135 lines
4.7 KiB
Java
135 lines
4.7 KiB
Java
package cn.heavenlybook;
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import ai.djl.ModelException;
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import ai.djl.inference.Predictor;
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import ai.djl.modality.Classifications;
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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.BoundingBox;
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import ai.djl.modality.cv.output.DetectedObjects;
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import ai.djl.modality.cv.output.Rectangle;
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import ai.djl.repository.zoo.Criteria;
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import ai.djl.repository.zoo.ModelZoo;
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import ai.djl.repository.zoo.ZooModel;
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import ai.djl.translate.TranslateException;
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import cn.heavenlybook.utils.AnimalsClassification;
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import cn.heavenlybook.utils.FaceDetection;
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import cn.heavenlybook.utils.OpenCVImageUtil;
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import lombok.extern.java.Log;
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import org.bytedeco.javacv.CanvasFrame;
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import org.bytedeco.javacv.Frame;
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import org.bytedeco.javacv.OpenCVFrameConverter;
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import org.bytedeco.javacv.OpenCVFrameGrabber;
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import org.bytedeco.opencv.opencv_core.Mat;
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import org.bytedeco.opencv.opencv_core.Point;
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import org.bytedeco.opencv.opencv_core.Rect;
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import org.bytedeco.opencv.opencv_core.Scalar;
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import javax.swing.*;
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import java.awt.image.BufferedImage;
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import java.io.IOException;
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import java.util.List;
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import static org.bytedeco.opencv.global.opencv_imgproc.*;
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/**
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* @author ht
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*/
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@Log
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public class Main {
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public static void main(String[] args) throws IOException, ModelException, TranslateException {
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float shrink = 0.5f;
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float threshold = 0.7f;
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Criteria<Image, DetectedObjects> criteria = new FaceDetection().criteria(shrink, threshold);
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// 开启摄像头，获取图像（得到的图像为frame类型，需要转换为mat类型进行检测和识别）
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OpenCVFrameGrabber grabber = new OpenCVFrameGrabber(0);
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grabber.start();
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// Frame与Mat转换
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OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
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// 新建一个预览窗口
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CanvasFrame canvas = new CanvasFrame("人脸检测");
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canvas.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
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canvas.setVisible(true);
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canvas.setFocusable(true);
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// 窗口置顶
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if (canvas.isAlwaysOnTopSupported()) {
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canvas.setAlwaysOnTop(true);
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}
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Frame frame;
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try (ZooModel<Image, DetectedObjects> model = ModelZoo.loadModel(criteria);
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Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
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// 获取图像帧
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while (canvas.isVisible() && (frame = grabber.grab()) != null) {
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// 将获取的frame转化成mat数据类型
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Mat img = converter.convert(frame);
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BufferedImage buffImg = OpenCVImageUtil.mat2BufferedImage(img);
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Image image = ImageFactory.getInstance().fromImage(buffImg);
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// region 动物识别
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Classifications classifications = AnimalsClassification.predict(image);
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Classifications.Classification bestItem = classifications.best();
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System.out.println(bestItem.getClassName() + " : " + bestItem.getProbability());
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// List<Classifications.Classification> items = classifications.items();
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// List<String> names = new ArrayList<>();
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// List<Double> probs = new ArrayList<>();
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// for (int i = 0; i < items.size(); i++) {
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// Classifications.Classification item = items.get(i);
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// names.add(item.getClassName());
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// probs.add(item.getProbability());
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// }
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log.info(classifications.toJson());
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// endregion
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int imageWidth = image.getWidth();
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int imageHeight = image.getHeight();
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DetectedObjects detections = predictor.predict(image);
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List<DetectedObjects.DetectedObject> items = detections.items();
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// 遍历人脸
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for (DetectedObjects.DetectedObject item : items) {
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BoundingBox box = item.getBoundingBox();
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Rectangle rectangle = box.getBounds();
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int x = (int) (rectangle.getX() * imageWidth);
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int y = (int) (rectangle.getY() * imageHeight);
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Rect face =
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new Rect(
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x,
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y,
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(int) (rectangle.getWidth() * imageWidth),
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(int) (rectangle.getHeight() * imageHeight));
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// 绘制人脸矩形区域，scalar色彩顺序：BGR(蓝绿红)
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rectangle(img, face, new Scalar(0, 0, 255, 1));
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int posX = Math.max(face.tl().x() - 10, 0);
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int posY = Math.max(face.tl().y() - 10, 0);
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// 在人脸矩形上面绘制文字
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putText(
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img,
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"Face",
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new Point(posX, posY),
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FONT_HERSHEY_COMPLEX,
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1.0,
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new Scalar(0, 0, 255, 2.0));
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}
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// 显示视频图像
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canvas.showImage(frame);
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}
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}
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canvas.dispose();
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grabber.close();
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}
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}
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