如何在OpenHarmony上使用SeetaFace2人脸识别库？

简介

相信大部分同学们都已了解或接触过OpenAtom OpenHarmony（以下简称“OpenHarmony”）了，但你一定没在OpenHarmony上实现过人脸识别功能，跟着本文带你快速在OpenHarmony标准设备上基于SeetaFace2和OpenCV实现人脸识别。

项目效果

本项目实现了导入人脸模型、人脸框选和人脸识别三大功能，操作流程如下：

1. 录入页面点击右下角按钮，跳转拍摄页面进行拍照；

2. 选择一张或多张人脸作为训练模型，并设置对应的名字；

3. 选择一张未录入的人脸图片，点击框选按钮实现人脸图片框选功能；

4. 最后点击识别，应用会对当前图片进行匹配，最终在界面中显示识别结果。

快速上手

设备端开发

设备端通过OpenCV对图像进行处理并通过Seetaface2对图形数据进行人脸头像的识别，最终输出对应的NAPI接口提供给应用端调用。因此设备端开发主要涉及到OpenCV和Seetaface2的移植以及NAPI接口的开发。

OpenCV库移植

OpenCV是一个功能非常强大的开源计算机视觉库。此库已由知识体系工作组移植到了OpenHarmony中，后期还会将此库合入到主仓。在此库上主仓之前，我们只需要以下几个步骤就可以实现OpenCV的移植使用。

1. 通过以下命令下载已经移植好的OpenCV

git clone git@gitee.com:zhong-luping/ohos_opencv.git

2. 将OpenCV拷贝到OpenHarmony目录的third_party下

cp -raf opencv ~/openharmony/third_party/

3. 适当裁剪编译选项

打开OpenCV目录下的BUILD.gn，如下：

不需要video以及flann功能，将对应的模块注释即可。

import("//build/ohos.gni")
group("opencv") {
    deps = [
        "//third_party/opencv/modules/core:opencv_core",
      //  "//third_party/opencv/modules/flann:opencv_flann",
        "//third_party/opencv/modules/imgproc:opencv_imgproc",
        "//third_party/opencv/modules/ml:opencv_ml",
        "//third_party/opencv/modules/photo:opencv_photo",
        "//third_party/opencv/modules/dnn:opencv_dnn",
        "//third_party/opencv/modules/features2d:opencv_features2d",
        "//third_party/opencv/modules/imgcodecs:opencv_imgcodecs",
        "//third_party/opencv/modules/videoio:opencv_videoio",
        "//third_party/opencv/modules/calib3d:opencv_calib3d",
        "//third_party/opencv/modules/highgui:opencv_highgui",
        "//third_party/opencv/modules/objdetect:opencv_objdetect",
        "//third_party/opencv/modules/stitching:opencv_stitching",
        "//third_party/opencv/modules/ts:opencv_ts",
     //   "//third_party/opencv/modules/video:opencv_video",
       "//third_party/opencv/modules/gapi:opencv_gapi",
    ]
}

4. 添加依赖子系统的part_name，编译框架子系统会将编译出的库拷贝到系统文件中。

此项目中我们新建了一个SeetaFaceApp的子系统，该子系统中命名part_name为SeetafaceApi，所以我们需要在对应模块中的BUILD.gn中加上part_name=“SeetafaceApi”

以module/core为例：

ohos_shared_library("opencv_core"){
 sources = [ ... ]
configs = [  ... ]
deps = [ ... ]
part_name = "SeetafaceApi"
}

5. 编译工程需要添加OpenCV的依赖。

在生成NAPI的BUILD.gn中添加以下依赖：

deps += [ "//third_party/opencv:opencv" ]

至此，人脸识别中OpenCV的移植使用完成。

SeetaFace2库移植

SeetaFace2是中科视拓开源的第二代人脸识别库。包括了搭建一套全自动人脸识别系统所需的三个核心模块，即：人脸检测模块FaceDetector、面部关键点定位模块FaceLandmarker以及人脸特征提取与比对模块 FaceRecognizer。

