阈值为0.8，基础算法场景下阈值为0.75
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double threshold = ImageSimilarityUtils.openCVLoaded ? 0.8 : 0.75;
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double threshold = 0.8;
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if (similarity >= threshold) {
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// 相似度达标，允许考勤
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Reference in New Issue