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dengwenjie
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package cn.smartjavaai.face.algo;
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import ai.djl.MalformedModelException;
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import ai.djl.inference.Predictor;
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import ai.djl.modality.cv.Image;
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import ai.djl.modality.cv.ImageFactory;
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import ai.djl.modality.cv.output.DetectedObjects;
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import ai.djl.modality.cv.translator.ImageFeatureExtractorFactory;
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import ai.djl.repository.zoo.Criteria;
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import ai.djl.repository.zoo.ModelNotFoundException;
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import ai.djl.repository.zoo.ZooModel;
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import ai.djl.training.util.ProgressBar;
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import ai.djl.translate.TranslateException;
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import cn.smartjavaai.common.entity.Point;
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import cn.smartjavaai.common.entity.Rectangle;
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import cn.smartjavaai.face.*;
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import org.apache.commons.beanutils.BeanUtils;
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import org.apache.commons.compress.utils.Lists;
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import java.io.IOException;
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import java.io.InputStream;
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import java.lang.reflect.InvocationTargetException;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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import java.util.stream.Collectors;
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import java.util.stream.StreamSupport;
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/**
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* RetinaFace实现
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* @author dwj
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* @date 2025/2/19
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*/
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public class RetinaFace extends AbstractFaceAlgorithm {
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private Criteria<Image, DetectedObjects> criteria;
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/**
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* 特征图层的基础缩放比例
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*/
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public static final int[][] scales = {{16, 32}, {64, 128}, {256, 512}};
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/**
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* 特征图相对于原图的采样步长
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*/
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public static final int[] steps = {8, 16, 32};
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/**
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* 缩放系数
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*/
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public static final double[] variance = {0.1f, 0.2f};
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/**
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* 加载模型
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* @param config
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*/
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@Override
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public void loadModel(ModelConfig config) {
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FaceDetectionTranslator translator =
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new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, config.getMaxFaceCount(), scales, steps);
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criteria =
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Criteria.builder()
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.setTypes(Image.class, DetectedObjects.class)
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.optModelUrls("https://resources.djl.ai/test-models/pytorch/retinaface.zip")
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//.optModelPath(modelPath)
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// Load model from local file, e.g:
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.optModelName("retinaface") // specify model file prefix
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.optTranslator(translator)
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.optProgress(new ProgressBar())
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.optEngine("PyTorch") // Use PyTorch engine
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.build();
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}
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/**
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* 检测人脸
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* @param imagePath 图片路径
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* @return
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* @throws Exception
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*/
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@Override
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public FaceDetectedResult detect(String imagePath) throws Exception{
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Path facePath = Paths.get(imagePath);
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Image img = ImageFactory.getInstance().fromFile(facePath);
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try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
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Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
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DetectedObjects detection = predictor.predict(img);
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return convertToFaceDetectedResult(detection,img);
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}
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}
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/**
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* 检测人脸
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* @param imageInputStream 图片流
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* @return
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* @throws Exception
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*/
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@Override
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public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
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Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
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try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
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Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
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DetectedObjects detection = predictor.predict(img);
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return convertToFaceDetectedResult(detection,img);
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/*saveBoundingBoxImage(img, detection);
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return detection;*/
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}
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}
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/**
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* 转换为FaceDetectedResult
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* @param detection
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* @param img
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* @return
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*/
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private FaceDetectedResult convertToFaceDetectedResult(DetectedObjects detection, Image img){
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FaceDetectedResult faceDetectedResult = new FaceDetectedResult();
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List<Double> probabilities = new ArrayList<>(detection.getProbabilities());
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List<DetectedObjects.DetectedObject> detectedObjectList = detection.items();
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List<Rectangle> RectangleList = detectedObjectList.parallelStream()
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.map(obj -> {
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Rectangle rectangle = new Rectangle();
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List<Point> pointList = new ArrayList<>();
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ai.djl.modality.cv.output.Rectangle rectangleDjl = obj.getBoundingBox().getBounds();
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int x = (int)(rectangleDjl.getX() * (double)img.getWidth());
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int y = (int)(rectangleDjl.getY() * (double)img.getHeight());
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int width = (int)(rectangleDjl.getWidth() * (double)img.getWidth());
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int height = (int)(rectangleDjl.getHeight() * (double)img.getHeight());
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pointList.add(new Point(x,y));
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pointList.add(new Point(x + width,y));
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pointList.add(new Point(x,y + height));
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pointList.add(new Point(x + width,y + height));
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rectangle.setPointList(pointList);
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rectangle.setHeight(height);
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rectangle.setWidth(width);
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return rectangle;
