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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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24
smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/RetinaFace.java
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@@ -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);
}
/**