HALCON 22.11.0.0 / HALCON Operator Reference / Filters / Edges
ClassesClasses | | Operators

sobel_ampsobel_ampSobelAmpSobelAmpsobel_amp (Operator)

Name

sobel_ampsobel_ampSobelAmpSobelAmpsobel_amp — Detect edges (amplitude) using the Sobel operator.

Signature

sobel_amp(Image : EdgeAmplitude : FilterType, Size : )

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

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

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

HImage HImage::SobelAmp(const HString& FilterType, const HTuple& Size) const

HImage HImage::SobelAmp(const HString& FilterType, Hlong Size) const

HImage HImage::SobelAmp(const char* FilterType, Hlong Size) const

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

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

HImage HImage.SobelAmp(string filterType, HTuple size)

HImage HImage.SobelAmp(string filterType, int size)

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

Description

sobel_ampsobel_ampSobelAmpSobelAmpSobelAmpsobel_amp 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.) Here, thin(x) is equal to x for a vertical maximum (mask A) and a horizontal maximum (mask B), respectively, and 0 otherwise. Thus, for 'thin_sum_abs'"thin_sum_abs""thin_sum_abs""thin_sum_abs""thin_sum_abs""thin_sum_abs" and 'thin_max_abs'"thin_max_abs""thin_max_abs""thin_max_abs""thin_max_abs""thin_max_abs" the gradient image is thinned. For the filter types 'x'"x""x""x""x""x" and 'y'"y""y""y""y""y" if the input image is of type byte the output image is of type int1, of type int2 otherwise. 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_imageGaussImageGaussImageGaussImagegauss_image) or a binomial filter (see binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilterbinomial_filter) of size SizeSizeSizeSizesizesize-2. The Gaussian filter is selected for the above values of FilterTypeFilterTypeFilterTypeFilterTypefilterTypefilter_type. Here, SizeSizeSizeSizesizesize = 5, 7, 9, 11, or 13 must be used. The binomial filter is selected by appending '_binomial'"_binomial""_binomial""_binomial""_binomial""_binomial" to the above values of FilterTypeFilterTypeFilterTypeFilterTypefilterTypefilter_type. Here, SizeSizeSizeSizesizesize 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 SizeSizeSizeSizesizesize. Here, the first value of SizeSizeSizeSizesizesize 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_amp(I,E,FilterType,S)sobel_amp(I,E,FilterType,S)SobelAmp(I,E,FilterType,S)SobelAmp(I,E,FilterType,S)SobelAmp(I,E,FilterType,S)sobel_amp(I,E,FilterType,S)
for SizeSizeSizeSizesizesize > 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)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)GaussImage(F,G,S-2)gauss_image(F,G,S-2)
sobel_amp(G,E,FilterType,3)sobel_amp(G,E,FilterType,3)SobelAmp(G,E,FilterType,3)SobelAmp(G,E,FilterType,3)SobelAmp(G,E,FilterType,3)sobel_amp(G,E,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)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)BinomialFilter(F,G,S[0]-2,S[1]-2)binomial_filter(F,G,S[0]-2,S[1]-2)
sobel_amp(G,E,FilterType,3)sobel_amp(G,E,FilterType,3)SobelAmp(G,E,FilterType,3)SobelAmp(G,E,FilterType,3)SobelAmp(G,E,FilterType,3)sobel_amp(G,E,FilterType,3).

For sobel_ampsobel_ampSobelAmpSobelAmpSobelAmpsobel_amp special optimizations are implemented FilterTypeFilterTypeFilterTypeFilterTypefilterTypefilter_type = 'sum_abs'"sum_abs""sum_abs""sum_abs""sum_abs""sum_abs" that use SIMD technology. The actual application of these special optimizations is controlled by the system parameter 'sse2_enable'"sse2_enable""sse2_enable""sse2_enable""sse2_enable""sse2_enable" and 'avx2_enable'"avx2_enable""avx2_enable""avx2_enable""avx2_enable""avx2_enable", respectively (see set_systemset_systemSetSystemSetSystemSetSystemset_system). If 'sse2_enable'"sse2_enable""sse2_enable""sse2_enable""sse2_enable""sse2_enable" or 'avx2_enable'"avx2_enable""avx2_enable""avx2_enable""avx2_enable""avx2_enable"is set to 'true'"true""true""true""true""true" (and the SIMD instruction set is available), the internal calculations are performed using SIMD technology. Note that SIMD technology performs best on large, compact input regions. Depending on the input region and the capabilities of the hardware the execution of sobel_ampsobel_ampSobelAmpSobelAmpSobelAmpsobel_amp might even take significantly more time with SIMD technology than without.