关于SeetaFace2的移植请参照文档:SeetaFace2移植开发文档。

NAPI接口开发

关于OpenHarmony中的NAPI开发，参考视频：

OpenHarmony中napi的开发视频教程。本文将重点讲解NAPI接口如何实现OpenCV以及SeetaFace的调用。

1. 人脸框获取的NAPI接口的实现。

int GetRecognizePoints(const char *image_path)；

此接口主要是通过应用层输入一张图片，通过OpenCV的imread接口获取到图片数据，并通过人脸检测模块FaceDetector分析获得图片中所有的人脸矩形框（矩形框是以x,y,w,h的方式）并将人脸框矩形以数组的方式返回到应用层。

人脸框矩形获取的主要代码如下：

static int RecognizePoint(string image_path, FaceRect *rect, int num)
{
    if (rect == nullptr) {
        cerr << "NULL POINT!" << endl;
        LOGE("NULL POINT! \n");
        return -1;
    }
    seeta::ModelSetting::Device device = seeta::ModelSetting::CPU;
    int id = 0;
 
 
    /* 设置人脸识别模型。*/
    seeta::ModelSetting FD_model( "/system/usr/model/fd_2_00.dat", device, id );
    seeta::ModelSetting FL_model( "/system/usr/model/pd_2_00_pts81.dat", device, id );
 
 
    seeta::FaceDetector FD(FD_model);
    seeta::FaceLandmarker FL(FL_model);
 
 
    FD.set(seeta::FaceDetector::PROPERTY_VIDEO_STABLE, 1);
 
 
    /* 读取图片数据 */
    auto frame = imread(image_path);
    seeta::cv::ImageData simage = frame;
    if (simage.empty()) {
        cerr << "Can not open image: " << image_path << endl;
        LOGE("Can not open image: %{public}s", image_path.c_str());
        return -1;
    }
    /* 图片数据进行人脸识别处理 ，获取所有的人脸框数据对象*/
    auto faces = FD.detect(simage);
    if (faces.size <= 0) {
        cerr << "detect " << image_path << "failed!" << endl;
        LOGE("detect image: %s failed!", image_path.c_str());
        return -1;
    }
    for (int i = 0; (i < faces.size && i < num); i++) {
        /* 将所有人脸框对象数据以坐标形式输出*/
        auto &face = faces.data[i];
        memcpy(&rect[i], &(face.pos), sizeof(FaceRect));
    }
    return faces.size;
}

其中FD_model是人脸检测模型，而FL_model是面部关键点定位模型(此模型分为5点定位和81点定位，本项目中使用的是81点定位模型)，这些模型从开源项目中免费获取。

通过以上方式获取到对应的人脸矩形框后，再将矩形框以数组的方式返回到应用端：

string image = path;
p = (FaceRect *)malloc(sizeof(FaceRect) * MAX_FACE_RECT);
/* 根据图片进行人脸识别并获取人脸框坐标点 */
int retval = RecognizePoint(image, p, MAX_FACE_RECT);
if (retval <= napi_ok) {
    LOGE("GetNapiValueString failed!");
    free(p);
    return result;
} 
/*将所有坐标点以数组方式返回到应用端*/
for (int i = 0; i < retval; i++) {
    int arry_int[4] = {p[i].x, p[i].y, p[i].w, p[i].h};
    int arraySize = (sizeof(arry_int) / sizeof(arry_int[0]));
    for (int j = 0; j < arraySize; j++) {
        napi_value num_val;
        if (napi_create_int32(env, arry_int[j], &num_val) != napi_ok) {
            LOGE("napi_create_int32 failed!");
            return result;
        }
        napi_set_element(env, array, i*arraySize + j, num_val);
    }
}
if (napi_create_object(env, &result) != napi_ok) {
    LOGE("napi_create_object failed!");
    free(p);
    return result;
}
if (napi_set_named_property(env, result, "recognizeFrame", array) != napi_ok) {
    LOGE("napi_set_named_property failed!");
    free(p);
    return result;
}
LOGI("");
free(p);
return result;

其中array是通过napi_create_array创建的一个NAPI数组对象，通过 napi_set_element将所有的矩形框数据保存到array对象中，最后通过 napi_set_named_property将array转换成应用端可识别的对象类型result并将其返回。

