mirror of
https://github.com/geekwenjie/SmartJavaAI.git
synced 2026-09-14 22:18:42 +00:00
新增目标检测功能
This commit is contained in:
dengwenjie
@@ -1,87 +0,0 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
import cn.smartjavaai.face.entity.FaceResult;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.io.InputStream;
|
||||
|
||||
/**
|
||||
* 人脸识别算法
|
||||
* @author dwj
|
||||
*/
|
||||
public abstract class AbstractFaceAlgorithm implements FaceAlgorithm{
|
||||
@Override
|
||||
public void loadModel(ModelConfig config) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public void loadFaceFeatureModel(ModelConfig config) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceDetectedResult detect(String imagePath) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(String imagePath) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(InputStream inputStream) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float calculSimilar(float[] feature1, float[] feature2) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(String imagePath1, String imagePath2) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, String imagePath) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, InputStream inputStream) throws Exception {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(String imagePath) throws Exception{
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(InputStream inputStream) throws Exception{
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public long removeRegister(String... keys) throws Exception{
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public long clearFace() throws Exception{
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,143 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
import cn.smartjavaai.common.entity.DetectionResponse;
|
||||
import cn.smartjavaai.face.entity.FaceResult;
|
||||
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.InputStream;
|
||||
|
||||
/**
|
||||
* 人脸识别算法
|
||||
* @author dwj
|
||||
*/
|
||||
public abstract class AbstractFaceModel implements FaceModel {
|
||||
@Override
|
||||
public void loadModel(FaceModelConfig config) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(String imagePath) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(InputStream imageInputStream) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(BufferedImage image) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(byte[] imageData) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public void detectAndDraw(String imagePath, String outputPath) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public BufferedImage detectAndDraw(BufferedImage sourceImage) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(String imagePath) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(InputStream inputStream) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float calculSimilar(float[] feature1, float[] feature2) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(String imagePath1, String imagePath2) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(InputStream inputStream1, InputStream inputStream2) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, String imagePath) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, InputStream inputStream) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, BufferedImage sourceImage) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, byte[] imageData) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(String imagePath) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(InputStream inputStream) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public long removeRegister(String... keys) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public long clearFace() {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(BufferedImage sourceImage) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(byte[] imageData) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(BufferedImage sourceImage1, BufferedImage sourceImag2) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(byte[] imageData1, byte[] imageData2) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(BufferedImage sourceImage) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(byte[] imageData) {
|
||||
throw new UnsupportedOperationException("默认不支持该功能");
|
||||
}
|
||||
}
|
||||
@@ -1,133 +0,0 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
import ai.djl.MalformedModelException;
|
||||
import ai.djl.repository.zoo.ModelNotFoundException;
|
||||
import ai.djl.translate.TranslateException;
|
||||
import cn.smartjavaai.face.entity.FaceResult;
|
||||
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.IOException;
|
||||
import java.io.InputStream;
|
||||
|
||||
/**
|
||||
* 人脸识别算法
|
||||
* @author dwj
|
||||
*/
|
||||
public interface FaceAlgorithm {
|
||||
|
||||
/**
|
||||
* 加载模型
|
||||
* @param config
|
||||
* @throws Exception
|
||||
*/
|
||||
void loadModel(ModelConfig config) throws Exception; // 加载模型
|
||||
|
||||
/**
|
||||
* 加载人脸特征提取模型
|
||||
* @param config
|
||||
* @throws Exception
|
||||
*/
|
||||
void loadFaceFeatureModel(ModelConfig config) throws Exception; // 加载模型
|
||||
|
||||
/**
|
||||
* 人脸检测
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
FaceDetectedResult detect(String imagePath) throws Exception;
|
||||
|
||||
/**
|
||||
* 人脸检测
|
||||
* @param imageInputStream 图片输入流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
FaceDetectedResult detect(InputStream imageInputStream) throws Exception;
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
float[] featureExtraction(String imagePath) throws Exception;
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param inputStream 输入流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
float[] featureExtraction(InputStream inputStream) throws Exception;
|
||||
|
||||
/**
|
||||
* 计算相似度
|
||||
* @param feature1 图1特征
|
||||
* @param feature2 图2特征
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
float calculSimilar(float[] feature1, float[] feature2) throws Exception;
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param imagePath1 图1路径
|
||||
* @param imagePath2 图2路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
float featureComparison(String imagePath1, String imagePath2) throws Exception;
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param inputStream1 图1输入流
|
||||
* @param inputStream2 图2输入流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception;
|
||||
|
||||
/**
|
||||
* 注册人脸
|
||||
* @param key
|
||||
* @param imagePath
|
||||
* @return
|
||||
*/
|
||||
boolean register(String key, String imagePath) throws Exception;
|
||||
|
||||
/**
|
||||
* 注册人脸
|
||||
* @param key
|
||||
* @param inputStream
|
||||
* @return
|
||||
*/
|
||||
boolean register(String key, InputStream inputStream) throws Exception;
|
||||
|
||||
/**
|
||||
* 查询人脸
|
||||
* @param imagePath
|
||||
* @return
|
||||
*/
|
||||
FaceResult search(String imagePath) throws Exception;
|
||||
|
||||
/**
|
||||
* 查询人脸
|
||||
* @param inputStream
|
||||
* @return
|
||||
*/
|
||||
FaceResult search(InputStream inputStream) throws Exception;
|
||||
|
||||
/**
|
||||
* 删除已标记人脸
|
||||
* @param keys
|
||||
* @return
|
||||
*/
|
||||
long removeRegister(String... keys) throws Exception;
|
||||
|
||||
/**
|
||||
* 清空人脸库数据
|
||||
*/
|
||||
long clearFace() throws Exception;
|
||||
|
||||
}
|
||||
@@ -1,122 +0,0 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
import cn.smartjavaai.face.algo.FeatureExtractionAlgo;
|
||||
import cn.smartjavaai.face.algo.RetinaFace;
|
||||
import cn.smartjavaai.face.algo.SeetaFace6Algo;
|
||||
import cn.smartjavaai.face.algo.UltraLightFastGenericFace;
|
||||
|
||||
import java.util.Map;
|
||||
import java.util.concurrent.ConcurrentHashMap;
|
||||
|
||||
/**
|
||||
* 人脸算法工厂
|
||||
* @author dwj
|
||||
*/
|
||||
public class FaceAlgorithmFactory {
|
||||
|
||||
/**
|
||||
* 算法注册表
|
||||
*/
|
||||
private static final Map<String, Class<? extends FaceAlgorithm>> registry =
|
||||
new ConcurrentHashMap<>();
|
||||
|
||||
/**
|
||||
* 注册算法
|
||||
* @param name
|
||||
* @param clazz
|
||||
*/
|
||||
public static void registerAlgorithm(String name, Class<? extends FaceAlgorithm> clazz) {
|
||||
registry.put(name.toLowerCase(), clazz);
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用ModelConfig创建算法
|
||||
* @param config
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
public static FaceAlgorithm createFaceAlgorithm(ModelConfig config) throws Exception {
|
||||
Class<?> clazz = registry.get(config.getAlgorithmName().toLowerCase());
|
||||
if(clazz == null){
|
||||
System.out.println("No such algorithm: " + config.getAlgorithmName().toLowerCase());
|
||||
throw new IllegalArgumentException("Unsupported algorithm");
|
||||
}
|
||||
FaceAlgorithm algorithm = (FaceAlgorithm) clazz.newInstance();
|
||||
algorithm.loadModel(config);
|
||||
return algorithm;
|
||||
}
|
||||
|
||||
/**
|
||||
* 创建默认算法
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
public static FaceAlgorithm createFaceAlgorithm() throws Exception {
|
||||
// 初始化配置
|
||||
ModelConfig config = new ModelConfig();
|
||||
config.setAlgorithmName("retinaface");
|
||||
config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);
|
||||
config.setMaxFaceCount(FaceConfig.MAX_FACE_LIMIT);
|
||||
config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
|
||||
return createFaceAlgorithm(config);
|
||||
}
|
||||
|
||||
/**
|
||||
* 创建轻量级算法
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
public static FaceAlgorithm createLightFaceAlgorithm() throws Exception {
|
||||
// 初始化配置
|
||||
ModelConfig config = new ModelConfig();
|
||||
config.setAlgorithmName("ultralightfastgenericface");
|
||||
config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);
|
||||
config.setMaxFaceCount(FaceConfig.MAX_FACE_LIMIT);
|
||||
config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
|
||||
Class<?> clazz = registry.get(config.getAlgorithmName().toLowerCase());
|
||||
if(clazz == null){
|
||||
throw new IllegalArgumentException("Unsupported algorithm");
|
||||
}
|
||||
FaceAlgorithm algorithm = (FaceAlgorithm) clazz.newInstance();
|
||||
algorithm.loadModel(config);
|
||||
return algorithm;
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用ModelConfig创建人脸特征提取算法
|
||||
* @param config
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
public static FaceAlgorithm createFaceFeatureAlgorithm(ModelConfig config) throws Exception {
|
||||
Class<?> clazz = registry.get(config.getAlgorithmName().toLowerCase());
|
||||
if(clazz == null){
|
||||
throw new IllegalArgumentException("Unsupported algorithm");
|
||||
}
|
||||
FaceAlgorithm algorithm = (FaceAlgorithm) clazz.newInstance();
|
||||
algorithm.loadFaceFeatureModel(config);
|
||||
return algorithm;
|
||||
}
|
||||
|
||||
/**
|
||||
* 创建人脸特征提取算法
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
public static FaceAlgorithm createFaceFeatureAlgorithm() throws Exception {
|
||||
// 初始化配置
|
||||
ModelConfig config = new ModelConfig();
|
||||
config.setAlgorithmName("featureExtraction");
|
||||
return createFaceFeatureAlgorithm(config);
|
||||
}
|
||||
|
||||
// 初始化默认算法
|
||||
static {
|
||||
registerAlgorithm("retinaface", RetinaFace.class);
|
||||
registerAlgorithm("ultralightfastgenericface", UltraLightFastGenericFace.class);
|
||||
//人脸特征提取
|
||||
registerAlgorithm("featureExtraction", FeatureExtractionAlgo.class);
|
||||
registerAlgorithm("seetaface6", SeetaFace6Algo.class);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -1,40 +0,0 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
|
||||
import cn.smartjavaai.common.entity.Rectangle;
|
||||
|
||||
import java.util.List;
|
||||
|
||||
/**
|
||||
* 人脸检测结果
|
||||
* @author dwj
|
||||
*/
|
||||
public class FaceDetectedResult {
|
||||
|
||||
|
||||
/**
|
||||
* 置信度
|
||||
*/
|
||||
private List<Double> probabilities;
|
||||
|
||||
/**
|
||||
* 人脸框
|
||||
*/
|
||||
private List<Rectangle> rectangles;
|
||||
|
||||
public List<Double> getProbabilities() {
|
||||
return probabilities;
|
||||
}
|
||||
|
||||
public void setProbabilities(List<Double> probabilities) {
|
||||
this.probabilities = probabilities;
|
||||
}
|
||||
|
||||
public List<Rectangle> getRectangles() {
|
||||
return rectangles;
|
||||
}
|
||||
|
||||
public void setRectangles(List<Rectangle> rectangles) {
|
||||
this.rectangles = rectangles;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,206 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
import cn.smartjavaai.common.entity.DetectionResponse;
|
||||
import cn.smartjavaai.face.entity.FaceResult;
|
||||
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.InputStream;
|
||||
|
||||
/**
|
||||
* 人脸识别算法
|
||||
* @author dwj
|
||||
*/
|
||||
public interface FaceModel {
|
