人脸识别dlib下载与安装

链接

https://dlib.net/

点击下载


选择opencv

查看案例

// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt/* This example program shows how to find frontal human faces in an image and estimate their pose. The pose takes the form of 68 landmarks. These are points on the face such as the corners of the mouth, along the eyebrows, on the eyes, and so forth. This example is essentially just a version of the face_landmark_detection_ex.cpp example modified to use OpenCV's VideoCapture object to read from a camera instead of files. Finally, note that the face detector is fastest when compiled with at least SSE2 instructions enabled. So if you are using a PC with an Intel or AMD chip then you should enable at least SSE2 instructions. If you are using cmake to compile this program you can enable them by using one of the following commands when you create the build project: cmake path_to_dlib_root/examples -DUSE_SSE2_INSTRUCTIONS=ON cmake path_to_dlib_root/examples -DUSE_SSE4_INSTRUCTIONS=ON cmake path_to_dlib_root/examples -DUSE_AVX_INSTRUCTIONS=ON This will set the appropriate compiler options for GCC, clang, Visual Studio, or the Intel compiler. If you are using another compiler then you need to consult your compiler's manual to determine how to enable these instructions. Note that AVX is the fastest but requires a CPU from at least 2011. SSE4 is the next fastest and is supported by most current machines. */#include<dlib/opencv.h>#include<opencv2/highgui/highgui.hpp>#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>using namespace dlib;using namespace std;intmain(){try{cv::VideoCapturecap(0);if(!cap.isOpened()){cerr<<"Unable to connect to camera"<<endl;return1;}image_window win;// Load face detection and pose estimation models.frontal_face_detector detector=get_frontal_face_detector();shape_predictor pose_model;deserialize("shape_predictor_68_face_landmarks.dat")>>pose_model;// Grab and process frames until the main window is closed by the user.while(!win.is_closed()){// Grab a framecv::Mat temp;if(!cap.read(temp)){break;}// Turn OpenCV's Mat into something dlib can deal with. Note that this just// wraps the Mat object, it doesn't copy anything. So cimg is only valid as// long as temp is valid. Also don't do anything to temp that would cause it// to reallocate the memory which stores the image as that will make cimg// contain dangling pointers. This basically means you shouldn't modify temp// while using cimg.cv_image<bgr_pixel>cimg(temp);// Detect facesstd::vector<rectangle>faces=detector(cimg);// Find the pose of each face.std::vector<full_object_detection>shapes;for(unsignedlongi=0;i<faces.size();++i)shapes.push_back(pose_model(cimg,faces[i]));// Display it all on the screenwin.clear_overlay();win.set_image(cimg);win.add_overlay(render_face_detections(shapes));}}catch(serialization_error&e){cout<<"You need dlib's default face landmarking model file to run this example."<<endl;cout<<"You can get it from the following URL: "<<endl;cout<<" http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2"<<endl;cout<<endl<<e.what()<<endl;}catch(exception&e){cout<<e.what()<<endl;}}

配置Camke

根据提示勾选，构建

获取最小大小的库

生成静态链接库

将其放入与对应的文件中

把dlib放入include 目录

下载模型

https://dlib.net/files/http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2

将模型放入models文件夹中