2. 人脸搜索识别初始化与逆初始化。

1. int FaceSearchInit()；

2. int FaceSearchDeinit()；

这2个接口主要是提供给人脸搜索以及识别调用的，初始化主要包含模型的注册以及识别模块的初始化：

static  int FaceSearchInit(FaceSearchInfo *info)
{
    if (info == NULL) {
        info = (FaceSearchInfo *)malloc(sizeof(FaceSearchInfo));
        if (info == nullptr) {
            cerr << "NULL POINT!" << endl;
            return -1;
        }
    }
 
 
    seeta::ModelSetting::Device device = seeta::ModelSetting::CPU;
    int id = 0;
    seeta::ModelSetting FD_model( "/system/usr/model/fd_2_00.dat", device, id );
    seeta::ModelSetting PD_model( "/system/usr//model/pd_2_00_pts5.dat", device, id );
    seeta::ModelSetting FR_model( "/system/usr/model/fr_2_10.dat", device, id );
 
 
    info->engine = make_shared<seeta::FaceEngine>(FD_model, PD_model, FR_model, 2, 16);
    info->engine->FD.set( seeta::FaceDetector::PROPERTY_MIN_FACE_SIZE, 80);
 
 
    info->GalleryIndexMap.clear();
 
 
    return 0;
}

而逆初始化就是做一些内存的释放。

static void FaceSearchDeinit(FaceSearchInfo *info, int need_delete)
{
    if (info != nullptr) {
        if (info->engine != nullptr) {
        }
 
 
        info->GalleryIndexMap.clear();
        if (need_delete) {
            free(info);
            info = nullptr;
        }
    }
}

3. 人脸搜索识别注册接口的实现。

int FaceSearchRegister(const char *value);

需要注意的是，该接口需要应用端传入一个json数据的参数，主要包含注册人脸的名字，图片以及图片个数，如{“name”:“刘德华”,“sum”:“2”,“image”:{“11.jpg”,“12.jpg”}}。而解析参数的时候需要调用 napi_get_named_property对json数据的各个对象进行解析，具体代码如下：

napi_get_cb_info(env, info, &argc, &argv, &thisVar, &data);
napi_value object = argv;
napi_value value = nullptr;
 
 
if (napi_get_named_property(env, object, (const char *)"name", &value) == napi_ok) {
    char name[64] = {0};
    if (GetNapiValueString(env, value, (char *)name, sizeof(name)) < 0) {
        LOGE("GetNapiValueString failed!");
        return result;
    }
    reg_info.name = name;
}
LOGI("name = %{public}s", reg_info.name.c_str());
if (napi_get_named_property(env, object, (const char *)"sum", &value) == napi_ok) {
     
    if (napi_get_value_uint32(env, value, &sum) != napi_ok) {
        LOGE("napi_get_value_uint32 failed!");
        return result;
    }
}
LOGI("sum = %{public}d", sum);
if (napi_get_named_property(env, object, (const char *)"image", &value) == napi_ok) {
    bool res = false;
    if (napi_is_array(env, value, &res) != napi_ok || res == false) {
        LOGE("napi_is_array failed!");
        return result;
    }
    for (int i = 0; i < sum; i++) {
        char image[256] = {0};
        napi_value imgPath = nullptr;
        if (napi_get_element(env, value, i, &imgPath) != napi_ok) {
            LOGE("napi_get_element failed!");
            return result;
        }
        if (GetNapiValueString(env, imgPath, (char *)image, sizeof(image)) < 0) {
            LOGE("GetNapiValueString failed!");
            return result;
        }
        reg_info.path = image;
        if (FaceSearchRegister(g_FaceSearch, reg_info) != napi_ok) {
            retval = -1;
            break;
        }
    }
}

通过napi_get_cb_info获取从应用端传来的参数，并通过 napi_get_named_property获取对应的name以及图片个数，最后通过napi_get_element获取图片数组中的各个image，将name和image通过FaceSearchRegister接口将图片和名字注册到SeetaFace2模块的识别引擎中。具体实现如下：

static int FaceSearchRegister(FaceSearchInfo &info, RegisterInfo &gegister)
{
    if (info.engine == nullptr) {
        cerr << "NULL POINT!" << endl;
        return -1;
    }
 
 
    seeta::cv::ImageData image = cv::imread(gegister.path);
    auto id = info.engine->Register(image);
    if (id >= 0) {
        info.GalleryIndexMap.insert(make_pair(id, gegister.name));
    }
 
 
    return 0;
}

注册完数据后，后续可以通过该引擎来识别对应的图片。

4. 获取人脸搜索识别结果接口的实现。

char *FaceSearchGetRecognize(const char *image_path);

