Operator Reference

HALCON Operator Reference / Filters / Edges

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ClassesClasses | | Operators

sobel_dirsobel_dirSobelDirSobelDirsobel_dir (Operator)

sobel_dirsobel_dirSobelDirSobelDirsobel_dir — Detect edges (amplitude and direction) using the Sobel operator.

Signature

sobel_dir(Image : EdgeAmplitude, EdgeDirection : FilterType, Size : )

Herror sobel_dir(const Hobject Image, Hobject* EdgeAmplitude, Hobject* EdgeDirection, const char* FilterType, const Hlong Size)

Herror T_sobel_dir(const Hobject Image, Hobject* EdgeAmplitude, Hobject* EdgeDirection, const Htuple FilterType, const Htuple Size)

void SobelDir(const HObject& Image, HObject* EdgeAmplitude, HObject* EdgeDirection, const HTuple& FilterType, const HTuple& Size)

HImage HImage::SobelDir(HImage* EdgeDirection, const HString& FilterType, const HTuple& Size) const

HImage HImage::SobelDir(HImage* EdgeDirection, const HString& FilterType, Hlong Size) const

HImage HImage::SobelDir(HImage* EdgeDirection, const char* FilterType, Hlong Size) const

HImage HImage::SobelDir(HImage* EdgeDirection, const wchar_t* FilterType, Hlong Size) const   ( Windows only)

static void HOperatorSet.SobelDir(HObject image, out HObject edgeAmplitude, out HObject edgeDirection, HTuple filterType, HTuple size)

HImage HImage.SobelDir(out HImage edgeDirection, string filterType, HTuple size)

HImage HImage.SobelDir(out HImage edgeDirection, string filterType, int size)

def sobel_dir(image: HObject, filter_type: str, size: MaybeSequence[int]) -> Tuple[HObject, HObject]

Description

sobel_dirsobel_dirSobelDirSobelDirsobel_dir calculates first derivative of an image and is used as an edge detector. The filter is based on the following filter masks: A = 1 2 1 0 0 0 -1 -2 -1 B = 1 0 -1 2 0 -2 1 0 -1 These masks are used differently, according to the selected filter type. (In the following, a and b denote the results of convolving an image with A and B for one particular pixel.)

• 'sum_sqrt'"sum_sqrt""sum_sqrt""sum_sqrt""sum_sqrt":

• 'sum_abs'"sum_abs""sum_abs""sum_abs""sum_abs":

For a Sobel operator with size 3x3, the corresponding filters A and B are applied directly, while for larger filter sizes the input image is first smoothed using a Gaussian filter (see gauss_imagegauss_imageGaussImageGaussImagegauss_image) or a binomial filter (see binomial_filterbinomial_filterBinomialFilterBinomialFilterbinomial_filter) of size SizeSizeSizesizesize-2. The Gaussian filter is selected for the above values of FilterTypeFilterTypeFilterTypefilterTypefilter_type. Here, SizeSizeSizesizesize = 5, 7, 9, 11, or 13 must be used. The binomial filter is selected by appending '_binomial'"_binomial""_binomial""_binomial""_binomial" to the above values of FilterTypeFilterTypeFilterTypefilterTypefilter_type. Here, SizeSizeSizesizesize can be selected between 5 and 39. Furthermore, it is possible to select different amounts of smoothing the column and row direction by passing two values in SizeSizeSizesizesize. Here, the first value of SizeSizeSizesizesize corresponds to the mask width (smoothing in the column direction), while the second value corresponds to the mask height (smoothing in the row direction) of the binomial filter. The binomial filter can only be used for images of type byte, uint2 and real. Since smoothing reduces the edge amplitudes, in this case the edge amplitudes are multiplied by a factor of 2 to prevent information loss. Therefore,

sobel_dir(I,Amp,Dir,FilterType,S)sobel_dir(I,Amp,Dir,FilterType,S)SobelDir(I,Amp,Dir,FilterType,S)SobelDir(I,Amp,Dir,FilterType,S)sobel_dir(I,Amp,Dir,FilterType,S)

for SizeSizeSizesizesize > 3 is conceptually equivalent to

scale_image(I,F,2,0)scale_image(I,F,2,0)ScaleImage(I,F,2,0)ScaleImage(I,F,2,0)scale_image(I,F,2,0)

gauss_image(F,G,S-2)gauss_image(F,G,S-2)GaussImage(F,G,S-2)GaussImage(F,G,S-2)gauss_image(F,G,S-2)

sobel_dir(G,Amp,Dir,FilterType,3)sobel_dir(G,Amp,Dir,FilterType,3)SobelDir(G,Amp,Dir,FilterType,3)SobelDir(G,Amp,Dir,FilterType,3)sobel_dir(G,Amp,Dir,FilterType,3)

or to

scale_image(I,F,2,0)scale_image(I,F,2,0)ScaleImage(I,F,2,0)ScaleImage(I,F,2,0)scale_image(I,F,2,0)

binomial_filter(F,G,S[0]-2,S[1]-2)binomial_filter(F,G,S[0]-2,S[1]-2)BinomialFilter(F,G,S[0]-2,S[1]-2)BinomialFilter(F,G,S[0]-2,S[1]-2)binomial_filter(F,G,S[0]-2,S[1]-2)

sobel_dir(G,Amp,Dir,FilterType,3)sobel_dir(G,Amp,Dir,FilterType,3)SobelDir(G,Amp,Dir,FilterType,3)SobelDir(G,Amp,Dir,FilterType,3)sobel_dir(G,Amp,Dir,FilterType,3).

