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人脸识别定义
人脸识别系统主要包括四个组成部分，分别为：人脸图像采集及检测、人脸图像预处理、人脸图像特征提取以及匹配与识别。
本文图片及学习代码来源，更多可前往
实现人脸识别流程
代码结构
代码实现
一、构建springboot项目添加pom依赖
5.6.0
0.11.0
1.8
UTF-8
UTF-8
UTF-8
gov.nist.math
jama
1.0.3
net.java.dev.jna
jna
${jna.version}
junit
junit
4.12
test
org.bytedeco
opencv-platform
4.5.1-1.5.5
org.bytedeco
javacv
1.5.5
org.bytedeco
javacv-platform
1.5.5
com.alibaba
fastjson
1.2.62
ai.djl
api
${djl.version}
ai.djl.pytorch
pytorch-model-zoo
${djl.version}
ai.djl.pytorch
pytorch-engine
${djl.version}
ai.djl.pytorch
pytorch-native-auto
1.8.1
二、创建转换器
package com.xgc.aideep.face.translator;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.output.*;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDArrays;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.NDManager;
import ai.djl.ndarray.types.DataType;
import ai.djl.ndarray.types.Shape;
import ai.djl.translate.Batchifier;
import ai.djl.translate.Translator;
import ai.djl.translate.TranslatorContext;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
/**
* @author gc.x
* @date 2022-04
*/
public class FaceDetectionTranslator implements Translator {
private double confThresh;
private double nmsThresh;
private int topK;
private double[] variance;
private int[][] scales;
private int[] steps;
private int width;
private int height;
public FaceDetectionTranslator(
double confThresh,
double nmsThresh,
double[] variance,
int topK,
int[][] scales,
int[] steps) {
this.confThresh = confThresh;
this.nmsThresh = nmsThresh;
this.variance = variance;
this.topK = topK;
this.scales = scales;
this.steps = steps;
}
/**
* {@inheritDoc}
*/
@Override
public NDList processInput(TranslatorContext ctx, Image input) {
width = input.getWidth();
height = input.getHeight();
NDArray array = input.toNDArray(ctx.getNDManager(), Image.Flag.COLOR);
array = array.transpose(2, 0, 1).flip(0); // HWC -> CHW RGB -> BGR
// The network by default takes float32
if (!array.getDataType().equals(DataType.FLOAT32)) {
array = array.toType(DataType.FLOAT32, false);
}
NDArray mean =
ctx.getNDManager().create(new float[]{104f, 117f, 123f}, new Shape(3, 1, 1));
array = array.sub(mean);
return new NDList(array);
}
/**
* {@inheritDoc}
*/
@Override
public DetectedObjects processOutput(TranslatorContext ctx, NDList list) {
NDManager manager = ctx.getNDManager();
double scaleXY = variance[0];
double scaleWH = variance[1];
NDArray prob = list.get(1).get(":, 1:");
prob =
NDArrays.stack(
new NDList(
prob.argMax(1).toType(DataType.FLOAT32, false),
prob.max(new int[]{1})));
NDArray boxRecover = boxRecover(manager, width, height, scales, steps);
NDArray boundingBoxes = list.get(0);
NDArray bbWH = boundingBoxes.get(":, 2:").mul(scaleWH).exp().mul(boxRecover.get(":, 2:"));
NDArray bbXY =
boundingBoxes
.get(":, :2")
.mul(scaleXY)
.mul(boxRecover.get(":, 2:"))
.add(boxRecover.get(":, :2"))
.sub(bbWH.mul(0.5f));
boundingBoxes = NDArrays.concat(new NDList(bbXY, bbWH), 1);
NDArray landms = list.get(2);
landms = decodeLandm(landms, boxRecover, scaleXY);
// filter the result below the threshold
NDArray cutOff = prob.get(1).gt(confThresh);
boundingBoxes = boundingBoxes.transpose().booleanMask(cutOff, 1).transpose();
landms = landms.transpose().booleanMask(cutOff, 1).transpose();
prob = prob.booleanMask(cutOff, 1);
// start categorical filtering
long[] order = prob.get(1).argSort().get(":" + topK).toLongArray();
prob = prob.transpose();
List retNames = new ArrayList();
List retProbs = new ArrayList();
List retBB = new ArrayList();
Map recorder = new ConcurrentHashMap();
for (int i = order.length - 1; i >= 0; i--) {
long currMaxLoc = order[i];
float[] classProb = prob.get(currMaxLoc).toFloatArray();
int classId = (int) classProb[0];
double probability = classProb[1];
double[] boxArr = boundingBoxes.get(currMaxLoc).toDoubleArray();
double[] landmsArr = landms.get(currMaxLoc).toDoubleArray();
Rectangle rect = new Rectangle(boxArr[0], boxArr[1], boxArr[2], boxArr[3]);
List boxes = recorder.getOrDefault(classId, new ArrayList());
boolean belowIoU = true;
for (BoundingBox box : boxes) {
if (box.getIoU(rect) > nmsThresh) {
