mirror of
https://github.com/geekwenjie/SmartJavaAI.git
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1、人脸模块：新增小视科技（MiniVision）活体检测模型
2、人脸模块：新增阿里通义工作室活体检测模型 3、人脸模块：新增2个表情识别模型 4、人脸模块：新增InsightFace、ElasticFace人脸识别模型 5、人脸模块：新增Seetaface6质量评估模型 6、目标检测模块：开放更多自定义模型参数 7、人脸模块：支持base64图片 8、实现接口 AutoCloseable，支持资源的自动释放 9、OCR模块：解决加方向矫正后无法连续识别bug 10、人脸模块：解决人脸更新后缓存问题 11、优化部分功能
This commit is contained in:
dengwenjie
7
examples/objectdetection-example/.gitignore
vendored
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7
examples/objectdetection-example/.gitignore
vendored
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@@ -0,0 +1,7 @@
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||||
.idea
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||||
.idea/
|
||||
target
|
||||
log
|
||||
*.iml
|
||||
/.settings/
|
||||
/logging.file_IS_UNDEFINED/
|
||||
58
examples/objectdetection-example/README.md
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58
examples/objectdetection-example/README.md
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@@ -0,0 +1,58 @@
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# 目标检测示例
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||||
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## 📁 项目结构
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```
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objectdetection-example/
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├── src/
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│ ├── main/
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│ │ ├── java/
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│ │ │ └── smartai/examples/objectdetection/
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│ │ │ ├── ObjectDetection.java
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│ │ │ └── ViewerFrame.java
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```
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---
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## 🧩 功能模块说明
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### 1. 目标检测 [ObjectDetection.java]
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- **功能**：核心目标检测类，包含多个测试方法，展示了如何使用不同的模型进行目标检测
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|
||||
---
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||||
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||||
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||||
## ⚙️ 配置要求
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||||
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||||
- **运行环境**：
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||||
- JDK 1.8 或更高版本
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||||
- IntelliJ IDEA 推荐作为开发 IDE
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||||
- **依赖库**：
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- OpenCV、DJL、SmartJavaAI SDK
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- **模型路径**：
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- 所有模型需下载并配置正确的路径（参考各 demo 注释中的链接）
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|
||||
---
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||||
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## 🚀 快速开始
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1. 克隆项目到本地：
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|
||||
2. 导入项目至 IntelliJ IDEA。
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|
||||
3. 根据需要修改模型路径（见各 demo 中注释）。
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|
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4. 运行对应的 JUnit 测试类方法即可体验各项功能。
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||||
|
||||
---
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||||
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## 📄 文档
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||||
|
||||
有关完整使用说明，请查阅 SmartJavaAI 官方文档：
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||||
[http://doc.smartjavaai.cn](http://doc.smartjavaai.cn)
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||||
|
||||
---
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||||
307
