数学之路-python计算实战(22)-机器视觉-sobel非线性滤波

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sobel非线性滤波，采用梯度模的近似方式

Sobel

Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.

C++: void Sobel(InputArray src, OutputArray dst, int ddepth, int dx, int dy, intksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )

Python: cv2.Sobel(src, ddepth, dx, dy[, dst[, ksize[, scale[, delta[, borderType]]]]]) → dst

C: void cvSobel(const CvArr* src, CvArr* dst, int xorder, int yorder, intaperture_size=3 )

Python: cv.Sobel(src, dst, xorder, yorder, apertureSize=3) → None

Parameters:

src – input image.
dst – output image of the same size and the same number of channels as src .
ddepth –
output image depth; the following combinations of src.depth() andddepth are supported:
- src.depth() = CV_8U, ddepth = -1/CV_16S/CV_32F/CV_64F
- src.depth() = CV_16U/CV_16S, ddepth = -1/CV_32F/CV_64F
- src.depth() = CV_32F, ddepth = -1/CV_32F/CV_64F
- src.depth() = CV_64F, ddepth = -1/CV_64F
when ddepth=-1, the destination image will have the same depth as the source; in the case of 8-bit input images it will result in truncated derivatives.
xorder – order of the derivative x.
yorder – order of the derivative y.
ksize – size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
scale – optional scale factor for the computed derivative values; by default, no scaling is applied (seegetDerivKernels() for details).
delta – optional delta value that is added to the results prior to storing them in dst.
borderType – pixel extrapolation method (seeborderInterpolate() for details).

In all cases except one, the $\texttt{ksize} \times\texttt{ksize}$ separable kernel is used to calculate the derivative. When $\texttt{ksize = 1}$ , the $3 \times 1$ or $1 \times 3$ kernel is used (that is, no Gaussian smoothing is done). ksize = 1 can only be used for the first or the second x- or y- derivatives.

There is also the special value ksize = CV_SCHARR (-1) that corresponds to the $3\times3$ Scharr filter that may give more accurate results than the $3\times3$ Sobel. The Scharr aperture is

$\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}$

for the x-derivative, or transposed for the y-derivative.

The function calculates an image derivative by convolving the image with the appropriate kernel:

$\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}$

The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder= 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of:

$\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}$

The second case corresponds to a kernel of:

$\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}$

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http://blog.csdn.net/myhaspl/

# -*- coding: utf-8 -*-   #非线性锐化滤波，sobel算子变换#code:myhaspl@myhaspl.comimport cv2fn="test6.jpg"myimg=cv2.imread(fn)img=cv2.cvtColor(myimg,cv2.COLOR_BGR2GRAY)jgimg=cv2.Sobel(img,0,1,1)cv2.imshow('src',img)cv2.imshow('dst',jgimg)cv2.waitKey()cv2.destroyAllWindows()

Sobel

使用扩展 Sobel 算子计算一阶、二阶、三阶或混合图像差分

void cvSobel( const CvArr* src, CvArr* dst, int xorder, int yorder, int aperture_size=3 );

src: 输入图像.
dst: 输出图像.
xorder: x 方向上的差分阶数
yorder: y 方向上的差分阶数
aperture_size: 扩展 Sobel 核的大小，必须是 1, 3, 5 或 7。除了尺寸为 1，其它情况下， aperture_size ×aperture_size 可分离内核将用来计算差分。对 aperture_size=1的情况，使用 3x1 或 1x3 内核（不进行高斯平滑操作）。这里有一个特殊变量 CV_SCHARR (=-1)，对应 3x3 Scharr 滤波器，可以给出比 3x3 Sobel 滤波更精确的结果。

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