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})
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.collect(Collectors.toList());
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faceDetectedResult.setProbabilities(probabilities);
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faceDetectedResult.setRectangles(RectangleList);
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return faceDetectedResult;
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}
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/**
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* 特征提取
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* @param imagePath 图片路径
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* @return
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* @throws Exception
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*/
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@Override
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public float[] featureExtraction(String imagePath) throws Exception {
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Path imageFile = Paths.get(imagePath);
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Image img = ImageFactory.getInstance().fromFile(imageFile);
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img.getWrappedImage();
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List<Float> mean =
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Arrays.asList(
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127.5f / 255.0f,
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127.5f / 255.0f,
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127.5f / 255.0f,
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128.0f / 255.0f,
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128.0f / 255.0f,
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128.0f / 255.0f);
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String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
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Criteria<Image, float[]> criteria =
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Criteria.builder()
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.setTypes(Image.class, float[].class)
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.optModelUrls(
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"https://resources.djl.ai/test-models/pytorch/face_feature.zip")
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.optModelName("face_feature") // specify model file prefix
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.optArgument("normalize", normalize)
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.optTranslatorFactory(new ImageFeatureExtractorFactory())
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.optProgress(new ProgressBar())
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.optEngine("PyTorch") // Use PyTorch engine
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.build();
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try (ZooModel<Image, float[]> model = criteria.loadModel()) {
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Predictor<Image, float[]> predictor = model.newPredictor();
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return predictor.predict(img);
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}
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}
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/**
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* 特征提取
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* @param inputStream 输入流
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* @return
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* @throws Exception
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*/
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@Override
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public float[] featureExtraction(InputStream inputStream) throws Exception {
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Image img = ImageFactory.getInstance().fromInputStream(inputStream);
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img.getWrappedImage();
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List<Float> mean =
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Arrays.asList(
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127.5f / 255.0f,
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127.5f / 255.0f,
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127.5f / 255.0f,
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128.0f / 255.0f,
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128.0f / 255.0f,
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128.0f / 255.0f);
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String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
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Criteria<Image, float[]> criteria =
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Criteria.builder()
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.setTypes(Image.class, float[].class)
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.optModelUrls(
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"https://resources.djl.ai/test-models/pytorch/face_feature.zip")
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.optModelName("face_feature") // specify model file prefix
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.optArgument("normalize", normalize)
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.optTranslatorFactory(new ImageFeatureExtractorFactory())
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.optProgress(new ProgressBar())
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.optEngine("PyTorch") // Use PyTorch engine
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.build();
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try (ZooModel<Image, float[]> model = criteria.loadModel()) {
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Predictor<Image, float[]> predictor = model.newPredictor();
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return predictor.predict(img);
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}
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}
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/**
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* 计算相似度
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* @param feature1 图1特征
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* @param feature2 图2特征
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* @return
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* @throws Exception
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*/
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@Override
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public float calculSimilar(float[] feature1, float[] feature2) throws Exception {
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float ret = 0.0f;
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float mod1 = 0.0f;
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float mod2 = 0.0f;
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int length = feature1.length;
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for (int i = 0; i < length; ++i) {
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ret += feature1[i] * feature2[i];
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mod1 += feature1[i] * feature1[i];
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mod2 += feature2[i] * feature2[i];
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}
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return (float) ((ret / Math.sqrt(mod1) / Math.sqrt(mod2) + 1) / 2.0f);
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}
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/**
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* 特征比较
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* @param imagePath1 图1路径
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* @param imagePath2 图2路径
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* @return
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* @throws Exception
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*/
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@Override
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public float featureComparison(String imagePath1, String imagePath2) throws Exception {
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float[] feature1 = featureExtraction(imagePath1);
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float[] feature2 = featureExtraction(imagePath2);
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return calculSimilar(feature1, feature2);
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}
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/**
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* 特征比较
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* @param inputStream1 图1输入流
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* @param inputStream2 图2输入流
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* @return
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* @throws Exception
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*/
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@Override
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public float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception {
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float[] feature1 = featureExtraction(inputStream1);
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float[] feature2 = featureExtraction(inputStream2);
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return calculSimilar(feature1, feature2);
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}
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/*@Override
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public float[] recognize(FaceRegion region) {
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return new float[0];
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}*/
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/*@Override
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public void loadModel(ModelConfig config) throws Exception {
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}*/
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}
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