examples/objectdetection-example/pom.xml
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307
examples/objectdetection-example/pom.xml
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@@ -0,0 +1,307 @@
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<?xml version="1.0" encoding="UTF-8"?>
|
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
|
||||
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
|
||||
<modelVersion>4.0.0</modelVersion>
|
||||
|
||||
<groupId>cn.smartjavaai</groupId>
|
||||
<artifactId>examples</artifactId>
|
||||
<version>1.0.0-SNAPSHOT</version>
|
||||
|
||||
<properties>
|
||||
<maven.compiler.source>11</maven.compiler.source>
|
||||
<maven.compiler.target>11</maven.compiler.target>
|
||||
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
|
||||
<smartjavaai.version>1.0.19</smartjavaai.version>
|
||||
<!--如果打包运行，需要替换成你的main-->
|
||||
<exec.mainClass>smartai.examples.objectdetection.ObjectDetection</exec.mainClass>
|
||||
|
||||
<javacv.version>1.5.10</javacv.version>
|
||||
|
||||
<javacv.platform.macosx-arm64>macosx-arm64</javacv.platform.macosx-arm64>
|
||||
<javacv.platform.linux-x86_64>linux-x86_64</javacv.platform.linux-x86_64>
|
||||
<javacv.platform.linux-arm64>linux-arm64</javacv.platform.linux-arm64>
|
||||
<javacv.platform.windows-x86_64>windows-x86_64</javacv.platform.windows-x86_64>
|
||||
|
||||
|
||||
<djl.platform.windows-x86_64>win-x86_64</djl.platform.windows-x86_64>
|
||||
<djl.platform.linux-x86_64>linux-x86_64</djl.platform.linux-x86_64>
|
||||
<djl.platform.linux-aarch64>linux-aarch64</djl.platform.linux-aarch64>
|
||||
<djl.platform.osx-aarch64>osx-aarch64</djl.platform.osx-aarch64>
|
||||
</properties>
|
||||
|
||||
<dependencyManagement>
|
||||
<dependencies>
|
||||
<dependency>
|
||||
<groupId>cn.smartjavaai</groupId>
|
||||
<artifactId>smartjavaai-bom</artifactId>
|
||||
<version>${smartjavaai.version}</version>
|
||||
<type>pom</type>
|
||||
<!-- 注意这里是import -->
|
||||
<scope>import</scope>
|
||||
</dependency>
|
||||
</dependencies>
|
||||
</dependencyManagement>
|
||||
|
||||
|
||||
<dependencies>
|
||||
<dependency>
|
||||
<groupId>commons-cli</groupId>
|
||||
<artifactId>commons-cli</artifactId>
|
||||
<version>1.9.0</version>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>commons-io</groupId>
|
||||
<artifactId>commons-io</artifactId>
|
||||
<version>2.17.0</version>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.apache.logging.log4j</groupId>
|
||||
<artifactId>log4j-slf4j2-impl</artifactId>
|
||||
<version>2.24.1</version>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.testng</groupId>
|
||||
<artifactId>testng</artifactId>
|
||||
<version>7.10.2</version>
|
||||
<scope>test</scope>
|
||||
</dependency>
|
||||
|
||||
|
||||
<dependency>
|
||||
<groupId>ch.qos.logback</groupId>
|
||||
<artifactId>logback-classic</artifactId>
|
||||
<version>1.2.3</version>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.slf4j</groupId>
|
||||
<artifactId>slf4j-api</artifactId>
|
||||
<version>1.7.30</version>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>com.alibaba</groupId>
|
||||
<artifactId>fastjson</artifactId>
|
||||
<version>1.2.83</version>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>junit</groupId>
|
||||
<artifactId>junit</artifactId>
|
||||
<version>4.13.2</version>
|
||||
</dependency>
|
||||
|
||||
<!--目标检测模块-->
|
||||
<dependency>
|
||||
<groupId>cn.smartjavaai</groupId>
|
||||
<artifactId>smartjavaai-objectdetection</artifactId>
|
||||
</dependency>
|
||||
|
||||
|
||||
<dependency>
|
||||
<groupId>ai.djl.pytorch</groupId>
|
||||
<artifactId>pytorch-jni</artifactId>
|
||||
<version>2.5.1-0.32.0</version>
|
||||
<scope>runtime</scope>
|
||||
</dependency>
|
||||
|
||||
|
||||
<!-- windows平台 (保留对应平台的配置，可以减小包大小)-->
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>javacpp</artifactId>
|
||||
<version>${javacv.version}</version>
|
||||
<classifier>${javacv.platform.windows-x86_64}</classifier>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>ffmpeg</artifactId>
|
||||
<version>6.1.1-1.5.10</version>
|
||||
<classifier>${javacv.platform.windows-x86_64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>openblas</artifactId>