||||
|
||||
/**
|
||||
* 加载模型
|
||||
* @param config
|
||||
*/
|
||||
void loadModel(FaceModelConfig config); // 加载模型
|
||||
|
||||
|
||||
/**
|
||||
* 人脸检测
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
*/
|
||||
DetectionResponse detect(String imagePath);
|
||||
|
||||
/**
|
||||
* 人脸检测
|
||||
* @param imageInputStream 图片输入流
|
||||
* @return
|
||||
*/
|
||||
DetectionResponse detect(InputStream imageInputStream);
|
||||
|
||||
/**
|
||||
* 人脸检测
|
||||
* @param image BufferedImage
|
||||
* @return
|
||||
*/
|
||||
DetectionResponse detect(BufferedImage image);
|
||||
|
||||
/**
|
||||
* 人脸检测
|
||||
* @param imageData
|
||||
* @return
|
||||
*/
|
||||
DetectionResponse detect(byte[] imageData);
|
||||
|
||||
/**
|
||||
* 检测并绘制人脸
|
||||
* @param imagePath 图片输入路径（包含文件名称）
|
||||
* @param outputPath 图片输出路径（包含文件名称）
|
||||
*/
|
||||
void detectAndDraw(String imagePath, String outputPath);
|
||||
|
||||
/**
|
||||
* 检测并绘制人脸
|
||||
* @param sourceImage
|
||||
* @return
|
||||
*/
|
||||
BufferedImage detectAndDraw(BufferedImage sourceImage);
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
*/
|
||||
float[] featureExtraction(String imagePath);
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param inputStream 输入流
|
||||
* @return
|
||||
*/
|
||||
float[] featureExtraction(InputStream inputStream);
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param sourceImage BufferedImage图片数据
|
||||
* @return
|
||||
*/
|
||||
float[] featureExtraction(BufferedImage sourceImage);
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param imageData 图片字节流
|
||||
* @return
|
||||
*/
|
||||
float[] featureExtraction(byte[] imageData);
|
||||
|
||||
/**
|
||||
* 计算相似度
|
||||
* @param feature1 图1特征
|
||||
* @param feature2 图2特征
|
||||
* @return
|
||||
*/
|
||||
float calculSimilar(float[] feature1, float[] feature2);
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param imagePath1 图1路径
|
||||
* @param imagePath2 图2路径
|
||||
* @return
|
||||
*/
|
||||
float featureComparison(String imagePath1, String imagePath2);
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param sourceImage1 图1BufferedImage
|
||||
* @param sourceImag2 图2BufferedImage
|
||||
* @return
|
||||
*/
|
||||
float featureComparison(BufferedImage sourceImage1, BufferedImage sourceImag2);
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param inputStream1 图1输入流
|
||||
* @param inputStream2 图2输入流
|
||||
* @return
|
||||
*/
|
||||
float featureComparison(InputStream inputStream1, InputStream inputStream2);
|
||||
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param imageData1
|
||||
* @param imageData2
|
||||
* @return
|
||||
*/
|
||||
float featureComparison(byte[] imageData1, byte[] imageData2);
|
||||
|
||||
/**
|
||||
* 注册人脸
|
||||
* @param key
|
||||
* @param imagePath
|
||||
* @return
|
||||
*/
|
||||
boolean register(String key, String imagePath);
|
||||
|
||||
/**
|
||||
* 注册人脸
|
||||
* @param key
|
||||
* @param inputStream
|
||||
* @return
|
||||
*/
|
||||
boolean register(String key, InputStream inputStream);
|
||||
|
||||
/**
|
||||
* 注册人脸
|
||||
* @param key
|
||||
* @param sourceImage
|
||||
* @return
|
||||
*/
|
||||
boolean register(String key, BufferedImage sourceImage);
|
||||
|
||||
|
||||
/**
|
||||
* 注册人脸
|
||||
* @param key
|
||||
* @param imageData
|
||||
* @return
|
||||
*/
|
||||
boolean register(String key, byte[] imageData);
|
||||
|
||||
/**
|
||||
* 查询人脸
|
||||
* @param imagePath
|
||||
* @return
|
||||
*/
|
||||
FaceResult search(String imagePath);
|
||||
|
||||
/**
|
||||
* 查询人脸
|
||||
* @param inputStream
|
||||
* @return
|
||||
*/
|
||||
FaceResult search(InputStream inputStream);
|
||||
|
||||
/**
|
||||
* 查询人脸
|
||||
* @param sourceImage
|
||||
* @return
|
||||
*/
|
||||
FaceResult search(BufferedImage sourceImage);
|
||||
|
||||
/**
|
||||
* 查询人脸
|
||||
* @param imageData
|
||||
* @return
|
||||
*/
|
||||
FaceResult search(byte[] imageData);
|
||||
|
||||
/**
|
||||
* 删除已标记人脸
|
||||
* @param keys
|
||||
* @return
|
||||
*/
|
||||
long removeRegister(String... keys);
|
||||
|
||||
/**
|
||||
* 清空人脸库数据
|
||||
*/
|
||||
long clearFace();
|
||||
|
||||
}
|
||||
@@ -0,0 +1,45 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
import cn.smartjavaai.common.enums.DeviceEnum;
|
||||
import lombok.Data;
|
||||
|
||||
/**
|
||||
* 模型配置
|
||||
* @author dwj
|
||||
*/
|
||||
@Data
|
||||
public class FaceModelConfig {
|
||||
|
||||
/**
|
||||
* 人脸算法名称
|
||||
*/
|
||||
private FaceModelEnum modelEnum;
|
||||
|
||||
/**
|
||||
* 置信度阈值
|
||||
*/
|
||||
private double confidenceThreshold = FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD;
|
||||
|
||||
/**
|
||||
* 非极大抑制阈值 作用：消除重叠检测框，保留最优结果
|
||||
*/
|
||||
private double nmsThresh = FaceConfig.NMS_THRESHOLD;
|
||||
|
||||
/**
|
||||
* 模型路径
|
||||
*/
|
||||
private String modelPath;
|
||||
|
||||
/**
|
||||
* 人脸库路径
|
||||
*/
|
||||
private String faceDbPath;
|
||||
|
||||
/**
|
||||
* 设备类型
|
||||
*/
|
||||
private DeviceEnum device;
|
||||
|
||||
|
||||
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
import cn.smartjavaai.face.model.FeatureExtractionModel;
|
||||
import cn.smartjavaai.face.model.RetinaFaceModel;
|
||||
import cn.smartjavaai.face.model.SeetaFace6Model;
|
||||
import cn.smartjavaai.face.model.UltraLightFastGenericFaceModel;
|
||||
|
||||
/**
|
||||
* 人脸模型枚举
|
||||
* @author dwj
|
||||
* @date 2025/4/10
|
||||
*/
|
||||
public enum FaceModelEnum {
|
||||
|
||||
RETINA_FACE("RetinaFaceModel"),
|
||||
ULTRA_LIGHT_FAST_GENERIC_FACE("UltraLightFastGenericFaceModel"),
|
||||
FACENET_FEATURE_EXTRACTION("FeatureExtractionModel"),
|
||||
SEETA_FACE6_MODEL("SeetaFace6Model");
|
||||
|
||||
private final String modelClassName;
|
||||
|
||||
FaceModelEnum(String modelClassName) {
|
||||
this.modelClassName = modelClassName;
|
||||
}
|
||||
|
||||
public String getModelClassName() {
|
||||
return modelClassName;
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据名称获取枚举 (忽略大小写和下划线变体)
|
||||
*/
|
||||
public static FaceModelEnum fromName(String name) {
|
||||
String formatted = name.trim().toUpperCase().replaceAll("[-_]", "");
|
||||
for (FaceModelEnum model : values()) {
|
||||
if (model.name().replaceAll("_", "").equals(formatted)) {
|
||||
return model;
|
||||
}
|
||||
}
|
||||
throw new IllegalArgumentException("未知模型名称: " + name);
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
@@ -0,0 +1,127 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
import cn.smartjavaai.face.exception.FaceException;
|
||||
import cn.smartjavaai.face.model.FeatureExtractionModel;
|
||||
import cn.smartjavaai.face.model.RetinaFaceModel;
|
||||
import cn.smartjavaai.face.model.SeetaFace6Model;
|
||||
import cn.smartjavaai.face.model.UltraLightFastGenericFaceModel;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
|
||||
import java.util.Map;
|
||||
import java.util.Objects;
|
||||
import java.util.concurrent.ConcurrentHashMap;
|
||||
|
||||
/**
|
||||
* 人脸算法工厂
|
||||
* @author dwj
|
||||
*/
|
||||
@Slf4j
|
||||
public class FaceModelFactory {
|
||||
|
||||
// 使用 volatile 和双重检查锁定来确保线程安全的单例模式
|
||||
private static volatile FaceModelFactory instance;
|
||||
|
||||
private static final ConcurrentHashMap<String, FaceModel> modelMap = new ConcurrentHashMap<>();
|
||||
|
||||
/**
|
||||
* 算法注册表
|
||||
*/
|
||||
private static final Map<String, Class<? extends FaceModel>> registry =
|
||||
new ConcurrentHashMap<>();
|
||||
|
||||
|
||||
public static FaceModelFactory getInstance() {
|
||||
if (instance == null) {
|
||||
synchronized (FaceModelFactory.class) {
|
||||
if (instance == null) {
|
||||
instance = new FaceModelFactory();
|
||||
}
|
||||
}
|
||||
}
|
||||
return instance;
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* 注册算法
|
||||
* @param name
|
||||
* @param clazz
|
||||
*/
|
||||
private static void registerAlgorithm(String name, Class<? extends FaceModel> clazz) {
|
||||
registry.put(name.toLowerCase(), clazz);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 获取模型（通过配置）
|
||||
* @param config
|
||||
* @return
|
||||
*/
|
||||
public FaceModel getModel(FaceModelConfig config) {
|
||||
if(Objects.isNull(config) || Objects.isNull(config.getModelEnum())){
|
||||
throw new FaceException("未配置人脸模型");
|
||||
}
|
||||
return modelMap.computeIfAbsent(config.getModelEnum().name(), k -> {
|
||||
return createFaceModel(config);
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取默认模型
|
||||
* @return
|
||||
*/
|
||||
public FaceModel getModel() {
|
||||
// 初始化默认配置
|
||||
FaceModelConfig config = new FaceModelConfig();
|
||||
config.setModelEnum(FaceModelEnum.RETINA_FACE);
|
||||
config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);
|
||||
config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
|
||||
return getModel(config);
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用ModelConfig创建算法
|
||||
* @param config
|
||||
* @return
|
||||
*/
|
||||
private FaceModel createFaceModel(FaceModelConfig config) {
|
||||
Class<?> clazz = registry.get(config.getModelEnum().getModelClassName().toLowerCase());
|
||||
if(clazz == null){
|
||||
throw new FaceException("Unsupported algorithm");
|
||||
}
|
||||
FaceModel algorithm = null;
|
||||
try {
|
||||
algorithm = (FaceModel) clazz.newInstance();
|
||||
} catch (InstantiationException | IllegalAccessException e) {
|
||||
throw new FaceException(e);
|
||||
}
|
||||
algorithm.loadModel(config);
|
||||
return algorithm;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 获取轻量级人脸模型
|
||||
* @return
|
||||
*/
|
||||
public FaceModel getLightFaceModel() {
|
||||
// 初始化默认配置
|
||||
FaceModelConfig config = new FaceModelConfig();
|
||||
config.setModelEnum(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE);
|
||||
config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);
|
||||
config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
|
||||
return getModel(config);
|
||||
}
|
||||
|
||||
|
||||
// 初始化默认算法
|
||||
static {
|
||||
registerAlgorithm("retinafacemodel", RetinaFaceModel.class);
|
||||
registerAlgorithm("ultralightfastgenericfacemodel", UltraLightFastGenericFaceModel.class);
|
||||
//人脸特征提取
|
||||
registerAlgorithm("featureextractionmodel", FeatureExtractionModel.class);
|
||||
registerAlgorithm("seetaface6model", SeetaFace6Model.class);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -1,88 +0,0 @@
|
||||
package cn.smartjavaai.face;
|
||||
|
||||
/**
|
||||
* 模型配置
|
||||
* @author dwj
|
||||
*/
|
||||
|
||||
public class ModelConfig {
|
||||
|
||||
/**
|
||||
* 人脸算法名称
|
||||
*/
|
||||
private String algorithmName;
|
||||
|
||||
/**
|
||||
* 置信度阈值
|
||||
*/
|
||||
private double confidenceThreshold;
|
||||
|
||||
/**
|
||||
* 非极大抑制阈值 作用：消除重叠检测框，保留最优结果
|
||||
*/
|
||||
private double nmsThresh;
|
||||
|
||||
/**
|
||||
* 最大检测人脸数量
|
||||
*/
|
||||
private int maxFaceCount;
|
||||
|
||||
/**
|
||||
* 模型路径
|
||||
*/
|
||||
private String modelPath;
|
||||
|
||||
/**
|
||||
* 人脸库路径
|
||||
*/
|
||||
private String faceDbPath;
|
||||
|
||||
|
||||
public String getAlgorithmName() {
|
||||
return algorithmName;
|
||||
}
|
||||
|
||||
public void setAlgorithmName(String algorithmName) {
|
||||
this.algorithmName = algorithmName;
|
||||
}
|
||||
|
||||
public double getConfidenceThreshold() {
|
||||
return confidenceThreshold;
|
||||
}
|
||||
|
||||
public void setConfidenceThreshold(double confidenceThreshold) {
|
||||
this.confidenceThreshold = confidenceThreshold;
|
||||
}
|
||||
|
||||
public double getNmsThresh() {
|
||||
return nmsThresh;
|
||||
}
|
||||
|
||||
public void setNmsThresh(double nmsThresh) {
|
||||
this.nmsThresh = nmsThresh;
|
||||
}
|
||||
|
||||
public int getMaxFaceCount() {
|
||||
return maxFaceCount;
|
||||
}
|
||||
|
||||
public void setMaxFaceCount(int maxFaceCount) {
|
||||
this.maxFaceCount = maxFaceCount;
|
||||
}
|
||||
|
||||
public String getModelPath() {
|
||||
return modelPath;
|
||||
}
|
||||
|
||||
public void setModelPath(String modelPath) {
|
||||
this.modelPath = modelPath;
|
||||
}
|
||||
|
||||
public String getFaceDbPath() {
|
||||
return faceDbPath;
|
||||
}
|
||||
|
||||
public void setFaceDbPath(String faceDbPath) {
|
||||
this.faceDbPath = faceDbPath;
|
||||
}
|
||||
}
|
||||
@@ -1,162 +0,0 @@
|
||||
package cn.smartjavaai.face.algo;
|
||||
|
||||
import ai.djl.Device;
|
||||
import ai.djl.inference.Predictor;
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.ImageFactory;
|
||||
import ai.djl.modality.cv.output.DetectedObjects;
|
||||
import ai.djl.modality.cv.translator.ImageFeatureExtractorFactory;
|
||||
import ai.djl.repository.zoo.Criteria;
|
||||
import ai.djl.repository.zoo.ZooModel;
|
||||
import ai.djl.training.util.ProgressBar;
|
||||
import cn.smartjavaai.common.entity.Point;
|
||||
import cn.smartjavaai.common.entity.Rectangle;
|
||||
