主要還是項目要求吧需要進行人臉68個關鍵點檢測然后網上尋找一番發(fā)現(xiàn)DLIB庫使用還是挺高的然后就在上篇博客中簡單記錄了一下DLIB 19.17的配置過程但是還是要嘗試下它的程序的。LZ修改過的版本由于保密原因就不進行展示(≧▽≦)/啦啦啦給出官方給的源碼吧。#includedlib/image_processing/frontal_face_detector.h#includedlib/image_processing/render_face_detections.h#includedlib/image_processing.h#includedlib/gui_widgets.h#includedlib/image_io.h#includeiostreamusing namespace dlib;using namespace std;// ----------------------------------------------------------------------------------------intmain(intargc,char**argv){try{// This example takes in a shape model file and then a list of images to// process. We will take these filenames in as command line arguments.// Dlib comes with example images in the examples/faces folder so give// those as arguments to this program.//這里要去對應的網址下載關鍵點檢測的數(shù)據(jù)網速還是很渣渣if(argc1){coutCall this program like this:endl;cout./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpgendl;cout\nYou can get the shape_predictor_68_face_landmarks.dat file from:\n;couthttp://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2endl;return0;}// We need a face detector. We will use this to get bounding boxes for// each face in an image.// 初始化檢測器frontal_face_detector detectorget_frontal_face_detector();// And we also need a shape_predictor. This is the tool that will predict face// landmark positions given an image and face bounding box. Here we are just// loading the model from the shape_predictor_68_face_landmarks.dat file you gave// as a command line argument.shape_predictor sp;deserialize(argv[1])sp;image_window win,win_faces;// Loop over all the images provided on the command line.for(inti2;iargc;i){coutprocessing image argv[i]endl;array2drgb_pixelimg;load_image(img,argv[i]);// Make the image larger so we can detect small faces.pyramid_up(img);// Now tell the face detector to give us a list of bounding boxes// around all the faces in the image.std::vectorrectangledetsdetector(img);coutNumber of faces detected: dets.size()endl;// Now we will go ask the shape_predictor to tell us the pose of// each face we detected.std::vectorfull_object_detectionshapes;for(unsignedlongj0;jdets.size();j){full_object_detection shapesp(img,dets[j]);coutnumber of parts: shape.num_parts()endl;coutpixel position of first part: shape.part(0)endl;coutpixel position of second part: shape.part(1)endl;// You get the idea, you can get all the face part locations if// you want them. Here we just store them in shapes so we can// put them on the screen.shapes.push_back(shape);}// Now lets view our face poses on the screen.win.clear_overlay();win.set_image(img);win.add_overlay(render_face_detections(shapes));// We can also extract copies of each face that are cropped, rotated upright,// and scaled to a standard size as shown here:dlib::arrayarray2drgb_pixelface_chips;extract_image_chips(img,get_face_chip_details(shapes),face_chips);win_faces.set_image(tile_images(face_chips));coutHit enter to process the next image...endl;cin.get();}}catch(exceptione){cout\nexception thrown!endl;coute.what()endl;}}其實上述代碼LZ覺得官方還是很良心的仔細看看基本上每一步都講解的非常清楚下面是對應的CMakeLists.txt。cmake_minimum_required(VERSION 3.14) project(gaze_correction_v1) set(CMAKE_CXX_STANDARD 11) #opencv find_package(OpenCV REQUIRED) include_directories(${OpenCV_INCLUDE_DIRS}) #DLIB19.17 add_subdirectory(/home/felaim/Documents/software/dlib-19.17/dlib dlib_build) #include_directories(/usr/local/include/dlib) add_executable(gaze_correction_v1 main.cpp) target_link_libraries(gaze_correction_v1 ${OpenCV_LIBS} dlib::dlib )LZ把include_directories部分給注釋掉了因為不注釋匯報一個錯說DLIB路徑不能include類似錯誤啦(≧▽≦)/啦啦啦后面進行測試通常情況下C版本的基本上都會比python版本的速度要快一些但是LZ運行了上述代碼居然奇慢無比處理一張640*480的圖片要2000多ms然后LZ就瘋狂百度發(fā)現(xiàn)其他小伙伴測試人臉檢測速度也不快大概50ms~100ms左右但是LZ電腦好歹也是i7給我的感覺仿佛是個i3。。。后來總算找到了原因LZ編譯成debug模式DLIB庫在Debug模式下奇慢非常慢最后修改成release模式人臉檢測大概需要38ms關鍵點檢測需要3ms雖然速度還可以但是遠遠達不到項目要求/(ㄒoㄒ)/~~