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@@ -1,42 +1,44 @@
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# FastDeploy Android AAR 包使用文档
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FastDeploy Android SDK 目前支持图像分类、目标检测、OCR文字识别、语义分割和人脸检测等任务，对更多的AI任务支持将会陆续添加进来。以下为各个任务对应的API文档，在Android下使用FastDeploy中集成的模型，只需以下几个步骤：
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- 模型初始化
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- 调用`predict`接口
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- 可视化验证（可选）
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English | [简体中文](README_CN.md)
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|图像分类|目标检测|OCR文字识别|人像分割|人脸检测|
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|:---:|:---:|:---:|:---:|:---:|
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# FastDeploy Android AAR Package
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Currently FastDeploy Android SDK supports image classification, target detection, OCR text recognition, semantic segmentation and face detection. More AI tasks will be added in the future. The following is the API documents for each task. To use the models integrated in FastDeploy on Android, you only need to take the following steps.
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- Model initialization
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- Calling the `predict` interface
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- Visualization validation (optional)
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|Image Classification|Target Detection|OCR Text Recognition|Portrait Segmentation|Face Detection|
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|:---:|:---:|:---:|:---:|:---:|
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||||||
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## 内容目录
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## Content
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- [下载及配置SDK](#SDK)
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- [图像分类API](#Classification)
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- [目标检测API](#Detection)
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- [语义分割API](#Segmentation)
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- [OCR文字识别API](#OCR)
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- [人脸检测API](#FaceDetection)
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- [识别结果说明](#VisionResults)
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- [RuntimeOption说明](#RuntimeOption)
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- [可视化接口API](#Visualize)
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- [模型使用示例](#Demo)
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- [App示例工程使用方式](#App)
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- [Download and Configure SDK](#SDK)
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- [Image Classification API](#Classification)
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- [Target Detection API](#Detection)
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- [Semantic Segmentation API](#Segmentation)
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- [OCR Text Recognition API ](#OCR)
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- [Face Detection API](#FaceDetection)
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- [Identification Result Description](#VisionResults)
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- [Runtime Option Description](#RuntimeOption)
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- [Visualization Interface ](#Visualize)
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- [Examples of How to Use Models](#Demo)
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- [How to Use the App Sample Project](#App)
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## 下载及配置SDK
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## Download and Configure SDK
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<div id="SDK"></div>
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### 下载 FastDeploy Android SDK
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Release版本（Java SDK 目前仅支持Android，当前版本为 1.0.0）
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### Download FastDeploy Android SDK
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The release version is as follows (Java SDK currently supports Android only, and current version is 1.0.0):
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| 平台 | 文件 | 说明 |
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| Platform | File | Description |
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| :--- | :--- | :---- |
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| Android Java SDK | [fastdeploy-android-sdk-1.0.0.aar](https://bj.bcebos.com/fastdeploy/release/android/fastdeploy-android-sdk-1.0.0.aar) | NDK 20 编译产出, minSdkVersion 15,targetSdkVersion 28 |
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| Android Java SDK | [fastdeploy-android-sdk-1.0.0.aar](https://bj.bcebos.com/fastdeploy/release/android/fastdeploy-android-sdk-1.0.0.aar) | NDK 20 compiles, minSdkVersion 15,targetSdkVersion 28 |
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更多预编译库信息，请参考: [download_prebuilt_libraries.md](../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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For more information for pre-compile library, please refer to: [download_prebuilt_libraries.md](../../docs/cn/build_and_install/download_prebuilt_libraries.md).
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### 配置 FastDeploy Android SDK
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## Configure FastDeploy Android SDK
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首先，将fastdeploy-android-sdk-xxx.aar拷贝到您Android工程的libs目录下，其中`xxx`表示您所下载的SDK的版本号。
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First, please copy fastdeploy-android-sdk-xxx.aar to the libs directory of your Android project, where `xxx` indicates the version number of the SDK you download.
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```shell
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├── build.gradle
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├── libs
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@@ -45,7 +47,7 @@ Release版本（Java SDK 目前仅支持Android，当前版本为 1.0.0）
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└── src
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```
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然后，在您的Android工程中的build.gradble引入FastDeploy SDK，如下：
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Then, please add FastDeploy SDK to build.gradble in your Android project.
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```java
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dependencies {
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implementation fileTree(include: ['*.aar'], dir: 'libs')
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@@ -54,349 +56,349 @@ dependencies {
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}
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```
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## 图像分类API
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## Image Classification API
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<div id="Classification"></div>
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### PaddleClasModel Java API 说明
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- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleClasModel初始化参数说明如下：
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- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
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- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
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- configFile: String, 模型推理的预处理配置文件，如 infer_cfg.yml
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- labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 imagenet1k_label_list.txt，每一行包含一个label
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- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
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### PaddleClasModel Java API Introduction
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- Model initialization API: Model initialization API contains two methods, you can initialize directly through the constructor, or call init function at the appropriate program node. PaddleClasModel initialization parameters are described as follows:
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- modelFile: String, path to the model file in paddle format, e.g. model.pdmodel.
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- paramFile: String, path to the parameter file in paddle format, e.g. model.pdiparams.
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- configFile: String, preprocessing configuration file of model inference, e.g. infer_cfg.yml.
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- labelFile: String, optional, path to the label file, for visualization, e.g. imagenet1k_label_list.txt, in which each line contains a label.
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- option: RuntimeOption, optional, model initialization option. If this parameter is not passed, the default runtime option will be used.
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```java
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// 构造函数: constructor w/o label file
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public PaddleClasModel(); // 空构造函数，之后可以调用init初始化
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// Constructor w/o label file
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public PaddleClasModel(); // An empty constructor, which can be initialised by calling init function later.
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public PaddleClasModel(String modelFile, String paramsFile, String configFile);
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public PaddleClasModel(String modelFile, String paramsFile, String configFile, String labelFile);
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public PaddleClasModel(String modelFile, String paramsFile, String configFile, RuntimeOption option);
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public PaddleClasModel(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
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// 手动调用init初始化: call init manually w/o label file
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// Call init manually w/o label file
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public boolean init(String modelFile, String paramsFile, String configFile, RuntimeOption option);
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public boolean init(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
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```
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- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
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- Model prediction API: Model prediction API includes direct prediction API and API with visualization function. Direct prediction means that no image is saved and no result is rendered to Bitmap, but only the inference result is predicted. Prediction and visualization means to predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap (currently supports Bitmap in format ARGB8888), which can be displayed in camera later.
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```java
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// 直接预测：不保存图片以及不渲染结果到Bitmap上
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// Directly predict: do not save images or render result to Bitmap.
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public ClassifyResult predict(Bitmap ARGB8888Bitmap)；
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// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
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// Predict and visualize: predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap.
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public ClassifyResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float scoreThreshold);
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public ClassifyResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float scoreThreshold); // 只渲染 不保存图片
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public ClassifyResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float scoreThreshold); // Only rendering images without saving.
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```
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- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
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- Model resource release API: Calling function release() API can release model resources, and true means successful release, false means failure. Calling function initialized() can determine whether the model is initialized successfully, and true means successful initialization, false means failure.
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```java
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public boolean release(); // 释放native资源
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public boolean initialized(); // 检查是否初始化成功
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public boolean release(); // Release native resources.
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public boolean initialized(); // Check if initialization is successful.
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```
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## 目标检测API
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## Target Detection API
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<div id="Detection"></div>
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### PicoDet Java API 说明
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- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PicoDet初始化参数说明如下：
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- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
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- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
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- configFile: String, 模型推理的预处理配置文件，如 infer_cfg.yml
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- labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 coco_label_list.txt，每一行包含一个label
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- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
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### PicoDet Java API Introduction
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- Model initialization API: Model initialization API contains two methods, you can initialize directly through the constructor, or call init function at the appropriate program node. PicoDet initialization parameters are described as follows:
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- modelFile: String, path to the model file in paddle format, e.g. model.pdmodel.
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- paramFile: String, path to the parameter file in paddle format, e.g. model.pdiparams.
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- configFile: String, preprocessing configuration file of model inference, e.g. infer_cfg.yml.
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- labelFile: String, optional, path to the label file, for visualization, e.g. coco_label_list.txt, in which each line contains a label.
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- option: RuntimeOption, optional, model initialization option. If this parameter is not passed, the default runtime option will be used.
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```java
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// 构造函数: constructor w/o label file
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public PicoDet(); // 空构造函数，之后可以调用init初始化
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// Constructor w/o label file.
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public PicoDet(); // An empty constructor, which can be initialised by calling init function later.
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public PicoDet(String modelFile, String paramsFile, String configFile);
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public PicoDet(String modelFile, String paramsFile, String configFile, String labelFile);
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public PicoDet(String modelFile, String paramsFile, String configFile, RuntimeOption option);
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public PicoDet(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
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// 手动调用init初始化: call init manually w/o label file
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// Call init manually w/o label file.