|
||||
<version>0.3.26-1.5.10</version>
|
||||
<classifier>${javacv.platform.windows-x86_64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>opencv</artifactId>
|
||||
<version>4.9.0-1.5.10</version>
|
||||
<classifier>${javacv.platform.windows-x86_64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>ai.djl.pytorch</groupId>
|
||||
<artifactId>pytorch-native-cpu</artifactId>
|
||||
<classifier>${djl.platform.windows-x86_64}</classifier>
|
||||
<version>2.5.1</version>
|
||||
<scope>runtime</scope>
|
||||
</dependency>
|
||||
|
||||
|
||||
|
||||
<!-- linux x86 平台 (保留对应平台的配置，可以减小包大小)-->
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>javacpp</artifactId>
|
||||
<version>${javacv.version}</version>
|
||||
<classifier>${javacv.platform.linux-x86_64}</classifier>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>ffmpeg</artifactId>
|
||||
<version>6.1.1-1.5.10</version>
|
||||
<classifier>${javacv.platform.linux-x86_64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>openblas</artifactId>
|
||||
<version>0.3.26-1.5.10</version>
|
||||
<classifier>${javacv.platform.linux-x86_64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>opencv</artifactId>
|
||||
<version>4.9.0-1.5.10</version>
|
||||
<classifier>${javacv.platform.linux-x86_64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>ai.djl.pytorch</groupId>
|
||||
<artifactId>pytorch-native-cpu</artifactId>
|
||||
<classifier>${djl.platform.linux-x86_64}</classifier>
|
||||
<version>2.5.1</version>
|
||||
<scope>runtime</scope>
|
||||
</dependency>
|
||||
|
||||
|
||||
<!-- macOS M系列 平台 (保留对应平台的配置，可以减小包大小)-->
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>javacpp</artifactId>
|
||||
<version>${javacv.version}</version>
|
||||
<classifier>${javacv.platform.macosx-arm64}</classifier>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>ffmpeg</artifactId>
|
||||
<version>6.1.1-1.5.10</version>
|
||||
<classifier>${javacv.platform.macosx-arm64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>openblas</artifactId>
|
||||
<version>0.3.26-1.5.10</version>
|
||||
<classifier>${javacv.platform.macosx-arm64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>opencv</artifactId>
|
||||
<version>4.9.0-1.5.10</version>
|
||||
<classifier>${javacv.platform.macosx-arm64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>ai.djl.pytorch</groupId>
|
||||
<artifactId>pytorch-native-cpu</artifactId>
|
||||
<classifier>${djl.platform.osx-aarch64}</classifier>
|
||||
<version>2.5.1</version>
|
||||
<scope>runtime</scope>
|
||||
</dependency>
|
||||
|
||||
|
||||
<!-- linux aarch64 平台 (保留对应平台的配置，可以减小包大小)-->
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>javacpp</artifactId>
|
||||
<version>${javacv.version}</version>
|
||||
<classifier>${javacv.platform.linux-arm64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>ffmpeg</artifactId>
|
||||
<version>6.1.1-1.5.10</version>
|
||||
<classifier>${javacv.platform.linux-arm64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>openblas</artifactId>
|
||||
<version>0.3.26-1.5.10</version>
|
||||
<classifier>${javacv.platform.linux-arm64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>org.bytedeco</groupId>
|
||||
<artifactId>opencv</artifactId>
|
||||
<version>4.9.0-1.5.10</version>
|
||||
<classifier>${javacv.platform.linux-arm64}</classifier>
|
||||
</dependency>
|
||||
|
||||
<dependency>
|
||||
<groupId>ai.djl.pytorch</groupId>
|
||||
<artifactId>pytorch-native-cpu-precxx11</artifactId>
|
||||
<classifier>${djl.platform.linux-aarch64}</classifier>
|
||||
<version>2.5.1</version>
|
||||
<scope>runtime</scope>
|
||||
</dependency>
|
||||
|
||||
|
||||
|
||||
</dependencies>
|
||||
|
||||
<build>
|
||||
<plugins>
|
||||
<plugin>
|
||||
<groupId>org.apache.maven.plugins</groupId>
|
||||
<artifactId>maven-shade-plugin</artifactId>
|
||||
<version>3.5.0</version>
|
||||
<executions>
|
||||
<execution>
|
||||
<phase>package</phase>
|
||||
<goals><goal>shade</goal></goals>
|
||||
<configuration>
|
||||
<createDependencyReducedPom>false</createDependencyReducedPom>
|
||||
<transformers>
|
||||
<transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