该接口实现了通过传入一张图片，在识别引擎中进行搜索识别。如果识别引擎中有类似的人脸注册，则返回对应人脸注册时的名字，否则返回不识别(ignored)字样。该方法是通过异步回调的方式实现的：

// 创建async work，创建成功后通过最后一个参数(commandStrData->asyncWork)返回async work的handle
napi_value resourceName = nullptr;
napi_create_string_utf8(env, "FaceSearchGetPersonRecognizeMethod", NAPI_AUTO_LENGTH, &resourceName);
napi_create_async_work(env, nullptr, resourceName, FaceSearchRecognizeExecuteCB, FaceSearchRecognizeCompleteCB,
        (void *)commandStrData, &commandStrData->asyncWork);
 
 
// 将刚创建的async work加到队列，由底层去调度执行
napi_queue_async_work(env, commandStrData->asyncWork);

其中FaceSearchRecognizeExecuteCB实现了人脸识别

static void FaceSearchRecognizeExecuteCB(napi_env env, void *data)
{
    CommandStrData *commandStrData = dynamic_cast<CommandStrData*>((CommandStrData *)data);
    if (commandStrData == nullptr) {
        HILOG_ERROR("nullptr point!", __FUNCTION__, __LINE__);
        return;
    }
 
 
    FaceSearchInfo faceSearch = *(commandStrData->mFaceSearch);
    commandStrData->result = FaceSearchSearchRecognizer(faceSearch, commandStrData->filename);
    LOGI("Recognize result : %s !", __FUNCTION__, __LINE__, commandStrData->result.c_str());
}

FaceSearchRecognizeCompleteCB函数通过napi_resolve_deferred接口将识别结果返回到应用端。

static void FaceSearchRecognizeCompleteCB(napi_env env, napi_status status, void *data)
{
    CommandStrData *commandStrData = dynamic_cast<CommandStrData*>((CommandStrData *)data);
    napi_value result;
 
 
    if (commandStrData == nullptr || commandStrData->deferred == nullptr) {
        LOGE("nullptr", __FUNCTION__, __LINE__);
        if (commandStrData != nullptr) {
            napi_delete_async_work(env, commandStrData->asyncWork);
            delete commandStrData;
        }
 
 
        return;
    }
 
 
    const char *result_str = (const char *)commandStrData->result.c_str();
    if (napi_create_string_utf8(env, result_str, strlen(result_str), &result) != napi_ok) {
        LOGE("napi_create_string_utf8 failed!", __FUNCTION__, __LINE__);
        napi_delete_async_work(env, commandStrData->asyncWork);
        delete commandStrData;
        return;
    }
 
 
    napi_resolve_deferred(env, commandStrData->deferred, result);
    napi_delete_async_work(env, commandStrData->asyncWork);
 
 
    delete commandStrData;
}

通过人脸特征提取与比对模块，对传入的数据与已注册的数据进行对比，并通过返回对比的相似度来进行判断当前人脸是否为可识别的，最后返回识别结果。具体实现代码：

static string FaceSearchSearchRecognizer(FaceSearchInfo &info, string filename)
{
    if (info.engine == nullptr) {
        cerr << "NULL POINT!" << endl;
        return "recognize error 0";
    }
    string name;
    float threshold = 0.7f;
    seeta::QualityAssessor QA;
    auto frame = cv::imread(filename);
    if (frame.empty()) {
        LOGE("read image %{public}s failed!", filename.c_str());
        return "recognize error 1!";
    }
    seeta::cv::ImageData image = frame;
    std::vector<SeetaFaceInfo> faces = info.engine->DetectFaces(image);
 
 
    for (SeetaFaceInfo &face : faces) {
        int64_t index = 0;
        float similarity = 0;
 
 
        auto points = info.engine->DetectPoints(image, face);
 
 
        auto score = QA.evaluate(image, face.pos, points.data());
        if (score == 0) {
            name = "ignored";
        } else {
            auto queried = info.engine->QueryTop(image, points.data(), 1, &index, &similarity);
            // no face queried from database
            if (queried < 1) continue;
                // similarity greater than threshold, means recognized
            if( similarity > threshold ) {
                name = info.GalleryIndexMap[index];
            }
        }
    }
    LOGI("name : %{public}s \n", name.length() > 0 ? name.c_str() : "null");
    return name.length() > 0 ? name : "recognize failed";
}