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public boolean init(String modelFile, String paramsFile, String configFile, RuntimeOption option);
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public boolean init(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
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```
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- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
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- Model prediction API: Model prediction API includes direct prediction API and API with visualization function. Direct prediction means that no image is saved and no result is rendered to Bitmap, but only the inference result is predicted. Prediction and visualization means to predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap (currently supports Bitmap in format ARGB8888), which can be displayed in camera later.
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```java
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// 直接预测：不保存图片以及不渲染结果到Bitmap上
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// Directly predict: do not save images or render result to Bitmap.
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public DetectionResult predict(Bitmap ARGB8888Bitmap)；
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// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
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// Predict and visualize: predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap.
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public DetectionResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float scoreThreshold);
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public DetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float scoreThreshold); // 只渲染 不保存图片
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public DetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float scoreThreshold); // Only rendering images without saving.
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```
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- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
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- Model resource release API: Calling function release() API can release model resources, and true means successful release, false means failure. Calling function initialized() can determine whether the model is initialized successfully, and true means successful initialization, false means failure.
|
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```java
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public boolean release(); // 释放native资源
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public boolean initialized(); // 检查是否初始化成功
|
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public boolean release(); // Release native resources.
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public boolean initialized(); // Check if initialization is successful.
|
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```
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## OCR文字识别API
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## OCR Text Recognition API
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|
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<div id="OCR"></div>
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|
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### PP-OCRv2 & PP-OCRv3 Java API 说明
|
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- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。 PP-OCR初始化参数说明如下：
|
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- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
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- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 ppocr_keys_v1.txt，每一行包含一个label
|
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- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
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与其他模型不同的是，PP-OCRv2 和 PP-OCRv3 包含 DBDetector、Classifier和Recognizer等基础模型，以及PPOCRv2和PPOCRv3等pipeline类型。
|
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### PP-OCRv2 & PP-OCRv3 Java API Introduction
|
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- Model initialization API: Model initialization API contains two methods, you can initialize directly through the constructor, or call init function at the appropriate program node. PP-OCR initialization parameters are described as follows:
|
||||
- modelFile: String, path to the model file in paddle format, e.g. model.pdmodel.
|
||||
- paramFile: String, path to the parameter file in paddle format, e.g. model.pdiparams.
|
||||
- labelFile: String, optional, path to the label file, for visualization, e.g. ppocr_keys_v1.txt, in which each line contains a label.
|
||||
- option: RuntimeOption, optional, model initialization option. If this parameter is not passed, the default runtime option will be used.
|
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Unlike other models, PP-OCRv2 and PP-OCRv3 contain base models such as DBDetector, Classifier and Recognizer, and pipeline types such as PPOCRv2 and PPOCRv3.
|
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```java
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// 构造函数: constructor w/o label file
|
||||
// Constructor w/o label file
|
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public DBDetector(String modelFile, String paramsFile);
|
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public DBDetector(String modelFile, String paramsFile, RuntimeOption option);
|
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public Classifier(String modelFile, String paramsFile);
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public Classifier(String modelFile, String paramsFile, RuntimeOption option);
|
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public Recognizer(String modelFile, String paramsFile, String labelPath);
|
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public Recognizer(String modelFile, String paramsFile, String labelPath, RuntimeOption option);
|
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public PPOCRv2(); // 空构造函数，之后可以调用init初始化
|
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public PPOCRv2(); // An empty constructor, which can be initialised by calling init function later.
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// Constructor w/o classifier
|
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public PPOCRv2(DBDetector detModel, Recognizer recModel);
|
||||
public PPOCRv2(DBDetector detModel, Classifier clsModel, Recognizer recModel);
|
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public PPOCRv3(); // 空构造函数，之后可以调用init初始化
|
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public PPOCRv3(); // An empty constructor, which can be initialised by calling init function later.
|
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// Constructor w/o classifier
|
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public PPOCRv3(DBDetector detModel, Recognizer recModel);
|
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public PPOCRv3(DBDetector detModel, Classifier clsModel, Recognizer recModel);
|
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```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
- Model prediction API: Model prediction API includes direct prediction API and API with visualization function. Direct prediction means that no image is saved and no result is rendered to Bitmap, but only the inference result is predicted. Prediction and visualization means to predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap (currently supports Bitmap in format ARGB8888), which can be displayed in camera later.
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
// Directly predict: do not save images or render result to Bitmap.
|
||||
public OCRResult predict(Bitmap ARGB8888Bitmap)；
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
// Predict and visualize: predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap.
|
||||
public OCRResult predict(Bitmap ARGB8888Bitmap, String savedImagePath);
|
||||
public OCRResult predict(Bitmap ARGB8888Bitmap, boolean rendering); // 只渲染 不保存图片
|
||||
public OCRResult predict(Bitmap ARGB8888Bitmap, boolean rendering); // Only rendering images without saving.
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
- Model resource release API: Calling function release() API can release model resources, and true means successful release, false means failure. Calling function initialized() can determine whether the model is initialized successfully, and true means successful initialization, false means failure.
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
public boolean release(); // Release native resources.
|
||||
public boolean initialized(); // Check if initialization is successful.
|
||||
```
|
||||
|
||||
## 语义分割API
|
||||
## Semantic Segmentation API
|
||||
|
||||
<div id="Segmentation"></div>
|
||||
|
||||
### PaddleSegModel Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleSegModel初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- configFile: String, 模型推理的预处理配置文件，如 infer_cfg.yml
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
### PaddleSegModel Java API Introduction
|
||||
- Model initialization API: Model initialization API contains two methods, you can initialize directly through the constructor, or call init function at the appropriate program node. PaddleSegModel initialization parameters are described as follows:
|
||||
- modelFile: String, path to the model file in paddle format, e.g. model.pdmodel.
|
||||
- paramFile: String, path to the parameter file in paddle format, e.g. model.pdiparams.
|
||||
- configFile: String, preprocessing configuration file of model inference, e.g. infer_cfg.yml.
|
||||
- option: RuntimeOption, optional, model initialization option. If this parameter is not passed, the default runtime option will be used.
|
||||
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public PaddleSegModel(); // 空构造函数，之后可以调用init初始化
|
||||
// Constructor w/o label file
|
||||
public PaddleSegModel(); // An empty constructor, which can be initialised by calling init function later.
|
||||
public PaddleSegModel(String modelFile, String paramsFile, String configFile);
|
||||
public PaddleSegModel(String modelFile, String paramsFile, String configFile, RuntimeOption option);
|
||||
// 手动调用init初始化: call init manually w/o label file
|
||||
// Call init manually w/o label file
|
||||
public boolean init(String modelFile, String paramsFile, String configFile, RuntimeOption option);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
- Model prediction API: Model prediction API includes direct prediction API and API with visualization function. Direct prediction means that no image is saved and no result is rendered to Bitmap, but only the inference result is predicted. Prediction and visualization means to predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap (currently supports Bitmap in format ARGB8888), which can be displayed in camera later.
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
// Directly predict: do not save images or render result to Bitmap.
|
||||
public SegmentationResult predict(Bitmap ARGB8888Bitmap)；
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
// Predict and visualize: predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap.
|
||||
public SegmentationResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float weight);
|
||||
public SegmentationResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float weight); // 只渲染 不保存图片
|
||||
// 修改result，而非返回result，关注性能的用户可以将以下接口与SegmentationResult的CxxBuffer一起使用
|
||||
public SegmentationResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float weight); // Only rendering images without saving.
|
||||
// Modify result, but not return it. Concerning performance, you can use the following interface with CxxBuffer in SegmentationResult.
|
||||
public boolean predict(Bitmap ARGB8888Bitmap, SegmentationResult result)；
|
||||
public boolean predict(Bitmap ARGB8888Bitmap, SegmentationResult result, String savedImagePath, float weight);
|
||||
public boolean predict(Bitmap ARGB8888Bitmap, SegmentationResult result, boolean rendering, float weight);
|
||||
```
|
||||
- 设置竖屏或横屏模式: 对于 PP-HumanSeg系列模型，必须要调用该方法设置竖屏模式为true.
|
||||
- Set vertical or horizontal mode: For PP-HumanSeg series model, you should call this method to set the vertical mode to true.
|
||||
```java
|
||||
public void setVerticalScreenFlag(boolean flag);
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
- Model resource release API: Calling function release() API can release model resources, and true means successful release, false means failure. Calling function initialized() can determine whether the model is initialized successfully, and true means successful initialization, false means failure.
