简单移植dlib和opencv到Androd平台进行人脸检测

简单移植简单移植dlib和opencv到Androd平台进⾏⼈脸检测_android[object之前一直想做一下人脸检测，网上一大堆SDK全是收费的，而且基本上都不是本地检测。后来总算找到了dlib官方的demo速度实在太慢，后面有空会去学习一些优化经验，这篇文章直接从编译开始讲起集成，并且使用官方demo以及机器学习模型来进行人脸检测。首先下载dlib与opencv的新版，以及AndroidStudio的NDK工具准备。\h\hopencv-3.4.4\h之前我想用18的，18dlib现在大部分c库都有python脚本来进行编译，但是配一些环境也是麻烦，我们直接用AndroidStudio来进行编译即可。新建一个AndroidStudio的C++提了。直接看编译的项目结构。我们把下载的dlib库的的源文件全部copy到cpp目录下。然后打开我们的才CMakeLists.txtcmake_minimum_required(VERSIONset(DLIB_IN_PROJECT_BUILDfalse)set(BUILD_SHARED_LIBStrue)#编译成动态库set(DLIB_NO_GUI_SUPPORTtrue)#不需要gui支持set(ARM_NEON_IS_AVAILABLEtrue#开启neon优化set(DLIB_PNG_SUPPORTtrue)#打开PNG支持add_library(native-libSHAREDsrc/main/cpp/native-#Finally,youneedtotellCMakethatthisprogram,assignment_learning_ex,#dependsondlib.Youdothatwiththisstatement:然后打个build.gradle，主要就是配置一些cpuapplyplugin:applyplugin:'kotlin-applyapplyplugin:'kotlin-android-extensions'android{compileSdkVersion28defaultConfig{applicationId"pile"minSdkVersion23targetSdkVersionversionCodeversionNametestInstrumentationRunner"android.support.test.runner.AndroidJUnitRunner"externalNativeBuild{cmakeabiFilters'armeabi-}}}buildTypesdebugexternalNativeBuild{cmake{arguments'-DANDROID_PLATFORM=android-28','-DANDROID_TOOLCHAIN=clang',cFlags'-O3','-fsigned-char','-Wformat','-mfpu=neon','-mfloat-abi=softfp-frtti'//fulloptimization,chardatatypeisinlinecppFlags'-O3','-fexceptions','-fsigned-char',"-frtti-std=c++14",'-Wformat'}}minifyEnabledfalsedebuggabletrueproguardFilesgetDefaultProguardFile('proguard-android.txt'),'proguard-}}}到目前为止其实已经可以编译出来so文件了，但是最后竟然栽在了System.loadLibrary(“xxx”)上，加载dlib的so掉logger_kernel_1.cpp中的139行。上面的都完成之后，我们执行assembleDebug，编译出来so由于Opencv同样新建一个AndroidStudio的C++项目,我们将opencv和dlib的库文件都引入到AndroidStudio中，然后再build.gradle中配置一些编译参数，并且将opencv和dlib包含到项目中。这些步骤基本都十分雷同，我就不赘述了，大家可以直接参考源码。我重点说一下cmake以及c++代码。简单加入两个socmake_minimum_requiredcmake_minimum_required(VERSION3.4.1)set(PATH_TO_NATIVE${PATH_TO_MEDIACORE}/src/main/cpp)set(PATH_TO_PRE_BUILTinclude_directories(BEFORE${PATH_TO_MEDIACORE}/libs/include/)file(GLOBFFMPEG_DECODE_SOURCE"*.cpp")#包含当前目录的cpp文件add_definitions(-DDLIB_PNG_SUPPORT)#需要加入这个宏定义，不然会导致无法使用))很简单的功能，就是通过native层返回一个数组，生成bitmapclassclassMainActivity:AppCompatActivity()overridefunonCreate(savedInstanceState:Bundle?){valprogress=ProgressDialog(this)btn_detect.setOnClickListener{thread{valbyte=valb=Bitmap.createBitmap(byte,384,250,Bitmap.Config.ARGB_8888)runOnUiThread{}}}}}}companionobject//Usedtoloadthe'native-lib'libraryonapplicationinit}}privateexternalfun}native-简单返回一个intexternextern"C"JNIEXPORTjintArrayJNICALLJava_com_ty_opencvtest_MainActivity_detect(JNIEnv*env,jobject){jintArrayintArray=env-constjint*buf=reinterpret_cast<constjint*>(test_faced());env->SetIntArrayRegion(intArray,0,384*250,buf);return}关键的检测代码，我把demo检测人脸，每张脸保存68使用Opencv##include<dlib/image_processing/frontal_face_detector.h>#include<dlib/image_processing/render_face_detections.h>#include<dlib/image_processing.h>#include<dlib/gui_widgets.h>#include<dlib/image_io.h>#include<iostream>#include<opencv2/opencv.hpp>usingnamespacedlib;usingnamespacestd;#include<android/log.h>#defineLOG_TAG#defineLOGD(...)((void)android_log_print(ANDROID_LOG_DEBUG,LOG_TAG,VA_ARGSuchar*test_faced(){try{//frontal_face_detectordetector=//机器学习的模型，从sdshape_predictorsp;deserialize("/sdcard/dlibfolder/shape_predictor_68_face_landmarks.dat")>>sp;//导入实现的pngarray2d<rgb_pixel>load_image(img,"/sdcard/dlibfolder/header.png");constrectangle&rectOri=get_rect(img);////constrectangle&rectUp=floatscalY=rectUp.bottom()/rectOri.bottom();floatscalX=rectUp.right()/rectOri.right();LOGD("%f==%f",scalY,scalX);//opencvcv::Mattemp;//Nowtellthefacedetectortogiveusalistofbounding//aroundallthefacesinthe//std::vector<rectangle>dets=std::vector<full_object_detection>for(unsignedlongj=0;j<dets.size();++j){full_object_detectionshape=sp(img,dets[j]);}if(!shapes.empty())for(inti=0;i<68;i++)LOGD("%d==%d",shapes[0].part(i).x(),circle(temp,cvPoint(static_cast<int>(shapes[0].part(i).x()/sca