import cn.smartjavaai.face.AbstractFaceAlgorithm;
|
||||
import cn.smartjavaai.face.FaceDetectedResult;
|
||||
import cn.smartjavaai.face.FaceDetectionTranslator;
|
||||
import cn.smartjavaai.face.ModelConfig;
|
||||
import cn.smartjavaai.face.translator.FaceFeatureTranslator;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
|
||||
import java.io.InputStream;
|
||||
import java.nio.file.Path;
|
||||
import java.nio.file.Paths;
|
||||
import java.util.ArrayList;
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
/**
|
||||
* @author dwj
|
||||
*/
|
||||
public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
|
||||
|
||||
|
||||
private Criteria<Image, float[]> faceFeatureCriteria;
|
||||
|
||||
private Predictor<Image, float[]> predictor;
|
||||
|
||||
private ZooModel<Image, float[]> model;
|
||||
|
||||
public static final List<Float> mean =
|
||||
Arrays.asList(
|
||||
127.5f / 255.0f,
|
||||
127.5f / 255.0f,
|
||||
127.5f / 255.0f,
|
||||
128.0f / 255.0f,
|
||||
128.0f / 255.0f,
|
||||
128.0f / 255.0f);
|
||||
|
||||
|
||||
/**
|
||||
* 加载人脸特征提取模型
|
||||
* @param config
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public void loadFaceFeatureModel(ModelConfig config) throws Exception {
|
||||
String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
|
||||
faceFeatureCriteria =
|
||||
Criteria.builder()
|
||||
.setTypes(Image.class, float[].class)
|
||||
.optModelName("face_feature") // specify model file prefix
|
||||
.optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null :
|
||||
"https://resources.djl.ai/test-models/pytorch/face_feature.zip")
|
||||
.optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
|
||||
.optTranslator(new FaceFeatureTranslator())
|
||||
.optArgument("normalize", normalize)
|
||||
.optEngine("PyTorch") // Use PyTorch engine
|
||||
.optProgress(new ProgressBar())
|
||||
.build();
|
||||
|
||||
model = faceFeatureCriteria.loadModel();
|
||||
predictor = model.newPredictor();
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public float[] featureExtraction(String imagePath) throws Exception {
|
||||
Path imageFile = Paths.get(imagePath);
|
||||
Image img = ImageFactory.getInstance().fromFile(imageFile);
|
||||
img.getWrappedImage();
|
||||
return predictor.predict(img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param inputStream 输入流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public float[] featureExtraction(InputStream inputStream) throws Exception {
|
||||
Image img = ImageFactory.getInstance().fromInputStream(inputStream);
|
||||
img.getWrappedImage();
|
||||
return predictor.predict(img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 计算相似度
|
||||
* @param feature1 图1特征
|
||||
* @param feature2 图2特征
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public float calculSimilar(float[] feature1, float[] feature2) throws Exception {
|
||||
float ret = 0.0f;
|
||||
float mod1 = 0.0f;
|
||||
float mod2 = 0.0f;
|
||||
int length = feature1.length;
|
||||
for (int i = 0; i < length; ++i) {
|
||||
ret += feature1[i] * feature2[i];
|
||||
mod1 += feature1[i] * feature1[i];
|
||||
mod2 += feature2[i] * feature2[i];
|
||||
}
|
||||
return (float) ((ret / Math.sqrt(mod1) / Math.sqrt(mod2) + 1) / 2.0f);
|
||||
}
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param imagePath1 图1路径
|
||||
* @param imagePath2 图2路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public float featureComparison(String imagePath1, String imagePath2) throws Exception {
|
||||
float[] feature1 = featureExtraction(imagePath1);
|
||||
float[] feature2 = featureExtraction(imagePath2);
|
||||
return calculSimilar(feature1, feature2);
|
||||
}
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param inputStream1 图1输入流
|
||||
* @param inputStream2 图2输入流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception {
|
||||
float[] feature1 = featureExtraction(inputStream1);
|
||||
float[] feature2 = featureExtraction(inputStream2);
|
||||
return calculSimilar(feature1, feature2);
|
||||
}
|
||||
|
||||
/*@Override
|
||||
public float[] recognize(FaceRegion region) {
|
||||
return new float[0];
|
||||
}*/
|
||||
|
||||
/*@Override
|
||||
public void loadModel(ModelConfig config) throws Exception {
|
||||
|
||||
}*/
|
||||
}
|
||||
@@ -1,144 +0,0 @@
|
||||
package cn.smartjavaai.face.algo;
|
||||
|
||||
import ai.djl.MalformedModelException;
|
||||
import ai.djl.inference.Predictor;
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.ImageFactory;
|
||||
import ai.djl.modality.cv.output.DetectedObjects;
|
||||
import ai.djl.modality.cv.translator.ImageFeatureExtractorFactory;
|
||||
import ai.djl.repository.zoo.Criteria;
|
||||
import ai.djl.repository.zoo.ModelNotFoundException;
|
||||
import ai.djl.repository.zoo.ZooModel;
|
||||
import ai.djl.training.util.ProgressBar;
|
||||
import ai.djl.translate.TranslateException;
|
||||
import cn.smartjavaai.common.entity.Point;
|
||||
import cn.smartjavaai.common.entity.Rectangle;
|
||||
import cn.smartjavaai.face.*;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
|
||||
|
||||
import java.io.IOException;
|
||||
import java.io.InputStream;
|
||||
import java.lang.reflect.InvocationTargetException;
|
||||
import java.nio.file.Path;
|
||||
import java.nio.file.Paths;
|
||||
import java.util.ArrayList;
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
import java.util.stream.Collectors;
|
||||
import java.util.stream.StreamSupport;
|
||||
|
||||
/**
|
||||
* RetinaFace实现
|
||||
* @author dwj
|
||||
*/
|
||||
public class RetinaFace extends AbstractFaceAlgorithm {
|
||||
|
||||
private Criteria<Image, DetectedObjects> criteria;
|
||||
|
||||
private Criteria<Image, float[]> faceFeatureCriteria;
|
||||
|
||||
private Predictor<Image, DetectedObjects> predictor;
|
||||
|
||||
private ZooModel<Image, DetectedObjects> model;
|
||||
|
||||
/**
|
||||
* 特征图层的基础缩放比例
|
||||
*/
|
||||
public static final int[][] scales = {{16, 32}, {64, 128}, {256, 512}};
|
||||
/**
|
||||
* 特征图相对于原图的采样步长
|
||||
*/
|
||||
public static final int[] steps = {8, 16, 32};
|
||||
/**
|
||||
* 缩放系数
|
||||
*/
|
||||
public static final double[] variance = {0.1f, 0.2f};
|
||||
|
||||
|
||||
/**
|
||||
* 加载模型
|
||||
* @param config
|
||||
*/
|
||||
@Override
|
||||
public void loadModel(ModelConfig config) throws ModelNotFoundException, MalformedModelException, IOException {
|
||||
FaceDetectionTranslator translator =
|
||||
new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, config.getMaxFaceCount(), scales, steps);
|
||||
criteria =
|
||||
Criteria.builder()
|
||||
.setTypes(Image.class, DetectedObjects.class)
|
||||
.optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null : "https://resources.djl.ai/test-models/pytorch/retinaface.zip")
|
||||
// Load model from local file, e.g:
|
||||
.optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
|
||||
.optModelName(StringUtils.isNotBlank(config.getAlgorithmName()) ? config.getAlgorithmName() : "retinaface") // specify model file prefix
|
||||
.optTranslator(translator)
|
||||
.optProgress(new ProgressBar())
|
||||
.optEngine("PyTorch") // Use PyTorch engine
|
||||
.build();
|
||||
model = criteria.loadModel();
|
||||
predictor = model.newPredictor();
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* 检测人脸
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public FaceDetectedResult detect(String imagePath) throws Exception{
|
||||
Path facePath = Paths.get(imagePath);
|
||||
Image img = ImageFactory.getInstance().fromFile(facePath);
|
||||
DetectedObjects detection = predictor.predict(img);
|
||||
return convertToFaceDetectedResult(detection,img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 检测人脸
|
||||
* @param imageInputStream 图片流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
|
||||
Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
|
||||
DetectedObjects detection = predictor.predict(img);
|
||||
return convertToFaceDetectedResult(detection,img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 转换为FaceDetectedResult
|
||||
* @param detection
|
||||
* @param img
|
||||
* @return
|
||||
*/
|
||||
private FaceDetectedResult convertToFaceDetectedResult(DetectedObjects detection, Image img){
|
||||
FaceDetectedResult faceDetectedResult = new FaceDetectedResult();
|
||||
List<Double> probabilities = new ArrayList<>(detection.getProbabilities());
|
||||
List<DetectedObjects.DetectedObject> detectedObjectList = detection.items();
|
||||
List<Rectangle> RectangleList = detectedObjectList.parallelStream()
|
||||
.map(obj -> {
|
||||
Rectangle rectangle = new Rectangle();
|
||||
List<Point> pointList = new ArrayList<>();
|
||||
ai.djl.modality.cv.output.Rectangle rectangleDjl = obj.getBoundingBox().getBounds();
|
||||
int x = (int)(rectangleDjl.getX() * (double)img.getWidth());
|
||||
int y = (int)(rectangleDjl.getY() * (double)img.getHeight());
|
||||
int width = (int)(rectangleDjl.getWidth() * (double)img.getWidth());
|
||||
int height = (int)(rectangleDjl.getHeight() * (double)img.getHeight());
|
||||
pointList.add(new Point(x,y));
|
||||
pointList.add(new Point(x + width,y));
|
||||
pointList.add(new Point(x,y + height));
|
||||
pointList.add(new Point(x + width,y + height));
|
||||
rectangle.setPointList(pointList);
|
||||
rectangle.setHeight(height);
|
||||
rectangle.setWidth(width);
|
||||
return rectangle;
|
||||
})
|
||||
.collect(Collectors.toList());
|
||||
faceDetectedResult.setProbabilities(probabilities);
|
||||
faceDetectedResult.setRectangles(RectangleList);
|
||||
return faceDetectedResult;
|
||||
}
|
||||
}
|
||||
@@ -1,319 +0,0 @@
|
||||
package cn.smartjavaai.face.algo;
|
||||
|
||||
import cn.smartjavaai.common.entity.Point;
|
||||
import cn.smartjavaai.common.entity.Rectangle;
|
||||
import cn.smartjavaai.common.utils.ImageUtils;
|
||||
import cn.smartjavaai.face.AbstractFaceAlgorithm;
|
||||
import cn.smartjavaai.face.FaceDetectedResult;
|
||||
import cn.smartjavaai.face.ModelConfig;
|
||||
import cn.smartjavaai.face.dao.FaceDao;
|
||||
import cn.smartjavaai.face.entity.FaceData;
|
||||
import cn.smartjavaai.face.entity.FaceResult;
|
||||
import com.seetaface.NativeLoader;
|
||||
import com.seetaface.SeetaFace6JNI;
|
||||
import com.seetaface.model.RecognizeResult;
|
||||
import com.seetaface.model.SeetaImageData;
|
||||
import com.seetaface.model.SeetaRect;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
|
||||
import javax.imageio.ImageIO;
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.*;
|
||||
import java.nio.file.Path;
|
||||
import java.nio.file.Paths;
|
||||
import java.sql.SQLException;
|
||||
import java.util.ArrayList;
|
||||
import java.util.List;
|
||||
import java.util.Objects;
|
||||
|
||||
/**
|
||||
* SeetaFace6 人脸算法
|
||||
* @author dwj
|
||||
*/
|
||||
@Slf4j
|
||||
public class SeetaFace6Algo extends AbstractFaceAlgorithm {
|
||||
|
||||
|
||||
private ModelConfig config;
|
||||
|
||||
|
||||
@Override
|
||||
public void loadModel(ModelConfig config) throws Exception {
|
||||
this.config = config;
|
||||
if (NativeLoader.seetaFace6SDK == null) {
|
||||
synchronized (SeetaFace6JNI.class) {
|
||||
if(StringUtils.isBlank(config.getModelPath())){
|
||||
throw new Exception("modelPath is null");
|
||||
}
|
||||
//加载依赖库
|
||||
NativeLoader.loadNativeLibraries(config.getModelPath());
|
||||
log.info("Loading seetaFace6 library successfully.");
|
||||
NativeLoader.seetaFace6SDK = new SeetaFace6JNI();
|
||||
//加载模型
|
||||
boolean isSuccess = NativeLoader.seetaFace6SDK.initModel(config.getModelPath());
|
||||
if(!isSuccess){
|
||||
throw new Exception("seetaFace6模型初始化失败," + config.getModelPath());
|
||||
}
|
||||
log.info("Load seetaFace6 model success!");
|
||||
new Thread(new Runnable() {
|
||||
public void run() {
|
||||
try {
|
||||
log.info("start load faceDb...");
|
||||
loadFaceDb();
|
||||
log.info("Load faceDb success!");
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
}).start();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceDetectedResult detect(String imagePath) throws Exception {
|
||||
// 将图片路径转换为 BufferedImage
|
||||
BufferedImage image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
SeetaRect[] seetaResult = NativeLoader.seetaFace6SDK.detect(imageData);
|
||||
return convertToFaceDetectedResult(seetaResult);
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
|
||||
BufferedImage image = ImageIO.read(imageInputStream);
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
SeetaRect[] seetaResult = NativeLoader.seetaFace6SDK.detect(imageData);
|
||||
return convertToFaceDetectedResult(seetaResult);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(String imagePath) throws Exception {
|
||||
BufferedImage image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