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
public boolean release(); // Release native resources.
|
||||
public boolean initialized(); // Check if initialization is successful.
|
||||
```
|
||||
|
||||
## 人脸检测API
|
||||
## Face Detection API
|
||||
|
||||
<div id="FaceDetection"></div>
|
||||
|
||||
### SCRFD Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleSegModel初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
### SCRFD Java API Introduction
|
||||
- Model initialization API: Model initialization API contains two methods, you can initialize directly through the constructor, or call init function at the appropriate program node. PaddleSegModel initialization parameters are described as follows:
|
||||
- modelFile: String, path to the model file in paddle format, e.g. model.pdmodel.
|
||||
- paramFile: String, path to the parameter file in paddle format, e.g. model.pdiparams.
|
||||
- option: RuntimeOption, optional, model initialization option. If this parameter is not passed, the default runtime option will be used.
|
||||
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public SCRFD(); // 空构造函数，之后可以调用init初始化
|
||||
// Constructor w/o label file.
|
||||
public SCRFD(); // An empty constructor, which can be initialised by calling init function later.
|
||||
public SCRFD(String modelFile, String paramsFile);
|
||||
public SCRFD(String modelFile, String paramsFile, RuntimeOption option);
|
||||
// 手动调用init初始化: call init manually w/o label file
|
||||
// Call init manually w/o label file.
|
||||
public boolean init(String modelFile, String paramsFile, RuntimeOption option);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
- Model prediction API: Model prediction API includes direct prediction API and API with visualization function. Direct prediction means that no image is saved and no result is rendered to Bitmap, but only the inference result is predicted. Prediction and visualization means to predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap (currently supports Bitmap in format ARGB8888), which can be displayed in camera later.
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
// Directly predict: do not save images or render result to Bitmap.
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap)；
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, float confThreshold, float nmsIouThreshold)； // 设置置信度阈值和NMS阈值
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, float confThreshold, float nmsIouThreshold)； // Set confidence thresholds and NMS thresholds.
|
||||
// Predict and visualize: predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap.
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float confThreshold, float nmsIouThreshold);
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float confThreshold, float nmsIouThreshold); // 只渲染 不保存图片
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float confThreshold, float nmsIouThreshold); // Only rendering images without saving.
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
- Model resource release API: Calling function release() API can release model resources, and true means successful release, false means failure. Calling function initialized() can determine whether the model is initialized successfully, and true means successful initialization, false means failure.
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
public boolean release(); // Release native resources.
|
||||
public boolean initialized(); // Check if initialization is successful.
|
||||
```
|
||||
|
||||
### YOLOv5Face Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleSegModel初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
### YOLOv5Face Java API Introduction
|
||||
- Model initialization API: Model initialization API contains two methods, you can initialize directly through the constructor, or call init function at the appropriate program node. PaddleSegModel initialization parameters are described as follows:
|
||||
- modelFile: String, path to the model file in paddle format, e.g. model.pdmodel.
|
||||
- paramFile: String, path to the parameter file in paddle format, e.g. model.pdiparams.
|
||||
- option: RuntimeOption, optional, model initialization option. If this parameter is not passed, the default runtime option will be used.
|
||||
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public YOLOv5Face(); // 空构造函数，之后可以调用init初始化
|
||||
// Constructor w/o label file.
|
||||
public YOLOv5Face(); // An empty constructor, which can be initialised by calling init function later.
|
||||
public YOLOv5Face(String modelFile, String paramsFile);
|
||||
public YOLOv5Face(String modelFile, String paramsFile, RuntimeOption option);
|
||||
// 手动调用init初始化: call init manually w/o label file
|
||||
// Call init manually w/o label file.
|
||||
public boolean init(String modelFile, String paramsFile, RuntimeOption option);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
- Model prediction API: Model prediction API includes direct prediction API and API with visualization function. Direct prediction means that no image is saved and no result is rendered to Bitmap, but only the inference result is predicted. Prediction and visualization means to predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap (currently supports Bitmap in format ARGB8888), which can be displayed in camera later.
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
// Directly predict: do not save images or render result to Bitmap.
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap)；
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, float confThreshold, float nmsIouThreshold)； // 设置置信度阈值和NMS阈值
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, float confThreshold, float nmsIouThreshold)； // Set confidence thresholds and NMS thresholds.
|
||||
// Predict and visualize: predict the result and visualize it, and save the visualized image to the specified path, and render the result to Bitmap.
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float confThreshold, float nmsIouThreshold);
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float confThreshold, float nmsIouThreshold); // 只渲染 不保存图片
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float confThreshold, float nmsIouThreshold); // Only rendering images without saving.
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
- Model resource release API: Calling function release() API can release model resources, and true means successful release, false means failure. Calling function initialized() can determine whether the model is initialized successfully, and true means successful initialization, false means failure.
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
public boolean release(); // Release native resources.
|
||||
public boolean initialized(); // Check if initialization is successful.
|
||||
```
|
||||
|
||||
## 识别结果说明
|
||||
## Identification Result Description
|
||||
|
||||
<div id="VisionResults"></div>
|
||||
|
||||
- 图像分类ClassifyResult说明
|
||||
- Image classification result description
|
||||
```java
|
||||
public class ClassifyResult {
|
||||
public float[] mScores; // [n] 每个类别的得分(概率)
|
||||
public int[] mLabelIds; // [n] 分类ID 具体的类别类型
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
public float[] mScores; // [n] Scores of every class(probability).
|
||||
public int[] mLabelIds; // [n] Class ID, specific class type.
|
||||
public boolean initialized(); // To test whether the result is valid.
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的ClassifyResult说明: [api/vision_results/classification_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/classification_result.md)
|
||||
Other reference: C++/Python corresponding ClassifyResult description: [api/vision_results/classification_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/classification_result.md)
|
||||
|
||||
- 目标检测DetectionResult说明
|
||||
- Target detection result description
|
||||
```java
|
||||
public class DetectionResult {
|
||||
public float[][] mBoxes; // [n,4] 检测框 (x1,y1,x2,y2)
|
||||
public float[] mScores; // [n] 每个检测框得分(置信度，概率值)
|
||||
public int[] mLabelIds; // [n] 分类ID
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
public float[][] mBoxes; // [n,4] Detecting box (x1,y1,x2,y2).
|
||||
public float[] mScores; // [n] Score (confidence level, probability value) for each detecting box.
|
||||
public int[] mLabelIds; // [n] Class ID.
|
||||
public boolean initialized(); // To test whether the result is valid.
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的DetectionResult说明: [api/vision_results/detection_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/detection_result.md)
|
||||
Other reference: C++/Python corresponding DetectionResult description: [api/vision_results/detection_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/detection_result.md)
|
||||
|
||||
- OCR文字识别OCRResult说明
|
||||
- OCR text recognition result description
|
||||
```java
|
||||
public class OCRResult {
|
||||
public int[][] mBoxes; // [n,8] 表示单张图片检测出来的所有目标框坐标 每个框以8个int数值依次表示框的4个坐标点，顺序为左下，右下，右上，左上
|
||||
public String[] mText; // [n] 表示多个文本框内被识别出来的文本内容
|
||||
public float[] mRecScores; // [n] 表示文本框内识别出来的文本的置信度
|
||||
public float[] mClsScores; // [n] 表示文本框的分类结果的置信度
|
||||
public int[] mClsLabels; // [n] 表示文本框的方向分类类别
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
public int[][] mBoxes; // [n,8] indicates the coordinates of all target boxes detected in a single image. Each box is 8 int values representing the 4 coordinate points of the box, in the order of lower left, lower right, upper right, upper left.
|
||||
public String[] mText; // [n] indicates the content recognized in multiple text boxes.
|
||||
public float[] mRecScores; // [n] indicates the confidence level of the text recognized in the text box.
|
||||
public float[] mClsScores; // [n] indicates the confidence level of the classification result of the text.
|
||||
public int[] mClsLabels; // [n] indicates the direction classification category of the text box.
|
||||
public boolean initialized(); // To test whether the result is valid.
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的OCRResult说明: [api/vision_results/ocr_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/ocr_result.md)
|
||||
Other reference: C++/Python corresponding OCRResult description: [api/vision_results/ocr_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/ocr_result.md)
|
||||
|
||||
- 语义分割SegmentationResult结果说明
|
||||
- Semantic segmentation result description
|
||||
```java
|
||||
public class SegmentationResult {
|
||||
public int[] mLabelMap; // 预测到的label map 每个像素位置对应一个label HxW
|
||||
public float[] mScoreMap; // 预测到的得分 map 每个像素位置对应一个score HxW
|
||||
public long[] mShape; // label map实际的shape (H,W)
|
||||
public boolean mContainScoreMap = false; // 是否包含 score map
|
||||
// 用户可以选择直接使用CxxBuffer，而非通过JNI拷贝到Java层，
|
||||
// 该方式可以一定程度上提升性能
|
||||
public void setCxxBufferFlag(boolean flag); // 设置是否为CxxBuffer模式
|
||||
public boolean releaseCxxBuffer(); // 手动释放CxxBuffer!!!
