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

dst = cv.Sobel(src)
dst = cv.Sobel(..., 'OptionName', optionValue, ...)

Input

• src input image.

Output

• dst output image of the same size and the same number of channels as src.

Options

• KSize size of the extended Sobel kernel. Aperture size used to compute the second-derivative filters. The size must be positive and odd; it must be 1, 3, 5, or 7. default 3
• XOrder Order of the derivative x. default 1
• YOrder Order of the derivative y. default 1
• Scale Optional scale factor for the computed derivative values. By default, no scaling is applied (see cv.getDerivKernels for details). default 1
• Delta Optional delta value that is added to the results prior to storing them in dst. default 0
• BorderType Pixel extrapolation method. See cv.copyMakeBorder. default 'Default'
• DDepth output image depth, see combinations below; in the case of 8-bit input images it will result in truncated derivatives. When DDepth=-1 (default), the output image will have the same depth as the source.
  • SDepth = uint8 --> DDepth = -1, int16, single, double
  • SDepth = uint16, int16 --> DDepth = -1, single, double
  • SDepth = single --> DDepth = -1, single, double
  • SDepth = double --> DDepth = -1, double

In all cases except one, the KSize x KSize separable kernel is used to calculate the derivative. When KSize=1, the 3x1 or 1x3 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=-1 or KSize='Scharr' that corresponds to the 3x3 Scharr filter that may give more accurate results than the 3x3 Sobel. The Scharr aperture is:

[-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:

            partial^(XOrder+YOrder) src
dst = ---------------------------------------
       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:

[-1 0 1; -2 0 2; -1 0 1]

The second case corresponds to a kernel of:

[-1 -2 -1; 0 0 0; 1 2 1]