1. 新建MFC项目
点击完成。
2. 添加按钮
在“工具箱”中找到“Button”控件，添加至界面：
2. 配置opencv， 添加colordetector.h
#include<iostream>
#include "opencv2/opencv.hpp" using namespace std; class ColorDetector{
private:
//最小可接受距离
int minDist;
//目标色
cv::Vec3b target;
//结果图像
cv::Mat result;
public:
//构造函数
ColorDetector() : minDist()
{
//初始化默认参数
target[] = target[] = target[] = ;
}
//设置彩色距离阈值，阈值须为非负数
void setColorDistabceThreshold(int distance)
{
if (distance < )
distance = ;
minDist = distance;
}
//获取彩色距离阈值
int getColorDistanceThreshold() const
{
return minDist;
}
//设置需检测的颜色
void setTargetColor(unsigned char red, unsigned char green, unsigned char blue)
{
target[] = red;
target[] = green;
target[] = blue;
}
//设置需检测的颜色
void setTargetColor(cv::Vec3b color)
{
target = color;
}
//获取需检测的颜色
cv::Vec3b getTargetColor() const
{
return target;
}
//二值化处理函数
cv::Mat_<uchar> process(cv::Mat &image)
{
result.create(image.rows, image.cols, CV_8U);
//得到迭代器
cv::Mat_<cv::Vec3b>::const_iterator it = image.begin<cv::Vec3b>();
cv::Mat_<cv::Vec3b>::const_iterator itend = image.end<cv::Vec3b>();
cv::Mat_<uchar>::iterator itout = result.begin<uchar>(); while (it != itend)
{
if (getDistance(*it) < minDist)
{
*itout = ;
}
else
{
*itout = ;
}
it++;//更新输入迭代器
itout++;;//更新输出迭代器
}
return result;
}
//计算颜色距离
int getDistance(const cv::Vec3b &color) const
{
cv::Vec3b dist;
cv::absdiff(color, target, dist);
return cv::sum(dist)[];
}
}; class ColorDetectController
{
private:
ColorDetector* cdetect;//算法类
cv::Mat image;//待处理的图像
cv::Mat result;//结果
public:
ColorDetectController()
{
cdetect = new ColorDetector();
}
//设置色彩距离阈值
void setColorDistanceThreshold(int distance)
{
cdetect->setColorDistabceThreshold(distance);
}
//获取色彩距离阈值
int getColorDistancethreshold() const
{
return cdetect->getColorDistanceThreshold();
}
//设置要检测的颜色
void setTargetColor(unsigned char red,
unsigned char green, unsigned char blue)
{
cdetect->setTargetColor(red, green, blue);
}
//获取要检测的颜色
void getTargetColor(unsigned char& red, unsigned char& green, unsigned char& blue) const
{
cv::Vec3b color = cdetect->getTargetColor();
red = color[];
green = color[];
blue = color[];
}
//设置输入图像，通过文件读取
bool setInputImage(string filename)
{
image = cv::imread(filename);
if (!image.data)
return false;
else
return true;
}
//返回当前的输入图像
const cv::Mat getInputImage() const
{
return image;
}
//开始处理图像
void process()
{
result = cdetect->process(image);
}
//获取最近一次处理的结果
const cv::Mat getLastResult() const
{
return result;
}
//删除当前控制器创建的处理对象
~ColorDetectController()
{
delete cdetect;
}
};
在3_2模块间通信.h 添加头文件 #include "colordetector.h"
在3_2模块间通信Dlg.h 添加公有成员 ColorDetectController controller;
3. 右击“Button1”,选择属性，将”Caption“属性改为”打开图像“，ID 属性改为 OpenImg。双击按钮添加代码段：
CFileDialog dlg(TRUE, _T("*.bmp"), NULL,
OFN_FILEMUSTEXIST | OFN_PATHMUSTEXIST | OFN_HIDEREADONLY,
_T("image file(*.bmp;*,jpg)|*.bmp;*.jpg|ALL Files(*.*)|*.*||"), NULL);
dlg.m_ofn.lpstrTitle = _T("Open Image");
// if a filename has been selected
if (dlg.DoModal() == IDOK) {
// get the path of the selected filename
CString strMfc = dlg.GetPathName();
std::string filename = CT2CA(strMfc.GetBuffer());
// set and display the input image
controller.setInputImage(filename);
cv::imshow("Input Image", controller.getInputImage());
}右击“Button1”,选择属性，将”Caption“属性改为”处理图像“，ID 属性改为 ProcessImg。双击按钮添加代码段：
// target color is hard-coded here
controller.setTargetColor(, , );
// process the input image and display result
controller.process();
cv::imshow("Output Result", controller.getLastResult());