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
public int[] mLabelMap; // The predicted label map, each pixel position corresponds to a label HxW.
|
||||
public float[] mScoreMap; // The predicted score map, each pixel position corresponds to a score HxW.
|
||||
public long[] mShape; // The real shape(H,W) of label map.
|
||||
public boolean mContainScoreMap = false; // Whether score map is included.
|
||||
// You can choose to use CxxBuffer directly instead of copying it to JAVA layer through JNI.
|
||||
// This method can improve performance to some extent.
|
||||
public void setCxxBufferFlag(boolean flag); // Set whether the mode is CxxBuffer.
|
||||
public boolean releaseCxxBuffer(); // Release CxxBuffer manually!!!
|
||||
public boolean initialized(); // Check if the result is valid.
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的SegmentationResult说明: [api/vision_results/segmentation_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result.md)
|
||||
Other reference：C++/Python corresponding SegmentationResult description: [api/vision_results/segmentation_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result.md)
|
||||
|
||||
- 人脸检测FaceDetectionResult结果说明
|
||||
- Face detection result description
|
||||
```java
|
||||
public class FaceDetectionResult {
|
||||
public float[][] mBoxes; // [n,4] 检测框 (x1,y1,x2,y2)
|
||||
public float[] mScores; // [n] 每个检测框得分(置信度，概率值)
|
||||
public float[][] mLandmarks; // [nx?,2] 每个检测到的人脸对应关键点
|
||||
int mLandmarksPerFace = 0; // 每个人脸对应的关键点个数
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
public float[][] mBoxes; // [n,4] detection box (x1,y1,x2,y2)
|
||||
public float[] mScores; // [n] scores(confidence level, probability value) of every detection box
|
||||
public float[][] mLandmarks; // [nx?,2] Each detected face corresponding keypoint
|
||||
int mLandmarksPerFace = 0; // Each face corresponding keypoints number
|
||||
public boolean initialized(); // Check if the result is valid.
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的FaceDetectionResult说明: [api/vision_results/face_detection_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/face_detection_result.md)
|
||||
Other reference：C++/Python corresponding FaceDetectionResult description: [api/vision_results/face_detection_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/face_detection_result.md)
|
||||
|
||||
## RuntimeOption说明
|
||||
## Runtime Option Description
|
||||
|
||||
<div id="RuntimeOption"></div>
|
||||
|
||||
- RuntimeOption设置说明
|
||||
- RuntimeOption setting description
|
||||
```java
|
||||
public class RuntimeOption {
|
||||
public void enableLiteFp16(); // 开启fp16精度推理
|
||||
public void disableLiteFP16(); // 关闭fp16精度推理
|
||||
public void enableLiteInt8(); // 开启int8精度推理，针对量化模型
|
||||
public void disableLiteInt8(); // 关闭int8精度推理
|
||||
public void setCpuThreadNum(int threadNum); // 设置线程数
|
||||
public void setLitePowerMode(LitePowerMode mode); // 设置能耗模式
|
||||
public void setLitePowerMode(String modeStr); // 通过字符串形式设置能耗模式
|
||||
public void enableLiteFp16(); // Enable fp16 precision inference
|
||||
public void disableLiteFP16(); // Disable fp16 precision inference
|
||||
public void enableLiteInt8(); // Enable int8 precision inference, for quantized models
|
||||
public void disableLiteInt8(); // Disable int8 precision inference
|
||||
public void setCpuThreadNum(int threadNum); // Set number of threads.
|
||||
public void setLitePowerMode(LitePowerMode mode); // Set power mode.
|
||||
public void setLitePowerMode(String modeStr); // Set power mode by string.
|
||||
}
|
||||
```
|
||||
|
||||
## 可视化接口
|
||||
## Visualization Interface
|
||||
|
||||
<div id="Visualize"></div>
|
||||
|
||||
FastDeploy Android SDK同时提供一些可视化接口，可用于快速验证推理结果。以下接口均把结果result渲染在输入的Bitmap上。具体的可视化API接口如下：
|
||||
FastDeploy Android SDK also provides visual interfaces that can be used to quickly validate the inference results. The following interfaces all render the result in the input Bitmap.
|
||||
|
||||
```java
|
||||
public class Visualize {
|
||||
// 默认参数接口
|
||||
// Default parameter interface.
|
||||
public static boolean visClassification(Bitmap ARGB8888Bitmap, ClassifyResult result);
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result);
|
||||
public static boolean visFaceDetection(Bitmap ARGB8888Bitmap, FaceDetectionResult result);
|
||||
public static boolean visOcr(Bitmap ARGB8888Bitmap, OCRResult result);
|
||||
public static boolean visSegmentation(Bitmap ARGB8888Bitmap, SegmentationResult result);
|
||||
// 有可设置参数的可视化接口
|
||||
// visDetection: 可设置阈值（大于该阈值的框进行绘制）、框线大小、字体大小、类别labels等
|
||||
// Visual interface with configurable parameters.
|
||||
// visDetection: You can configure the threshold value (draw the boxes higher than the threshold), box line size, font size, labels, etc.
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result, float scoreThreshold);
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result, float scoreThreshold, int lineSize, float fontSize);
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result, String[] labels);
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result, String[] labels, float scoreThreshold, int lineSize, float fontSize);
|
||||
// visClassification: 可设置阈值（大于该阈值的框进行绘制）、字体大小、类别labels等
|
||||
// visClassification: You can configure the threshold value (draw the boxes higher than the threshold), font size, labels, etc.
|
||||
public static boolean visClassification(Bitmap ARGB8888Bitmap, ClassifyResult result, float scoreThreshold,float fontSize);
|
||||
public static boolean visClassification(Bitmap ARGB8888Bitmap, ClassifyResult result, String[] labels);
|
||||
public static boolean visClassification(Bitmap ARGB8888Bitmap, ClassifyResult result, String[] labels, float scoreThreshold,float fontSize);
|
||||
// visSegmentation: weight背景权重
|
||||
// visSegmentation: Background weight.
|
||||
public static boolean visSegmentation(Bitmap ARGB8888Bitmap, SegmentationResult result, float weight);
|
||||
// visFaceDetection: 线大小、字体大小等
|
||||
// visFaceDetection: String size, font size, etc.
|
||||
public static boolean visFaceDetection(Bitmap ARGB8888Bitmap, FaceDetectionResult result, int lineSize, float fontSize);
|
||||
}
|
||||
```
|
||||
对应的可视化类型为：
|
||||
The corresponding visualization types:
|
||||
```java
|
||||
import com.baidu.paddle.fastdeploy.vision.Visualize;
|
||||
```
|
||||
|
||||
## 模型使用示例
|
||||
## Examples of How to Use Models
|
||||
|
||||
<div id="Demo"></div>
|
||||
|
||||
- 模型调用示例1：使用构造函数以及默认的RuntimeOption
|
||||
- Example 1: Using constructor function and default RuntimeOption.
|
||||
```java
|
||||
import java.nio.ByteBuffer;
|
||||
import android.graphics.Bitmap;
|
||||
@@ -405,90 +407,92 @@ import android.opengl.GLES20;
|
||||
import com.baidu.paddle.fastdeploy.vision.DetectionResult;
|
||||
import com.baidu.paddle.fastdeploy.vision.detection.PicoDet;
|
||||
|
||||
// 初始化模型
|
||||
// Initialize model.
|
||||
PicoDet model = new PicoDet("picodet_s_320_coco_lcnet/model.pdmodel",
|
||||
"picodet_s_320_coco_lcnet/model.pdiparams",
|
||||
"picodet_s_320_coco_lcnet/infer_cfg.yml");
|
||||
|
||||
// 模型推理
|
||||
// Model inference.
|
||||
DetectionResult result = model.predict(ARGB8888ImageBitmap);
|
||||
|
||||
// 释放模型资源
|
||||
// Release model resources.
|
||||
model.release();
|
||||
```
|
||||
|
||||
- 模型调用示例2: 在合适的程序节点，手动调用init，并自定义RuntimeOption
|
||||
- Example 2: Manually call init function at appropriate program nodes, and customize RuntimeOption.
|
||||
```java
|
||||
// import 同上 ...
|
||||
// import id.
|
||||
import com.baidu.paddle.fastdeploy.RuntimeOption;
|
||||
import com.baidu.paddle.fastdeploy.LitePowerMode;
|
||||
import com.baidu.paddle.fastdeploy.vision.DetectionResult;
|
||||
import com.baidu.paddle.fastdeploy.vision.detection.PicoDet;
|
||||
// 新建空模型
|
||||
// Create a new empty model.
|
||||
PicoDet model = new PicoDet();
|
||||
// 模型路径
|
||||
// Model path.
|
||||
String modelFile = "picodet_s_320_coco_lcnet/model.pdmodel";
|
||||
String paramFile = "picodet_s_320_coco_lcnet/model.pdiparams";
|
||||
String configFile = "picodet_s_320_coco_lcnet/infer_cfg.yml";
|
||||
// 指定RuntimeOption
|
||||
// Set RuntimeOption.
