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JavaJDK1.8适配 完成从摄像头中检测动物

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Signed-off-by: JoeAllen <2494136784@qq.com>
This commit is contained in:
JoeAllen
2022-07-29 09:13:20 +08:00
parent 41179c52b2
commit 2c8c05dedf
5 changed files with 36 additions and 126 deletions
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pom.xml
+1 -6
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@@ -100,13 +100,8 @@
<dependency>
<groupId>org.testng</groupId>
<artifactId>testng</artifactId>
<version>7.6.0</version>
<version>7.5</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-nop</artifactId>
<version>1.7.25</version>
</dependency>
</dependencies>
</project>
src/main/java/cn/heavenlybook/Main.java
+10 -101
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@@ -1,36 +1,20 @@
package cn.heavenlybook;
import ai.djl.ModelException;
import ai.djl.inference.Predictor;
import ai.djl.modality.Classifications;
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.Rectangle;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ModelZoo;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.translate.TranslateException;
import cn.heavenlybook.utils.AnimalsClassification;
import cn.heavenlybook.utils.FaceDetection;
import cn.heavenlybook.utils.OpenCVImageUtil;
import lombok.extern.java.Log;
import org.bytedeco.javacv.CanvasFrame;
import org.bytedeco.javacv.Frame;
import org.bytedeco.javacv.OpenCVFrameConverter;
import org.bytedeco.javacv.OpenCVFrameGrabber;
import org.bytedeco.opencv.opencv_core.Mat;
import org.bytedeco.opencv.opencv_core.Point;
import org.bytedeco.opencv.opencv_core.Rect;
import org.bytedeco.opencv.opencv_core.Scalar;
import javax.swing.*;
import java.awt.image.BufferedImage;
import java.io.IOException;
import java.util.List;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
/**
* @author ht
@@ -38,97 +22,22 @@ import static org.bytedeco.opencv.global.opencv_imgproc.*;
@Log
public class Main {
public static void main(String[] args) throws IOException, ModelException, TranslateException {
float shrink = 0.5f;
float threshold = 0.7f;
Criteria<Image, DetectedObjects> criteria = new FaceDetection().criteria(shrink, threshold);
// 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）
OpenCVFrameGrabber grabber = new OpenCVFrameGrabber(0);
grabber.start();
// Frame与Mat转换
OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
// 新建一个预览窗口
CanvasFrame canvas = new CanvasFrame("人脸检测");
canvas.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
canvas.setVisible(true);
canvas.setFocusable(true);
// 窗口置顶
if (canvas.isAlwaysOnTopSupported()) {
canvas.setAlwaysOnTop(true);
}
Frame frame;
try (ZooModel<Image, DetectedObjects> model = ModelZoo.loadModel(criteria);
Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
// 获取图像帧
while (canvas.isVisible() && (frame = grabber.grab()) != null) {
// 将获取的frame转化成mat数据类型
Mat img = converter.convert(frame);
BufferedImage buffImg = OpenCVImageUtil.mat2BufferedImage(img);
Image image = ImageFactory.getInstance().fromImage(buffImg);
// region 动物识别
Classifications classifications = AnimalsClassification.predict(image);
Classifications.Classification bestItem = classifications.best();
System.out.println(bestItem.getClassName() + " : " + bestItem.getProbability());
// List<Classifications.Classification> items = classifications.items();
// List<String> names = new ArrayList<>();
// List<Double> probs = new ArrayList<>();
// for (int i = 0; i < items.size(); i++) {
// Classifications.Classification item = items.get(i);
// names.add(item.getClassName());
// probs.add(item.getProbability());
// }
log.info(classifications.toJson());
// endregion
int imageWidth = image.getWidth();
int imageHeight = image.getHeight();
DetectedObjects detections = predictor.predict(image);
List<DetectedObjects.DetectedObject> items = detections.items();
// 遍历人脸
