如何利用opencv训练自己的模型实现特定物体的识别

  [-info ]  # 记录样本数据的文件(就是我们刚才创建的info.data文件) [-img ] [-vec ]   # 输出文件，内含用于训练的正样本。 [-bg ]  # 背景图像的描述文件 [-num ]   #样本数量（默认为1000） [-bgcolor ]    #指定背景颜色 [-w ]#输出样本的宽度（以像素为单位） [-h ]#输出样本的高度（以像素为单位） 

D:\opencv3.4.12\opencv\build\x64\vc15\bin\opencv_createsamples.exe -info C:\Users\lng\Desktop\image\positive\info.dat -vec C:\Users\lng\Desktop\image\sample.vec -num 58 -bgcolor 0 -bgthresh 0 -w 24 -h 24 

 -data      #目录名，如不存在训练程序会创建它，用于存放训练好的分类器 -vec               #包含正样本的vec文件名 -bg    #背景描述文件 [-numPos ]   #每级分类器训练时所用的正样本数目 [-numNeg ]   #每级分类器训练时所用的负样本数目 [-numStages ]   #训练的分类器的级数 --cascadeParams-- [-featureType <{HAAR(default), LBP, HOG}>]  # 特征的类型： HAAR - 类Haar特征； LBP - 局部纹理模式特征 [-w ] #训练样本的尺寸（单位为像素） [-h ] #训练样本的尺寸（单位为像素） --boostParams-- [-minHitRate  = 0.995>] #分类器的每一级希望得到的最小检测率 [-maxFalseAlarmRate ] #分类器的每一级希望得到的最大误检率 

D:\opencv3.4.12\opencv\build\x64\vc15\bin\opencv_traincascade.exe -data C:\Users\lng\Desktop\image -vec C:\Users\lng\Desktop\image\sample.vec -bg bg.txt -numPos 50 -numNeg 500 -numStages 12 -feattureType HAAR -w 24 -h 24 -minHitRate 0.995 -maxFalseAlarmRate 0.5 

PARAMETERS: cascadeDirName: C:\Users\lng\Desktop\image vecFileName: C:\Users\lng\Desktop\image\sample.vec bgFileName: bg.txt numPos: 50 numNeg: 500 numStages: 12 precalcValBufSize[Mb] : 1024 precalcIdxBufSize[Mb] : 1024 acceptanceRatioBreakValue : -1 stageType: BOOST featureType: HAAR sampleWidth: 24 sampleHeight: 24 boostType: GAB minHitRate: 0.995 maxFalseAlarmRate: 0.5 weightTrimRate: 0.95 maxDepth: 1 maxWeakCount: 100 mode: BASIC Number of unique features given windowSize [24,24] : 162336 ===== TRAINING 0-stage =====  Training until now has taken 0 days 0 hours 0 minutes 1 seconds. ===== TRAINING 1-stage =====  Training until now has taken 0 days 0 hours 0 minutes 3 seconds. ===== TRAINING 2-stage =====  Training until now has taken 0 days 0 hours 0 minutes 5 seconds. ===== TRAINING 3-stage =====  Training until now has taken 0 days 0 hours 0 minutes 7 seconds. ===== TRAINING 4-stage =====  Training until now has taken 0 days 0 hours 0 minutes 11 seconds. ===== TRAINING 5-stage =====  Training until now has taken 0 days 0 hours 0 minutes 15 seconds. ===== TRAINING 6-stage ===== 

#include  #include  char* face_cascade_name = "C:\\Users\\lng\\Desktop\\image\\cascade.xml"; void faceRecongize(cv::CascadeClassifier faceCascade, cv::Mat frame); int main(){ cv::VideoCapture *videoCap = new cv::VideoCapture; cv::CascadeClassifier faceCascade; // 加载苹果分类器文件 if (!faceCascade.load(face_cascade_name)) { std::cout << "load face_cascade_name failed. " << std::endl; return -1; } // 打开摄像机 videoCap->open(0); if (!videoCap->isOpened()) { videoCap->release(); std::cout << "open camera failed"<< std::endl; return -1; } std::cout << "open camera success"<< std::endl; while(1){ cv::Mat frame; //读取视频帧 videoCap->read(frame); if (frame.empty()) { videoCap->release(); return -1; } //进行苹果识别 faceRecongize(faceCascade, frame); //窗口进行展示 imshow("face", frame); //等待回车键按下退出程序 if (cv::waitKey(30) == 13) { cv::destroyAllWindows(); return 0; } } system("pause"); return 0; } void faceRecongize(cv::CascadeClassifier faceCascade, cv::CascadeClassifier eyesCascade, cv::CascadeClassifier mouthCascade, cv::Mat frame) { std::vector faces; // 检测苹果 faceCascade.detectMultiScale(frame, faces, 1.1, 2, 0 | cv::CASCADE_SCALE_IMAGE, cv::Size(30, 30)); for (int i = 0; i  eyes; // 苹果上方区域写字进行标识 cv::Point centerText(faces[i].x + faces[i].width / 2 - 40, faces[i].y - 20); cv::putText(frame, "apple", centerText, cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 0, 255), 2); } } 

cmake_minimum_required (VERSION 3.5) project (faceRecongize2015) MESSAGE(STATUS "PROJECT_SOURCE_DIR " ${PROJECT_SOURCE_DIR}) SET(SRC_LISTS ${PROJECT_SOURCE_DIR}/src/main.cpp) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") #set(CMAKE_AUTOMOC ON) #set(CMAKE_AUTOUIC ON) #set(CMAKE_AUTORCC ON) # 配置头文件目录 include_directories(${PROJECT_SOURCE_DIR}/src) include_directories("D:\\opencv3.4.12\\opencv\\build\\include") include_directories("D:\\opencv3.4.12\\opencv\\build\\include\\opencv2") # 设置不显示命令框 if(MSVC) #set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /SUBSYSTEM:WINDOWS /ENTRY:mainCRTStartup") endif() # 添加库文件 set(PRO_OPENCV_LIB "D:\\opencv3.4.12\\opencv\\build\\x64\\vc15\\lib\\opencv_world3412.lib" "D:\\opencv3.4.12\\opencv\\build\\x64\\vc15\\lib\\opencv_world3412d.lib") IF(WIN32) # 生成可执行程序 ADD_EXECUTABLE(faceRecongize2015 ${SRC_LISTS}) # 链接库文件 TARGET_LINK_LIBRARIES(faceRecongize2015 ${PRO_OPENCV_LIB}) ENDIF() 

- src - mian.cpp - build_x64 - CMakeLists 

cmake -G "Visual Studio 14 2015 Win64" .. cmake --build ./ --config Release