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优化人脸检测速度

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dengwenjie
2025-03-08 10:47:55 +08:00
parent 4e97688315
commit 12cac51b8a
4 changed files with 59 additions and 35 deletions
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27
examples/src/main/java/smartai/examples/face/FaceDemo.java
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@@ -3,6 +3,7 @@ package smartai.examples.face;
import cn.smartjavaai.common.entity.Rectangle;
import cn.smartjavaai.face.*;
import com.alibaba.fastjson.JSONObject;
import org.apache.commons.lang3.time.StopWatch;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import smartai.examples.utils.ImageUtils;
@@ -28,7 +29,7 @@ public class FaceDemo {
public static void main(String[] args) {
try {
//detectFace();
//detectFace2();
verifyIDCard();
} catch (Exception e) {
e.printStackTrace();
@@ -42,10 +43,18 @@ public class FaceDemo {
* 应用场景：如监控摄像头、智能安防系统等需要高精度检测的场合
*/
public static void detectFace() throws Exception {
// 创建并启动计时器
StopWatch sw = StopWatch.createStarted();
//创建人脸算法
FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm();
sw.stop();
logger.info("创建人脸算法耗时：" + sw.getTime() + "ms");
sw.reset();
sw.start();
//使用图片路径检测
FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
sw.stop();
logger.info("人脸检测耗时：" + sw.getTime() + "ms");
logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
//使用图片流检测
File input = new File("src/main/resources/largest_selfie.jpg");
@@ -66,10 +75,18 @@ public class FaceDemo {
* 应用场景：如监控摄像头、智能安防系统等需要高精度检测的场合
*/
public static void detectFace2() throws Exception {
// 创建并启动计时器
StopWatch sw = StopWatch.createStarted();
//创建轻量人脸算法
FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createLightFaceAlgorithm();
sw.stop();
logger.info("创建人脸算法耗时：" + sw.getTime() + "ms");
sw.reset();
sw.start();
//使用图片路径检测
FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
sw.stop();
logger.info("人脸检测耗时：" + sw.getTime() + "ms");
logger.info("轻量人脸检测结果：{}", JSONObject.toJSONString(result));
//使用图片流检测
//File imageFile = new File("/Users/wenjie/Downloads/djl-master/examples/src/test/resources/largest_selfie.jpg");
@@ -87,10 +104,18 @@ public class FaceDemo {
* @throws Exception
*/
public static void verifyIDCard() throws Exception {
// 创建并启动计时器
StopWatch sw = StopWatch.createStarted();
//创建脸算法
FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceFeatureAlgorithm();
sw.stop();
logger.info("创建人脸算法耗时：" + sw.getTime() + "ms");
sw.reset();
sw.start();
//提取身份证人脸特征（图片仅供测试）
float[] featureIdCard = currentAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
sw.stop();
logger.info("人脸检测耗时：" + sw.getTime() + "ms");
//提取身份证人脸特征（从图片流获取）
//File input = new File("src/main/resources/kana1.jpg");
//float[] featureIdCard = currentAlgorithm.featureExtraction(new FileInputStream(input));

16
smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/FeatureExtractionAlgo.java
View File

@@ -33,6 +33,10 @@ 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,
@@ -62,6 +66,8 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
.optProgress(new ProgressBar())
.optEngine("PyTorch") // Use PyTorch engine
.build();
model = faceFeatureCriteria.loadModel();
predictor = model.newPredictor();
}
@@ -76,10 +82,7 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
Path imageFile = Paths.get(imagePath);
Image img = ImageFactory.getInstance().fromFile(imageFile);
img.getWrappedImage();
try (ZooModel<Image, float[]> model = faceFeatureCriteria.loadModel()) {
Predictor<Image, float[]> predictor = model.newPredictor();
return predictor.predict(img);
}
return predictor.predict(img);
}
/**
@@ -92,10 +95,7 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
public float[] featureExtraction(InputStream inputStream) throws Exception {
Image img = ImageFactory.getInstance().fromInputStream(inputStream);
img.getWrappedImage();
try (ZooModel<Image, float[]> model = faceFeatureCriteria.loadModel()) {
Predictor<Image, float[]> predictor = model.newPredictor();
return predictor.predict(img);
}
return predictor.predict(img);
}
/**

24
smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/RetinaFace.java
View File

@@ -38,6 +38,10 @@ public class RetinaFace extends AbstractFaceAlgorithm {
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
*/
@@ -57,7 +61,7 @@ public class RetinaFace extends AbstractFaceAlgorithm {
* @param config
*/
@Override
public void loadModel(ModelConfig config) {
public void loadModel(ModelConfig config) throws ModelNotFoundException, MalformedModelException, IOException {
FaceDetectionTranslator translator =
new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, config.getMaxFaceCount(), scales, steps);
criteria =
@@ -71,6 +75,8 @@ public class RetinaFace extends AbstractFaceAlgorithm {
.optProgress(new ProgressBar())
.optEngine("PyTorch") // Use PyTorch engine
.build();
model = criteria.loadModel();
predictor = model.newPredictor();
}
@@ -85,11 +91,8 @@ public class RetinaFace extends AbstractFaceAlgorithm {
public FaceDetectedResult detect(String imagePath) throws Exception{
Path facePath = Paths.get(imagePath);
Image img = ImageFactory.getInstance().fromFile(facePath);
try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
DetectedObjects detection = predictor.predict(img);
return convertToFaceDetectedResult(detection,img);
}
DetectedObjects detection = predictor.predict(img);
return convertToFaceDetectedResult(detection,img);
}
/**
@@ -101,13 +104,8 @@ public class RetinaFace extends AbstractFaceAlgorithm {
@Override
public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
DetectedObjects detection = predictor.predict(img);
return convertToFaceDetectedResult(detection,img);
/*saveBoundingBoxImage(img, detection);
return detection;*/
}
DetectedObjects detection = predictor.predict(img);
return convertToFaceDetectedResult(detection,img);
}
/**

27
smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/UltraLightFastGenericFace.java
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@@ -1,17 +1,20 @@
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;
@@ -41,6 +44,10 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
*/
private static final double[] variance = {0.1f, 0.2f};
private Predictor<Image, DetectedObjects> predictor;
private ZooModel<Image, DetectedObjects> model;
@@ -49,7 +56,7 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
* @param config
*/
@Override
public void loadModel(ModelConfig config) {
public void loadModel(ModelConfig config) throws ModelNotFoundException, MalformedModelException, IOException {
FaceDetectionTranslator translator =
new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, config.getMaxFaceCount(), scales, steps);
criteria =
@@ -60,6 +67,8 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
.optProgress(new ProgressBar())
.optEngine("PyTorch") // Use PyTorch engine
.build();
model = criteria.loadModel();
predictor = model.newPredictor();
}
/**
@@ -72,11 +81,8 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
public FaceDetectedResult detect(String imagePath) throws Exception{
Path facePath = Paths.get(imagePath);
Image img = ImageFactory.getInstance().fromFile(facePath);
try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
DetectedObjects detection = predictor.predict(img);
return convertToFaceDetectedResult(detection,img);
}
DetectedObjects detection = predictor.predict(img);
return convertToFaceDetectedResult(detection,img);
}
/**
@@ -88,13 +94,8 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
@Override
public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
DetectedObjects detection = predictor.predict(img);
return convertToFaceDetectedResult(detection,img);
/*saveBoundingBoxImage(img, detection);
return detection;*/
}
DetectedObjects detection = predictor.predict(img);
return convertToFaceDetectedResult(detection,img);
}
/**