如何在OpenHarmony上使用SeetaFace2人脸识别库? 精华

OpenHarmony开发者
发布于 2022-8-22 17:46
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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

如何在OpenHarmony上使用SeetaFace2人脸识别库?-鸿蒙开发者社区

   

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

如何在OpenHarmony上使用SeetaFace2人脸识别库?-鸿蒙开发者社区

   

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

如何在OpenHarmony上使用SeetaFace2人脸识别库?-鸿蒙开发者社区

   

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

如何在OpenHarmony上使用SeetaFace2人脸识别库?-鸿蒙开发者社区

   

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'
    })
}

 

参考文档
SeetaFace2移植开发文档:

​https://gitee.com/openharmony-sig/knowledge_demo_smart_home/blob/master/docs/SeetaFace2/%E4%BA%BA%E8%84%B8%E8%AF%86%E5%88%AB%E5%BA%93%E7%9A%84%E7%A7%BB%E6%A4%8D.md​

OpenHarmony中napi的开发视频教程:

​https://www.bilibili.com/video/BV1L44y1p7KE?spm_id_from=333.999.0.0​

RK3568快速上手:

​https://growing.openharmony.cn/mainPlay/learnPathMaps?id=27​

人脸识别应用:

​https://gitee.com/openharmony-sig/knowledge_demo_travel/blob/master/docs/FaceRecognition_ETS/README_zh.md​

应用开发准备:

​https://docs.openharmony.cn/pages/v3.2Beta/zh-cn/application-dev/quick-start/start-overview.md/​

eTS语言快速入门:

​https://docs.openharmony.cn/pages/v3.2Beta/zh-cn/application-dev/quick-start/start-with-ets.md/​

知识体系工作组:

​https://gitee.com/openharmony-sig/knowledge​

如何在OpenHarmony上使用SeetaFace2人脸识别库?-鸿蒙开发者社区

   

已于2023-4-21 15:14:43修改
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笨笨的婧婧
笨笨的婧婧

之前在codelab见过HarmonyOS的人脸识别:https://developer.huawei.com/consumer/cn/codelabsPortal/carddetails/HarmonyOS-BiometricAuthentication

回复
2022-8-26 11:10:38
一緑向北
一緑向北

OpenCV的人脸识别确实挺成熟的

回复
2022-8-26 14:50:34
hmyxd
hmyxd

都是开源项目确实能减少不少成本

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2022-8-26 18:26:35
无马牧马人
无马牧马人

SeetaFace2  有鸿蒙版本吗,就是已经写好BUILD.gn的版本;参照SeetaFace2  移植开发文档估计都需要写半天

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2023-1-11 17:46:11
OwenZhang3
OwenZhang3

已收藏

已于2023-1-13 16:54:25修改
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2023-1-13 16:54:05
无马牧马人
无马牧马人 回复了 无马牧马人
​SeetaFace2 有鸿蒙版本吗,就是已经写好BUILD.gn的版本;参照SeetaFace2 移植开发文档估计都需要写半天
1
回复
2023-2-2 15:02:14
安苒anran0
安苒anran0

好活,当赏!

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2023-8-2 19:24:49
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