|
||||
RuntimeOption option = new RuntimeOption();
|
||||
option.setCpuThreadNum(2);
|
||||
option.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
|
||||
option.enableLiteFp16();
|
||||
// 使用init函数初始化
|
||||
// Initiaze with init function.
|
||||
model.init(modelFile, paramFile, configFile, option);
|
||||
// Bitmap读取、模型预测、资源释放 同上 ...
|
||||
// Reading Bitmap, model prediction, resource release id.
|
||||
```
|
||||
|
||||
## App示例工程使用方式
|
||||
## How to Use the App Sample Project
|
||||
<div id="App"></div>
|
||||
|
||||
FastDeploy在java/android/app目录下提供了一些示例工程，以下将介绍示例工程的使用方式。由于java/android目录下同时还包含JNI工程，因此想要使用示例工程的用户还需要配置NDK，如果您只关心Java API的使用，并且不想配置NDK，可以直接跳转到以下详细的案例链接。
|
||||
FastDeploy provides some sample projects in the java/android/app directory. Since the java/android directory also contains JNI projects, users who want to use the sample projects also need to configure the NDK. If you only want to use the Java API and don't want to configure the NDK, you can jump to the detailed case links below.
|
||||
|
||||
- [图像分类App示例工程](../../examples/vision/classification/paddleclas/android)
|
||||
- [目标检测App示例工程](../../examples/vision/detection/paddledetection/android)
|
||||
- [OCR文字识别App示例工程](../../examples/vision/ocr/PP-OCRv2/android)
|
||||
- [人像分割App示例工程](../../examples/vision/segmentation/paddleseg/android)
|
||||
- [人脸检测App示例工程](../../examples/vision/facedet/scrfd/android)
|
||||
- [App sample project of image classification](../../examples/vision/classification/paddleclas/android)
|
||||
- [App sample project of target detection](../../examples/vision/detection/paddledetection/android)
|
||||
- [App sample project of OCR text detection](../../examples/vision/ocr/PP-OCRv2/android)
|
||||
- [App sample project of portrait segmentation](../../examples/vision/segmentation/paddleseg/android)
|
||||
- [App sample project of face detection](../../examples/vision/facedet/scrfd/android)
|
||||
|
||||
### 环境准备
|
||||
### Prepare for Environment
|
||||
|
||||
1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Stuido 官网](https://developer.android.com/studio)。
|
||||
2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`
|
||||
1. Install Android Studio tools in your local environment, please refer to [Android Stuido official website](https://developer.android.com/studio) for detailed installation method.
|
||||
2. Get an Android phone and turn on USB debugging mode. How to turn on: ` Phone Settings -> Find Developer Options -> Turn on Developer Options and USB Debug Mode`.
|
||||
|
||||
**注意**：如果您的 Android Studio 尚未配置 NDK ，请根据 Android Studio 用户指南中的[安装及配置 NDK 和 CMake ](https://developer.android.com/studio/projects/install-ndk)内容，预先配置好 NDK 。您可以选择最新的 NDK 版本，或者使用 FastDeploy Android 预测库版本一样的 NDK
|
||||
|
||||
### 部署步骤
|
||||
**Notes**：If your Android Studio is not configured with an NDK, please configure the it according to [Installing and Configuring NDK and CMake](https://developer.android.com/studio/projects/install-ndk) in the Android Studio User Guide. You can either choose the latest NDK version or use the same version as the FastDeploy Android prediction library.
|
||||
|
||||
1. App示例工程位于 `fastdeploy/java/android/app` 目录
|
||||
2. 用 Android Studio 打开 `fastdeploy/java/android` 工程，注意是`java/android`目录
|
||||
3. 手机连接电脑，打开 USB 调试和文件传输模式，并在 Android Studio 上连接自己的手机设备（手机需要开启允许从 USB 安装软件权限）
|
||||
### Configuration Steps
|
||||
|
||||
1. The App sample project is located in directory `fastdeploy/java/android/app`.
|
||||
2. Open `fastdeploy/java/android` project by Android Studio, please note that the directory is `java/android`.
|
||||
3. Connect your phone to your computer, turn on USB debugging and file transfer mode, and connect your own mobile device on Android Studio (your phone needs to be enabled to allow software installation from USB).
|
||||
|
||||
<p align="center">
|
||||
<img width="1440" alt="image" src="https://user-images.githubusercontent.com/31974251/203257262-71b908ab-bb2b-47d3-9efb-67631687b774.png">
|
||||
</p>
|
||||
|
||||
> **注意：**
|
||||
>> 如果您在导入项目、编译或者运行过程中遇到 NDK 配置错误的提示，请打开 ` File > Project Structure > SDK Location`，修改 `Andriod NDK location` 为您本机配置的 NDK 所在路径。本工程默认使用的NDK版本为20.
|
||||
>> 如果您是通过 Andriod Studio 的 SDK Tools 下载的 NDK (见本章节"环境准备")，可以直接点击下拉框选择默认路径。
|
||||
>> 还有一种 NDK 配置方法，你可以在 `java/android/local.properties` 文件中手动完成 NDK 路径配置，如下图所示
|
||||
>> 如果以上步骤仍旧无法解决 NDK 配置错误，请尝试根据 Andriod Studio 官方文档中的[更新 Android Gradle 插件](https://developer.android.com/studio/releases/gradle-plugin?hl=zh-cn#updating-plugin)章节，尝试更新Android Gradle plugin版本。
|
||||
> **Notes:**
|
||||
>> If you encounter an NDK configuration error during importing, compiling or running the program, please open ` File > Project Structure > SDK Location` and change `Andriod NDK location` to your locally configured NDK path. The default NDK version in this project is 20.
|
||||
>> If you downloaded the NDK through SDK Tools in Andriod Studio (see "Prepare for Environment" in this section), you can select the default path by clicking the drop-down box.
|
||||
>> There is another way to configure the NDK: you can do it manually in the file `java/android/local.properties`, as shown above.
|
||||
>> If the above steps still can't solve the configuration error, please try to update Android Gradle plugin version according to section [Updating Android Gradle plugin](https://developer.android.com/studio/releases/gradle-plugin?hl=zh-cn#updating-plugin) in official Andriod Studio documentation.
|
||||
|
||||
4. 点击 Run 按钮，自动编译 APP 并安装到手机。(该过程会自动下载预编译的 FastDeploy Android 库 以及 模型文件，需要联网)
|
||||
成功后效果如下，图一：APP 安装到手机；图二： APP 打开后的效果，会自动识别图片中的物体并标记；图三：APP设置选项，点击右上角的设置图片，可以设置不同选项进行体验。
|
||||
|
||||
| APP 图标 | APP 效果 | APP设置项
|
||||
4. Click the Run button to automatically compile the APP and install it to your phone. (The process will automatically download the pre-compiled FastDeploy Android library and model files, internet connection required.)
|
||||
The success interface is as follows. Figure 1: Install APP on phone; Figure 2: The opening interface, it will automatically recognize the objects in the picture and mark them; Figure 3: APP setting options, click setting in the upper right corner, and you can set different options.
|
||||
|
||||
| APP icon | APP effect | APP setting options
|
||||
| --- | --- | --- |
|
||||
|  |  |  |
|
||||
|
||||
### 切换不同的场景
|
||||
App示例工程只需要在AndroidManifest.xml中切换不同的Activity即可编译不同场景的App进行体验。
|
||||
### Switch Between Different Scenarios
|
||||
App sample project only needs to switch between different Activity in AndroidManifest.xml to compile App in different scenarios.