The edge directions are returned in EdgeDirectionEdgeDirectionEdgeDirectionedgeDirectionedge_direction, and are stored in 2-degree steps, i.e., an edge direction of degrees in mathematically positive sense and with respect to the horizontal axis is stored as x / 2 in the edge direction image. Furthermore, the direction of the change of intensity is taken into account. Let denote the image gradient. Then the following edge directions are returned as :

intensity increase		edge direction [deg]
from bottom to top	0 / +	0
from lower right to upper left	- / +	]0,90[
from right to left	- / 0	90
from upper right to lower left	- / -	]90,180[
from top to bottom	0 / -	180
from upper left to lower right	+ / -	]180,270[
from left to right	+ / 0	270
from lower left to upper right	+ / +	]270,360[.

Points with edge amplitude 0 are assigned the edge direction 255 (undefined direction).

sobel_ampsobel_ampSobelAmpSobelAmpsobel_amp can be executed on OpenCL devices. Note that when using gaussian filtering for SizeSizeSizesizesize > 3, the results can vary from the CPU implementation.

Attention

Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.

Execution Information

• Supports OpenCL compute devices.
• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Automatically parallelized on tuple level.
• Automatically parallelized on channel level.
• Automatically parallelized on domain level.

Parameters

ImageImageImageimageimage (input_object)  (multichannel-)image(-array) → objectHImageHObjectHObjectHobject (byte / int2 / uint2 / real)

Input image.

EdgeAmplitudeEdgeAmplitudeEdgeAmplitudeedgeAmplitudeedge_amplitude (output_object)  (multichannel-)image(-array) → objectHImageHObjectHObjectHobject * (byte / int2 / uint2 / real)

Edge amplitude (gradient magnitude) image.

EdgeDirectionEdgeDirectionEdgeDirectionedgeDirectionedge_direction (output_object)  (multichannel-)image(-array) → objectHImageHObjectHObjectHobject * (direction)

Edge direction image.

FilterTypeFilterTypeFilterTypefilterTypefilter_type (input_control)  string → HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Filter type.

Default: 'sum_abs' "sum_abs" "sum_abs" "sum_abs" "sum_abs"

List of values: 'sum_abs'"sum_abs""sum_abs""sum_abs""sum_abs", 'sum_abs_binomial'"sum_abs_binomial""sum_abs_binomial""sum_abs_binomial""sum_abs_binomial", 'sum_sqrt'"sum_sqrt""sum_sqrt""sum_sqrt""sum_sqrt", 'sum_sqrt_binomial'"sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial"

List of values (for compute devices): 'sum_abs'"sum_abs""sum_abs""sum_abs""sum_abs", 'sum_sqrt'"sum_sqrt""sum_sqrt""sum_sqrt""sum_sqrt", 'sum_abs_binomial'"sum_abs_binomial""sum_abs_binomial""sum_abs_binomial""sum_abs_binomial", 'sum_sqrt_binomial'"sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial"

SizeSizeSizesizesize (input_control)  integer(-array) → HTupleMaybeSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Size of filter mask.

Default: 3

List of values: 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39

Example (HDevelop)

read_image(Image,'fabrik')
sobel_dir(Image,Amp,Dir,'sum_abs',3)
threshold(Amp,Edg,128,255)

Example (C)

read_image(&Image,"fabrik");
sobel_dir(Image,&Amp,&Dir,"sum_abs",3);
threshold(Amp,&Edg,128.0,255.0);

Example (HDevelop)

read_image(Image,'fabrik')
sobel_dir(Image,Amp,Dir,'sum_abs',3)
threshold(Amp,Edg,128,255)

Example (HDevelop)

read_image(Image,'fabrik')
sobel_dir(Image,Amp,Dir,'sum_abs',3)
threshold(Amp,Edg,128,255)

Result

sobel_dirsobel_dirSobelDirSobelDirsobel_dir returns 2 ( H_MSG_TRUE) if all parameters are correct. If the input is empty the behavior can be set via set_system('no_object_result',<Result>)set_system("no_object_result",<Result>)SetSystem("no_object_result",<Result>)SetSystem("no_object_result",<Result>)set_system("no_object_result",<Result>). If necessary, an exception is raised.

Possible Predecessors

binomial_filterbinomial_filterBinomialFilterBinomialFilterbinomial_filter, gauss_filtergauss_filterGaussFilterGaussFiltergauss_filter, mean_imagemean_imageMeanImageMeanImagemean_image, anisotropic_diffusionanisotropic_diffusionAnisotropicDiffusionAnisotropicDiffusionanisotropic_diffusion, sigma_imagesigma_imageSigmaImageSigmaImagesigma_image

Possible Successors

nonmax_suppression_dirnonmax_suppression_dirNonmaxSuppressionDirNonmaxSuppressionDirnonmax_suppression_dir, hysteresis_thresholdhysteresis_thresholdHysteresisThresholdHysteresisThresholdhysteresis_threshold, thresholdthresholdThresholdThresholdthreshold

Alternatives

edges_imageedges_imageEdgesImageEdgesImageedges_image, frei_dirfrei_dirFreiDirFreiDirfrei_dir, kirsch_dirkirsch_dirKirschDirKirschDirkirsch_dir, prewitt_dirprewitt_dirPrewittDirPrewittDirprewitt_dir, robinson_dirrobinson_dirRobinsonDirRobinsonDirrobinson_dir

See also

robertsrobertsRobertsRobertsroberts, laplacelaplaceLaplaceLaplacelaplace, highpass_imagehighpass_imageHighpassImageHighpassImagehighpass_image, bandpass_imagebandpass_imageBandpassImageBandpassImagebandpass_image

Module

Foundation

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