临时提交

examples/face-example/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.0.23</smartjavaai.version>
+        <smartjavaai.version>1.0.24</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.face.facedet.FaceDetDemo</exec.mainClass>
 
@@ -255,6 +255,14 @@
             <scope>runtime</scope>
         </dependency>
 
+        <dependency>
+            <groupId>ai.djl.pytorch</groupId>
+            <artifactId>pytorch-native-cpu</artifactId>
+            <classifier>linux-aarch64</classifier>
+            <scope>runtime</scope>
+            <version>2.5.1</version>
+        </dependency>
+
 
 
     </dependencies>

examples/face-example/src/main/java/smartai/examples/face/PythonTranslator.java

@@ -0,0 +1,118 @@
+/*
+ * Copyright 2023 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.face;
+
+import ai.djl.ModelException;
+import ai.djl.inference.Predictor;
+import ai.djl.modality.Classifications;
+import ai.djl.modality.Input;
+import ai.djl.modality.Output;
+import ai.djl.modality.cv.Image;
+import ai.djl.ndarray.NDList;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.translate.NoBatchifyTranslator;
+import ai.djl.translate.TranslateException;
+import ai.djl.translate.TranslatorContext;
+import ai.djl.util.JsonUtils;
+import ai.djl.util.Utils;
+
+import com.google.gson.reflect.TypeToken;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.lang.reflect.Type;
+import java.net.URL;
+import java.nio.file.Paths;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+
+public class PythonTranslator implements NoBatchifyTranslator<byte[], Classifications> {
+
+    private ZooModel<Input, Output> model;
+    private Predictor<Input, Output> predictor;
+
+    @Override
+    public void prepare(TranslatorContext ctx) throws ModelException, IOException {
+        if (predictor == null) {
+            Criteria<Input, Output> criteria =
+                    Criteria.builder()
+                            .setTypes(Input.class, Output.class)
+                            .optModelPath(Paths.get("src/test/python"))
+                            .optEngine("Python")
+                            .build();
+            model = criteria.loadModel();
+            predictor = model.newPredictor();
+        }
+    }
+
+//    @Override
+//    public NDList processInput(TranslatorContext ctx, String url)
+//            throws IOException, TranslateException {
+//        Input input = new Input();
+//        try (InputStream is = new URL(url).openStream()) {
+//            input.add("data", Utils.toByteArray(is));
+//        }
+//        input.addProperty("Content-Type", "image/jpeg");
+//        // calling preprocess() function in model.py
+//        input.addProperty("handler", "preprocess");
+//        Output output = predictor.predict(input);
+//        if (output.getCode() != 200) {
+//            throw new TranslateException("Python preprocess() failed: " + output.getMessage());
+//        }
+//
+//        return output.getDataAsNDList(ctx.getNDManager());
+//    }
+
+    @Override
+    public NDList processInput(TranslatorContext ctx, byte[] image)
+            throws IOException, TranslateException {
+        Input input = new Input();
+        input.add("data", image);
+        input.addProperty("Content-Type", "image/jpeg");
+        // calling preprocess() function in model.py
+        input.addProperty("handler", "preprocess");
+        Output output = predictor.predict(input);
+        if (output.getCode() != 200) {
+            throw new TranslateException("Python preprocess() failed: " + output.getMessage());
+        }
+        return output.getDataAsNDList(ctx.getNDManager());
+    }
+
+    @Override
+    public Classifications processOutput(TranslatorContext ctx, NDList list)
+            throws TranslateException {
+        Input input = new Input();
+        input.add("data", list);
+        // calling postprocess() function in processing.py
+        input.addProperty("handler", "postprocess");
+        Output output = predictor.predict(input);
+        if (output.getCode() != 200) {
+            throw new TranslateException("Python postprocess() failed: " + output.getMessage());
+        }
+
+        String json = output.getData().getAsString();
+        System.out.println("json:" + json);
+        return null;
+    }
+
+    public void close() {
+        if (predictor != null) {
+            predictor.close();
+            model.close();
+            predictor = null;
+            model = null;
+        }
+    }
+}

