說說Darknet 如何和QT qtcreator配置使用

說說Darknet 如何和QT qtcreator配置使用；

darknet yolov系列讓作視覺工程的C++程式設計師喜不自勝！為啥？終於不用額外研究python也可玩轉AI影像識別了，而且嵌入式友好、靈活、可擴充套件。感謝darknet的創作者，無私的提供給工程技術人員。

這裡記錄以下自己使用的點滴經驗，幫助尚未掌握的同行更容易把握這個好用AI影像識別引擎。

1、darknet編譯，都好說，網路上已經介紹的很清楚了，這裡不再贅述，至於你要用哪個版本，這裡還是推薦https://github.com/AlexeyAB/darknet

2、這個版本有windows/linux的，我主要介紹使用ubuntu18.04的吧，按照你是否安裝CUDNN和電腦的硬體配置修改makefile;然後生成libdarknet.so檔案。

3、其實darknet自帶的有libdarknet.so怎麼用的example樣例程式碼，就在yolo_console_dll.cpp 幾乎相關的API使用都示範，如果大家工程化應用darknet yolov*就可以參考這個檔案。

4、廢話少說，上程式碼，真刀真槍！首先宣告，你已經新建了qtcreator 工程，把下面程式碼直接拷貝即可；

#include <QCoreApplication>
#define GPU
#define OPENCV
#include <iostream>
#include <fstream>
#include <QDebug>
#include <opencv2/opencv.hpp>
#include <include/yolo_v2_class.hpp>
#include <include/darknet.h>


std::vector<std::string> objects_names_from_file(std::string const filename) {
    std::ifstream file(filename);
    std::vector<std::string> file_lines;
    if (!file.is_open()) return file_lines;
    for(std::string line; getline(file, line);) file_lines.push_back(line);
    std::cout << "object names loaded \n";
    return file_lines;
}

void draw_boxes(cv::Mat mat_img, std::vector<bbox_t> result_vec, std::vector<std::string> obj_names,
    int current_det_fps = -1, int current_cap_fps = -1)
{
    int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };

    for (auto &i : result_vec) {
        cv::Scalar color = obj_id_to_color(i.obj_id);
        cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), color, 2);
        if (obj_names.size() > i.obj_id) {
            std::string obj_name = obj_names[i.obj_id];
            if (i.track_id > 0) obj_name += " - " + std::to_string(i.track_id);
            cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0);
            int max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2);
            max_width = std::max(max_width, (int)i.w + 2);
            //max_width = std::max(max_width, 283);
            std::string coords_3d;
            if (!std::isnan(i.z_3d)) {
                std::stringstream ss;
                ss << std::fixed << std::setprecision(2) << "x:" << i.x_3d << "m y:" << i.y_3d << "m z:" << i.z_3d << "m ";
                coords_3d = ss.str();
                cv::Size const text_size_3d = getTextSize(ss.str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, 1, 0);
                int const max_width_3d = (text_size_3d.width > i.w + 2) ? text_size_3d.width : (i.w + 2);
                if (max_width_3d > max_width) max_width = max_width_3d;
            }

            cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 35, 0)),
                cv::Point2f(std::min((int)i.x + max_width, mat_img.cols - 1), std::min((int)i.y, mat_img.rows - 1)),
                color, CV_FILLED, 8, 0);
            putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 16), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2);
            if(!coords_3d.empty()) putText(mat_img, coords_3d, cv::Point2f(i.x, i.y-1), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);
        }
    }
    if (current_det_fps >= 0 && current_cap_fps >= 0) {
        std::string fps_str = "FPS detection: " + std::to_string(current_det_fps) + "   FPS capture: " + std::to_string(current_cap_fps);
        putText(mat_img, fps_str, cv::Point2f(10, 20), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(50, 255, 0), 2);
    }
}

int main()
{
    //獲取使用GPU的指定ID
       gpu_index = 0;

       //#define GPU
   #ifndef GPU
       gpu_index = -1;
   #else
       if(gpu_index >= 0){
           cuda_set_device(gpu_index);
       }
   #endif
    //初始化相機
    /// \brief mtwoCamera
    ///
    ///
    cv::VideoCapture video;
  int cameraWidth=320;
  int cameraHeight=480;
    video.open(0);

    video.set(CV_CAP_PROP_FRAME_WIDTH, cameraWidth*2);//寬度
    video.set(CV_CAP_PROP_FRAME_HEIGHT, cameraHeight);//高度
    video.set(CV_CAP_PROP_FPS, 30);//幀率 幀/秒
    video.set(CV_CAP_PROP_BRIGHTNESS, 1);//亮度 1
    video.set(CV_CAP_PROP_CONTRAST,40);//對比度 40
    video.set(CV_CAP_PROP_SATURATION, 50);//飽和度 50
    video.set(CV_CAP_PROP_HUE, 50);//色調 50
    video.set(CV_CAP_PROP_EXPOSURE, 50);//曝光 50


    if( !video.isOpened() )
    {
        printf("Read Video failed!\n");
        return 0;
    }
    cv::Mat frame;


    bool iscla=false;

    std::string cfg_file = "/home/sunkit/Qtkit/DarknetConsol/darknet-master/cfg/yolov4.cfg";
    std::string weights_file = "/home/sunkit/Qtkit/DarknetConsol/darknet-master/yolov4.weights";
    std::string  names_file = "/home/sunkit/Qtkit/DarknetConsol/darknet-master/data/coco.names";
    std::string  filename = "/home/sunkit/Qtkit/DarknetConsol/darknet-master/data/dog.jpg";

