OpenCV使用Cmake和MinGW编译安装在QT中配置运行

一、软件环境：

Win7-64bit
Qt-5.14.0
MinGW-7.3.0-32bit
CMake-3.16.5
Opencv3.4.7

我的主机上已经有QT和MinGW，只需下载CMake和Opencv。
1、opencv下载地址：
https://sourceforge.net/p/opencvlibrary/activity/?page=0&limit=100#5dfd53dae8ba7c1415f4b10f
运行解压得到opencv文件夹主要包括 build 和sources 两个文件夹 。

2、CMake下载地址：https://cmake.org/download/ cmake-3.17.0-win64-x64.msi

要先添加MinGW的环境变量, D:\Qt\Qt5.14.0\Tools\mingw730_32\bin，还有之前的Qt环境变量，以及CMake的环境变量

二、使用 CMake 生成 OpenCV 的 Makefile

打开 cmake-gui，设置源码和生成路径：

点击 Configure，设置编译器
Specify the generator for this project: MinGW Makefiles
Specify native compilers
Next
Compilers C: D:\Qt\Qt5.14.0\Tools\mingw730_32\bin\gcc.exe
Compilers C++:D:\Qt\Qt5.14.0\Tools\mingw730_32\bin\g++.exe
Finish
编译配置：
勾选 WITH_QT
勾选 WITH_OPENGL
点击 Configure，再次配置：
不勾选 WITH_IPP
设置 QT_MAKE_EXECUTABLE 为 E:\Qt\Qt5.9.3\5.9.3\mingw53_32\bin\qmake.exe
设置 Qt5Concurrent_DIR 为 D:/Qt/Qt5.14.0/5.14.0/mingw73_32/lib/cmake/Qt5Concurrent
设置 Qt5Core_DIR 为 D:/Qt/Qt5.14.0/5.14.0/mingw73_32/lib/cmake/Qt5Core
设置 Qt5Gui_DIR 为 D:/Qt/Qt5.14.0/5.14.0/mingw73_32/lib/cmake/Qt5Gui
设置 Qt5Test_DIR 为 D:/Qt/Qt5.14.0/5.14.0/mingw73_32/lib/cmake/Qt5Test
设置 Qt5Widgets_DIR 为 D:/Qt/Qt5.14.0/5.14.0/mingw73_32/lib/cmake/Qt5Widgets
设置 Qt5OpenGL_DIR 为D:/Qt/Qt5.14.0/5.14.0/mingw73_32/lib/cmake/Qt5OpenGL
设置 CMAKE_BUILD_TYPE 为 Release
不勾选 ENABLE_PRECOMPILED_HEADERS

发生错误：
1、无法下载opencv_ffmpeg.dll文件

Call Stack (most recent call first): 3rdparty/ffmpeg/ffmpeg.cmake:20
(ocv_download) cmake/OpenCVFindLibsVideo.cmake:227
(download_win_ffmpeg) CMakeLists.txt:803 (include) FFMPEG: Download:
ffmpeg_version.cmake

Couldn’t connect to server from the Internet. Perhaps direct
connections are not allowed in the current network. To use proxy
please check/specify these environment variables:
-http_proxy/https_proxy -and/or HTTP_PROXY/HTTPS_PROXY

CMake Warning at cmake/OpenCVDownload.cmake:193 (message): FFMPEG:
Download failed: 7;“Couldn’t connect to server”

solution：
https://www.cnblogs.com/huluwa508/p/10142718.html
直接下载：
https://raw.githubusercontent.com/opencv/opencv_3rdparty/d284fd09e76966e95f08e1ac3a1a28b227447501/ffmpeg/opencv_ffmpeg.dll
然后放到目标路径 D:\opencv\build\3rdparty\ffmpeg\opencv_ffmpeg.dll

2、如何去掉cmake编译Opencv时的Cmake

Warning：“OpenCVGenSetupVars.cmake:54”

solution：https://blog.csdn.net/allen101zhang/article/details/104632077

点击 Generate 生成 Makefile

$ cd D:\opencv\build
$ mingw32-make -j 8

编译的过程发生了很多错误：
1、

Linking CXX shared library …\bin\libopencv_dnn331.dll
Build target opencv_dnn
Makefile:161: recipe for target ‘all’ failed
mingw32-make: *** [all] Error 2

solution：
用mingw32-make 使用单线程
2、chmod’ 不是内部或外部命令，也不是可运行的程序 或批处理文件
在 D:\opencv\sources\cmake\OpenCVPCHSupport.cmake 中使用了 chmod 命令，然而 Windows 下不支持 chmod 命令，因此报错。
solution:
参考：fix ‘chmod’ is not recognized as an internal or external command in Windows #15433