examples/face-example/src/main/java/smartai/examples/face/Test.java

@@ -0,0 +1,99 @@
+package smartai.examples.face;
+
+import ai.djl.Application;
+import ai.djl.Device;
+import ai.djl.MalformedModelException;
+import ai.djl.inference.Predictor;
+import ai.djl.modality.Classifications;
+import ai.djl.modality.audio.Audio;
+import ai.djl.modality.audio.AudioFactory;
+import ai.djl.modality.audio.translator.SpeechRecognitionTranslatorFactory;
+import ai.djl.repository.Artifact;
+import ai.djl.repository.MRL;
+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.translate.TranslateException;
+import lombok.extern.slf4j.Slf4j;
+
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * @author dwj
+ * @date 2025/7/29
+ */
+@Slf4j
+public class Test {
+
+    public static void main(String[] args) throws ModelNotFoundException, MalformedModelException, IOException, TranslateException {
+//        PythonTranslator translator = new PythonTranslator();
+//        Criteria<byte[], Classifications> criteria =
+//                Criteria.builder()
+//                        .setTypes(byte[].class, Classifications.class)
+//                        .optModelPath(Paths.get("/Users/wenjie/Documents/develop/model/arcfaceresnet100-11-int8.onnx"))
+//                        .optEngine("OnnxRuntime")
+//                        .optTranslator(translator)
+//                        .build();
+//        String path = "/Users/wenjie/Downloads/facetest/jsy.jpg";
+//        try (ZooModel<byte[], Classifications> model = criteria.loadModel();
+//             Predictor<byte[], Classifications> predictor = model.newPredictor()) {
+//            byte[] data = Files.readAllBytes(Paths.get(path));
+//            Classifications ret = predictor.predict(data);
+//            System.out.println(ret);
+//        }
+//
+//        // unload python model
+//        translator.close();
+
+
+        // Load model.
+        // Wav2Vec2 model is a speech model that accepts a float array corresponding to the raw
+        // waveform of the speech signal.
+
+//        String url = "/Users/wenjie/Downloads/20210601_u2++_conformer_exp/final.pt";
+//        Criteria<Audio, String> criteria =
+//                Criteria.builder()
+//                        .setTypes(Audio.class, String.class)
+////                        .optModelUrls(url)
+//                        .optModelPath(Paths.get(url))
+//                        .optDevice(Device.cpu()) // torchscript model only support CPU
+//                        .optTranslatorFactory(new SpeechRecognitionTranslatorFactory())
+////                        .optModelName("data.pkl")
+//                        .optEngine("PyTorch")
+//                        .build();
+//
+//        // Read in audio file
+//        String wave = "https://resources.djl.ai/audios/speech.wav";
+//        Audio audio = AudioFactory.newInstance().fromUrl(wave);
+//        try (ZooModel<Audio, String> model = criteria.loadModel();
+//             Predictor<Audio, String> predictor = model.newPredictor()) {
+//             String result = predictor.predict(audio);
+//             log.info("Result: {}", result);
+//        }
+
+        boolean withArtifacts =
+                args.length > 0 && ("--artifact".equals(args[0]) || "-a".equals(args[0]));
+        if (!withArtifacts) {
+            log.info("============================================================");
+            log.info("user ./gradlew listModel --args='-a' to show artifact detail");
+            log.info("============================================================");
+        }
+        Map<Application, List<Artifact>> models = ModelZoo.listModels();
+        for (Map.Entry<Application, List<Artifact>> entry : models.entrySet()) {
+            String appName = entry.getKey().toString();
+            for (Artifact artifact : entry.getValue()) {
+                if (withArtifacts) {
+                    log.info("{} djl://{}", appName, artifact);
+                } else {
+                    log.info("{} {}", appName, artifact);
+                }
+            }
+        }
+    }
+}

examples/face-example/src/main/java/smartai/examples/face/facedet/FaceDetDemo.java

