如何在OpenHarmony上使用SeetaFace2人脸识别库？
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OpenCVgit 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; memcpy(&rect, &(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 = {p.x, p.y, p.w, p.h}; int arraySize = (sizeof(arry_int) / sizeof(arry_int)); for (int j = 0; j < arraySize; j++) { napi_value num_val; if (napi_create_int32(env, arry_int, &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：staticint 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 = {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 = {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; } } } 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文件并添加以下代码：{ "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/star3. 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。// 框选当前人脸select(){ try{ CommonLog.info(TAG,`select start`) // 获取人脸左上和右下坐标 let res = SeetafaceApp.GetRecognizePoints(this.url) CommonLog.info(TAG,`select start1` + JSON.stringify(res)) let points = res.recognizeFrame as Array<number> CommonLog.info(TAG,`select success ${JSON.stringify(res)}`) let faceNumber = Math.floor(points.length/4) // 框选出所有人脸 this.context.lineWidth = 5 this.context.strokeStyle = Color.Yellow.toString() for (let index = 0; index < faceNumber; index++) { let baseNumber = 4 * index this.context.strokeRect(points,points,points,points) this.context.save() } CommonLog.info(TAG,`strokeRect success `) }catch(err){ CommonLog.error(TAG,`select fail ${JSON.stringify(err)}`) }}
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' })}
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.mdOpenHarmony中napi的开发视频教程：https://www.bilibili.com/video/BV1L44y1p7KE?spm_id_from=333.999.0.0RK3568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
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