绪：模板匹配是在一幅图像中寻找与另一幅模板图像最匹配（相似）部分的技术；最常见的模板匹配方法：（1）平方差匹配法 method=TM_SQDIFF（2）归一化平方差匹配法 method=TM_SQDIFF_NORMED（3）相关匹配法 method=TM_CCORR（4）归一化相关匹配法 method=TM_CCORR_NORMED（5）系数匹配法 method=TM_CCOEFF（6）归一化相关系数匹配法 method=TM_CCOEFF_NORMED本经验以归一化相关系数匹配方法为例，进行编写程序；方法/步骤1/7分步阅读

相似性度量：归一化相关系数匹配法：

其公式如下所示：

进行变换得；

其中，I为待匹配图像；M为匹配图像；

m和n分别为匹配图像的宽度和长度；

N=m×n；

r的值域范围在，值为1.0表示待匹配图像和匹配图像完全匹配

2/7

代码编写：

先把公式中各分量的计算公式写成函数，总共有5个；

//∑∑M(i,j)

double MFun(Mat _mask_img)

{

int _width = _mask_img.cols;

int _height = _mask_img.rows;

double m_sum = 0.f;

unsigned char* p_data = (unsigned char*)_mask_img.data;

for (int j=0;j_width;j++)//lie

{

for (int i=0;i_height;i++)//hang

{

m_sum = p_data;

}

}

return m_sum;

}

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//∑∑M(i,j)^2

double M2Fun(Mat _mask_img)

{

int _width = _mask_img.cols;

int _height = _mask_img.rows;

double m_sum = 0.f;

unsigned char* p_data = (unsigned char*)_mask_img.data;

for (int j=0;j_width;j++)

{

for (int i=0;i_height;i++)

{

double p_data_1 = p_data;

m_sum = p_data_1 * p_data_1;

}

}

return m_sum;

}

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//∑∑I(i,j)

double IFun(Mat _raw_img,Mat _mask_img,int _u,int _v)

{

double m_sum;

unsigned char* p_data = (unsigned char*)_raw_img.data;

int _height = _mask_img.rows;

int _width = _mask_img.cols;

for (int j=0;j_width;j++)

{

for (int i=0;i_height;i++)

{

m_sum = p_data;

}

}

return m_sum;

}

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//∑∑I(i,j)^2

double I2Fun(Mat _raw_img,Mat _mask_img,int _u,int _v)

{

double m_sum;

unsigned char* p_data = (unsigned char*)_raw_img.data;

int _height = _mask_img.rows;

int _width = _mask_img.cols;

for (int j=0;j_width;j++)

{

for (int i=0;i_height;i++)

{

double temp_data = p_data;

m_sum = temp_data * temp_data;

}

}

return m_sum;

}

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//∑∑IM

double IMFun(Mat _raw_img,Mat _mask_img,int _u,int _v)

{

double m_sum;

unsigned char* p_data_1 = (unsigned char*)_raw_img.data;

unsigned char* p_data_2 = (unsigned char*)_mask_img.data;

int _height = _mask_img.rows;

int _width = _mask_img.cols;

for (int j=0;j_width;j++)

{

for (int i=0;i_height;i++)

{

m_sum = p_data_1 * p_data_2;

}

}

return m_sum;

}

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匹配结果值进行保存，

程序如下：

Mat MaskMatchFun(Mat _raw_img,Mat _mask_img)

{

int mask_width = _mask_img.cols;

int mask_height = _mask_img.rows;

int mask_big = mask_width * mask_height;//m*n

int raw_width = _raw_img.cols;

int raw_height = _raw_img.rows;

int real_height = raw_height - mask_height + 1;//l-m+1

int real_width = raw_width - mask_height + 1;

double M_Value = MFun(_mask_img);//m

double M2_Value = M2Fun(_mask_img);//m^2

Mat dist_img(real_height,real_width,CV_32FC1);

float* p_data = (float*) dist_img.data;

for (int j=0;jreal_width;j++)

{

for (int i=0;ireal_height;i++)

{

double temp1 = IMFun(_raw_img,_mask_img,i,j);

double temp2 = IFun(_raw_img,_mask_img,i,j);

double fenzi = mask_big*temp1-temp2*M_Value;

double temp3 = I2Fun(_raw_img,_mask_img,i,j);

double temp4 = (mask_big*temp3-temp2*temp2)*(mask_big*M2_Value-M_Value*M_Value);

double fenmu = sqrt(temp4);

p_data = fenzi/fenmu;

}

}

return dist_img;

}

模板匹配归一化相关系数相似度测量匹配编程