|
||||
|
||||
<p align="center">
|
||||
<img width="788" alt="image" src="https://user-images.githubusercontent.com/31974251/203258255-b422d3e2-6004-465f-86b6-9fa61a27c6c2.png">
|
||||
</p>
|
||||
|
||||
- 图像分类场景
|
||||
- Image classification scenario
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
@@ -502,7 +506,7 @@ App示例工程只需要在AndroidManifest.xml中切换不同的Activity即可
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
- 目标检测场景
|
||||
- Target detection scenario
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
@@ -516,7 +520,7 @@ App示例工程只需要在AndroidManifest.xml中切换不同的Activity即可
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
- OCR文字识别场景
|
||||
- OCR text detection scenario
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
@@ -530,7 +534,7 @@ App示例工程只需要在AndroidManifest.xml中切换不同的Activity即可
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
- 人像分割场景
|
||||
- Portrait segmentation scenario
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
@@ -544,7 +548,7 @@ App示例工程只需要在AndroidManifest.xml中切换不同的Activity即可
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
- 人脸检测场景
|
||||
- Face detection scenario
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
|
||||
562
java/android/README_CN.md
Normal file
562
java/android/README_CN.md
Normal file
@@ -0,0 +1,562 @@
|
||||
简体中文 | [English](README.md)
|
||||
|
||||
# FastDeploy Android AAR 包使用文档
|
||||
FastDeploy Android SDK 目前支持图像分类、目标检测、OCR文字识别、语义分割和人脸检测等任务，对更多的AI任务支持将会陆续添加进来。以下为各个任务对应的API文档，在Android下使用FastDeploy中集成的模型，只需以下几个步骤：
|
||||
- 模型初始化
|
||||
- 调用`predict`接口
|
||||
- 可视化验证（可选）
|
||||
|
||||
|图像分类|目标检测|OCR文字识别|人像分割|人脸检测|
|
||||
|:---:|:---:|:---:|:---:|:---:|
|
||||
||||||
|
||||
|
||||
## 内容目录
|
||||
|
||||
- [下载及配置SDK](#SDK)
|
||||
- [图像分类API](#Classification)
|
||||
- [目标检测API](#Detection)
|
||||
- [语义分割API](#Segmentation)
|
||||
- [OCR文字识别API](#OCR)
|
||||
- [人脸检测API](#FaceDetection)
|
||||
- [识别结果说明](#VisionResults)
|
||||
- [RuntimeOption说明](#RuntimeOption)
|
||||
- [可视化接口API](#Visualize)
|
||||
- [模型使用示例](#Demo)
|
||||
- [App示例工程使用方式](#App)
|
||||
|
||||
## 下载及配置SDK
|
||||
<div id="SDK"></div>
|
||||
|
||||
### 下载 FastDeploy Android SDK
|
||||
Release版本（Java SDK 目前仅支持Android，当前版本为 1.0.0）
|
||||
|
||||
| 平台 | 文件 | 说明 |
|
||||
| :--- | :--- | :---- |
|
||||
| Android Java SDK | [fastdeploy-android-sdk-1.0.0.aar](https://bj.bcebos.com/fastdeploy/release/android/fastdeploy-android-sdk-1.0.0.aar) | NDK 20 编译产出, minSdkVersion 15,targetSdkVersion 28 |
|
||||
|
||||
更多预编译库信息，请参考: [download_prebuilt_libraries.md](../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
### 配置 FastDeploy Android SDK
|
||||
|
||||
首先，将fastdeploy-android-sdk-xxx.aar拷贝到您Android工程的libs目录下，其中`xxx`表示您所下载的SDK的版本号。
|
||||
```shell
|
||||
├── build.gradle
|
||||
├── libs
|
||||
│ └── fastdeploy-android-sdk-xxx.aar
|
||||
├── proguard-rules.pro
|
||||
└── src
|
||||
```
|
||||
|
||||
然后，在您的Android工程中的build.gradble引入FastDeploy SDK，如下：
|
||||
```java
|
||||
dependencies {
|
||||
implementation fileTree(include: ['*.aar'], dir: 'libs')
|
||||
implementation 'com.android.support:appcompat-v7:28.0.0'
|
||||
// ...
|
||||
}
|
||||
```
|
||||
|
||||
## 图像分类API
|
||||
|
||||
<div id="Classification"></div>
|
||||
|
||||
### PaddleClasModel Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleClasModel初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- configFile: String, 模型推理的预处理配置文件，如 infer_cfg.yml
|
||||
- labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 imagenet1k_label_list.txt，每一行包含一个label
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
|
||||
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public PaddleClasModel(); // 空构造函数，之后可以调用init初始化
|
||||
public PaddleClasModel(String modelFile, String paramsFile, String configFile);
|
||||
public PaddleClasModel(String modelFile, String paramsFile, String configFile, String labelFile);
|
||||
public PaddleClasModel(String modelFile, String paramsFile, String configFile, RuntimeOption option);
|
||||
public PaddleClasModel(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
|
||||
// 手动调用init初始化: call init manually w/o label file
|
||||
public boolean init(String modelFile, String paramsFile, String configFile, RuntimeOption option);
|
||||
public boolean init(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
public ClassifyResult predict(Bitmap ARGB8888Bitmap)；
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
public ClassifyResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float scoreThreshold);
|
||||
public ClassifyResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float scoreThreshold); // 只渲染 不保存图片
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
```
|
||||
|
||||
## 目标检测API
|
||||
|
||||
<div id="Detection"></div>
|
||||
|
||||
### PicoDet Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PicoDet初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- configFile: String, 模型推理的预处理配置文件，如 infer_cfg.yml
|
||||
- labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 coco_label_list.txt，每一行包含一个label
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public PicoDet(); // 空构造函数，之后可以调用init初始化
|
||||
public PicoDet(String modelFile, String paramsFile, String configFile);
|
||||
public PicoDet(String modelFile, String paramsFile, String configFile, String labelFile);
|
||||
public PicoDet(String modelFile, String paramsFile, String configFile, RuntimeOption option);
|
||||
public PicoDet(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
|
||||
// 手动调用init初始化: call init manually w/o label file
|
||||
public boolean init(String modelFile, String paramsFile, String configFile, RuntimeOption option);
|
||||
public boolean init(String modelFile, String paramsFile, String configFile, String labelFile, RuntimeOption option);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
public DetectionResult predict(Bitmap ARGB8888Bitmap)；
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
public DetectionResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float scoreThreshold);
|
||||
public DetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float scoreThreshold); // 只渲染 不保存图片
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
```
|
||||
|
||||
## OCR文字识别API
|
||||
|
||||
<div id="OCR"></div>
|
||||
|
||||
### PP-OCRv2 & PP-OCRv3 Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。 PP-OCR初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- labelFile: String, 可选参数，表示label标签文件所在路径，用于可视化，如 ppocr_keys_v1.txt，每一行包含一个label
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
与其他模型不同的是，PP-OCRv2 和 PP-OCRv3 包含 DBDetector、Classifier和Recognizer等基础模型，以及PPOCRv2和PPOCRv3等pipeline类型。
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public DBDetector(String modelFile, String paramsFile);
|
||||
public DBDetector(String modelFile, String paramsFile, RuntimeOption option);
|
||||
public Classifier(String modelFile, String paramsFile);
|
||||
public Classifier(String modelFile, String paramsFile, RuntimeOption option);
|
||||
public Recognizer(String modelFile, String paramsFile, String labelPath);
|
||||
public Recognizer(String modelFile, String paramsFile, String labelPath, RuntimeOption option);
|
||||
public PPOCRv2(); // 空构造函数，之后可以调用init初始化
|
||||
// Constructor w/o classifier
|
||||
public PPOCRv2(DBDetector detModel, Recognizer recModel);
|
||||
public PPOCRv2(DBDetector detModel, Classifier clsModel, Recognizer recModel);
|
||||
public PPOCRv3(); // 空构造函数，之后可以调用init初始化
|
||||
// Constructor w/o classifier
|
||||
public PPOCRv3(DBDetector detModel, Recognizer recModel);
|
||||
public PPOCRv3(DBDetector detModel, Classifier clsModel, Recognizer recModel);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
public OCRResult predict(Bitmap ARGB8888Bitmap)；
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
public OCRResult predict(Bitmap ARGB8888Bitmap, String savedImagePath);
|
||||
public OCRResult predict(Bitmap ARGB8888Bitmap, boolean rendering); // 只渲染 不保存图片
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
```
|
||||
|
||||
## 语义分割API
|
||||
|
||||
<div id="Segmentation"></div>
|
||||
|
||||
### PaddleSegModel Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleSegModel初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- configFile: String, 模型推理的预处理配置文件，如 infer_cfg.yml
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public PaddleSegModel(); // 空构造函数，之后可以调用init初始化
|
||||
public PaddleSegModel(String modelFile, String paramsFile, String configFile);
|
||||
public PaddleSegModel(String modelFile, String paramsFile, String configFile, RuntimeOption option);
|
||||
// 手动调用init初始化: call init manually w/o label file
|
||||
public boolean init(String modelFile, String paramsFile, String configFile, RuntimeOption option);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
public SegmentationResult predict(Bitmap ARGB8888Bitmap)；
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
public SegmentationResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float weight);
|
||||
public SegmentationResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float weight); // 只渲染 不保存图片
|
||||
// 修改result，而非返回result，关注性能的用户可以将以下接口与SegmentationResult的CxxBuffer一起使用
|
||||
public boolean predict(Bitmap ARGB8888Bitmap, SegmentationResult result)；
|
||||
public boolean predict(Bitmap ARGB8888Bitmap, SegmentationResult result, String savedImagePath, float weight);
|
||||
public boolean predict(Bitmap ARGB8888Bitmap, SegmentationResult result, boolean rendering, float weight);
|
||||
```
|
||||
- 设置竖屏或横屏模式: 对于 PP-HumanSeg系列模型，必须要调用该方法设置竖屏模式为true.