sobel_ampsobel_ampSobelAmpSobelAmpSobelAmpsobel_amp can be executed on OpenCL devices for the filter types 'sum_abs'"sum_abs""sum_abs""sum_abs""sum_abs""sum_abs", 'sum_sqrt'"sum_sqrt""sum_sqrt""sum_sqrt""sum_sqrt""sum_sqrt", 'x'"x""x""x""x""x" and 'y'"y""y""y""y""y" (as well as their binomial variants). Note that when using gaussian filtering for SizeSizeSizeSizesizesize > 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

Parameters

ImageImageImageImageimageimage (input_object)  (multichannel-)image(-array) → objectHImageHObjectHImageHobject (byte / int2 / uint2 / real)

Input image.

EdgeAmplitudeEdgeAmplitudeEdgeAmplitudeEdgeAmplitudeedgeAmplitudeedge_amplitude (output_object)  (multichannel-)image(-array) → objectHImageHObjectHImageHobject * (int1 / int2 / uint2 / real)

Edge amplitude (gradient magnitude) image.

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

Filter type.

Default value: 'sum_abs' "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", 'sum_abs_binomial'"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", 'sum_sqrt_binomial'"sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial", 'thin_max_abs'"thin_max_abs""thin_max_abs""thin_max_abs""thin_max_abs""thin_max_abs", 'thin_max_abs_binomial'"thin_max_abs_binomial""thin_max_abs_binomial""thin_max_abs_binomial""thin_max_abs_binomial""thin_max_abs_binomial", 'thin_sum_abs'"thin_sum_abs""thin_sum_abs""thin_sum_abs""thin_sum_abs""thin_sum_abs", 'thin_sum_abs_binomial'"thin_sum_abs_binomial""thin_sum_abs_binomial""thin_sum_abs_binomial""thin_sum_abs_binomial""thin_sum_abs_binomial", 'x'"x""x""x""x""x", 'x_binomial'"x_binomial""x_binomial""x_binomial""x_binomial""x_binomial", 'y'"y""y""y""y""y", 'y_binomial'"y_binomial""y_binomial""y_binomial""y_binomial""y_binomial"

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

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

Size of filter mask.

Default value: 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_amp(Image,Amp,'sum_abs',3)
threshold(Amp,Edg,128,255)

Example (C)

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

Example (HDevelop)

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

Example (HDevelop)

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

Example (HDevelop)

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

Result

sobel_ampsobel_ampSobelAmpSobelAmpSobelAmpsobel_amp 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>)SetSystem("no_object_result",<Result>)set_system("no_object_result",<Result>). If necessary, an exception is raised.

Possible Predecessors

binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilterbinomial_filter, gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter, mean_imagemean_imageMeanImageMeanImageMeanImagemean_image, anisotropic_diffusionanisotropic_diffusionAnisotropicDiffusionAnisotropicDiffusionAnisotropicDiffusionanisotropic_diffusion, sigma_imagesigma_imageSigmaImageSigmaImageSigmaImagesigma_image

Possible Successors

thresholdthresholdThresholdThresholdThresholdthreshold, nonmax_suppression_ampnonmax_suppression_ampNonmaxSuppressionAmpNonmaxSuppressionAmpNonmaxSuppressionAmpnonmax_suppression_amp, gray_skeletongray_skeletonGraySkeletonGraySkeletonGraySkeletongray_skeleton

Alternatives

frei_ampfrei_ampFreiAmpFreiAmpFreiAmpfrei_amp, robertsrobertsRobertsRobertsRobertsroberts, kirsch_ampkirsch_ampKirschAmpKirschAmpKirschAmpkirsch_amp, prewitt_ampprewitt_ampPrewittAmpPrewittAmpPrewittAmpprewitt_amp, robinson_amprobinson_ampRobinsonAmpRobinsonAmpRobinsonAmprobinson_amp

See also

laplacelaplaceLaplaceLaplaceLaplacelaplace, highpass_imagehighpass_imageHighpassImageHighpassImageHighpassImagehighpass_image, bandpass_imagebandpass_imageBandpassImageBandpassImageBandpassImagebandpass_image

Module

Foundation

ClassesClasses | | Operators
HALCON Operator Reference 22.11.0.0 Copyright © 1996-2022 MVTec Software GmbH
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