|
||||
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
|
||||
<mainClass>${exec.mainClass}</mainClass>
|
||||
</transformer>
|
||||
</transformers>
|
||||
</configuration>
|
||||
</execution>
|
||||
</executions>
|
||||
</plugin>
|
||||
</plugins>
|
||||
</build>
|
||||
|
||||
<repositories>
|
||||
<repository>
|
||||
<id>aliyunmaven</id>
|
||||
<name>阿里云公共仓库</name>
|
||||
<url>https://maven.aliyun.com/repository/public</url>
|
||||
<releases>
|
||||
<enabled>true</enabled>
|
||||
</releases>
|
||||
<snapshots>
|
||||
<enabled>false</enabled>
|
||||
</snapshots>
|
||||
</repository>
|
||||
</repositories>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</project>
|
||||
@@ -0,0 +1,249 @@
|
||||
package smartai.examples.objectdetection;
|
||||
|
||||
import ai.djl.Application;
|
||||
import ai.djl.MalformedModelException;
|
||||
import ai.djl.modality.cv.Image;
|
||||
import ai.djl.modality.cv.ImageFactory;
|
||||
import ai.djl.modality.cv.output.*;
|
||||
import ai.djl.modality.cv.output.Rectangle;
|
||||
import ai.djl.repository.zoo.Criteria;
|
||||
import ai.djl.repository.zoo.ModelNotFoundException;
|
||||
import ai.djl.repository.zoo.ModelZoo;
|
||||
import ai.djl.repository.zoo.ZooModel;
|
||||
import ai.djl.training.util.ProgressBar;
|
||||
import cn.smartjavaai.common.entity.DetectionInfo;
|
||||
import cn.smartjavaai.common.entity.DetectionRectangle;
|
||||
import cn.smartjavaai.common.entity.DetectionResponse;
|
||||
import cn.smartjavaai.common.entity.R;
|
||||
import cn.smartjavaai.common.enums.DeviceEnum;
|
||||
import cn.smartjavaai.common.enums.face.LivenessStatus;
|
||||
import cn.smartjavaai.common.utils.ImageUtils;
|
||||
import cn.smartjavaai.common.utils.OpenCVUtils;
|
||||
import cn.smartjavaai.face.model.liveness.LivenessDetModel;
|
||||
import cn.smartjavaai.objectdetection.config.DetectorModelConfig;
|
||||
import cn.smartjavaai.objectdetection.enums.DetectorModelEnum;
|
||||
import cn.smartjavaai.objectdetection.exception.DetectionException;
|
||||
import cn.smartjavaai.objectdetection.model.DetectorModel;
|
||||
import cn.smartjavaai.objectdetection.model.ObjectDetectionModelFactory;
|
||||
import com.alibaba.fastjson.JSONObject;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import nu.pattern.OpenCV;
|
||||
import org.junit.Assert;
|
||||
import org.junit.Test;
|
||||
import org.opencv.core.Mat;
|
||||
import org.opencv.core.Size;
|
||||
import org.opencv.imgproc.Imgproc;
|
||||
import org.opencv.videoio.VideoCapture;
|
||||
import org.opencv.videoio.Videoio;
|
||||
|
||||
import javax.imageio.ImageIO;
|
||||
import javax.swing.*;
|
||||
import java.awt.*;
|
||||
import java.awt.image.BufferedImage;
|
||||
import java.io.File;
|
||||
import java.io.IOException;
|
||||
import java.nio.file.Paths;
|
||||
import java.util.ArrayList;
|
||||
import java.util.Iterator;
|
||||
import java.util.List;
|
||||
import java.util.Objects;
|
||||
import java.util.concurrent.Callable;
|
||||
import java.util.concurrent.ExecutorService;
|
||||
import java.util.concurrent.Executors;
|
||||
import java.util.concurrent.Future;
|
||||
|
||||
/**
|
||||
* 目标检测模型demo
|
||||
* 支持功能：目标检测
|
||||
* 模型下载地址：https://pan.baidu.com/s/10aTOLBlR6EG-sq6g0OkAWg?pwd=1234 提取码: 1234
|
||||
* @author dwj
|
||||
*/
|
||||
@Slf4j
|
||||
public class ObjectDetection {
|
||||
|
||||
|
||||
//设备类型
|
||||
public static DeviceEnum device = DeviceEnum.CPU;
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* 使用默认模型检测：YOLO11N
|
||||
*/
|
||||
@Test
|
||||
public void objectDetection(){
|
||||
//默认cpu
|
||||
try (DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel()){
|
||||
DetectionResponse detectionResponse = detectorModel.detect("src/main/resources/object_detection.jpg");
|
||||
log.info("目标检测结果：{}", JSONObject.toJSONString(detectionResponse));
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 指定模型检测(19种模型可选)
|
||||
*/
|
||||
@Test
|
||||
public void objectDetection2(){
|
||||
DetectorModelConfig config = new DetectorModelConfig();
|
||||
config.setModelEnum(DetectorModelEnum.SSD_300_RESNET50);//检测模型，目前支持19种预置模型
|
||||
config.setDevice(device);
|
||||
try (DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config)){