    Detector detector(cfg_file, weights_file);

    auto obj_names = objects_names_from_file(names_file);
    std::cout << "input image or video filename: ";


    while (true)
    {
        // mtwoCamera.getCamFramfirst();
        //grab and retrieve each frames of the video sequentially

        video >> frame;

        if( frame.empty() )
        {
            std::cout << "frame empty"<<std::endl;
            break;
        }


        //wait for 40 milliseconds
        int c = cvWaitKey(40);
        //exit the loop if user press "Esc" key  (ASCII value of "Esc" is 27)
        if (27 == char(c)) break;
        if(char(c)=='c')//標定計算
        {

             iscla=!iscla;


        }
      cv::Mat mat_img =frame;
       if(iscla)
       {
               auto det_image = detector.mat_to_image_resize(mat_img);
               //std::cout << det_image->w << std::endl;
               //std::cout << det_image->h << std::endl;
              // std::cout << mat_img.size()<< std::endl;
               double timeConsume,start;
               start = static_cast<double>(cv::getTickCount());
               std::vector<bbox_t> result_vec = detector.detect_resized(*det_image, mat_img.size().width, mat_img.size().height,0.4);
               timeConsume = ((double)cv::getTickCount() - start)*1000 / cv::getTickFrequency();
              // qDebug()<<"time count:"<<timeConsume<<endl;
               draw_boxes(mat_img, result_vec, obj_names);


       }
      cv::imshow("frame",mat_img);

   }
}

5、效果如下，效果喜人，這也告訴大家AI 還是個什麼程度呢？各位都有自己的見解。

6、qtcreator 工程配置環境，沒有這個配置環境就是純耍帥，不實在。

QT -= gui

CONFIG += c++11 console
CONFIG -= app_bundle

# The following define makes your compiler emit warnings if you use
# any Qt feature that has been marked deprecated (the exact warnings
# depend on your compiler). Please consult the documentation of the
# deprecated API in order to know how to port your code away from it.
DEFINES += QT_DEPRECATED_WARNINGS

# You can also make your code fail to compile if it uses deprecated APIs.
# In order to do so, uncomment the following line.
# You can also select to disable deprecated APIs only up to a certain version of Qt.
#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000    # disables all the APIs deprecated before Qt 6.0.0

SOURCES += \
        main.cpp

# Default rules for deployment.
qnx: target.path = /tmp/$${TARGET}/bin
else: unix:!android: target.path = /opt/$${TARGET}/bin
!isEmpty(target.path): INSTALLS += target


unix: CONFIG += link_pkgconfig
unix: PKGCONFIG += /usr/local/lib/pkgconfig/opencv.pc
#CUDA
DEFINES += GPU CUDNN
LIBS += \
    -L"/usr/local/lib" \
    -L"/usr/local/cuda/lib64" \
    -lcudart -lcufft
DEPENDPATH += .
INCLUDEPATH += /usr/local/cuda/include
QMAKE_LIBDIR += /usr/local/cuda/lib64

CUDA_DIR = /usr/local/cuda
CUDA_SDK = /usr/local/cuda
SYSTEM_NAME = linux
SYSTEM_TYPE = 64
CUDA_ARCH = compute_61
CUDA_CODE = [sm_61,compute_61]

CUDA_OBJECTS_DIR = ./Release/obj
CUDA_LIBS = cudart cufft
CUDA_INC = $$join(INCLUDEPATH,'" -I"','-I"','"')

NVCC_OPTIONS = --use_fast_math
NVCC_LIBS = $$join(CUDA_LIBS,' -l','-l','')

CONFIG(Release){
    cuda.input = CUDA_SOURCES
    cuda.output = $$CUDA_OBJECTS_DIR/${QMAKE_FILE_BASE}_cuda.o
    cuda.commands = $$CUDA_DIR/bin/nvcc $$NVCC_OPTIONS $$CUDA_INC $$NVCC_LIBS --machine $$SYSTEM_TYPE -arch=$$CUDA_ARCH -O3 -c -o ${QMAKE_FILE_OUT} ${QMAKE_FILE_NAME}
    cuda.dependency_type = TYPE_C
    QMAKE_EXTRA_COMPILERS += cuda
}

win32:CONFIG(release, debug|release): LIBS += -L$$PWD/./release/ -ldarknet
else:win32:CONFIG(debug, debug|release): LIBS += -L$$PWD/./debug/ -ldarknet
else:unix: LIBS += -L$$PWD/./ -ldarknet

INCLUDEPATH += $$PWD/.
DEPENDPATH += $$PWD/.

結語：

有時候你會遇見darknet 無法執行，無厘頭阿，怎麼回事呢，這個就要深入瞭解了，比如你的深度模型網路檔案開頭沒有改為如下：

[net]
#batch=64
#subdivisions=8
batch=1
subdivisions=1
# Training
#width=512
#height=512
width=416
height=416
channels=3

注意：如果你的膝上型電腦顯示卡記憶體不大於6G，建議使用416，不多解釋。