void CDog::setResponse(const float sigma, vector<vector<float>>& res)
{
vector<float> gauss;
setGaussI(sigma, gauss);
vector<vector<cv::Vec3f> > vvftmp;
vvftmp.resize((int)m_image.size());
for (int y = 0; y < (int)m_image.size(); ++y)
vvftmp[y].resize((int)m_image[y].size());
//求一阶导
vector<vector<cv::Vec3f> > restmp = m_image;
convolveX(restmp, m_mask, gauss, vvftmp);
convolveY(restmp, m_mask, gauss, vvftmp);
res.resize((int)m_image.size());
for (int y = 0; y < (int)m_image.size(); ++y)
{
res[y].resize((int)m_image[y].size());
for (int x = 0; x < (int)m_image[y].size(); ++x)
res[y][x] = norm(restmp[y][x]); //对应点的一阶导(向量)的模
}
}
bool Kernel<T, U>::Convolve1D_Y(const Image<T>& iImage, Image<U>& oImage) const
{
return convolveY(iImage, oImage);
}