3、Videoio模块问题

mingw32-make[2]: ***
[modules/videoio/CMakeFiles/opencv_videoio.dir/src/cap_dshow.cpp.obj]
Error 1 CMakeFiles\Makefile2:2867: recipe for target
‘modules/videoio/CMakeFiles/opencv_videoio.dir/all’ failed
mingw32-make[1]: ***
[modules/videoio/CMakeFiles/opencv_videoio.dir/all] Error 2
Makefile:161: recipe for target ‘all’ failed mingw32-make: *** [all]
Error 2

solution：
在opencv的源码文件cap_dshow.cpp中添加
#define STRSAFE_NO_DEPRECATE

4、test_common.hpp: No such file or directory

方法一：（issues/15381）
在 cmake-gui 编译配置中：
*
不勾选 ENABLE_PRECOMPILED_HEADERS（禁用预编译头）

然后重新 Configure-Generate-mingw32-make

方法二：
把 E:\opencv-3.4.7\opencv-3.4.7\modules\dnn\test\test_common.hpp 复制到 E:\opencv-3.4.7\opencv-3.4.7-build\modules\dnn\ 目录下
然后继续 mingw32-make

原文链接：https://blog.csdn.net/fengxueniu/article/details/77478669

5、 Anaconoda冲突问题错误

mingw32-make[1]: ***
[modules/python3/CMakeFiles/opencv_python3.dir/all] Error 2
Makefile:161: recipe for target ‘all’ failed mingw32-make: *** [all]
Error 2

由于本人之前电脑装了Anaconda，所以在cmake进行configure之后总是自动检测到python路径并且为其自动build了python的opencv链接，所以后面我取消选中一切关于python的选项。

https://blog.csdn.net/KID_yuan/article/details/86769144
再重新 Configure-Generate-mingw32-make

mingw32-make install

添加 OpenCV 编译的库到环境变量
为系统变量 Path 添加 D:\opencv\build\install\x86\mingw\bin

三、新建 OpenCV 的 Qt 项目

1、在 .pro 文件里面添加：

INCLUDEPATH += D:/opencv/build/install/include
LIBS += D:/opencv/build/install/x86/mingw/bin/libopencv_*.dll


2、在 MainWindow.cpp 中如下

#include "mainwindow.h"
#include "ui_mainwindow.h"

#include 
#include 
using namespace cv;

MainWindow::MainWindow(QWidget *parent)
: QMainWindow(parent)
, ui(new Ui::MainWindow)
{
ui->setupUi(this);
// read an image
cv::Mat image = cv::imread("C:\\Users\\Administrator\\Desktop\\111.jpg");
// create image window named "My Image"
cv::namedWindow("My Image", WINDOW_NORMAL);
//WINDOW_NORMAL跟QT集成的时候会使用，允许修改窗口大小。
// show the image on window
cv::imshow("My Image", image);
}

MainWindow::~MainWindow()
{
delete ui;
}

运行结果：

四、Qt+opencv dnn模块调用tensorflow模型图像检测
1、在 .pro 文件里面添加：

INCLUDEPATH += D:/opencv/build/install/include
LIBS += D:/opencv/build/install/x86/mingw/bin/libopencv_*.dll
2、点击构建，完成之后，把ssd_mobilenet_v1_coco_11_06_2017放在构建目录下（build-XXX_Qt_5_14_0_MinGW_32_bit-Debug）。
mian.cpp中：

#include 
#include
#include
#include 
#include
#include
#include

using namespace std;
using namespace cv;

const size_t inWidth = 300;
const size_t inHeight = 300;
const float WHRatio = inWidth / (float)inHeight;
const char* classNames[]= {"background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "background", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "background", "backpack",
"umbrella", "background", "background", "handbag", "tie", "suitcase", "frisbee","skis", "snowboard", "sports ball", "kite", "baseball bat","baseball glove", "skateboard", "surfboard", "tennis racket","bottle", "background", "wine glass", "cup", "fork", "knife", "spoon","bowl", "banana", "apple", "sandwich", "orange","broccoli", "carrot", "hot dog", "pizza", "donut",
"cake", "chair", "couch", "potted plant", "bed", "background", "dining table", "background", "background", "toilet", "background","tv", "laptop", "mouse", "remote", "keyboard",
"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "background","book", "clock", "vase", "scissors","teddy bear", "hair drier", "toothbrush"};

int main(int argc, char *argv[])
{
QApplication a(argc, argv);
clock_t start, finish;
double totaltime;
Mat frame;

String weights = "./ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb";
String prototxt = "./ssd_mobilenet_v1_coco_11_06_2017/ssd_mobilenet_v1_coco.pbtxt";
dnn::Net net = cv::dnn::readNetFromTensorflow(weights, prototxt);
frame = imread("C:/Users/Administrator/Desktop/person.jpg");

start = clock();
Size frame_size = frame.size();

Size cropSize;
if (frame_size.width / (float)frame_size.height > WHRatio)
{
cropSize = Size(static_cast<int>(frame_size.height * WHRatio),
frame_size.height);
}
else
{
cropSize = Size(frame_size.width,
static_cast<int>(frame_size.width / WHRatio));
}

Rect crop(Point((frame_size.width - cropSize.width) / 2,
(frame_size.height - cropSize.height) / 2),
cropSize);

cv::Mat blob = cv::dnn::blobFromImage(frame, 1. / 255, Size(300, 300));
// 1*3*300*300
cout << "blob size: " << blob.size << endl;
net.setInput(blob);
Mat output = net.forward();
// 1*1*100*7
cout << "output size: " << output.size << endl;