return NativeLoader.seetaFace6SDK.extractMaxFace(imageData);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(InputStream inputStream) throws Exception {
|
||||
BufferedImage image = ImageIO.read(inputStream);
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
return NativeLoader.seetaFace6SDK.extractMaxFace(imageData);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float calculSimilar(float[] feature1, float[] feature2) throws Exception {
|
||||
return NativeLoader.seetaFace6SDK.calculateSimilarity(feature1, feature2);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(String imagePath1, String imagePath2) throws Exception {
|
||||
return featureComparison(new FileInputStream(Paths.get(imagePath1).toAbsolutePath().toString()),
|
||||
new FileInputStream(Paths.get(imagePath2).toAbsolutePath().toString()));
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception {
|
||||
BufferedImage image1 = ImageIO.read(inputStream1);
|
||||
BufferedImage image2 = ImageIO.read(inputStream2);
|
||||
SeetaImageData imageData1 = new SeetaImageData(image1.getWidth(), image1.getHeight(), 3);
|
||||
imageData1.data = ImageUtils.getMatrixBGR(image1);
|
||||
|
||||
SeetaImageData imageData2 = new SeetaImageData(image2.getWidth(), image2.getHeight(), 3);
|
||||
imageData2.data = ImageUtils.getMatrixBGR(image2);
|
||||
//裁剪
|
||||
byte[][] cropImg1 = NativeLoader.seetaFace6SDK.crop(imageData1);
|
||||
byte[][] cropImg2 = NativeLoader.seetaFace6SDK.crop(imageData2);
|
||||
if(cropImg1 == null || cropImg1.length == 0){
|
||||
throw new Exception("未发现人脸");
|
||||
}
|
||||
if(cropImg2 == null || cropImg2.length == 0){
|
||||
throw new Exception("未发现人脸");
|
||||
}
|
||||
|
||||
BufferedImage cropImage1 = ImageUtils.bgrToBufferedImage(cropImg1[0], 256, 256);
|
||||
BufferedImage cropImage2 = ImageUtils.bgrToBufferedImage(cropImg2[0], 256, 256);
|
||||
|
||||
SeetaImageData cropImageData1 = new SeetaImageData(cropImage1.getWidth(), cropImage1.getHeight(), 3);
|
||||
cropImageData1.data = ImageUtils.getMatrixBGR(cropImage1);
|
||||
SeetaImageData cropImageData2 = new SeetaImageData(cropImage2.getWidth(), cropImage2.getHeight(), 3);
|
||||
cropImageData2.data = ImageUtils.getMatrixBGR(cropImage2);
|
||||
return NativeLoader.seetaFace6SDK.compare(cropImageData1, cropImageData2);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 转换为FaceDetectedResult
|
||||
* @param seetaResult
|
||||
* @return
|
||||
*/
|
||||
private FaceDetectedResult convertToFaceDetectedResult(SeetaRect[] seetaResult){
|
||||
FaceDetectedResult faceDetectedResult = new FaceDetectedResult();
|
||||
List<Rectangle> RectangleList = new ArrayList<Rectangle>();
|
||||
List<Double> probabilities = new ArrayList<Double>();
|
||||
if(seetaResult != null && seetaResult.length > 0){
|
||||
for(SeetaRect rect : seetaResult){
|
||||
Rectangle rectangle = new Rectangle();
|
||||
List<Point> pointList = new ArrayList<>();
|
||||
pointList.add(new Point(rect.x,rect.y));
|
||||
pointList.add(new Point(rect.x + rect.width,rect.y));
|
||||
pointList.add(new Point(rect.x,rect.y + rect.height));
|
||||
pointList.add(new Point(rect.x + rect.width,rect.y + rect.height));
|
||||
rectangle.setPointList(pointList);
|
||||
rectangle.setHeight(rect.height);
|
||||
rectangle.setWidth(rect.width);
|
||||
RectangleList.add(rectangle);
|
||||
probabilities.add(new Double(rect.score));
|
||||
}
|
||||
}
|
||||
faceDetectedResult.setProbabilities(probabilities);
|
||||
faceDetectedResult.setRectangles(RectangleList);
|
||||
return faceDetectedResult;
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, String imagePath) throws Exception {
|
||||
return register(key, new FileInputStream(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public boolean register(String key, InputStream inputStream) throws Exception {
|
||||
if(!checkFaceDb()){
|
||||
throw new Exception("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
//裁剪人脸
|
||||
BufferedImage image = ImageIO.read(inputStream);
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
byte[][] bytes = NativeLoader.seetaFace6SDK.crop(imageData);
|
||||
if (bytes == null || bytes.length == 0) {
|
||||
log.info("register face fail: key={}, error=no valid face", key);
|
||||
return false;
|
||||
}
|
||||
long index = NativeLoader.seetaFace6SDK.registerCroppedFace(bytes[0]);
|
||||
if (index < 0) {
|
||||
log.info("register face fail: key={}, index={}", key, index);
|
||||
return false;
|
||||
}
|
||||
//持久化到sqlite数据库
|
||||
FaceData face = new FaceData();
|
||||
face.setKey(key);
|
||||
face.setIndex(index);
|
||||
face.setImgData(bytes[0]);
|
||||
new FaceDao(config.getFaceDbPath()).save(face);
|
||||
return true;
|
||||
}
|
||||
|
||||
public boolean register(String key, FaceData faceData) throws Exception {
|
||||
long index = NativeLoader.seetaFace6SDK.registerCroppedFace(faceData.getImgData());
|
||||
if (index < 0) {
|
||||
log.info("register face fail: key={}, index={}", key, index);
|
||||
return false;
|
||||
}
|
||||
int rows = new FaceDao(config.getFaceDbPath()).updateIndex(index, faceData);
|
||||
return rows > 0;
|
||||
}
|
||||
|
||||
|
||||
|
||||
@Override
|
||||
public FaceResult search(String imagePath) throws Exception {
|
||||
return search(new FileInputStream(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(InputStream inputStream) throws Exception{
|
||||
if(!checkFaceDb()){
|
||||
throw new Exception("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
BufferedImage image = ImageIO.read(inputStream);
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
RecognizeResult recognizeResult = NativeLoader.seetaFace6SDK.query(imageData);
|
||||
return searchFaceDb(recognizeResult);
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public long removeRegister(String... keys) throws Exception {
|
||||
if(!checkFaceDb()){
|
||||
throw new Exception("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
List<Long> list = new FaceDao(config.getFaceDbPath()).findIndexList(keys);
|
||||
if (list == null) {
|
||||
return 0;
|
||||
}
|
||||
long[] array = list.stream().mapToLong(Long::longValue).toArray();
|
||||
long rows = NativeLoader.seetaFace6SDK.delete(array);
|
||||
new FaceDao(config.getFaceDbPath()).deleteFace(keys);
|
||||
return rows;
|
||||
}
|
||||
|
||||
@Override
|
||||
public long clearFace() throws Exception{
|
||||
if(!checkFaceDb()){
|
||||
throw new Exception("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
long rows = NativeLoader.seetaFace6SDK.delete(new long[]{-1});
|
||||
new FaceDao(config.getFaceDbPath()).deleteAll();
|
||||
return rows;
|
||||
}
|
||||
|
||||
/**
|
||||
* 检查是否存在人脸库
|
||||
* @return
|
||||
*/
|
||||
private boolean checkFaceDb(){
|
||||
if(Objects.nonNull(config) && StringUtils.isNotBlank(config.getFaceDbPath())){
|
||||
File file = new File(config.getFaceDbPath());
|
||||
return file.exists() && file.isFile();
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
private FaceResult searchFaceDb(RecognizeResult recognizeResult) throws SQLException, ClassNotFoundException {
|
||||
if(recognizeResult != null && recognizeResult.index >= 0){
|
||||
String key = new FaceDao(config.getFaceDbPath()).findKeyByIndex(recognizeResult.index);
|
||||
return new FaceResult(key, recognizeResult.similar);
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 加载人脸库
|
||||
* @throws SQLException
|
||||
* @throws ClassNotFoundException
|
||||
*/
|
||||
private void loadFaceDb() throws SQLException, ClassNotFoundException {
|
||||
if(!checkFaceDb()){
|
||||
log.info("未配置人脸库");
|
||||
return;
|
||||
}
|
||||
//分页查询人脸库
|
||||
int pageNo = 0, pageSize = 100;
|
||||
while (true) {
|
||||
List<FaceData> list = new FaceDao(config.getFaceDbPath()).findFace(pageNo, pageSize);
|
||||
if (list == null) {
|
||||
break;
|
||||
}
|
||||
list.forEach(face -> {
|
||||
try {
|
||||
register(face.getKey(), face);
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
});
|
||||
if (list.size() < pageSize) {
|
||||
break;
|
||||
}
|
||||
pageNo++;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,135 +0,0 @@
|
||||
package cn.smartjavaai.face.algo;
|
||||
|
||||
import ai.djl.MalformedModelException;
|
||||
import ai.djl.inference.Predictor;
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.ImageFactory;
|
||||
import ai.djl.modality.cv.output.DetectedObjects;
|
||||
import ai.djl.modality.cv.translator.ImageFeatureExtractorFactory;
|
||||
import ai.djl.repository.zoo.Criteria;
|
||||
import ai.djl.repository.zoo.ModelNotFoundException;
|
||||
import ai.djl.repository.zoo.ZooModel;
|
||||
import ai.djl.training.util.ProgressBar;
|
||||
import cn.smartjavaai.common.entity.Point;
|
||||
import cn.smartjavaai.common.entity.Rectangle;
|
||||
import cn.smartjavaai.face.*;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.io.InputStream;
|
||||
import java.nio.file.Path;
|
||||
import java.nio.file.Paths;
|
||||
import java.util.ArrayList;
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
/**
|
||||
* @author dwj
|
||||
*/
|
||||
public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
|
||||
|
||||
|
||||
private Criteria<Image, DetectedObjects> criteria;
|
||||
|
||||
/**
|
||||
* 特征图层的基础缩放比例
|
||||
*/
|
||||
private static final int[][] scales = {{10, 16, 24}, {32, 48}, {64, 96}, {128, 192, 256}};
|
||||
/**
|
||||
* 特征图相对于原图的采样步长
|
||||
*/
|
||||
private static final int[] steps = {8, 16, 32, 64};
|
||||
/**
|
||||
* 缩放系数
|
||||
*/
|
||||
private static final double[] variance = {0.1f, 0.2f};
|
||||
|
||||
private Predictor<Image, DetectedObjects> predictor;
|
||||
|
||||
private ZooModel<Image, DetectedObjects> model;
|
||||
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* 加载模型
|
||||
* @param config
|
||||
*/
|
||||
@Override
|
||||
public void loadModel(ModelConfig config) throws ModelNotFoundException, MalformedModelException, IOException {
|
||||
FaceDetectionTranslator translator =
|
||||
new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, config.getMaxFaceCount(), scales, steps);
|
||||
criteria =
|
||||
Criteria.builder()
|
||||
.setTypes(Image.class, DetectedObjects.class)
|
||||
.optModelUrls("https://resources.djl.ai/test-models/pytorch/ultranet.zip")
|
||||
.optTranslator(translator)
|
||||
.optProgress(new ProgressBar())
|
||||
.optEngine("PyTorch") // Use PyTorch engine
|
||||
.build();
|
||||
model = criteria.loadModel();
|
||||
predictor = model.newPredictor();
|
||||
}
|
||||
|
||||
/**
|
||||
* 检测人脸
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public FaceDetectedResult detect(String imagePath) throws Exception{
|
||||
Path facePath = Paths.get(imagePath);
|
||||
Image img = ImageFactory.getInstance().fromFile(facePath);
|
||||
DetectedObjects detection = predictor.predict(img);
|
||||
return convertToFaceDetectedResult(detection,img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 检测人脸
|
||||
* @param imageInputStream 图片输入流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
|
||||
Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
|
||||
DetectedObjects detection = predictor.predict(img);
|
||||
return convertToFaceDetectedResult(detection,img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 转换检测结果
|
||||
* @param detection
|
||||
* @param img
|
||||
* @return
|
||||
*/
|
||||
private FaceDetectedResult convertToFaceDetectedResult(DetectedObjects detection, Image img){
|
||||
FaceDetectedResult faceDetectedResult = new FaceDetectedResult();
|
||||
List<Double> probabilities = new ArrayList<>(detection.getProbabilities());
|
||||
List<DetectedObjects.DetectedObject> detectedObjectList = detection.items();
|
||||
List<Rectangle> RectangleList = detectedObjectList.parallelStream()
|
||||
.map(obj -> {
|
||||
Rectangle rectangle = new Rectangle();
|
||||
List<Point> pointList = new ArrayList<>();
|
||||
ai.djl.modality.cv.output.Rectangle rectangleDjl = obj.getBoundingBox().getBounds();
|
||||
int x = (int)(rectangleDjl.getX() * (double)img.getWidth());
|
||||
int y = (int)(rectangleDjl.getY() * (double)img.getHeight());
|
||||
int width = (int)(rectangleDjl.getWidth() * (double)img.getWidth());
|
||||
int height = (int)(rectangleDjl.getHeight() * (double)img.getHeight());
|
||||
pointList.add(new Point(x,y));
|
||||
pointList.add(new Point(x + width,y));
|
||||
pointList.add(new Point(x,y + height));
|
||||
pointList.add(new Point(x + width,y + height));
|
||||
rectangle.setPointList(pointList);
|
||||
rectangle.setHeight(height);
|
||||
rectangle.setWidth(width);
|
||||
return rectangle;
|
||||
})
|
||||
.collect(Collectors.toList());
|
||||
faceDetectedResult.setProbabilities(probabilities);
|
||||
faceDetectedResult.setRectangles(RectangleList);
|
||||
return faceDetectedResult;
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,30 @@
|
||||
package cn.smartjavaai.face.exception;
|
||||
|
||||
/**
|
||||
* 人脸检测异常
|
||||