@@ -67,7 +67,7 @@ public class FaceDetDemo {
         //高精度模型，速度慢
         config.setModelEnum(FaceDetModelEnum.RETINA_FACE);//人脸检测模型
         //下载模型并替换本地路径，下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
-        config.setModelPath("/Users/xxx/Documents/develop/model/retinaface.pt");
+        config.setModelPath("/Users/wenjie/Documents/develop/face_model/retinaface.pt");
         config.setConfidenceThreshold(FaceDetectConstant.DEFAULT_CONFIDENCE_THRESHOLD);//只返回相似度大于该值的人脸
         config.setNmsThresh(FaceDetectConstant.NMS_THRESHOLD);//用于去除重复的人脸框，当两个框的重叠度超过该值时，只保留一个
         return FaceDetModelFactory.getInstance().getModel(config);
@@ -95,12 +95,21 @@ public class FaceDetDemo {
     @Test
     public void testFaceDetect(){
         try {
-            FaceDetModel faceModel = FaceDetModelFactory.getInstance().getModel();
+            FaceDetModel faceModel = getFaceDetModel();
             R<DetectionResponse> detectedResult = faceModel.detect(imgPath);
-            if(detectedResult.isSuccess()){
-                log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult.getData()));
+//            if(detectedResult.isSuccess()){
+//                log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult.getData()));
+//            }else{
+//                log.info("人脸检测失败：{}", detectedResult.getMessage());
+//            }
+
+            long start = System.currentTimeMillis();
+            R<DetectionResponse> detectedResult2 = faceModel.detect("/Users/wenjie/Downloads/facetest/surprise.png");
+            log.info("耗时：{}", System.currentTimeMillis() - start);
+            if(detectedResult2.isSuccess()){
+                log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult2.getData()));
             }else{
-                log.info("人脸检测失败：{}", detectedResult.getMessage());
+                log.info("人脸检测失败：{}", detectedResult2.getMessage());
             }
         } catch (Exception e) {
             e.printStackTrace();

examples/face-example/src/main/java/smartai/examples/face/facerec/FaceRecDemo.java

@@ -63,7 +63,7 @@ public class FaceRecDemo {
         //高精度模型，速度慢
         config.setModelEnum(FaceDetModelEnum.RETINA_FACE);//人脸检测模型
         //下载模型并替换本地路径，下载地址：https://pan.baidu.com/s/10l22x5fRz_gwLr8EAHa1Jg?pwd=1234 提取码: 1234
-        config.setModelPath("/Users/xxx/Documents/develop/model/retinaface.pt");
+//        config.setModelPath("/Users/wenjie/Documents/develop/model/retinaface.pt");
         config.setConfidenceThreshold(FaceDetectConstant.DEFAULT_CONFIDENCE_THRESHOLD);//只返回相似度大于该值的人脸
         config.setNmsThresh(FaceDetectConstant.NMS_THRESHOLD);//用于去除重复的人脸框，当两个框的重叠度超过该值时，只保留一个
         config.setDevice(device);
@@ -170,7 +170,7 @@ public class FaceRecDemo {
         FaceRecConfig config = new FaceRecConfig();
         //高精度模型，速度慢, 追求速度请更换高速模型，具体其他模型参数可以查看文档：http://doc.smartjavaai.cn/face.html
         config.setModelEnum(FaceRecModelEnum.ELASTIC_FACE_MODEL);//人脸检测模型
-        config.setModelPath("/Users/xxx/Documents/develop/model/elasticface.pt");
+        config.setModelPath("/Users/wenjie/Documents/develop/model/elasticface.pt");
         //裁剪人脸：如果图片已经是裁剪过的，则请将此参数设置为false
         config.setCropFace(true);
         //开启人脸对齐：适用于人脸不正的场景，开启将提升人脸特征准确度，关闭可以提升性能

