视觉图像：模板匹配二
1、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[-1,1]，值为1.0表示待匹配图像和匹配图像完全匹配
2、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[i*_width+j];
}
}
return m_sum;
}
3、//∑∑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[i*_width+j];
m_sum = p_data_1 * p_data_1;
}
}
return m_sum;
}
4、//∑∑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[(_u+i)*_width+(j+_v)];
}
}
return m_sum;
}
5、//∑∑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[(i+_u)*_width+(j+_v)];
m_sum = temp_data * temp_data;
}
}
return m_sum;
}
6、//∑∑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[(i+_u)*_width+(j+_v)] * p_data_2[i*_width+j];
}
}
return m_sum;
}
7、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;j<real_width;j++)
{
for (int i=0;i<real_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[i*real_width+j] = fenzi/fenmu;
}
}
return dist_img;
}