belowIoU = false;
break;
}
}
if (belowIoU) {
List keyPoints = new ArrayList();
for (int j = 0; j < 5; j++) { // 5 face landmarks
double x = landmsArr[j * 2];
double y = landmsArr[j * 2 + 1];
keyPoints.add(new Point(x * width, y * height));
}
Landmark landmark =
new Landmark(boxArr[0], boxArr[1], boxArr[2], boxArr[3], keyPoints);
boxes.add(landmark);
recorder.put(classId, boxes);
String className = "Face"; // classes.get(classId)
retNames.add(className);
retProbs.add(probability);
retBB.add(landmark);
}
}
return new DetectedObjects(retNames, retProbs, retBB);
}
private NDArray boxRecover(
NDManager manager, int width, int height, int[][] scales, int[] steps) {
int[][] aspectRatio = new int[steps.length][2];
for (int i = 0; i < steps.length; i++) {
int wRatio = (int) Math.ceil((float) width / steps[i]);
int hRatio = (int) Math.ceil((float) height / steps[i]);
aspectRatio[i] = new int[]{hRatio, wRatio};
}
List defaultBoxes = new ArrayList();
for (int idx = 0; idx < steps.length; idx++) {
int[] scale = scales[idx];
for (int h = 0; h < aspectRatio[idx][0]; h++) {
for (int w = 0; w < aspectRatio[idx][1]; w++) {
for (int i : scale) {
double skx = i * 1.0 / width;
double sky = i * 1.0 / height;
double cx = (w + 0.5) * steps[idx] / width;
double cy = (h + 0.5) * steps[idx] / height;
defaultBoxes.add(new double[]{cx, cy, skx, sky});
}
}
}
}
double[][] boxes = new double[defaultBoxes.size()][defaultBoxes.get(0).length];
for (int i = 0; i < defaultBoxes.size(); i++) {
boxes[i] = defaultBoxes.get(i);
}
return manager.create(boxes).clip(0.0, 1.0);
}
// decode face landmarks, 5 points per face
private NDArray decodeLandm(NDArray pre, NDArray priors, double scaleXY) {
NDArray point1 =
pre.get(":, :2").mul(scaleXY).mul(priors.get(":, 2:")).add(priors.get(":, :2"));
NDArray point2 =
pre.get(":, 2:4").mul(scaleXY).mul(priors.get(":, 2:")).add(priors.get(":, :2"));
NDArray point3 =
pre.get(":, 4:6").mul(scaleXY).mul(priors.get(":, 2:")).add(priors.get(":, :2"));
NDArray point4 =
pre.get(":, 6:8").mul(scaleXY).mul(priors.get(":, 2:")).add(priors.get(":, :2"));
NDArray point5 =
pre.get(":, 8:10").mul(scaleXY).mul(priors.get(":, 2:")).add(priors.get(":, :2"));
return NDArrays.concat(new NDList(point1, point2, point3, point4, point5), 1);
}
/**
* {@inheritDoc}
*/
@Override
public Batchifier getBatchifier() {
return Batchifier.STACK;
}
}
package com.xgc.aideep.face.translator;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.transform.Normalize;
import ai.djl.modality.cv.transform.ToTensor;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.translate.Batchifier;
import ai.djl.translate.Pipeline;
import ai.djl.translate.Translator;
import ai.djl.translate.TranslatorContext;
/**
* @author gc.x
* @date 2022-04
*/
public final class FaceFeatureTranslator implements Translator {
public FaceFeatureTranslator() {
}
@Override
public NDList processInput(TranslatorContext ctx, Image input) {
NDArray array = input.toNDArray(ctx.getNDManager(), Image.Flag.COLOR);
Pipeline pipeline = new Pipeline();
pipeline
// .add(new Resize(160))
.add(new ToTensor())
.add(
new Normalize(
new float[]{127.5f / 255.0f, 127.5f / 255.0f, 127.5f / 255.0f},
new float[]{128.0f / 255.0f, 128.0f / 255.0f, 128.0f / 255.0f}));
return pipeline.transform(new NDList(array));
}
@Override
public float[] processOutput(TranslatorContext ctx, NDList list) {
NDList result = new NDList();
long numOutputs = list.singletonOrThrow().getShape().get(0);
for (int i = 0; i < numOutputs; i++) {
result.add(list.singletonOrThrow().get(i));
}
float[][] embeddings = result.stream().map(NDArray::toFloatArray).toArray(float[][]::new);
float[] feature = new float[embeddings.length];
for (int i = 0; i < embeddings.length; i++) {
feature[i] = embeddings[i][0];
}
return feature;
}
@Override
public Batchifier getBatchifier() {
return Batchifier.STACK;
}
}
三、加载模型
package com.xgc.aideep.face.model;
import ai.djl.MalformedModelException;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.output.DetectedObjects;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ModelNotFoundException;
