主要还是项目要求吧需要进行人脸68个关键点检测然后网上寻找一番发现DLIB库使用还是挺高的然后就在上篇博客中简单记录了一下DLIB 19.17的配置过程但是还是要尝试下它的程序的。LZ修改过的版本由于保密原因就不进行展示(≧▽≦)/啦啦啦给出官方给的源码吧。#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.//这里要去对应的网址下载关键点检测的数据网速还是很渣渣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.// 初始化检测器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;}}其实上述代码LZ觉得官方还是很良心的仔细看看基本上每一步都讲解的非常清楚下面是对应的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部分给注释掉了因为不注释汇报一个错说DLIB路径不能include类似错误啦(≧▽≦)/啦啦啦后面进行测试通常情况下C版本的基本上都会比python版本的速度要快一些但是LZ运行了上述代码居然奇慢无比处理一张640*480的图片要2000多ms然后LZ就疯狂百度发现其他小伙伴测试人脸检测速度也不快大概50ms~100ms左右但是LZ电脑好歹也是i7给我的感觉仿佛是个i3。。。后来总算找到了原因LZ编译成debug模式DLIB库在Debug模式下奇慢非常慢最后修改成release模式人脸检测大概需要38ms关键点检测需要3ms虽然速度还可以但是远远达不到项目要求/(ㄒoㄒ)/~~