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100 lines
4.7 KiB
Markdown
100 lines
4.7 KiB
Markdown
# SmartJavaAI离线下载模型案例
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**SmartJavaAI**如果未指定模型地址，系统将自动下载模型至本地。因此，无论模型是否通过离线方式下载，SmartJavaAI 最终都会在离线环境下运行模型。
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### 1. 安装人脸算法依赖
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在 Maven 项目的 `pom.xml` 中添加 SmartJavaAI的人脸算法依赖：
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```xml
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<dependencies>
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<dependency>
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<groupId>ink.numberone</groupId>
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<artifactId>smartjavaai-face</artifactId>
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<version>1.0.2</version>
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</dependency>
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</dependencies>
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```
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### 2. 下载模型
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| 模型名称 | 下载地址 | 文件大小 | 适用场景 |
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| :-----------------------: | :----------------------------------------------------------: | :------: | :------------: |
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| retinaface | [下载](https://resources.djl.ai/test-models/pytorch/retinaface.zip) | 110MB | 高精度人脸检测 |
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| ultralightfastgenericface | [下载](https://resources.djl.ai/test-models/pytorch/ultranet.zip) | 1.7MB | 高速人脸检测 |
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| featureExtraction | [下载](https://resources.djl.ai/test-models/pytorch/face_feature.zip) | 104MB | 人脸特征提取 |
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### 3. 人脸检测代码示例（离线下载模型）
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```java
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// 初始化配置
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ModelConfig config = new ModelConfig();
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config.setAlgorithmName("retinaface");//人脸算法模型，目前支持：retinaface及ultralightfastgenericface
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//config.setAlgorithmName("ultralightfastgenericface");//轻量模型
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config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);//置信度阈值
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config.setMaxFaceCount(FaceConfig.MAX_FACE_LIMIT);//每张特征图保留的最大候选框数量
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//nms阈值:控制重叠框的合并程度,取值越低，合并越多重叠框（减少误检但可能漏检）；取值越高，保留更多框（增加检出但可能引入冗余）
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config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
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//模型下载地址：
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//retinaface: https://resources.djl.ai/test-models/pytorch/retinaface.zip
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//ultralightfastgenericface: https://resources.djl.ai/test-models/pytorch/ultranet.zip
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//改为模型存放路径
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config.setModelPath("/Users/xxx/Documents/develop/face_model/retinaface.pt");
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//创建人脸算法
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FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm(config);
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//使用图片路径检测
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FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
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logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
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//使用图片流检测
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File input = new File("src/main/resources/largest_selfie.jpg");
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//FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(input));
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//logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
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BufferedImage image = ImageIO.read(input);
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//创建保存路径
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Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
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//绘制人脸框
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ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
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```
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### 3. 人证核验示例（离线下载模型）
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人证核验步骤：
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（1）提取身份证人脸特征，
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（2）提取实时人脸特征
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（3）特征比对
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```java
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// 初始化配置
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ModelConfig config = new ModelConfig();
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config.setAlgorithmName("featureExtraction");
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//模型下载地址：https://resources.djl.ai/test-models/pytorch/face_feature.zip
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//改为模型存放路径
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config.setModelPath("/Users/xxx/Documents/develop/face_model/face_feature.pt");
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//创建脸算法
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FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceFeatureAlgorithm(config);
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//提取身份证人脸特征（图片仅供测试）
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float[] featureIdCard = currentAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
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//提取身份证人脸特征（从图片流获取）
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//File input = new File("src/main/resources/kana1.jpg");
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//float[] featureIdCard = currentAlgorithm.featureExtraction(new FileInputStream(input));
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logger.info("身份证人脸特征：{}", JSONObject.toJSONString(featureIdCard));
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//提取实时人脸特征（图片仅供测试）
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float[] realTimeFeature = currentAlgorithm.featureExtraction("src/main/resources/kana2.jpg");
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logger.info("实时人脸特征：{}", JSONObject.toJSONString(realTimeFeature));
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if(realTimeFeature != null){
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if(currentAlgorithm.calculSimilar(featureIdCard, realTimeFeature) > 0.8){
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logger.info("人脸核验通过");
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}else{
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logger.info("人脸核验不通过");
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}
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}
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```
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## 完整代码
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`📁 examples/src/main/java/smartai/examples/face`
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└── 📄[FaceDemo.java](https://github.com/geekwenjie/SmartJavaAI/blob/master/examples/src/main/java/smartai/examples/face/FaceDemo.java) <sub>*（基于JDK11构建的完整可执行示例）*</sub>
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