examples/face-example/src/test/python/model.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+#
+# Copyright 2023 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.txt" file accompanying this file. This file is distributed on an "AS IS"
+# BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied. See the License for
+# the specific language governing permissions and limitations under the License.
+"""
+PyTorch resnet18 pre/post processing example.
+"""
+
+import json
+import logging
+import os
+from typing import Optional, Any
+import sklearn
+
+import torch
+import torch.nn.functional as F
+from torchvision import transforms
+
+from djl_python import Input
+from djl_python import Output
+
+
+class Processing(object):
+
+    def __init__(self):
+        self.topK = 5
+        self.image_processing = None
+        self.mapping = None
+        self.initialized = False
+
+    def initialize(self, properties: dict):
+        """
+        Initialize model.
+        """
+        self.image_processing = transforms.Compose([
+            transforms.Resize(112),
+            transforms.CenterCrop(112),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                 std=[0.229, 0.224, 0.225])
+        ])
+        #self.mapping = self.load_label_mapping("index_to_name.json")
+        self.initialized = True
+
+    def preprocess(self, inputs: Input) -> Output:
+        outputs = Output()
+        try:
+            batch = inputs.get_batches()
+            images = []
+            for i, item in enumerate(batch):
+                image = self.image_processing(item.get_as_image())
+                images.append(image)
+            images = torch.stack(images)
+            outputs.add_as_numpy(images.detach().numpy())
+            outputs.add_property("content-type", "tensor/ndlist")
+        except Exception as e:
+            logging.exception("pre-process failed")
+            # error handling
+            outputs = Output().error(str(e))
+
+        return outputs
+
+    def postprocess(self, inputs: Input) -> Output:
+        outputs = Output()
+        try:
+            data = inputs.get_as_numpy(0)[0]
+            item = torch.from_numpy(data)
+            print("data shape:", item.shape)
+            embedding = sklearn.preprocessing.normalize(item).flatten()
+            outputs.add(embedding)
+        except Exception as e:
+            logging.exception("post-process failed")
+            # error handling
+            outputs = Output().error(str(e))
+
+        return outputs
+
+    @staticmethod
+    def load_label_mapping(mapping_file_path: Any) -> dict:
+        if not os.path.isfile(mapping_file_path):
+            raise Exception('mapping file not found: ' + mapping_file_path)
+
+        with open(mapping_file_path) as f:
+            mapping = json.load(f)
+        if not isinstance(mapping, dict):
+            raise Exception('mapping file should be in "class":"label" format')
+
+        for key, value in mapping.items():
+            new_value = value
+            if isinstance(new_value, list):
+                new_value = value[-1]
+            if not isinstance(new_value, str):
+                raise Exception(
+                    'labels in mapping must be either str or [str]')
+            mapping[key] = new_value
+        return mapping
+
+
+_service = Processing()
+
+
+def preprocess(inputs: Input) -> Output:
+    return _service.preprocess(inputs)
+
+
+def postprocess(inputs: Input) -> Output:
+    return _service.postprocess(inputs)
+
+
+def handle(inputs: Input) -> Optional[Output]:
+    """
+    Default handler function
+    """
+    if not _service.initialized:
+        # stateful model
+        _service.initialize(inputs.get_properties())
+
+    return None

examples/objectdetection-example/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.0.23</smartjavaai.version>
+        <smartjavaai.version>1.0.24</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.objectdetection.ObjectDetection</exec.mainClass>
 
@@ -34,7 +34,7 @@
         <dependencies>
             <dependency>
                 <groupId>cn.smartjavaai</groupId>
-                <artifactId>smartjavaai-bom</artifactId>
+                <artifactId>bom</artifactId>
                 <version>${smartjavaai.version}</version>
                 <type>pom</type>
                 <!-- 注意这里是import -->
@@ -94,7 +94,7 @@
         <!--目标检测模块-->
         <dependency>
             <groupId>cn.smartjavaai</groupId>
-            <artifactId>smartjavaai-objectdetection</artifactId>
+            <artifactId>vision</artifactId>
         </dependency>
 
 

examples/objectdetection-example/src/main/java/smartai/examples/objectdetection/ObjectDetection.java

@@ -2,6 +2,9 @@ package smartai.examples.objectdetection;
 
 import ai.djl.Application;
 import ai.djl.MalformedModelException;
+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.*;
@@ -11,6 +14,7 @@ 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 ai.djl.translate.TranslateException;
 import cn.smartjavaai.common.config.Config;
 import cn.smartjavaai.common.entity.DetectionInfo;
 import cn.smartjavaai.common.entity.DetectionRectangle;
@@ -42,6 +46,7 @@ import java.awt.*;
 import java.awt.image.BufferedImage;
 import java.io.File;
 import java.io.IOException;
+import java.net.URL;
 import java.nio.file.Paths;
 import java.util.*;
 import java.util.List;
@@ -64,6 +69,32 @@ public class ObjectDetection {
     //设备类型
     public static DeviceEnum device = DeviceEnum.CPU;
 
+    public static void main(String[] args) throws ModelException, TranslateException, IOException {
+        Classifications classification = predict();
+        log.info("{}", classification);
+    }
+
+
+    public static Classifications predict() throws IOException, ModelException, TranslateException {
+
+        Config.setCachePath("/Users/wenjie/smartjavaai_cache");
+        URL url = new URL("https://resources.djl.ai/images/action_dance.jpg");
+        // Use DJL PyTorch model zoo model
+        Criteria<URL, Classifications> criteria =
+                Criteria.builder()
+                        .setTypes(URL.class, Classifications.class)
+                        .optModelUrls(
+                                "djl://ai.djl.mxnet/action_recognition")
+                        .optEngine("MXNet")
+                        .optProgress(new ProgressBar())
+                        .build();
+
+        try (ZooModel<URL, Classifications> inception = criteria.loadModel();
+             Predictor<URL, Classifications> action = inception.newPredictor()) {
+            return action.predict(url);
+        }
+    }
+
     @BeforeClass
     public static void beforeAll() throws IOException {
         //修改缓存路径
@@ -95,6 +126,8 @@ public class ObjectDetection {
         try {
             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.setModelEnum(DetectorModelEnum.YOLOV12_OFFICIAL);
+            config.setModelPath("yolov11s");
             // 指定允许的类别
 //            config.setAllowedClasses(Arrays.asList("person"));
             //指定返回检测数量
@@ -205,6 +238,32 @@ public class ObjectDetection {
         }
     }
 