至此，所有的NAPI接口已经开发完成。

5. NAPI库编译开发完NAPI接口后，我们需要将我们编写的库加入到系统中进行编译，我们需要添加一个自己的子系统。

首先在库目录下新建一个ohos.build

{
    "subsystem": "SeetafaceApp",
    "parts": {
        "SeetafaceApi": {
            "module_list": [
               "//seetaface:seetafaceapp_napi"
            ],
            "test_list": [ ]
        }
    }
}

其次同一目录新建一个BUILD.gn，将库源文件以及对应的依赖加上，如下：

import("//build/ohos.gni")
 
 
config("lib_config") {
    cflags_cc = [
        "-frtti",
        "-fexceptions",
        "-DCVAPI_EXPORTS",
        "-DOPENCV_ALLOCATOR_STATS_COUNTER_TYPE=int",
        "-D_USE_MATH_DEFINES",
        "-D__OPENCV_BUILD=1",
        "-D__STDC_CONSTANT_MACROS",
        "-D__STDC_FORMAT_MACROS",
        "-D__STDC_LIMIT_MACROS",
        "-O2",
        "-Wno-error=header-hygiene",
    ]
}
 
 
ohos_shared_library("seetafaceapp_napi") {
    sources = [
        "app.cpp",
    ]
 
 
    include_dirs = [
        "./",
        "//third_party/opencv/include",
        "//third_party/opencv/common",
        "//third_party/opencv/modules/core/include",
        "//third_party/opencv/modules/highgui/include",
        "//third_party/opencv/modules/imgcodecs/include",
        "//third_party/opencv/modules/imgproc/include",
        "//third_party/opencv/modules/calib3d/include",
        "//third_party/opencv/modules/dnn/include",
        "//third_party/opencv/modules/features2d/include",
        "//third_party/opencv/modules/flann/include",
        "//third_party/opencv/modules/ts/include",
        "//third_party/opencv/modules/video/include",
        "//third_party/opencv/modules/videoio/include",
        "//third_party/opencv/modules/ml/include",
        "//third_party/opencv/modules/objdetect/include",
        "//third_party/opencv/modules/photo/include",
        "//third_party/opencv/modules/stitching/include",
        "//third_party/SeetaFace2/FaceDetector/include",
        "//third_party/SeetaFace2/FaceLandmarker/include",
        "//third_party/SeetaFace2/FaceRecognizer/include",
        "//third_party/SeetaFace2/QualityAssessor/include",
        "//base/accessibility/common/log/include",
        "//base/hiviewdfx/hilog_lite/interfaces/native/innerkits"
    ]
 
 
    deps = [ "//foundation/ace/napi:ace_napi" ]
    deps += [ "//third_party/opencv:opencv" ]
    deps += [ "//third_party/SeetaFace2:SeetaFace2" ]
 
 
    external_deps = [
        "hiviewdfx_hilog_native:libhilog",
    ]
 
 
    configs = [
       ":lib_config"
    ]
 
 
    # 指定库生成的路径
    relative_install_dir = "module"
    # 子系统及其组件，后面会引用
    subsystem_name = "SeetafaceApp"
    part_name = "SeetafaceApi"
}