for (DetectedObjects.DetectedObject item : items) {
BoundingBox box = item.getBoundingBox();
Rectangle rectangle = box.getBounds();
int x = (int) (rectangle.getX() * imageWidth);
int y = (int) (rectangle.getY() * imageHeight);
Rect face =
new Rect(
x,
y,
(int) (rectangle.getWidth() * imageWidth),
(int) (rectangle.getHeight() * imageHeight));
// 绘制人脸矩形区域，scalar色彩顺序：BGR(蓝绿红)
rectangle(img, face, new Scalar(0, 0, 255, 1));
int posX = Math.max(face.tl().x() - 10, 0);
int posY = Math.max(face.tl().y() - 10, 0);
// 在人脸矩形上面绘制文字
putText(
img,
"Face",
new Point(posX, posY),
FONT_HERSHEY_COMPLEX,
1.0,
new Scalar(0, 0, 255, 2.0));
}
// 显示视频图像
canvas.showImage(frame);
}
// 获取图像帧
while ((frame = grabber.grab()) != null) {
// 将获取的frame转化成mat数据类型
Mat img = converter.convert(frame);
BufferedImage buffImg = OpenCVImageUtil.mat2BufferedImage(img);
Image image = ImageFactory.getInstance().fromImage(buffImg);
Classifications classifications = AnimalsClassification.predict(image);
Classifications.Classification bestItem = classifications.best();
log.info(bestItem.getClassName() + " : " + bestItem.getProbability());
log.info(classifications.toJson());
}
canvas.dispose();
grabber.close();
}
}
src/main/java/cn/heavenlybook/utils/AnimalsClassification.java
+19 -17
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@@ -59,25 +59,27 @@ public final class AnimalsClassification {
return new Classifications(names, probs);
}
static Criteria<Image, Classifications> criteria;
static ZooModel<Image, Classifications> rotateModel;
static Predictor<Image, Classifications> classifier;
public static Classifications classfier(Image img)
throws IOException, ModelException, TranslateException {
Criteria<Image, Classifications> criteria =
Criteria.builder()
.optEngine("PaddlePaddle")
.setTypes(Image.class, Classifications.class)
.optModelUrls("https://aias-home.oss-cn-beijing.aliyuncs.com/models/animals.zip")
// .optModelUrls("/Users/calvin/model/animals/")
.optModelName("inference")
.optTranslator(new AnimalTranslator())
.optProgress(new ProgressBar())
.build();
try (ZooModel<Image, Classifications> rotateModel = ModelZoo.loadModel(criteria)) {
try (Predictor<Image, Classifications> classifier = rotateModel.newPredictor()) {
Classifications classifications = classifier.predict(img);
return classifications;
}
}
criteria =
criteria == null
? Criteria.builder()
.optEngine("PaddlePaddle")
.setTypes(Image.class, Classifications.class)
.optModelUrls("https://aias-home.oss-cn-beijing.aliyuncs.com/models/animals.zip")
// .optModelUrls("/Users/calvin/model/animals/")
.optModelName("inference")
.optTranslator(new AnimalTranslator())
.optProgress(new ProgressBar())
.build()
: criteria;
rotateModel = rotateModel == null ? ModelZoo.loadModel(criteria) : rotateModel;
classifier = classifier == null ? rotateModel.newPredictor() : classifier;
return classifier.predict(img);
}
}
src/main/java/cn/heavenlybook/utils/LightAnimalsClassification.java
+4 -2
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@@ -37,7 +37,9 @@ public final class LightAnimalsClassification {
Classifications.Classification item = items.get(i);
double prob = item.getProbability();
probArr[i] = prob;
if (prob > max) max = prob;
if (prob > max) {
max = prob;
}
}
for (int i = 0; i < items.size(); i++) {
@@ -69,7 +71,7 @@ public final class LightAnimalsClassification {
.optProgress(new ProgressBar())
.build();
try (ZooModel rotateModel = ModelZoo.loadModel(criteria)) {
try (ZooModel<Image, Classifications> rotateModel = ModelZoo.loadModel(criteria)) {
try (Predictor<Image, Classifications> classifier = rotateModel.newPredictor()) {
Classifications classifications = classifier.predict(img);
return classifications;
src/test/java/Tests.java → src/test/java/cn/heavenlybook/Tests.java
+2
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@@ -1,3 +1,5 @@
package cn.heavenlybook;
import org.testng.annotations.Test;
/**