+    /**
+     * tensorflow目标检测
+     */
+    @Test
+    public void objectDetection3(){
+        try {
+            DetectorModelConfig config = new DetectorModelConfig();
+            config.setModelEnum(DetectorModelEnum.TENSORFLOW2_OFFICIAL);
+            config.setModelPath("/Users/wenjie/Documents/develop/model/tensorflow/ssd_mobilenet_v2_320x320_coco17_tpu-8");
+//            config.putCustomParam("synsetUrl", "https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt");
+//            config.putCustomParam("synsetPath", "/Users/wenjie/Downloads/mscoco_label_map.pbtxt.txt");
+            config.putCustomParam("synsetFileName", "mscoco.pbtxt");
+            // 指定允许的类别
+//            config.setAllowedClasses(Arrays.asList("person"));
+            //指定返回检测数量
+            config.setTopK(100);
+            config.setDevice(device);
+            DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config);
+            DetectionResponse detectionResponse = detectorModel.detect("src/main/resources/dog_bike_car.jpg");
+            detectorModel.detectAndDraw("src/main/resources/dog_bike_car.jpg", "output/dog_bike_car_detect.jpg");
+            log.info("目标检测结果：{}", JSONObject.toJSONString(detectionResponse));
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
 
     /**
      * 摄像头目标检测

examples/ocr-examples/output/table_ch2_result.html

@@ -0,0 +1,5 @@
+<style>
+table { border-collapse: collapse; }
+td, th, table { border: 1px solid black; padding: 5px; }
+</style>
+<html><body><table><thead><tr><td>主要财务比率</td><td>2020</td><td>2021</td><td>2022E</td><td>2023E</td><td>2024E</td></tr></thead><tbody><tr><td>成长能力</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>营业收入</td><td>97.08%</td><td>33.28%</td><td>65.00%</td><td>42.10%</td><td>21.00%</td></tr><tr><td>营业利润</td><td>165.21%</td><td>22.38%</td><td>31.65%</td><td>64.55%</td><td>36.68%</td></tr><tr><td>归属於母公司净利润</td><td>164.75%</td><td>24.17%</td><td>39.44%</td><td>64.13%</td><td>38.63%</td></tr><tr><td>获利能力</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>毛利率</td><td>25.45%</td><td>23.01%</td><td>16.80%</td><td>17.00%</td><td>18.00%</td></tr><tr><td>净利率</td><td>13.98%</td><td>13.03%</td><td>11.01%</td><td>12.72%</td><td>14.57%</td></tr><tr><td>ROE</td><td>19.29%</td><td>19.25%</td><td>20.77%</td><td>47.11%</td><td>35.24%</td></tr><tr><td>ROIC</td><td>44.53%</td><td>41.55%</td><td>44.21%</td><td>32.59%</td><td>62.14%</td></tr><tr><td>偿债能力</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>资产负债率</td><td>48.28%</td><td>54.90%</td><td>57.79%</td><td>65.62%</td><td>58.84%</td></tr><tr><td>净负债率</td><td>-39.12%</td><td>-36.03%</td><td>6.62%</td><td>8.70%</td><td>5.28%</td></tr><tr><td>流动比率</td><td>1.77</td><td>1.74</td><td>1.60</td><td>1.41</td><td>1.65</td></tr><tr><td>速动比率</td><td>1.26</td><td>1.07</td><td>0.85</td><td>0.62</td><td>0.81</td></tr><tr><td>营运能力</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>应收账款周转率</td><td>5.16</td><td>4.59</td><td>4.11</td><td>5.24</td><td>5.24</td></tr><tr><td>存货周转率</td><td>3.48</td><td>2.89</td><td>2.55</td><td>2.77</td><td>2.63</td></tr><tr><td>总资产周转率</td><td>0.80</td><td>0.78</td><td>0.93</td><td>1.21</td><td>1.22</td></tr><tr><td>每股指标（元）</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>每股收益</td><td>0.84</td><td>1.04</td><td>1.45</td><td>2.38</td><td>3.30</td></tr><tr><td>每股经营现金流</td><td>0.03</td><td>0.04</td><td>-2.54</td><td>4.28</td><td>-1.13</td></tr><tr><td>每股净资产</td><td>4.34</td><td>5.40</td><td>6.97</td><td>5.05</td><td>9.35</td></tr><tr><td>估值比率</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>市盈率</td><td>41.30</td><td>33.26</td><td>23.85</td><td>14.53</td><td>10.48</td></tr><tr><td>市净率</td><td>7.97</td><td>6.40</td><td>4.95</td><td>6.85</td><td>3.69</td></tr><tr><td>EV/EBITDA</td><td>5.08</td><td>22.72</td><td>23.65</td><td>14.40</td><td>10.60</td></tr><tr><td>EV/EBIT</td><td>5.33</td><td>24.19</td><td>25.45</td><td>15.05</td><td>10.95</td></tr></tbody></table></body></html>