添加完对应的文件后，我们需要将我们的子系统添加到系统中进行编译，打开build/subsystem_config.json并在最后添加以下代码：

"SeetafaceApp": {
  "path": "seetaface",
  "name": "SeetafaceApp"
}

添加完子系统再修改产对应的品配置

打开productdefine/common/products/rk3568.json并在最后添加以下代码：

"SeetafaceApp:SeetafaceApi":{}

做完以上修改后我们就可以通过以下命令直接编译NAPI的库文件了：

./build.sh --product-name rk3568 --ccache

参考RK3568快速上手-镜像烧录完成烧录即可。

应用端开发

在完成设备NAPI功能开发后，应用端通过调用NAPI组件中暴露给应用的人脸识别接口，即可实现对应功能。接下来就带着大家使用NAPI实现人脸识别功能。

开发准备

1. 下载DevEco Studio 3.0 Beta4；

2. 搭建开发环境，参考开发准备；

3. 了解属性eTS开发，参考eTS语言快速入门；

SeetaFace2初始化

1. 首先将SeetaFace2 NAPI接口声明文件放置于SDK目录/api下；

2. 然后导入SeetaFace2 NAPI模块；ck-start/star

3. 调用初始化接口；

// 首页实例创建后
async aboutToAppear() {
  await StorageUtils.clearModel();
  CommonLog.info(TAG,'aboutToAppear')
  // 初始化人脸识别
  let res = SeetafaceApp.FaceSearchInit()
  CommonLog.info(TAG,`FaceSearchInit res=${res}`)
  this.requestPermissions()
}
 
 
// 请求权限
requestPermissions(){
  CommonLog.info(TAG,'requestPermissions')
  let context = featureAbility.getContext()
  context.requestPermissionsFromUser(PERMISSIONS, 666,(res)=>{
    this.getMediaImage()
  })
}

获取所有人脸图片

通过文件管理模块fileio和媒体库管理mediaLibrary，获取指定应用数据目录下所有的图片信息，并将路径赋值给faceList，faceList数据用于Image组件提供url进行加载图片

// 获取所有图片
async getMediaImage(){
  let context = featureAbility.getContext();
  // 获取本地应用沙箱路径
  let localPath = await context.getOrCreateLocalDir()
  CommonLog.info(TAG, `localPath:${localPath}`)
  let facePath = localPath + "/files"
  // 获取所有照片
  this.faceList = await FileUtil.getImagePath(facePath)
}

设置人脸模型

获取选中的人脸图片地址和输入的名字，调用SeetafaceApp.FaceSearchRegister(params)进行设置人脸模型。其中参数params由name名字、image图片地址集合和sum图片数量组成。

async submit(name) {
    if (!name || name.length == 0) {
        CommonLog.info(TAG, 'name is empty')
        return
    }
    let selectArr = this.faceList.filter(item => item.isSelect)
    if (selectArr.length == 0) {
        CommonLog.info(TAG, 'faceList is empty')
        return
    }
    // 关闭弹窗
    this.dialogController.close()
    try {
        let urls = []
        let files = []
        selectArr.forEach(item => {
            let source = item.url.replace('file://', '')
            CommonLog.info(TAG, `source:${source}`)
            urls.push(item.url)
            files.push(source)
        })
 
 
        // 设置人脸识别模型参数
        let params = {
            name: name,
            image: files,
            sum: files.length
        }
        CommonLog.info(TAG, 'FaceSearchRegister' + JSON.stringify(params))
        let res = SeetafaceApp.FaceSearchRegister(params)
        CommonLog.info(TAG, 'FaceSearchRegister res ' + res)
        // 保存已设置的人脸模型到轻量存储
        let data = {
            name:name,
            urls:urls
        }
        let modelStr = await StorageUtils.getModel()
        let modelList = JSON.parse(modelStr)
        modelList.push(data)
        StorageUtils.setModel(modelList)
        router.back()
    } catch (err) {
        CommonLog.error(TAG, 'submit fail ' + err)
    }
}

实现框选人脸

调用SeetafaceApp.GetRecognizePoints传入当前图片地址，获取到人脸左上和右下坐标，再通过CanvasRenderingContext2D对象绘画出人脸框。

实现人脸识别

调用SeetafaceApp.FaceSearchGetRecognize(url)，传入图片地址对人脸进行识别并返回对应识别出来的名字。

// 人脸识别
recognize(){
    SeetafaceApp.FaceSearchGetRecognize(this.url).then(res=>{
        CommonLog.info(TAG,'recognize suceess' + JSON.stringify(res))
        if(res && res != 'ignored' && res != "recognize failed" && res != 'recognize error 1!'){
            // 赋值识别到的人物模型
            this.name = res
        }else{
            this.name = '未识别到该模型'
        }
    }).catch(err=>{
        CommonLog.error(TAG,'recognize' + err)
        this.name = '未识别到该模型'
    })
}

为了帮助到大家能够更有效的学习OpenHarmony 开发的内容，下面特别准备了一些相关的参考学习资料：