* @author dwj
|
||||
* @date 2025/4/4
|
||||
*/
|
||||
public class FaceException extends RuntimeException{
|
||||
|
||||
public FaceException() {
|
||||
super();
|
||||
}
|
||||
|
||||
public FaceException(String message, Throwable cause, boolean enableSuppression, boolean writableStackTrace) {
|
||||
super(message, cause, enableSuppression, writableStackTrace);
|
||||
}
|
||||
|
||||
public FaceException(String message, Throwable cause) {
|
||||
super(message, cause);
|
||||
}
|
||||
|
||||
public FaceException(String message) {
|
||||
super(message);
|
||||
}
|
||||
|
||||
public FaceException(Throwable cause) {
|
||||
super(cause);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,34 @@
|
||||
package cn.smartjavaai.face.factory;
|
||||
|
||||
import ai.djl.inference.Predictor;
|
||||
import ai.djl.repository.zoo.ZooModel;
|
||||
import cn.smartjavaai.face.model.SeetaFace6Model;
|
||||
import com.seetaface.SeetaFace6JNI;
|
||||
import org.apache.commons.pool2.BasePooledObjectFactory;
|
||||
import org.apache.commons.pool2.PooledObject;
|
||||
import org.apache.commons.pool2.impl.DefaultPooledObject;
|
||||
|
||||
/**
|
||||
* Predictor 工厂类
|
||||
* @author dwj
|
||||
* @date 2025/4/8
|
||||
*/
|
||||
public class SeetaFace6Factory extends BasePooledObjectFactory<SeetaFace6JNI> {
|
||||
|
||||
@Override
|
||||
public SeetaFace6JNI create() {
|
||||
return new SeetaFace6JNI();
|
||||
}
|
||||
|
||||
@Override
|
||||
public PooledObject<SeetaFace6JNI> wrap(SeetaFace6JNI obj) {
|
||||
return new DefaultPooledObject<>(obj);
|
||||
}
|
||||
|
||||
@Override
|
||||
public void destroyObject(PooledObject<SeetaFace6JNI> p) {
|
||||
//p.getObject().dispose(); // 如果需要释放 native 资源
|
||||
SeetaFace6JNI object = p.getObject();
|
||||
object = null;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,259 @@
|
||||
package cn.smartjavaai.face.model;
|
||||
|
||||
import ai.djl.Device;
|
||||
import ai.djl.MalformedModelException;
|
||||
import ai.djl.inference.Predictor;
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.ImageFactory;
|
||||
import ai.djl.repository.zoo.Criteria;
|
||||
import ai.djl.repository.zoo.ModelNotFoundException;
|
||||
import ai.djl.repository.zoo.ZooModel;
|
||||
import ai.djl.training.util.ProgressBar;
|
||||
import cn.smartjavaai.common.enums.DeviceEnum;
|
||||
import cn.smartjavaai.common.pool.PredictorFactory;
|
||||
import cn.smartjavaai.common.utils.FileUtils;
|
||||
import cn.smartjavaai.common.utils.ImageUtils;
|
||||
import cn.smartjavaai.face.AbstractFaceModel;
|
||||
import cn.smartjavaai.face.FaceModelConfig;
|
||||
import cn.smartjavaai.face.exception.FaceException;
|
||||
import cn.smartjavaai.face.translator.FaceFeatureTranslator;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
import org.apache.commons.pool2.ObjectPool;
|
||||
import org.apache.commons.pool2.impl.GenericObjectPool;
|
||||
import org.apache.commons.pool2.impl.GenericObjectPoolConfig;
|
||||
|
||||
import javax.imageio.ImageIO;
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.ByteArrayInputStream;
|
||||
import java.io.IOException;
|
||||
import java.io.InputStream;
|
||||
import java.nio.file.Paths;
|
||||
import java.time.Duration;
|
||||
import java.util.Arrays;
|
||||
import java.util.List;
|
||||
import java.util.Objects;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
/**
|
||||
* @author dwj
|
||||
*/
|
||||
@Slf4j
|
||||
public class FeatureExtractionModel extends AbstractFaceModel implements AutoCloseable{
|
||||
|
||||
|
||||
private ObjectPool<Predictor<Image, float[]>> predictorPool;
|
||||
|
||||
|
||||
private ZooModel<Image, float[]> model;
|
||||
|
||||
public static final List<Float> mean =
|
||||
Arrays.asList(
|
||||
127.5f / 255.0f,
|
||||
127.5f / 255.0f,
|
||||
127.5f / 255.0f,
|
||||
128.0f / 255.0f,
|
||||
128.0f / 255.0f,
|
||||
128.0f / 255.0f);
|
||||
|
||||
|
||||
/**
|
||||
* 加载人脸特征提取模型
|
||||
* @param config
|
||||
*/
|
||||
@Override
|
||||
public void loadModel(FaceModelConfig config) {
|
||||
Device device = null;
|
||||
if(!Objects.isNull(config.getDevice())){
|
||||
device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu();
|
||||
}
|
||||
String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
|
||||
Criteria<Image, float[]> faceFeatureCriteria =
|
||||
Criteria.builder()
|
||||
.setTypes(Image.class, float[].class)
|
||||
.optModelName("face_feature") // specify model file prefix
|
||||
.optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null :
|
||||
"https://resources.djl.ai/test-models/pytorch/face_feature.zip")
|
||||
.optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
|
||||
.optTranslator(new FaceFeatureTranslator())
|
||||
.optArgument("normalize", normalize)
|
||||
.optDevice(device)
|
||||
.optEngine("PyTorch") // Use PyTorch engine
|
||||
.optProgress(new ProgressBar())
|
||||
.build();
|
||||
|
||||
try {
|
||||
model = faceFeatureCriteria.loadModel();
|
||||
// 创建池子：每个线程独享 Predictor
|
||||
this.predictorPool = new GenericObjectPool<>(new PredictorFactory<>(model));
|
||||
log.info("当前设备: " + model.getNDManager().getDevice());
|
||||
} catch (IOException | ModelNotFoundException | MalformedModelException e) {
|
||||
throw new FaceException("模型加载失败", e);
|
||||
}
|
||||
}
|
||||
|
||||
private float[] featureExtraction(Image image){
|
||||
image.getWrappedImage();
|
||||
Predictor<Image, float[]> predictor = null;
|
||||
try {
|
||||
predictor = predictorPool.borrowObject();
|
||||
return predictor.predict(image);
|
||||
} catch (Exception e) {
|
||||
throw new FaceException("目标检测错误", e);
|
||||
}finally {
|
||||
if (predictor != null) {
|
||||
try {
|
||||
predictorPool.returnObject(predictor); //归还
|
||||
log.info("释放资源");
|
||||
} catch (Exception e) {
|
||||
log.warn("归还Predictor失败", e);
|
||||
try {
|
||||
predictor.close(); // 归还失败才销毁
|
||||
} catch (Exception ex) {
|
||||
log.error("关闭Predictor失败", ex);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
*/
|
||||
@Override
|
||||
public float[] featureExtraction(String imagePath) {
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
Image img = null;
|
||||
try {
|
||||
img = ImageFactory.getInstance().fromFile(Paths.get(imagePath));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片", e);
|
||||
}
|
||||
return featureExtraction(img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 特征提取
|
||||
* @param inputStream 输入流
|
||||
* @return
|
||||
*/
|
||||
@Override
|
||||
public float[] featureExtraction(InputStream inputStream) {
|
||||
if(Objects.isNull(inputStream)){
|
||||
throw new FaceException("图像输入流无效");
|
||||
}
|
||||
Image img = null;
|
||||
try {
|
||||
img = ImageFactory.getInstance().fromInputStream(inputStream);
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片输入流", e);
|
||||
}
|
||||
return featureExtraction(img);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(BufferedImage sourceImage) {
|
||||
if(!ImageUtils.isImageValid(sourceImage)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
Image img = ImageFactory.getInstance().fromImage(sourceImage);
|
||||
return featureExtraction(img);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(byte[] imageData) {
|
||||
if(Objects.isNull(imageData)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
try {
|
||||
return featureExtraction(ImageIO.read(new ByteArrayInputStream(imageData)));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片字节流", e);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 计算相似度
|
||||
* @param feature1 图1特征
|
||||
* @param feature2 图2特征
|
||||
* @return
|
||||
*/
|
||||
@Override
|
||||
public float calculSimilar(float[] feature1, float[] feature2) {
|
||||
float ret = 0.0f;
|
||||
float mod1 = 0.0f;
|
||||
float mod2 = 0.0f;
|
||||
int length = feature1.length;
|
||||
for (int i = 0; i < length; ++i) {
|
||||
ret += feature1[i] * feature2[i];
|
||||
mod1 += feature1[i] * feature1[i];
|
||||
mod2 += feature2[i] * feature2[i];
|
||||
}
|
||||
return (float) ((ret / Math.sqrt(mod1) / Math.sqrt(mod2) + 1) / 2.0f);
|
||||
}
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param imagePath1 图1路径
|
||||
* @param imagePath2 图2路径
|
||||
* @return
|
||||
*/
|
||||
@Override
|
||||
public float featureComparison(String imagePath1, String imagePath2) {
|
||||
if(!FileUtils.isFileExists(imagePath1) || !FileUtils.isFileExists(imagePath2)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
float[] feature1 = featureExtraction(imagePath1);
|
||||
float[] feature2 = featureExtraction(imagePath2);
|
||||
return calculSimilar(feature1, feature2);
|
||||
}
|
||||
|
||||
/**
|
||||
* 特征比较
|
||||
* @param inputStream1 图1输入流
|
||||
* @param inputStream2 图2输入流
|
||||
* @return
|
||||
*/
|
||||
@Override
|
||||
public float featureComparison(InputStream inputStream1, InputStream inputStream2) {
|
||||
if(Objects.isNull(inputStream1) || Objects.isNull(inputStream2)){
|
||||
throw new FaceException("图像输入流无效");
|
||||
}
|
||||
float[] feature1 = featureExtraction(inputStream1);
|
||||
float[] feature2 = featureExtraction(inputStream2);
|
||||
return calculSimilar(feature1, feature2);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(BufferedImage sourceImage1, BufferedImage sourceImag2) {
|
||||
if(!ImageUtils.isImageValid(sourceImage1) || !ImageUtils.isImageValid(sourceImag2)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
float[] feature1 = featureExtraction(sourceImage1);
|
||||
float[] feature2 = featureExtraction(sourceImag2);
|
||||
return calculSimilar(feature1, feature2);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(byte[] imageData1, byte[] imageData2) {
|
||||
if(Objects.isNull(imageData1) || Objects.isNull(imageData2)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
float[] feature1 = featureExtraction(imageData1);
|
||||
float[] feature2 = featureExtraction(imageData2);
|
||||
return calculSimilar(feature1, feature2);
|
||||
}
|
||||
|
||||
@Override
|
||||
public void close() {
|
||||
if (predictorPool != null) {
|
||||
predictorPool.close();
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,243 @@
|
||||
package cn.smartjavaai.face.model;
|
||||
|
||||
import ai.djl.Device;
|
||||
import ai.djl.MalformedModelException;
|
||||
import ai.djl.inference.Predictor;
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.ImageFactory;
|
||||
import ai.djl.modality.cv.output.DetectedObjects;
|
||||
import ai.djl.repository.zoo.Criteria;
|
||||
import ai.djl.repository.zoo.ModelNotFoundException;
|
||||
import ai.djl.repository.zoo.ZooModel;
|
||||
import ai.djl.training.util.ProgressBar;
|
||||
import cn.smartjavaai.common.entity.DetectionResponse;
|
||||
import cn.smartjavaai.common.enums.DeviceEnum;
|
||||
import cn.smartjavaai.common.pool.PredictorFactory;
|
||||
import cn.smartjavaai.common.utils.FileUtils;
|
||||
import cn.smartjavaai.common.utils.ImageUtils;
|
||||
import cn.smartjavaai.face.*;
|
||||
import cn.smartjavaai.face.exception.FaceException;
|
||||
import cn.smartjavaai.face.translator.FaceDetectionTranslator;
|
||||
import cn.smartjavaai.face.utils.FaceUtils;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
import org.apache.commons.pool2.ObjectPool;
|
||||
import org.apache.commons.pool2.impl.GenericObjectPool;
|
||||
import org.apache.commons.pool2.impl.GenericObjectPoolConfig;
|
||||
|
||||
|
||||
import javax.imageio.ImageIO;
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.*;
|
||||
import java.nio.file.Files;
|
||||
import java.nio.file.Path;
|
||||
import java.nio.file.Paths;
|
||||
import java.time.Duration;
|
||||
import java.util.Objects;
|
||||
|
||||
/**
|
||||
* RetinaFace实现
|
||||
* @author dwj
|
||||
*/
|
||||
@Slf4j
|
||||
public class RetinaFaceModel extends AbstractFaceModel implements AutoCloseable{
|
||||
|
||||
|
||||
private ObjectPool<Predictor<Image, DetectedObjects>> predictorPool;
|
||||
|
||||
private ZooModel<Image, DetectedObjects> model;
|
||||
|
||||
/**
|
||||
* 特征图层的基础缩放比例
|
||||
*/
|
||||
public static final int[][] scales = {{16, 32}, {64, 128}, {256, 512}};
|
||||
/**
|
||||
* 特征图相对于原图的采样步长
|
||||
*/
|
||||
public static final int[] steps = {8, 16, 32};
|
||||
/**
|
||||
* 缩放系数
|
||||
*/
|
||||
public static final double[] variance = {0.1f, 0.2f};
|
||||
|
||||
|
||||
/**
|
||||
* 加载模型
|
||||
* @param config
|
||||
*/
|
||||
@Override
|
||||
public void loadModel(FaceModelConfig config){
|
||||
Device device = null;
|
||||
if(!Objects.isNull(config.getDevice())){
|
||||
device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu();
|
||||
}
|
||||
FaceDetectionTranslator translator =
|
||||
new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, FaceConfig.MAX_FACE_LIMIT, scales, steps);
|
||||
Criteria<Image, DetectedObjects> criteria =
|
||||
Criteria.builder()
|
||||
.setTypes(Image.class, DetectedObjects.class)