examples/ocr-examples/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.0.23</smartjavaai.version>
+        <smartjavaai.version>1.0.24</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.ocr.common.OcrRecognizeDemo</exec.mainClass>
 

examples/ocr-examples/src/main/java/smartai/examples/ocr/common/OcrRecognizeDemo.java

@@ -61,7 +61,7 @@ public class OcrRecognizeDemo {
         //指定文本识别模型
         recModelConfig.setRecModelEnum(CommonRecModelEnum.PP_OCR_V5_MOBILE_REC_MODEL);
         //指定识别模型位置，需要更改为自己的模型路径（下载地址请查看文档）
-        recModelConfig.setRecModelPath("/Users/wenjie/Documents/develop/model/ocr/PP-OCRv5_mobile_rec_infer/PP-OCRv5_mobile_rec_infer.onnx");
+        recModelConfig.setRecModelPath("/Users/wenjie/Documents/develop/model/ocr/PP-OCRv5_server_rec_infer/PP-OCRv5_server_rec.onnx");
         recModelConfig.setDevice(device);
         recModelConfig.setTextDetModel(getDetectionModel());
         return OcrModelFactory.getInstance().getRecModel(recModelConfig);
@@ -76,7 +76,7 @@ public class OcrRecognizeDemo {
         //指定检测模型
         config.setModelEnum(CommonDetModelEnum.PP_OCR_V5_MOBILE_DET_MODEL);
         //指定模型位置，需要更改为自己的模型路径（下载地址请查看文档）
-        config.setDetModelPath("/Users/wenjie/Documents/develop/model/ocr/PP-OCRv5_mobile_det_infer/PP-OCRv5_mobile_det_infer.onnx");
+        config.setDetModelPath("/Users/wenjie/Documents/develop/model/ocr/PP-OCRv5_server_det_infer/PP-OCRv5_server_det.onnx");
         config.setDevice(device);
         return OcrModelFactory.getInstance().getDetModel(config);
     }
@@ -127,7 +127,7 @@ public class OcrRecognizeDemo {
             OcrCommonRecModel recModel = getRecModel();
             //不带方向矫正，分行返回文本
             OcrRecOptions options = new OcrRecOptions(false, true);
-            OcrInfo ocrInfo = recModel.recognize("src/main/resources/ocr_2.jpg",options);
+            OcrInfo ocrInfo = recModel.recognize("/Users/wenjie/Downloads/49421755855753_.pic_hd.jpg",options);
             log.info("OCR识别结果：{}", JSONObject.toJSONString(ocrInfo));
         } catch (Exception e) {
             e.printStackTrace();

examples/speech-examples/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.0.23</smartjavaai.version>
+        <smartjavaai.version>1.0.24</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.speech.asr.common.OcrRecognizeDemo</exec.mainClass>
 

examples/translation-example/pom.xml

@@ -12,7 +12,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-        <smartjavaai.version>1.0.23</smartjavaai.version>
+        <smartjavaai.version>1.0.24</smartjavaai.version>
         <!--如果打包运行，需要替换成你的main-->
         <exec.mainClass>smartai.examples.nlp.translation.TranslationDemo</exec.mainClass>