图像梯度计算加速

Posted 2020-12-13 almn

常规方法：

 1 void SobelAmplitude(Mat &sobelx, Mat &sobely, Mat &SobelXY) {
 2     SobelXY = Mat::zeros(sobelx.size(), CV_32FC1);
 3     for (int i = 0; i < SobelXY.rows; i++) {
 4         float *data = SobelXY.ptr<float>(i);
 5         short *datax = sobelx.ptr<short>(i);
 6         short *datay = sobely.ptr<short>(i);
 7         for (int j = 0; j < SobelXY.cols; j++) {
 8             data[j] = sqrt(datax[j] * datax[j] + datay[j] * datay[j]);
 9         }
10     }
11 //    convertScaleAbs(SobelXY, SobelXY);
12 }

上述，图像在x和y方向上的梯度数据类型为CV_16S。

当然，也可以为uchar！
自己修改，uchar类型，就有很多种方式，比如：

查表法：提供者：GiantPandaCV 

unsigned char Table[65026];
for (int Y = 0; Y < 65026; Y++) Table[Y] = (sqrtf(Y + 0.0f) + 0.5f);

 for (int X = 0; X < Width; X++)
{
int GX = First[X] - First[X + 2] + (Second[X] - Second[X + 2]) * 2 + Third[X] - Third[X + 2];
int GY = First[X] + First[X + 2] + (First[X + 1] - Third[X + 1]) * 2 - Third[X] - Third[X + 2];
LinePD[X] = Table[min(GX * GX + GY * GY, 65025)];
}