|
||||
.optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null : "https://resources.djl.ai/test-models/pytorch/retinaface.zip")
|
||||
// Load model from local file, e.g:
|
||||
.optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
|
||||
.optModelName("retinaface") // specify model file prefix
|
||||
.optTranslator(translator)
|
||||
.optDevice(device)
|
||||
.optProgress(new ProgressBar())
|
||||
.optEngine("PyTorch") // Use PyTorch engine
|
||||
.build();
|
||||
try {
|
||||
model = criteria.loadModel();
|
||||
// 创建池子：每个线程独享 Predictor
|
||||
this.predictorPool = new GenericObjectPool<>(new PredictorFactory<>(model));
|
||||
log.info("当前设备: " + model.getNDManager().getDevice());
|
||||
} catch (IOException | ModelNotFoundException | MalformedModelException e) {
|
||||
throw new FaceException("模型加载失败", e);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* 检测人脸
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public DetectionResponse detect(String imagePath){
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
Image img = null;
|
||||
try {
|
||||
img = ImageFactory.getInstance().fromFile(Paths.get(imagePath));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效的图片", e);
|
||||
}
|
||||
DetectedObjects detection = detect(img);
|
||||
return FaceUtils.convertToDetectionResponse(detection,img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 检测人脸
|
||||
* @param imageInputStream 图片流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public DetectionResponse detect(InputStream imageInputStream){
|
||||
if(Objects.isNull(imageInputStream)){
|
||||
throw new FaceException("图像输入流无效");
|
||||
}
|
||||
try {
|
||||
Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
|
||||
DetectedObjects detection = detect(img);
|
||||
return FaceUtils.convertToDetectionResponse(detection,img);
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片输入流", e);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(BufferedImage image) {
|
||||
if(!ImageUtils.isImageValid(image)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
Image img = ImageFactory.getInstance().fromImage(image);
|
||||
DetectedObjects detection = detect(img);
|
||||
return FaceUtils.convertToDetectionResponse(detection,img);
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(byte[] imageData) {
|
||||
if(Objects.isNull(imageData)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
try {
|
||||
return detect(ImageIO.read(new ByteArrayInputStream(imageData)));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("错误的图像", e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public void detectAndDraw(String imagePath, String outputPath) {
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
try {
|
||||
Image img = ImageFactory.getInstance().fromFile(Paths.get(imagePath));
|
||||
DetectedObjects detectedObjects = detect(img);
|
||||
if(Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
|
||||
throw new FaceException("未识别到人脸");
|
||||
}
|
||||
img.drawBoundingBoxes(detectedObjects);
|
||||
Path output = Paths.get(outputPath);
|
||||
log.info("Saving to {}", output.toAbsolutePath().toString());
|
||||
img.save(Files.newOutputStream(output), "png");
|
||||
} catch (IOException e) {
|
||||
throw new FaceException(e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public BufferedImage detectAndDraw(BufferedImage sourceImage) {
|
||||
if(!ImageUtils.isImageValid(sourceImage)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
Image img = ImageFactory.getInstance().fromImage(sourceImage);
|
||||
DetectedObjects detectedObjects = detect(img);
|
||||
if(Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
|
||||
throw new FaceException("未识别到人脸");
|
||||
}
|
||||
img.drawBoundingBoxes(detectedObjects);
|
||||
try {
|
||||
ByteArrayOutputStream outputStream = new ByteArrayOutputStream();
|
||||
// 调用 save 方法将 Image 写入字节流
|
||||
img.save(outputStream, "png");
|
||||
// 将字节流转换为 BufferedImage
|
||||
byte[] imageBytes = outputStream.toByteArray();
|
||||
return ImageIO.read(new ByteArrayInputStream(imageBytes));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("导出图片失败", e);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 人脸检测
|
||||
* @param image
|
||||
* @return
|
||||
*/
|
||||
private DetectedObjects detect(Image image){
|
||||
Predictor<Image, DetectedObjects> predictor = null;
|
||||
try {
|
||||
predictor = predictorPool.borrowObject();
|
||||
return predictor.predict(image);
|
||||
} catch (Exception e) {
|
||||
throw new FaceException("目标检测错误", e);
|
||||
}finally {
|
||||
if (predictor != null) {
|
||||
try {
|
||||
predictorPool.returnObject(predictor); //归还
|
||||
log.info("释放资源");
|
||||
} catch (Exception e) {
|
||||
log.warn("归还Predictor失败", e);
|
||||
try {
|
||||
predictor.close(); // 归还失败才销毁
|
||||
} catch (Exception ex) {
|
||||
log.error("关闭Predictor失败", ex);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public void close() {
|
||||
if (predictorPool != null) {
|
||||
predictorPool.close();
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,585 @@
|
||||
package cn.smartjavaai.face.model;
|
||||
|
||||
import cn.smartjavaai.common.entity.DetectionResponse;
|
||||
import cn.smartjavaai.common.utils.FileUtils;
|
||||
import cn.smartjavaai.common.utils.ImageUtils;
|
||||
import cn.smartjavaai.face.AbstractFaceModel;
|
||||
import cn.smartjavaai.face.FaceModelConfig;
|
||||
import cn.smartjavaai.face.dao.FaceDao;
|
||||
import cn.smartjavaai.face.entity.FaceData;
|
||||
import cn.smartjavaai.face.entity.FaceResult;
|
||||
import cn.smartjavaai.face.exception.FaceException;
|
||||
import cn.smartjavaai.face.utils.FaceUtils;
|
||||
import com.seetaface.NativeLoader;
|
||||
import com.seetaface.SeetaFace6JNI;
|
||||
import com.seetaface.model.RecognizeResult;
|
||||
import com.seetaface.model.SeetaImageData;
|
||||
import com.seetaface.model.SeetaRect;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
|
||||
import javax.imageio.ImageIO;
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.*;
|
||||
import java.nio.file.Path;
|
||||
import java.nio.file.Paths;
|
||||
import java.sql.SQLException;
|
||||
import java.util.List;
|
||||
import java.util.Objects;
|
||||
|
||||
/**
|
||||
* SeetaFace6 人脸算法
|
||||
* @author dwj
|
||||
*/
|
||||
@SuppressWarnings("AliMissingOverrideAnnotation")
|
||||
@Slf4j
|
||||
public class SeetaFace6Model extends AbstractFaceModel {
|
||||
|
||||
|
||||
private FaceModelConfig config;
|
||||
|
||||
private static final Object lock = new Object(); // 全局锁
|
||||
|
||||
|
||||
@Override
|
||||
public void loadModel(FaceModelConfig config) {
|
||||
this.config = config;
|
||||
if (NativeLoader.seetaFace6SDK == null) {
|
||||
synchronized (lock) {
|
||||
if(StringUtils.isBlank(config.getModelPath())){
|
||||
throw new FaceException("modelPath is null");
|
||||
}
|
||||
//加载依赖库
|
||||
NativeLoader.loadNativeLibraries(config.getModelPath());
|
||||
log.info("Loading seetaFace6 library successfully.");
|
||||
NativeLoader.seetaFace6SDK = new SeetaFace6JNI();
|
||||
//加载模型
|
||||
boolean isSuccess = NativeLoader.seetaFace6SDK.initModel(config.getModelPath());
|
||||
if(!isSuccess){
|
||||
throw new FaceException("seetaFace6模型初始化失败," + config.getModelPath());
|
||||
}
|
||||
log.info("Load seetaFace6 model success!");
|
||||
new Thread(new Runnable() {
|
||||
public void run() {
|
||||
try {
|
||||
log.info("start load faceDb...");
|
||||
loadFaceDb();
|
||||
log.info("Load faceDb success!");
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
}).start();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(String imagePath) {
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
// 将图片路径转换为 BufferedImage
|
||||
BufferedImage image = null;
|
||||
try {
|
||||
image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片路径", e);
|
||||
}
|
||||
return detect(image);
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(InputStream imageInputStream) {
|
||||
if(Objects.isNull(imageInputStream)){
|
||||
throw new FaceException("图像输入流无效");
|
||||
}
|
||||
BufferedImage image = null;
|
||||
try {
|
||||
image = ImageIO.read(imageInputStream);
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片输入流", e);
|
||||
}
|
||||
return detect(image);
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(BufferedImage image) {
|
||||
if(!ImageUtils.isImageValid(image)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
synchronized (lock) {
|
||||
SeetaRect[] seetaResult = NativeLoader.seetaFace6SDK.detect(imageData);
|
||||
return FaceUtils.convertToDetectionResponse(seetaResult, config);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(byte[] imageData) {
|
||||
if(Objects.isNull(imageData)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
try {
|
||||
return detect(ImageIO.read(new ByteArrayInputStream(imageData)));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("错误的图像", e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public void detectAndDraw(String imagePath, String outputPath) {
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
try {
|
||||
//创建保存路径
|
||||
Path imageOutputPath = Paths.get(outputPath);
|
||||
BufferedImage image = null;
|
||||
try {
|
||||
image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片路径", e);
|
||||
}
|
||||
DetectionResponse result = detect(image);
|
||||
if(Objects.isNull(result) || Objects.isNull(result.getRectangleList()) || result.getRectangleList().isEmpty()){
|
||||
throw new FaceException("未识别到人脸");
|
||||
}
|
||||
//绘制人脸框
|
||||
FaceUtils.drawBoundingBoxes(image, result, imageOutputPath.toAbsolutePath().toString());
|
||||
} catch (IOException e) {
|
||||
throw new FaceException(e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public BufferedImage detectAndDraw(BufferedImage sourceImage) {
|
||||
if(!ImageUtils.isImageValid(sourceImage)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
DetectionResponse detectedObjects = detect(sourceImage);
|
||||
if(Objects.isNull(detectedObjects) || Objects.isNull(detectedObjects.getRectangleList()) || detectedObjects.getRectangleList().isEmpty()){
|
||||
throw new FaceException("未识别到人脸");
|
||||
}
|
||||
//绘制人脸框
|
||||
try {
|
||||
return FaceUtils.drawBoundingBoxes(sourceImage, detectedObjects);
|
||||
} catch (IOException e) {
|
||||
throw new RuntimeException(e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(BufferedImage image) {
|
||||
if(!ImageUtils.isImageValid(image)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
synchronized (lock) {
|
||||
return NativeLoader.seetaFace6SDK.extractMaxFace(imageData);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(String imagePath) {
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
BufferedImage image = null;
|
||||
try {
|
||||
image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片路径", e);
|
||||
}
|
||||
return featureExtraction(image);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(InputStream inputStream) {
|
||||
if(Objects.isNull(inputStream)){
|
||||
throw new FaceException("图像输入流无效");
|
||||
}
|
||||
BufferedImage image = null;
|
||||
try {
|
||||
image = ImageIO.read(inputStream);
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片输入流", e);
|
||||
}
|
||||
return featureExtraction(image);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float[] featureExtraction(byte[] imageData) {
|
||||
if(Objects.isNull(imageData)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
try {
|
||||
return featureExtraction(ImageIO.read(new ByteArrayInputStream(imageData)));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("错误的图像", e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public float calculSimilar(float[] feature1, float[] feature2) {
|
||||
if(Objects.isNull(feature1) || Objects.isNull(feature2)){
|
||||
throw new FaceException("特征向量无效");
|
||||
}
|
||||
synchronized (lock) {
|
||||
return NativeLoader.seetaFace6SDK.calculateSimilarity(feature1, feature2);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(String imagePath1, String imagePath2) {
|
||||
if(!FileUtils.isFileExists(imagePath1) || !FileUtils.isFileExists(imagePath2)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
BufferedImage image1 = null;
|
||||
BufferedImage image2 = null;
|
||||
try {
|
||||
image1 = ImageIO.read(new File(Paths.get(imagePath1).toAbsolutePath().toString()));
|
||||
image2 = ImageIO.read(new File(Paths.get(imagePath2).toAbsolutePath().toString()));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片路径", e);
|
||||
}
|
||||
return featureComparison(image1, image2);
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(InputStream inputStream1, InputStream inputStream2) {
|
||||
if(Objects.isNull(inputStream1) || Objects.isNull(inputStream2)){
|
||||
throw new FaceException("图像输入流无效");
|
||||
}
|
||||
BufferedImage image1 = null;