|
||||
DetectionResponse detectionResponse = detectorModel.detect("src/main/resources/dog_bike_car.jpg");
|
||||
log.info("目标检测结果：{}", JSONObject.toJSONString(detectionResponse));
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 人脸检测并绘制检测结果
|
||||
*/
|
||||
@Test
|
||||
public void objectDetectionAndDraw(){
|
||||
try (DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel()){
|
||||
detectorModel.detectAndDraw("src/main/resources/object_detection.jpg","output/object_detection_detected.png");
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 人脸检测并绘制检测结果,返回BufferedImage
|
||||
*/
|
||||
@Test
|
||||
public void objectDetectionAndDraw2(){
|
||||
try (DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel()){
|
||||
String imagePath = "src/main/resources/object_detection.jpg";
|
||||
BufferedImage image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
|
||||
//可以根据后续业务场景使用detectedImage
|
||||
BufferedImage detectedImage = detectorModel.detectAndDraw(image);
|
||||
Assert.assertNotNull("detectedImage null", detectedImage);
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* 使用yolo官方模型检测物品识别
|
||||
*/
|
||||
@Test
|
||||
public void objectDetectionWithOfficialModel(){
|
||||
DetectorModelConfig config = new DetectorModelConfig();
|
||||
config.setThreshold(0.3f);
|
||||
//也支持YoloV8：YOLOV8_OFFICIAL 模型可以从文档中提供的地址下载
|
||||
config.setModelEnum(DetectorModelEnum.YOLOV12_OFFICIAL);//检测模型，目前支持19种模型
|
||||
// 指定模型路径，需要更改为自己的模型路径
|
||||
config.setModelPath("/Users/xxx/Documents/yolov12n.onnx");
|
||||
config.setDevice(device);
|
||||
//一定要将yolo官方的类别文件：synset.txt（文档中下载）放在模型同目录下，否则报错
|
||||
try (DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config)){
|
||||
DetectionResponse detect = detectorModel.detect("src/main/resources/dog_bike_car.jpg");
|
||||
log.info("目标检测结果：{}", JSONObject.toJSONString(detect));
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用自己训练的模型检测
|
||||
*/
|
||||
@Test
|
||||
public void objectDetectionWithCustomModel(){
|
||||
DetectorModelConfig config = new DetectorModelConfig();
|
||||
//也支持YoloV8：YOLOV8_CUSTOM 模型需要自己训练，训练教程可以查看文档
|
||||
config.setModelEnum(DetectorModelEnum.YOLOV12_CUSTOM);//自定义YOLOV12模型
|
||||
// 指定模型路径，需要更改为自己的模型路径
|
||||
config.setModelPath("/Users/xxx/Documents/develop/fire_model/best.onnx");
|
||||
config.putCustomParam("width", 640);//resize 宽
|
||||
config.putCustomParam("height", 640);// resize 高
|
||||
config.putCustomParam("nmsThreshold", 0.5f);
|
||||
config.setDevice(device);
|
||||
try (DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config)){
|
||||
DetectionResponse detect = detectorModel.detect("src/main/resources/dog_bike_car.jpg");
|
||||
log.info("目标检测结果：{}", JSONObject.toJSONString(detect));
|
||||
} catch (Exception e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 摄像头目标检测
|
||||
* 注意事项：如果视频比较卡，可以使用轻量的检测模型
|
||||
*/
|
||||
@Test
|
||||
public void testDetectCamera(){
|
||||
try (DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel()){
|
||||
OpenCV.loadShared();
|
||||
VideoCapture capture = new VideoCapture(0);
|
||||
if (!capture.isOpened()) {
|
||||
System.out.println("No camera detected");
|
||||
return;
|
||||
}
|
||||
|
||||
double ratio =
|
||||
capture.get(Videoio.CAP_PROP_FRAME_WIDTH)
|
||||
/ capture.get(Videoio.CAP_PROP_FRAME_HEIGHT);
|
||||
Dimension screenSize = Toolkit.getDefaultToolkit().getScreenSize();
|
||||
int height = (int) (screenSize.height * 0.65f);
|
||||
int width = (int) (height * ratio);
|
||||
if (width > screenSize.width) {
|
||||
width = screenSize.width;
|
||||
}
|
||||
|
||||
Mat image = new Mat();
|
||||
boolean captured = false;
|
||||
for (int i = 0; i < 10; ++i) {
|
||||
captured = capture.read(image);
|
||||
if (captured) {
|
||||
break;
|
||||
}
|
||||
|
||||
try {
|
||||
Thread.sleep(50);
|
||||
} catch (InterruptedException ignore) {
|
||||
// ignore
|
||||
}
|
||||
}
|
||||
if (!captured) {
|
||||
JOptionPane.showConfirmDialog(null, "Failed to capture image from WebCam.");
|
||||
}
|
||||
ViewerFrame frame = new ViewerFrame(width, height);
|
||||
ImageFactory factory = ImageFactory.getInstance();
|
||||
Size size = new Size(width, height);