Mat detectionMat(output.size[2], output.size[3], CV_32F, output.ptr<float>());

frame = frame(crop);
float confidenceThreshold = 0.50;
for (int i = 0; i < detectionMat.rows; i++)
{
float confidence = detectionMat.at<float>(i, 2);

if (confidence > confidenceThreshold)
{
size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));

int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);

ostringstream ss;
ss << confidence;
String conf(ss.str());

Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));

rectangle(frame, object, Scalar(0, 255, 0), 1);
//cout << "objectClass:" << objectClass << endl;
String label = String(classNames[objectClass]) + ": " + conf;
//cout << "label"<
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
Size(labelSize.width, labelSize.height + baseLine)),
Scalar(0, 255, 0), CV_FILLED);
putText(frame, label, Point(xLeftBottom, yLeftBottom),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
}
}
finish = clock();
totaltime = finish - start;
cout << "Recognition time " << totaltime <<"ms"<< endl;
namedWindow("result", 0);
imshow("result", frame);
waitKey(0);
return a.exec();
}

结果：

五、Qt+opencv dnn模块调用tensorflow模型视频检测

mian.cpp中：

#include 
#include
#include
#include 
#include
#include
#include

using namespace std;
using namespace cv;

const size_t inWidth = 300;
const size_t inHeight = 300;
const float WHRatio = inWidth / (float)inHeight;
const char* classNames[]= {"background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "background", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "background", "backpack",
"umbrella", "background", "background", "handbag", "tie", "suitcase", "frisbee","skis", "snowboard", "sports ball", "kite", "baseball bat","baseball glove", "skateboard", "surfboard", "tennis racket","bottle", "background", "wine glass", "cup", "fork", "knife", "spoon","bowl", "banana", "apple", "sandwich", "orange","broccoli", "carrot", "hot dog", "pizza", "donut",
"cake", "chair", "couch", "potted plant", "bed", "background", "dining table", "background", "background", "toilet", "background","tv", "laptop", "mouse", "remote", "keyboard",
"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "background","book", "clock", "vase", "scissors","teddy bear", "hair drier", "toothbrush"};

int main(int argc, char *argv[])
{
QApplication a(argc, argv);
clock_t start, finish;
double totaltime;
Mat frame;
VideoCapture capture;
capture.open(0);
String weights = "./ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb";
String prototxt = "./ssd_mobilenet_v1_coco_11_06_2017/ssd_mobilenet_v1_coco.pbtxt";
dnn::Net net = cv::dnn::readNetFromTensorflow(weights, prototxt);

while (capture.read(frame))
{
start = clock();
Size frame_size = frame.size();

Size cropSize;
if (frame_size.width / (float)frame_size.height > WHRatio)
{
cropSize = Size(static_cast<int>(frame_size.height * WHRatio),
frame_size.height);
}
else
{
cropSize = Size(frame_size.width,
static_cast<int>(frame_size.width / WHRatio));
}

Rect crop(Point((frame_size.width - cropSize.width) / 2,
(frame_size.height - cropSize.height) / 2),
cropSize);

cv::Mat blob = cv::dnn::blobFromImage(frame, 1. / 255, Size(300, 300));
//cout << "blob size: " << blob.size << endl;
net.setInput(blob);
Mat output = net.forward();
//cout << "output size: " << output.size << endl;
Mat detectionMat(output.size[2], output.size[3], CV_32F, output.ptr<float>());

frame = frame(crop);
float confidenceThreshold = 0.50;
for (int i = 0; i < detectionMat.rows; i++)
{
float confidence = detectionMat.at<float>(i, 2);

if (confidence > confidenceThreshold)
{
size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));

int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
ostringstream ss;
ss << confidence;
String conf(ss.str());

Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));

rectangle(frame, object, Scalar(0, 255, 0), 2);
//cout << "objectClass:" << objectClass << endl;
String label = String(classNames[objectClass]) + ": " + conf;
//cout << "label"<
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
Size(labelSize.width, labelSize.height + baseLine)),
Scalar(0, 255, 0), CV_FILLED);
putText(frame, label, Point(xLeftBottom, yLeftBottom),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
}
}
finish = clock();
totaltime = finish - start;
cout << "Recognition frame time " << totaltime <<"ms"<< endl;
namedWindow("result", 0);
imshow("result", frame);
char c = waitKey(5);
if (c == 27)
{ // ESC退出
break;
}
}
capture.release();
waitKey(0);
return a.exec();
}

结果：

参考博客：
https://blog.huihut.com/2017/12/03/CompiledOpenCVRunInQt/

https://wiki.qt.io/How_to_setup_Qt_and_openCV_on_Windows

https://blog.csdn.net/yuwenlingfeng/article/details/79154489?depth_1-utm_source=distribute.pc_relevant.none-task&utm_source=distribute.pc_relevant.none-task
https://blog.csdn.net/m0_38133212/article/details/87908101

https://blog.csdn.net/m0_38133212/article/details/88032546