import ai.djl.repository.zoo.ModelZoo;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.training.util.ProgressBar;
import com.xgc.aideep.face.translator.FaceDetectionTranslator;
import java.io.IOException;
/**
* @author gc.x
* @date 2022-04
*/
public final class FaceDetectionModel {
private ZooModel model;
private static String model_path = "models/ultranet.zip";
public void init(int topK, double confThresh, double nmsThresh) throws MalformedModelException, ModelNotFoundException, IOException {
this.model = ModelZoo.loadModel(detectCriteria(topK, confThresh, nmsThresh));
}
public ZooModel getModel() {
return model;
}
public void close() {
this.model.close();
}
private Criteria detectCriteria(int topK, double confThresh, double nmsThresh) {
double[] variance = {0.1f, 0.2f};
int[][] scales = {{10, 16, 24}, {32, 48}, {64, 96}, {128, 192, 256}};
int[] steps = {8, 16, 32, 64};
FaceDetectionTranslator translator =
new FaceDetectionTranslator(confThresh, nmsThresh, variance, topK, scales, steps);
Criteria criteria =
Criteria.builder()
.setTypes(Image.class, DetectedObjects.class)
.optModelUrls(model_path)
.optTranslator(translator)
.optEngine("PyTorch") // Use PyTorch engine
.optProgress(new ProgressBar())
.build();
return criteria;
}
}
package com.xgc.aideep.face.model;
import ai.djl.MalformedModelException;
import ai.djl.modality.cv.Image;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ModelNotFoundException;
import ai.djl.repository.zoo.ModelZoo;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.training.util.ProgressBar;
import com.xgc.aideep.face.translator.FaceFeatureTranslator;
import java.io.IOException;
/**
* @author gc.x
* @date 2022-04
*/
public final class FaceFeatureModel {
private ZooModel model;
private static String model_path = "models/face_feature.zip";
public void init() throws MalformedModelException, ModelNotFoundException, IOException {
this.model = ModelZoo.loadModel(detectCriteria());
}
public ZooModel getModel() {
return model;
}
public void close() {
this.model.close();
}
private Criteria detectCriteria() {
Criteria criteria =
Criteria.builder()
.setTypes(Image.class, float[].class)
.optModelName("face_feature") // specify model file prefix
.optModelUrls(model_path)
.optTranslator(new FaceFeatureTranslator())
.optEngine("PyTorch") // Use PyTorch engine
.optProgress(new ProgressBar())
.build();
return criteria;
}
}
四、目标检测
package com.xgc.aideep.face.service.impl;
import ai.djl.ModelException;
import ai.djl.inference.Predictor;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.ImageFactory;
import ai.djl.modality.cv.output.BoundingBox;
import ai.djl.modality.cv.output.DetectedObjects;
import ai.djl.modality.cv.output.Point;
import ai.djl.modality.cv.output.Rectangle;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDManager;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.translate.TranslateException;
import com.xgc.aideep.face.entity.FaceObject;
import com.xgc.aideep.face.model.FaceDetectionModel;
import com.xgc.aideep.face.service.FaceDetectService;
import com.xgc.aideep.face.service.FaceFeatureService;
import com.xgc.aideep.face.util.*;
import lombok.RequiredArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.javacv.Java2DFrameUtils;
import org.bytedeco.opencv.opencv_core.Mat;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import java.awt.image.BufferedImage;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
/**
* 目标检测服务
*
* @author gc.x
* @date 2022-04
*/
@Slf4j
@RequiredArgsConstructor
@Service
public class DetectServiceImpl implements FaceDetectService {
@Autowired
private FaceFeatureService featureService;
@Autowired
private FaceDetectionModel faceDetectionModel;
public List faceDetect(BufferedImage image)
throws IOException, ModelException, TranslateException {
ZooModel model = faceDetectionModel.getModel();
try (Predictor predictor = model.newPredictor()) {
Image djlImg = ImageFactory.getInstance().fromImage(image);
DetectedObjects detections = predictor.predict(djlImg);
List list = detections.items();
List faceObjects = new ArrayList();
for (DetectedObjects.DetectedObject detectedObject : list) {