|
||||
```java
|
||||
public void setVerticalScreenFlag(boolean flag);
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
```
|
||||
|
||||
## 人脸检测API
|
||||
|
||||
<div id="FaceDetection"></div>
|
||||
|
||||
### SCRFD Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleSegModel初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public SCRFD(); // 空构造函数，之后可以调用init初始化
|
||||
public SCRFD(String modelFile, String paramsFile);
|
||||
public SCRFD(String modelFile, String paramsFile, RuntimeOption option);
|
||||
// 手动调用init初始化: call init manually w/o label file
|
||||
public boolean init(String modelFile, String paramsFile, RuntimeOption option);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap)；
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, float confThreshold, float nmsIouThreshold)； // 设置置信度阈值和NMS阈值
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float confThreshold, float nmsIouThreshold);
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float confThreshold, float nmsIouThreshold); // 只渲染 不保存图片
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
```
|
||||
|
||||
### YOLOv5Face Java API 说明
|
||||
- 模型初始化 API: 模型初始化API包含两种方式，方式一是通过构造函数直接初始化；方式二是，通过调用init函数，在合适的程序节点进行初始化。PaddleSegModel初始化参数说明如下：
|
||||
- modelFile: String, paddle格式的模型文件路径，如 model.pdmodel
|
||||
- paramFile: String, paddle格式的参数文件路径，如 model.pdiparams
|
||||
- option: RuntimeOption，可选参数，模型初始化option。如果不传入该参数则会使用默认的运行时选项。
|
||||
|
||||
```java
|
||||
// 构造函数: constructor w/o label file
|
||||
public YOLOv5Face(); // 空构造函数，之后可以调用init初始化
|
||||
public YOLOv5Face(String modelFile, String paramsFile);
|
||||
public YOLOv5Face(String modelFile, String paramsFile, RuntimeOption option);
|
||||
// 手动调用init初始化: call init manually w/o label file
|
||||
public boolean init(String modelFile, String paramsFile, RuntimeOption option);
|
||||
```
|
||||
- 模型预测 API：模型预测API包含直接预测的API以及带可视化功能的API。直接预测是指，不保存图片以及不渲染结果到Bitmap上，仅预测推理结果。预测并且可视化是指，预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap(目前支持ARGB8888格式的Bitmap), 后续可将该Bitmap在camera中进行显示。
|
||||
```java
|
||||
// 直接预测：不保存图片以及不渲染结果到Bitmap上
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap)；
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, float confThreshold, float nmsIouThreshold)； // 设置置信度阈值和NMS阈值
|
||||
// 预测并且可视化：预测结果以及可视化，并将可视化后的图片保存到指定的途径，以及将可视化结果渲染在Bitmap上
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, String savedImagePath, float confThreshold, float nmsIouThreshold);
|
||||
public FaceDetectionResult predict(Bitmap ARGB8888Bitmap, boolean rendering, float confThreshold, float nmsIouThreshold); // 只渲染 不保存图片
|
||||
```
|
||||
- 模型资源释放 API：调用 release() API 可以释放模型资源，返回true表示释放成功，false表示失败；调用 initialized() 可以判断模型是否初始化成功，true表示初始化成功，false表示失败。
|
||||
```java
|
||||
public boolean release(); // 释放native资源
|
||||
public boolean initialized(); // 检查是否初始化成功
|
||||
```
|
||||
|
||||
## 识别结果说明
|
||||
|
||||
<div id="VisionResults"></div>
|
||||
|
||||
- 图像分类ClassifyResult说明
|
||||
```java
|
||||
public class ClassifyResult {
|
||||
public float[] mScores; // [n] 每个类别的得分(概率)
|
||||
public int[] mLabelIds; // [n] 分类ID 具体的类别类型
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的ClassifyResult说明: [api/vision_results/classification_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/classification_result.md)
|
||||
|
||||
- 目标检测DetectionResult说明
|
||||
```java
|
||||
public class DetectionResult {
|
||||
public float[][] mBoxes; // [n,4] 检测框 (x1,y1,x2,y2)
|
||||
public float[] mScores; // [n] 每个检测框得分(置信度，概率值)
|
||||
public int[] mLabelIds; // [n] 分类ID
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的DetectionResult说明: [api/vision_results/detection_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/detection_result.md)
|
||||
|
||||
- OCR文字识别OCRResult说明
|
||||
```java
|
||||
public class OCRResult {
|
||||
public int[][] mBoxes; // [n,8] 表示单张图片检测出来的所有目标框坐标 每个框以8个int数值依次表示框的4个坐标点，顺序为左下，右下，右上，左上
|
||||
public String[] mText; // [n] 表示多个文本框内被识别出来的文本内容
|
||||
public float[] mRecScores; // [n] 表示文本框内识别出来的文本的置信度
|
||||
public float[] mClsScores; // [n] 表示文本框的分类结果的置信度
|
||||
public int[] mClsLabels; // [n] 表示文本框的方向分类类别
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的OCRResult说明: [api/vision_results/ocr_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/ocr_result.md)
|
||||
|
||||
- 语义分割SegmentationResult结果说明
|
||||
```java
|
||||
public class SegmentationResult {
|
||||
public int[] mLabelMap; // 预测到的label map 每个像素位置对应一个label HxW
|
||||
public float[] mScoreMap; // 预测到的得分 map 每个像素位置对应一个score HxW
|
||||
public long[] mShape; // label map实际的shape (H,W)
|
||||
public boolean mContainScoreMap = false; // 是否包含 score map
|
||||
// 用户可以选择直接使用CxxBuffer，而非通过JNI拷贝到Java层，
|
||||
// 该方式可以一定程度上提升性能
|
||||
public void setCxxBufferFlag(boolean flag); // 设置是否为CxxBuffer模式
|
||||
public boolean releaseCxxBuffer(); // 手动释放CxxBuffer!!!
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的SegmentationResult说明: [api/vision_results/segmentation_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result.md)
|
||||
|
||||
- 人脸检测FaceDetectionResult结果说明
|
||||
```java
|
||||
public class FaceDetectionResult {
|
||||
public float[][] mBoxes; // [n,4] 检测框 (x1,y1,x2,y2)
|
||||
public float[] mScores; // [n] 每个检测框得分(置信度，概率值)
|
||||
public float[][] mLandmarks; // [nx?,2] 每个检测到的人脸对应关键点
|
||||
int mLandmarksPerFace = 0; // 每个人脸对应的关键点个数
|
||||
public boolean initialized(); // 检测结果是否有效
|
||||
}
|
||||
```
|
||||
其他参考：C++/Python对应的FaceDetectionResult说明: [api/vision_results/face_detection_result.md](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/face_detection_result.md)
|
||||
|
||||
## RuntimeOption说明
|
||||
|
||||
<div id="RuntimeOption"></div>
|
||||
|
||||
- RuntimeOption设置说明
|
||||
```java
|
||||
public class RuntimeOption {
|
||||
public void enableLiteFp16(); // 开启fp16精度推理
|
||||
public void disableLiteFP16(); // 关闭fp16精度推理
|
||||
public void enableLiteInt8(); // 开启int8精度推理，针对量化模型
|
||||
public void disableLiteInt8(); // 关闭int8精度推理
|
||||
public void setCpuThreadNum(int threadNum); // 设置线程数
|
||||
public void setLitePowerMode(LitePowerMode mode); // 设置能耗模式
|
||||
public void setLitePowerMode(String modeStr); // 通过字符串形式设置能耗模式
|
||||
}
|
||||
```
|
||||
|
||||
## 可视化接口
|
||||
|
||||
<div id="Visualize"></div>
|
||||
|
||||
FastDeploy Android SDK同时提供一些可视化接口，可用于快速验证推理结果。以下接口均把结果result渲染在输入的Bitmap上。具体的可视化API接口如下：
|
||||
|
||||
```java
|
||||
public class Visualize {
|
||||
// 默认参数接口
|
||||
public static boolean visClassification(Bitmap ARGB8888Bitmap, ClassifyResult result);
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result);
|
||||
public static boolean visFaceDetection(Bitmap ARGB8888Bitmap, FaceDetectionResult result);
|
||||
public static boolean visOcr(Bitmap ARGB8888Bitmap, OCRResult result);
|
||||
public static boolean visSegmentation(Bitmap ARGB8888Bitmap, SegmentationResult result);
|
||||
// 有可设置参数的可视化接口
|
||||
// visDetection: 可设置阈值（大于该阈值的框进行绘制）、框线大小、字体大小、类别labels等
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result, float scoreThreshold);
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result, float scoreThreshold, int lineSize, float fontSize);
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result, String[] labels);
|
||||
public static boolean visDetection(Bitmap ARGB8888Bitmap, DetectionResult result, String[] labels, float scoreThreshold, int lineSize, float fontSize);
|
||||
// visClassification: 可设置阈值（大于该阈值的框进行绘制）、字体大小、类别labels等
|
||||
public static boolean visClassification(Bitmap ARGB8888Bitmap, ClassifyResult result, float scoreThreshold,float fontSize);
|
||||
public static boolean visClassification(Bitmap ARGB8888Bitmap, ClassifyResult result, String[] labels);
|
||||
public static boolean visClassification(Bitmap ARGB8888Bitmap, ClassifyResult result, String[] labels, float scoreThreshold,float fontSize);
|
||||
// visSegmentation: weight背景权重
|
||||
public static boolean visSegmentation(Bitmap ARGB8888Bitmap, SegmentationResult result, float weight);
|
||||
// visFaceDetection: 线大小、字体大小等
|
||||
public static boolean visFaceDetection(Bitmap ARGB8888Bitmap, FaceDetectionResult result, int lineSize, float fontSize);
|
||||
}
|
||||
```
|
||||
对应的可视化类型为：
|
||||
```java
|
||||
import com.baidu.paddle.fastdeploy.vision.Visualize;
|
||||
```
|
||||
|
||||
## 模型使用示例
|
||||
|
||||
<div id="Demo"></div>
|
||||
|
||||
- 模型调用示例1：使用构造函数以及默认的RuntimeOption
|
||||
```java
|
||||
import java.nio.ByteBuffer;
|
||||
import android.graphics.Bitmap;
|
||||
import android.opengl.GLES20;
|
||||
|
||||
import com.baidu.paddle.fastdeploy.vision.DetectionResult;
|
||||
import com.baidu.paddle.fastdeploy.vision.detection.PicoDet;
|
||||
|
||||
// 初始化模型
|
||||
PicoDet model = new PicoDet("picodet_s_320_coco_lcnet/model.pdmodel",
|
||||
"picodet_s_320_coco_lcnet/model.pdiparams",
|
||||
"picodet_s_320_coco_lcnet/infer_cfg.yml");
|
||||
|
||||
// 模型推理
|
||||
DetectionResult result = model.predict(ARGB8888ImageBitmap);
|
||||
|
||||
// 释放模型资源
|
||||
model.release();
|
||||
```
|
||||
|
||||
- 模型调用示例2: 在合适的程序节点，手动调用init，并自定义RuntimeOption
|
||||
```java
|
||||
// import 同上 ...