|
||||
BufferedImage image2 = null;
|
||||
try {
|
||||
image1 = ImageIO.read(inputStream1);
|
||||
image2 = ImageIO.read(inputStream2);
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片输入流", e);
|
||||
}
|
||||
return featureComparison(image1, image2);
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public float featureComparison(BufferedImage image1, BufferedImage image2) {
|
||||
if(!ImageUtils.isImageValid(image1) || !ImageUtils.isImageValid(image2)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
SeetaImageData imageData1 = new SeetaImageData(image1.getWidth(), image1.getHeight(), 3);
|
||||
imageData1.data = ImageUtils.getMatrixBGR(image1);
|
||||
|
||||
SeetaImageData imageData2 = new SeetaImageData(image2.getWidth(), image2.getHeight(), 3);
|
||||
imageData2.data = ImageUtils.getMatrixBGR(image2);
|
||||
synchronized (lock) {
|
||||
//裁剪
|
||||
byte[][] cropImg1 = NativeLoader.seetaFace6SDK.crop(imageData1);
|
||||
byte[][] cropImg2 = NativeLoader.seetaFace6SDK.crop(imageData2);
|
||||
if(cropImg1 == null || cropImg1.length == 0){
|
||||
throw new FaceException("未发现人脸");
|
||||
}
|
||||
if(cropImg2 == null || cropImg2.length == 0){
|
||||
throw new FaceException("未发现人脸");
|
||||
}
|
||||
BufferedImage cropImage1 = ImageUtils.bgrToBufferedImage(cropImg1[0], 256, 256);
|
||||
BufferedImage cropImage2 = ImageUtils.bgrToBufferedImage(cropImg2[0], 256, 256);
|
||||
|
||||
SeetaImageData cropImageData1 = new SeetaImageData(cropImage1.getWidth(), cropImage1.getHeight(), 3);
|
||||
cropImageData1.data = ImageUtils.getMatrixBGR(cropImage1);
|
||||
SeetaImageData cropImageData2 = new SeetaImageData(cropImage2.getWidth(), cropImage2.getHeight(), 3);
|
||||
cropImageData2.data = ImageUtils.getMatrixBGR(cropImage2);
|
||||
return NativeLoader.seetaFace6SDK.compare(cropImageData1, cropImageData2);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public float featureComparison(byte[] imageData1, byte[] imageData2) {
|
||||
if(Objects.isNull(imageData1) || Objects.isNull(imageData2)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
BufferedImage image1 = null;
|
||||
BufferedImage image2 = null;
|
||||
try {
|
||||
image1 = ImageIO.read(new ByteArrayInputStream(imageData1));
|
||||
image2 = ImageIO.read(new ByteArrayInputStream(imageData2));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片", e);
|
||||
}
|
||||
return featureComparison(image1, image2);
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, String imagePath) {
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
BufferedImage bufferedImage = null;
|
||||
try {
|
||||
bufferedImage = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片路径", e);
|
||||
}
|
||||
return register(key, bufferedImage);
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public boolean register(String key, BufferedImage image) {
|
||||
if(!checkFaceDb()){
|
||||
throw new FaceException("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
if(!ImageUtils.isImageValid(image)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
synchronized (lock) {
|
||||
byte[][] bytes = NativeLoader.seetaFace6SDK.crop(imageData);
|
||||
if (bytes == null || bytes.length == 0) {
|
||||
log.info("register face fail: key={}, error=no valid face", key);
|
||||
return false;
|
||||
}
|
||||
long index = NativeLoader.seetaFace6SDK.registerCroppedFace(bytes[0]);
|
||||
if (index < 0) {
|
||||
log.info("register face fail: key={}, index={}", key, index);
|
||||
return false;
|
||||
}
|
||||
//持久化到sqlite数据库
|
||||
FaceData face = new FaceData();
|
||||
face.setKey(key);
|
||||
face.setIndex(index);
|
||||
face.setImgData(bytes[0]);
|
||||
try {
|
||||
new FaceDao(config.getFaceDbPath()).save(face);
|
||||
} catch (SQLException | ClassNotFoundException e) {
|
||||
throw new FaceException("保存人脸库失败", e);
|
||||
}
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, byte[] imageData) {
|
||||
if(Objects.isNull(imageData)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
BufferedImage bufferedImage = null;
|
||||
try {
|
||||
bufferedImage = ImageIO.read(new ByteArrayInputStream(imageData));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException(e);
|
||||
}
|
||||
return register(key, bufferedImage);
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean register(String key, InputStream inputStream) {
|
||||
if(!checkFaceDb()){
|
||||
throw new FaceException("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
if(Objects.isNull(inputStream)){
|
||||
throw new FaceException("图像输入流无效");
|
||||
}
|
||||
BufferedImage image = null;
|
||||
try {
|
||||
image = ImageIO.read(inputStream);
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效的图片输入流", e);
|
||||
}
|
||||
return register(key, image);
|
||||
}
|
||||
|
||||
/**
|
||||
* 注册已裁剪后人脸
|
||||
* @param key
|
||||
* @param faceData
|
||||
* @return
|
||||
*/
|
||||
private boolean register(String key, FaceData faceData) {
|
||||
synchronized (lock) {
|
||||
long index = NativeLoader.seetaFace6SDK.registerCroppedFace(faceData.getImgData());
|
||||
if (index < 0) {
|
||||
log.info("register face fail: key={}, index={}", key, index);
|
||||
return false;
|
||||
}
|
||||
int rows = 0;
|
||||
try {
|
||||
rows = new FaceDao(config.getFaceDbPath()).updateIndex(index, faceData);
|
||||
} catch (SQLException | ClassNotFoundException e) {
|
||||
throw new FaceException(e);
|
||||
}
|
||||
return rows > 0;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
@Override
|
||||
public FaceResult search(String imagePath) {
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
BufferedImage bufferedImage = null;
|
||||
try {
|
||||
bufferedImage = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片路径", e);
|
||||
}
|
||||
return search(bufferedImage);
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(InputStream inputStream) {
|
||||
if(Objects.isNull(inputStream)){
|
||||
throw new FaceException("图像输入流无效");
|
||||
}
|
||||
if(!checkFaceDb()){
|
||||
throw new FaceException("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
BufferedImage image = null;
|
||||
try {
|
||||
image = ImageIO.read(inputStream);
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片输入流", e);
|
||||
}
|
||||
return search(image);
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(BufferedImage image) {
|
||||
if(!ImageUtils.isImageValid(image)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
SeetaImageData imageData = new SeetaImageData(image.getWidth(), image.getHeight(), 3);
|
||||
imageData.data = ImageUtils.getMatrixBGR(image);
|
||||
synchronized (lock) {
|
||||
RecognizeResult recognizeResult = NativeLoader.seetaFace6SDK.query(imageData);
|
||||
return searchFaceDb(recognizeResult);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public FaceResult search(byte[] imageData) {
|
||||
if(Objects.isNull(imageData)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
BufferedImage bufferedImage = null;
|
||||
try {
|
||||
bufferedImage = ImageIO.read(new ByteArrayInputStream(imageData));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException(e);
|
||||
}
|
||||
return search(bufferedImage);
|
||||
}
|
||||
|
||||
@Override
|
||||
public long removeRegister(String... keys) {
|
||||
if(keys == null || keys.length == 0){
|
||||
throw new FaceException("keys不允许为空");
|
||||
}
|
||||
if(!checkFaceDb()){
|
||||
throw new FaceException("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
synchronized (lock) {
|
||||
try {
|
||||
List<Long> list = new FaceDao(config.getFaceDbPath()).findIndexList(keys);
|
||||
if (list == null) {
|
||||
return 0;
|
||||
}
|
||||
long[] array = list.stream().mapToLong(Long::longValue).toArray();
|
||||
long rows = NativeLoader.seetaFace6SDK.delete(array);
|
||||
new FaceDao(config.getFaceDbPath()).deleteFace(keys);
|
||||
return rows;
|
||||
} catch (SQLException | ClassNotFoundException e) {
|
||||
throw new FaceException(e);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public long clearFace(){
|
||||
if(!checkFaceDb()){
|
||||
throw new FaceException("未找到人脸库，无法使用此功能（请检查是否配置人脸库路径）");
|
||||
}
|
||||
synchronized (lock) {
|
||||
long rows = NativeLoader.seetaFace6SDK.delete(new long[]{-1});
|
||||
try {
|
||||
new FaceDao(config.getFaceDbPath()).deleteAll();
|
||||
} catch (SQLException | ClassNotFoundException e) {
|
||||
throw new FaceException("删除人脸库失败", e);
|
||||
}
|
||||
return rows;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* 检查是否存在人脸库
|
||||
* @return
|
||||
*/
|
||||
private boolean checkFaceDb(){
|
||||
if(Objects.nonNull(config) && StringUtils.isNotBlank(config.getFaceDbPath())){
|
||||
File file = new File(config.getFaceDbPath());
|
||||
return file.exists() && file.isFile();
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
private FaceResult searchFaceDb(RecognizeResult recognizeResult) {
|
||||
if(recognizeResult != null && recognizeResult.index >= 0){
|
||||
String key = null;
|
||||
synchronized (lock) {
|
||||
try {
|
||||
key = new FaceDao(config.getFaceDbPath()).findKeyByIndex(recognizeResult.index);
|
||||
} catch (SQLException | ClassNotFoundException e) {
|
||||
throw new FaceException("查询人脸库失败", e);
|
||||
}
|
||||
return new FaceResult(key, recognizeResult.similar);
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 加载人脸库
|
||||
* @throws SQLException
|
||||
* @throws ClassNotFoundException
|
||||
*/
|
||||
private void loadFaceDb() {
|
||||
if(!checkFaceDb()){
|
||||
log.info("未配置人脸库");
|
||||
return;
|
||||
}
|
||||
//分页查询人脸库
|
||||
int pageNo = 0, pageSize = 100;
|
||||
while (true) {
|
||||
List<FaceData> list = null;
|
||||
try {
|
||||
list = new FaceDao(config.getFaceDbPath()).findFace(pageNo, pageSize);
|
||||
} catch (SQLException | ClassNotFoundException e) {
|
||||
throw new FaceException("查询人脸库失败", e);
|
||||
}
|
||||
if (list == null) {
|
||||
break;
|
||||
}
|
||||
list.forEach(face -> {
|
||||
try {
|
||||
register(face.getKey(), face);
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
});
|
||||
if (list.size() < pageSize) {
|
||||
break;
|
||||
}
|
||||
pageNo++;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,232 @@
|
||||
package cn.smartjavaai.face.model;
|
||||
|
||||
import ai.djl.Device;
|
||||
import ai.djl.MalformedModelException;
|
||||
import ai.djl.inference.Predictor;
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.ImageFactory;
|
||||
import ai.djl.modality.cv.output.DetectedObjects;
|
||||
import ai.djl.repository.zoo.Criteria;
|
||||
import ai.djl.repository.zoo.ModelNotFoundException;
|
||||
import ai.djl.repository.zoo.ZooModel;
|
||||
import ai.djl.training.util.ProgressBar;
|
||||
import cn.smartjavaai.common.entity.DetectionResponse;
|
||||
import cn.smartjavaai.common.enums.DeviceEnum;
|
||||
import cn.smartjavaai.common.pool.PredictorFactory;
|
||||
import cn.smartjavaai.common.utils.FileUtils;
|
||||
import cn.smartjavaai.common.utils.ImageUtils;
|
||||
import cn.smartjavaai.face.*;
|
||||
import cn.smartjavaai.face.exception.FaceException;
|
||||
import cn.smartjavaai.face.translator.FaceDetectionTranslator;
|
||||
import cn.smartjavaai.face.utils.FaceUtils;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
import org.apache.commons.pool2.ObjectPool;
|
||||
import org.apache.commons.pool2.impl.GenericObjectPool;
|
||||
import org.apache.commons.pool2.impl.GenericObjectPoolConfig;
|
||||
|
||||
import javax.imageio.ImageIO;
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.*;
|
||||
import java.nio.file.Paths;
|
||||
import java.time.Duration;
|
||||
import java.util.Objects;
|
||||
|
||||
/**
|
||||
* @author dwj
|
||||
*/
|
||||
@Slf4j
|
||||
public class UltraLightFastGenericFaceModel extends AbstractFaceModel implements AutoCloseable{
|
||||
|
||||
|
||||
private ObjectPool<Predictor<Image, DetectedObjects>> predictorPool;
|
||||
|
||||
/**
|
||||
* 特征图层的基础缩放比例
|
||||
*/
|
||||
private static final int[][] scales = {{10, 16, 24}, {32, 48}, {64, 96}, {128, 192, 256}};
|
||||
/**
|
||||
* 特征图相对于原图的采样步长
|
||||
*/
|
||||
private static final int[] steps = {8, 16, 32, 64};
|
||||
/**
|
||||
* 缩放系数
|
||||
*/
|
||||
private static final double[] variance = {0.1f, 0.2f};
|
||||
|
||||
|
||||
private ZooModel<Image, DetectedObjects> model;
|
||||
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* 加载模型
|
||||
* @param config
|
||||
*/
|
||||
@Override
|
||||
public void loadModel(FaceModelConfig config) {
|
||||
Device device = null;
|
||||
if(!Objects.isNull(config.getDevice())){
|
||||
device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu();
|
||||
}
|
||||
FaceDetectionTranslator translator =
|
||||
new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, FaceConfig.MAX_FACE_LIMIT, scales, steps);
|
||||
Criteria<Image, DetectedObjects> criteria =
|
||||
Criteria.builder()
|
||||