|
||||
|
||||
while (capture.isOpened()) {
|
||||
if (!capture.read(image)) {
|
||||
break;
|
||||
}
|
||||
Mat resizeImage = new Mat();
|
||||
Imgproc.resize(image, resizeImage, size);
|
||||
Image img = factory.fromImage(resizeImage);
|
||||
BufferedImage bufferedImage = OpenCVUtils.mat2Image(resizeImage);
|
||||
DetectionResponse detectedResult = detectorModel.detect(bufferedImage);
|
||||
if (Objects.isNull(detectedResult) || Objects.isNull(detectedResult.getDetectionInfoList()) || detectedResult.getDetectionInfoList().size() == 0){
|
||||
log.debug("未检测到物体");
|
||||
continue;
|
||||
}
|
||||
for(DetectionInfo detectionInfo : detectedResult.getDetectionInfoList()){
|
||||
DetectionRectangle detectionRectangle = detectionInfo.getDetectionRectangle();
|
||||
String text = detectionInfo.getObjectDetInfo().getClassName();
|
||||
ImageUtils.drawImageRectWithText(bufferedImage, detectionRectangle, text, Color.RED);
|
||||
}
|
||||
frame.showImage(bufferedImage);
|
||||
}
|
||||
|
||||
capture.release();
|
||||
System.exit(0);
|
||||
} catch (Exception e) {
|
||||
throw new RuntimeException(e);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
@@ -0,0 +1,70 @@
|
||||
/*
|
||||
* Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
|
||||
* with the License. A copy of the License is located at
|
||||
*
|
||||
* http://aws.amazon.com/apache2.0/
|
||||
*
|
||||
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
|
||||
* OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
|
||||
* and limitations under the License.
|
||||
*/
|
||||
package smartai.examples.objectdetection;
|
||||
|
||||
import javax.swing.*;
|
||||
import java.awt.*;
|
||||
import java.awt.image.BufferedImage;
|
||||
|
||||
public class ViewerFrame {
|
||||
|
||||
private JFrame frame;
|
||||
private ImagePanel imagePanel;
|
||||
|
||||
public ViewerFrame(int width, int height) {
|
||||
frame = new JFrame("Demo");
|
||||
imagePanel = new ImagePanel();
|
||||
frame.setLayout(new BorderLayout());
|
||||
frame.add(BorderLayout.CENTER, imagePanel);
|
||||
|
||||
JOptionPane.setRootFrame(frame);
|
||||
Dimension screenSize = Toolkit.getDefaultToolkit().getScreenSize();
|
||||
if (width > screenSize.width) {
|
||||
width = screenSize.width;
|
||||
}
|
||||
Dimension frameSize = new Dimension(width, height);
|
||||
frame.setSize(frameSize);
|
||||
frame.setLocation((screenSize.width - width) / 2, (screenSize.height - height) / 2);
|
||||
frame.setDefaultCloseOperation(WindowConstants.EXIT_ON_CLOSE);
|
||||
frame.setVisible(true);
|
||||
}
|
||||
|
||||
public void showImage(BufferedImage image) {
|
||||
imagePanel.setImage(image);
|
||||
SwingUtilities.invokeLater(
|
||||
() -> {
|
||||
frame.repaint();
|
||||
frame.pack();
|
||||
});
|
||||
}
|
||||
|
||||
private static final class ImagePanel extends JPanel {
|
||||
|
||||
private BufferedImage image;
|
||||
|
||||
void setImage(BufferedImage image) {
|
||||
this.image = image;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void paintComponent(Graphics g) {
|
||||
super.paintComponent(g);
|
||||
if (image == null) {
|
||||
return;
|
||||
}
|
||||
|
||||
g.drawImage(image, 0, 0, null);
|
||||
setPreferredSize(new Dimension(image.getWidth(), image.getHeight()));
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,3 @@
|
||||
Manifest-Version: 1.0
|
||||
Main-Class: smartai.examples.face.SeetaFace6LinuxDemo
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 160 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 463 KiB |
@@ -0,0 +1,14 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!-- 步骤2: 配置文件 (src/main/resources/logback.xml) -->
|
||||
<configuration scan="true" scanPeriod="30 seconds">
|
||||
<!-- 控制台日志输出 -->
|
||||
<appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
|
||||
<encoder>
|
||||
<pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %highlight(%-5level) %cyan(%logger{36}) - %msg%n</pattern>
|
||||
</encoder>
|
||||
</appender>
|
||||
|
||||
<root level="DEBUG">
|
||||
<appender-ref ref="CONSOLE" />
|
||||
</root>
|
||||
</configuration>
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 1.4 MiB |
Reference in New Issue
Block a user