|
||||
import com.baidu.paddle.fastdeploy.RuntimeOption;
|
||||
import com.baidu.paddle.fastdeploy.LitePowerMode;
|
||||
import com.baidu.paddle.fastdeploy.vision.DetectionResult;
|
||||
import com.baidu.paddle.fastdeploy.vision.detection.PicoDet;
|
||||
// 新建空模型
|
||||
PicoDet model = new PicoDet();
|
||||
// 模型路径
|
||||
String modelFile = "picodet_s_320_coco_lcnet/model.pdmodel";
|
||||
String paramFile = "picodet_s_320_coco_lcnet/model.pdiparams";
|
||||
String configFile = "picodet_s_320_coco_lcnet/infer_cfg.yml";
|
||||
// 指定RuntimeOption
|
||||
RuntimeOption option = new RuntimeOption();
|
||||
option.setCpuThreadNum(2);
|
||||
option.setLitePowerMode(LitePowerMode.LITE_POWER_HIGH);
|
||||
option.enableLiteFp16();
|
||||
// 使用init函数初始化
|
||||
model.init(modelFile, paramFile, configFile, option);
|
||||
// Bitmap读取、模型预测、资源释放 同上 ...
|
||||
```
|
||||
|
||||
## App示例工程使用方式
|
||||
<div id="App"></div>
|
||||
|
||||
FastDeploy在java/android/app目录下提供了一些示例工程，以下将介绍示例工程的使用方式。由于java/android目录下同时还包含JNI工程，因此想要使用示例工程的用户还需要配置NDK，如果您只关心Java API的使用，并且不想配置NDK，可以直接跳转到以下详细的案例链接。
|
||||
|
||||
- [图像分类App示例工程](../../examples/vision/classification/paddleclas/android)
|
||||
- [目标检测App示例工程](../../examples/vision/detection/paddledetection/android)
|
||||
- [OCR文字识别App示例工程](../../examples/vision/ocr/PP-OCRv2/android)
|
||||
- [人像分割App示例工程](../../examples/vision/segmentation/paddleseg/android)
|
||||
- [人脸检测App示例工程](../../examples/vision/facedet/scrfd/android)
|
||||
|
||||
### 环境准备
|
||||
|
||||
1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Stuido 官网](https://developer.android.com/studio)。
|
||||
2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`
|
||||
|
||||
**注意**：如果您的 Android Studio 尚未配置 NDK ，请根据 Android Studio 用户指南中的[安装及配置 NDK 和 CMake ](https://developer.android.com/studio/projects/install-ndk)内容，预先配置好 NDK 。您可以选择最新的 NDK 版本，或者使用 FastDeploy Android 预测库版本一样的 NDK
|
||||
|
||||
### 部署步骤
|
||||
|
||||
1. App示例工程位于 `fastdeploy/java/android/app` 目录
|
||||
2. 用 Android Studio 打开 `fastdeploy/java/android` 工程，注意是`java/android`目录
|
||||
3. 手机连接电脑，打开 USB 调试和文件传输模式，并在 Android Studio 上连接自己的手机设备（手机需要开启允许从 USB 安装软件权限）
|
||||
|
||||
<p align="center">
|
||||
<img width="1440" alt="image" src="https://user-images.githubusercontent.com/31974251/203257262-71b908ab-bb2b-47d3-9efb-67631687b774.png">
|
||||
</p>
|
||||
|
||||
> **注意：**
|
||||
>> 如果您在导入项目、编译或者运行过程中遇到 NDK 配置错误的提示，请打开 ` File > Project Structure > SDK Location`，修改 `Andriod NDK location` 为您本机配置的 NDK 所在路径。本工程默认使用的NDK版本为20.
|
||||
>> 如果您是通过 Andriod Studio 的 SDK Tools 下载的 NDK (见本章节"环境准备")，可以直接点击下拉框选择默认路径。
|
||||
>> 还有一种 NDK 配置方法，你可以在 `java/android/local.properties` 文件中手动完成 NDK 路径配置，如下图所示
|
||||
>> 如果以上步骤仍旧无法解决 NDK 配置错误，请尝试根据 Andriod Studio 官方文档中的[更新 Android Gradle 插件](https://developer.android.com/studio/releases/gradle-plugin?hl=zh-cn#updating-plugin)章节，尝试更新Android Gradle plugin版本。
|
||||
|
||||
4. 点击 Run 按钮，自动编译 APP 并安装到手机。(该过程会自动下载预编译的 FastDeploy Android 库 以及 模型文件，需要联网)
|
||||
成功后效果如下，图一：APP 安装到手机；图二： APP 打开后的效果，会自动识别图片中的物体并标记；图三：APP设置选项，点击右上角的设置图片，可以设置不同选项进行体验。
|
||||
|
||||
| APP 图标 | APP 效果 | APP设置项
|
||||
| --- | --- | --- |
|
||||
|  |  |  |
|
||||
|
||||
### 切换不同的场景
|
||||
App示例工程只需要在AndroidManifest.xml中切换不同的Activity即可编译不同场景的App进行体验。
|
||||
|
||||
<p align="center">
|
||||
<img width="788" alt="image" src="https://user-images.githubusercontent.com/31974251/203258255-b422d3e2-6004-465f-86b6-9fa61a27c6c2.png">
|
||||
</p>
|
||||
|
||||
- 图像分类场景
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
<!-- ... -->
|
||||
<activity android:name=".classification.ClassificationMainActivity">
|
||||
<!-- -->
|
||||
</activity>
|
||||
<activity
|
||||
android:name=".classification.ClassificationSettingsActivity"
|
||||
</activity>
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
- 目标检测场景
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
<!-- ... -->
|
||||
<activity android:name=".detection.DetectionMainActivity">
|
||||
<!-- -->
|
||||
</activity>
|
||||
<activity
|
||||
android:name=".detection.DetectionSettingsActivity"
|
||||
</activity>
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
- OCR文字识别场景
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
<!-- ... -->
|
||||
<activity android:name=".ocr.OcrMainActivity">
|
||||
<!-- -->
|
||||
</activity>
|
||||
<activity
|
||||
android:name=".ocr.OcrSettingsActivity"
|
||||
</activity>
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
- 人像分割场景
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
<!-- ... -->
|
||||
<activity android:name=".segmentation.SegmentationMainActivity">
|
||||
<!-- -->
|
||||
</activity>
|
||||
<activity
|
||||
android:name=".segmentation.SegmentationSettingsActivity"
|
||||
</activity>
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
- 人脸检测场景
|
||||
```xml
|
||||
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
package="com.baidu.paddle.fastdeploy.app.examples">
|
||||
<!-- ... -->
|
||||
<activity android:name=".facedet.FaceDetMainActivity">
|
||||
<!-- -->
|
||||
</activity>
|
||||
<activity
|
||||
android:name=".facedet.FaceDetSettingsActivity"
|
||||
</activity>
|
||||
</application>
|
||||
</manifest>
|
||||
```
|
||||
@@ -1 +0,0 @@
|
||||
- TODO
|
||||
Reference in New Issue
Block a user