.setTypes(Image.class, DetectedObjects.class)
|
||||
.optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null : "https://resources.djl.ai/test-models/pytorch/ultranet.zip")
|
||||
.optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
|
||||
.optTranslator(translator)
|
||||
.optProgress(new ProgressBar())
|
||||
.optDevice(device)
|
||||
.optEngine("PyTorch") // Use PyTorch engine
|
||||
.build();
|
||||
try {
|
||||
model = criteria.loadModel();
|
||||
// 创建池子：每个线程独享 Predictor
|
||||
this.predictorPool = new GenericObjectPool<>(new PredictorFactory<>(model));
|
||||
log.info("当前设备: " + model.getNDManager().getDevice());
|
||||
} catch (IOException | ModelNotFoundException | MalformedModelException e) {
|
||||
throw new FaceException("模型加载失败", e);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 检测人脸
|
||||
* @param imagePath 图片路径
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public DetectionResponse detect(String imagePath){
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
Image img = null;
|
||||
try {
|
||||
img = ImageFactory.getInstance().fromFile(Paths.get(imagePath));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效的图片", e);
|
||||
}
|
||||
DetectedObjects detection = detect(img);
|
||||
return FaceUtils.convertToDetectionResponse(detection,img);
|
||||
}
|
||||
|
||||
/**
|
||||
* 检测人脸
|
||||
* @param imageInputStream 图片流
|
||||
* @return
|
||||
* @throws Exception
|
||||
*/
|
||||
@Override
|
||||
public DetectionResponse detect(InputStream imageInputStream){
|
||||
try {
|
||||
Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
|
||||
DetectedObjects detection = detect(img);
|
||||
return FaceUtils.convertToDetectionResponse(detection,img);
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("无效图片输入流", e);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(BufferedImage image) {
|
||||
Image img = ImageFactory.getInstance().fromImage(image);
|
||||
DetectedObjects detection = detect(img);
|
||||
return FaceUtils.convertToDetectionResponse(detection,img);
|
||||
}
|
||||
|
||||
@Override
|
||||
public DetectionResponse detect(byte[] imageData) {
|
||||
if(Objects.isNull(imageData)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
try {
|
||||
return detect(ImageIO.read(new ByteArrayInputStream(imageData)));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("错误的图像", e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public void detectAndDraw(String imagePath, String outputPath) {
|
||||
if(!FileUtils.isFileExists(imagePath)){
|
||||
throw new FaceException("图像文件不存在");
|
||||
}
|
||||
try {
|
||||
Image img = ImageFactory.getInstance().fromFile(Paths.get(imagePath));
|
||||
DetectedObjects detectedObjects = detect(img);
|
||||
if(Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
|
||||
throw new FaceException("未识别到人脸");
|
||||
}
|
||||
img.drawBoundingBoxes(detectedObjects);
|
||||
ByteArrayOutputStream outputStream = new ByteArrayOutputStream();
|
||||
// 调用 save 方法将 Image 写入字节流
|
||||
img.save(new FileOutputStream(Paths.get(outputPath).toAbsolutePath().toString()), "png");
|
||||
} catch (IOException e) {
|
||||
throw new FaceException(e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public BufferedImage detectAndDraw(BufferedImage sourceImage) {
|
||||
if(!ImageUtils.isImageValid(sourceImage)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
Image img = ImageFactory.getInstance().fromImage(sourceImage);
|
||||
DetectedObjects detectedObjects = detect(img);
|
||||
if(Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
|
||||
throw new FaceException("未识别到人脸");
|
||||
}
|
||||
img.drawBoundingBoxes(detectedObjects);
|
||||
try {
|
||||
ByteArrayOutputStream outputStream = new ByteArrayOutputStream();
|
||||
// 调用 save 方法将 Image 写入字节流
|
||||
img.save(outputStream, "png");
|
||||
// 将字节流转换为 BufferedImage
|
||||
byte[] imageBytes = outputStream.toByteArray();
|
||||
return ImageIO.read(new ByteArrayInputStream(imageBytes));
|
||||
} catch (IOException e) {
|
||||
throw new FaceException("导出图片失败", e);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 人脸检测
|
||||
* @param image
|
||||
* @return
|
||||
*/
|
||||
private DetectedObjects detect(Image image){
|
||||
Predictor<Image, DetectedObjects> predictor = null;
|
||||
try {
|
||||
predictor = predictorPool.borrowObject();
|
||||
return predictor.predict(image);
|
||||
} catch (Exception e) {
|
||||
throw new FaceException("目标检测错误", e);
|
||||
}finally {
|
||||
if (predictor != null) {
|
||||
try {
|
||||
predictorPool.returnObject(predictor); //归还
|
||||
log.info("释放资源");
|
||||
} catch (Exception e) {
|
||||
log.warn("归还Predictor失败", e);
|
||||
try {
|
||||
predictor.close(); // 归还失败才销毁
|
||||
} catch (Exception ex) {
|
||||
log.error("关闭Predictor失败", ex);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public void close() {
|
||||
if (predictorPool != null) {
|
||||
predictorPool.close();
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -10,7 +10,7 @@
|
||||
* OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
|
||||
* and limitations under the License.
|
||||
*/
|
||||
package cn.smartjavaai.face;
|
||||
package cn.smartjavaai.face.translator;
|
||||
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.output.*;
|
||||
@@ -12,7 +12,7 @@ import ai.djl.translate.Translator;
|
||||
import ai.djl.translate.TranslatorContext;
|
||||
|
||||
/**
|
||||
* @author 邓文杰
|
||||
* @author dwj
|
||||
* @date 2025/3/31
|
||||
*/
|
||||
public final class FaceFeatureTranslator implements Translator<Image, float[]> {
|
||||
|
||||
@@ -0,0 +1,167 @@
|
||||
package cn.smartjavaai.face.utils;
|
||||
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.output.BoundingBox;
|
||||
import ai.djl.modality.cv.output.DetectedObjects;
|
||||
import cn.smartjavaai.common.entity.DetectionResponse;
|
||||
import cn.smartjavaai.common.entity.Point;
|
||||
import cn.smartjavaai.common.entity.DetectionRectangle;
|
||||
import cn.smartjavaai.common.utils.ImageUtils;
|
||||
import cn.smartjavaai.face.FaceModelConfig;
|
||||
import cn.smartjavaai.face.exception.FaceException;
|
||||
import com.seetaface.model.SeetaRect;
|
||||
|
||||
import javax.imageio.ImageIO;
|
||||
import java.awt.*;
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.File;
|
||||
import java.io.IOException;
|
||||
import java.util.ArrayList;
|
||||
import java.util.Iterator;
|
||||
import java.util.List;
|
||||
import java.util.Objects;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
/**
|
||||
* 人脸检测相关工具类
|
||||
* @author dwj
|
||||
* @date 2025/4/9
|
||||
*/
|
||||
public class FaceUtils {
|
||||
|
||||
|
||||
/**
|
||||
* 转换为FaceDetectedResult
|
||||
* @param detection
|
||||
* @param img
|
||||
* @return
|
||||
*/
|
||||
public static DetectionResponse convertToDetectionResponse(DetectedObjects detection, Image img){
|
||||
if(Objects.isNull(detection) || Objects.isNull(detection.getProbabilities())
|
||||
|| detection.getProbabilities().isEmpty() || Objects.isNull(detection.items()) || detection.items().isEmpty()){
|
||||
return null;
|
||||
}
|
||||
DetectionResponse detectionResponse = new DetectionResponse();
|
||||
List<DetectedObjects.DetectedObject> detectedObjectList = detection.items();
|
||||
List<DetectionRectangle> rectangleList = new ArrayList<DetectionRectangle>();
|
||||
Iterator iterator = detectedObjectList.iterator();
|
||||
int index = 0;
|
||||
while(iterator.hasNext()) {
|
||||
DetectedObjects.DetectedObject result = (DetectedObjects.DetectedObject)iterator.next();
|
||||
BoundingBox box = result.getBoundingBox();
|
||||
int x = (int)(box.getBounds().getX() * (double)img.getWidth());
|
||||
int y = (int)(box.getBounds().getY() * (double)img.getHeight());
|
||||
int width = (int)(box.getBounds().getWidth() * (double)img.getWidth());
|
||||
int height = (int)(box.getBounds().getHeight() * (double)img.getHeight());
|
||||
DetectionRectangle rectangle = new DetectionRectangle(x, y, width, height, detection.getProbabilities().get(index).floatValue());
|
||||
rectangleList.add(rectangle);
|
||||
index++;
|
||||
}
|
||||
detectionResponse.setRectangleList(rectangleList);
|
||||
return detectionResponse;
|
||||
}
|
||||
|
||||
/**
|
||||
* 转换为FaceDetectedResult
|
||||
* @param seetaResult
|
||||
* @return
|
||||
*/
|
||||
public static DetectionResponse convertToDetectionResponse(SeetaRect[] seetaResult, FaceModelConfig config){
|
||||
if(Objects.isNull(seetaResult) || seetaResult.length == 0){
|
||||
return null;
|
||||
}
|
||||
DetectionResponse detectionResponse = new DetectionResponse();
|
||||
List<DetectionRectangle> rectangleList = new ArrayList<DetectionRectangle>();
|
||||
for(SeetaRect rect : seetaResult){
|
||||
//过滤置信度
|
||||
if(config.getConfidenceThreshold() > 0 && rect.score < config.getConfidenceThreshold()){
|
||||
continue;
|
||||
}
|
||||
DetectionRectangle rectangle = new DetectionRectangle(rect.x, rect.y, rect.width, rect.height, rect.score);
|
||||
rectangleList.add(rectangle);
|
||||
}
|
||||
detectionResponse.setRectangleList(rectangleList);
|
||||
return detectionResponse;
|
||||
}
|
||||
|
||||
/**
|
||||
* 绘制人脸框
|
||||
* @param sourceImage
|
||||
* @param detectionResponse
|
||||
* @param savePath
|
||||
* @throws IOException
|
||||
*/
|
||||
public static void drawBoundingBoxes(BufferedImage sourceImage, DetectionResponse detectionResponse, String savePath) throws IOException {
|
||||
if(!ImageUtils.isImageValid(sourceImage)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
if(Objects.isNull(detectionResponse) || Objects.isNull(detectionResponse.getRectangleList()) || detectionResponse.getRectangleList().isEmpty()){
|
||||
throw new FaceException("无目标数据");
|
||||
}
|
||||
Graphics2D graphics = sourceImage.createGraphics();
|
||||
graphics.setColor(Color.RED);// 边框颜色
|
||||
graphics.setStroke(new BasicStroke(2)); // 线宽2像素
|
||||
graphics.setRenderingHint(RenderingHints.KEY_ANTIALIASING,
|
||||
RenderingHints.VALUE_ANTIALIAS_ON); // 抗锯齿
|
||||
int stroke = 2;
|
||||
for(DetectionRectangle rectangle : detectionResponse.getRectangleList()){
|
||||
graphics.setColor(Color.RED);// 边框颜色
|
||||
graphics.drawRect(rectangle.getX(), rectangle.getY(), rectangle.getWidth(), rectangle.getHeight());
|
||||
drawText(graphics, "face", rectangle.getX(), rectangle.getY(), stroke, 4);
|
||||
}
|
||||
graphics.dispose();
|
||||
ImageIO.write(sourceImage, "jpg", new File(savePath));
|
||||
}
|
||||
|
||||
/**
|
||||
* 绘制人脸框
|
||||
* @param sourceImage
|
||||
* @param detectionResponse
|
||||
* @throws IOException
|
||||
*/
|
||||
public static BufferedImage drawBoundingBoxes(BufferedImage sourceImage, DetectionResponse detectionResponse) throws IOException {
|
||||
if(!ImageUtils.isImageValid(sourceImage)){
|
||||
throw new FaceException("图像无效");
|
||||
}
|
||||
if(Objects.isNull(detectionResponse) || Objects.isNull(detectionResponse.getRectangleList()) || detectionResponse.getRectangleList().isEmpty()){
|
||||
throw new FaceException("无目标数据");
|
||||
}
|
||||
Graphics2D graphics = sourceImage.createGraphics();
|
||||
graphics.setColor(Color.RED);// 边框颜色
|
||||
graphics.setStroke(new BasicStroke(2)); // 线宽2像素
|
||||
graphics.setRenderingHint(RenderingHints.KEY_ANTIALIASING,
|
||||
RenderingHints.VALUE_ANTIALIAS_ON); // 抗锯齿
|
||||
int stroke = 2;
|
||||
for(DetectionRectangle rectangle : detectionResponse.getRectangleList()){
|
||||
graphics.setColor(Color.RED);// 边框颜色
|
||||
graphics.drawRect(rectangle.getX(), rectangle.getY(), rectangle.getWidth(), rectangle.getHeight());
|
||||
drawText(graphics, "face", rectangle.getX(), rectangle.getY(), stroke, 4);
|
||||
}
|
||||
graphics.dispose();
|
||||
return sourceImage;
|
||||
}
|
||||
|
||||
/**
|
||||
* 绘制文字
|
||||
* @param g
|
||||
* @param text
|
||||
* @param x
|
||||
* @param y
|
||||
* @param stroke
|
||||
* @param padding
|
||||
*/
|
||||
private static void drawText(Graphics2D g, String text, int x, int y, int stroke, int padding) {
|
||||
FontMetrics metrics = g.getFontMetrics();
|
||||
x += stroke / 2;
|
||||
y += stroke / 2;
|
||||
int width = metrics.stringWidth(text) + padding * 2 - stroke / 2;
|
||||
int height = metrics.getHeight() + metrics.getDescent();
|
||||
int ascent = metrics.getAscent();
|
||||
java.awt.Rectangle background = new java.awt.Rectangle(x, y, width, height);
|
||||
g.fill(background);
|
||||
g.setPaint(Color.WHITE);
|
||||
g.drawString(text, x + padding, y + ascent);
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
@@ -124,4 +124,12 @@ public class SeetaFace6JNI {
|
||||
*/
|
||||
public native int predictImage(SeetaImageData img);
|
||||
|
||||
public native void dispose();
|
||||
|
||||
@Override
|
||||
protected void finalize() throws Throwable {
|
||||
super.finalize();
|
||||
this.dispose();
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user