BoundingBox box = detectedObject.getBoundingBox();
Rectangle rectangle = box.getBounds();
// 抠人脸图
Rectangle subImageRect =
FaceUtil.getSubImageRect(
image, rectangle, djlImg.getWidth(), djlImg.getHeight(), 0f);
int x = (int) (subImageRect.getX());
int y = (int) (subImageRect.getY());
int w = (int) (subImageRect.getWidth());
int h = (int) (subImageRect.getHeight());
BufferedImage subImage = image.getSubimage(x, y, w, h);
Image img = DJLImageUtil.bufferedImage2DJLImage(subImage);
//获取特征向量
List feature = featureService.faceFeature(img);
FaceObject faceObject = new FaceObject();
faceObject.setFeature(feature);
faceObject.setBoundingBox(subImageRect);
faceObjects.add(faceObject);
}
return faceObjects;
}
}
public List faceDetect(Image djlImg) throws TranslateException, ModelException, IOException {
ZooModel model = faceDetectionModel.getModel();
try (Predictor predictor = model.newPredictor()) {
DetectedObjects detections = predictor.predict(djlImg);
List list = detections.items();
BufferedImage image = (BufferedImage) djlImg.getWrappedImage();
List faceObjects = new ArrayList();
for (DetectedObjects.DetectedObject detectedObject : list) {
BoundingBox box = detectedObject.getBoundingBox();
Rectangle rectangle = box.getBounds();
// 抠人脸图
// factor = 0.1f, 意思是扩大10%，防止人脸仿射变换后，人脸被部分截掉
Rectangle subImageRect =
FaceUtil.getSubImageRect(
image, rectangle, djlImg.getWidth(), djlImg.getHeight(), 1.0f);
int x = (int) (subImageRect.getX());
int y = (int) (subImageRect.getY());
int w = (int) (subImageRect.getWidth());
int h = (int) (subImageRect.getHeight());
BufferedImage subImage = image.getSubimage(x, y, w, h);
// 计算人脸关键点在子图中的新坐标
List points = (List) box.getPath();
double[][] pointsArray = FaceUtil.pointsArray(subImageRect, points);
// 转 buffered image 图片格式
BufferedImage converted3BGRsImg =
new BufferedImage(
subImage.getWidth(), subImage.getHeight(), BufferedImage.TYPE_3BYTE_BGR);
converted3BGRsImg.getGraphics().drawImage(subImage, 0, 0, null);
Mat mat = Java2DFrameUtils.toMat(converted3BGRsImg);
try (NDManager manager = NDManager.newBaseManager()) {
NDArray srcPoints = manager.create(pointsArray);
NDArray dstPoints = SVDUtil.point112x112(manager);
// 定制的5点仿射变换
Mat svdMat = NDArrayUtil.toOpenCVMat(manager, srcPoints, dstPoints);
// 换仿射变换矩阵
mat = FaceAlignment.get5WarpAffineImg(mat, svdMat);
// mat转bufferedImage类型
BufferedImage mat2BufferedImage = OpenCVImageUtil.mat2BufferedImage(mat);
int width = mat2BufferedImage.getWidth() > 112 ? 112 : mat2BufferedImage.getWidth();
int height = mat2BufferedImage.getHeight() > 112 ? 112 : mat2BufferedImage.getHeight();
mat2BufferedImage = mat2BufferedImage.getSubimage(0, 0, width, height);
Image img = DJLImageUtil.bufferedImage2DJLImage(mat2BufferedImage);
//获取特征向量
List feature = featureService.faceFeature(img);
FaceObject faceObject = new FaceObject();
faceObject.setFeature(feature);
faceObject.setBoundingBox(subImageRect);
faceObject.setScore((float) detectedObject.getProbability());
faceObjects.add(faceObject);
}
}
return faceObjects;
}
}
}
五、特征提取
package com.xgc.aideep.face.service.impl;
import ai.djl.inference.Predictor;
import ai.djl.modality.cv.Image;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.translate.TranslateException;
import com.xgc.aideep.face.model.FaceFeatureModel;
import com.xgc.aideep.face.service.FaceFeatureService;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import java.util.ArrayList;
import java.util.List;
/**
* 特征提取服务
* @author gc.x
* @date 2022-04
*/
@Slf4j
@Service
public class FeatureServiceImpl implements FaceFeatureService {
@Autowired
private FaceFeatureModel faceFeatureModel;
public List faceFeature(Image img) throws TranslateException {
ZooModel model = faceFeatureModel.getModel();
try (Predictor predictor = model.newPredictor()) {
float[] embeddings = predictor.predict(img);
List feature = new ArrayList();
if (embeddings != null) {
for (int i = 0; i < embeddings.length; i++) {
feature.add(embeddings[i]);
}
} else {
return null;
}
return feature;
}
}
}
六、特征比对（存储）
使用向量引擎milvus存储获取的人脸特征，业务人脸识别
七、源代码开源地址
https://gitee.com/giteeClass/ai-face
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