Operator Reference

rank_rectrank_rectRankRectRankRectrank_rect (Operator)

rank_rectrank_rectRankRectRankRectrank_rect — Compute a rank filter with rectangular masks.

Signature

rank_rect(Image : ImageRank : MaskWidth, MaskHeight, Rank : )

Herror rank_rect(const Hobject Image, Hobject* ImageRank, const Hlong MaskWidth, const Hlong MaskHeight, const Hlong Rank)

Herror T_rank_rect(const Hobject Image, Hobject* ImageRank, const Htuple MaskWidth, const Htuple MaskHeight, const Htuple Rank)

void RankRect(const HObject& Image, HObject* ImageRank, const HTuple& MaskWidth, const HTuple& MaskHeight, const HTuple& Rank)

HImage HImage::RankRect(Hlong MaskWidth, Hlong MaskHeight, Hlong Rank) const

static void HOperatorSet.RankRect(HObject image, out HObject imageRank, HTuple maskWidth, HTuple maskHeight, HTuple rank)

HImage HImage.RankRect(int maskWidth, int maskHeight, int rank)

def rank_rect(image: HObject, mask_width: int, mask_height: int, rank: int) -> HObject

Description

rank_rectrank_rectRankRectRankRectrank_rect performs a rank filter on the input image ImageImageImageimageimage with a rectangular mask of size MaskWidthMaskWidthMaskWidthmaskWidthmask_width × MaskHeightMaskHeightMaskHeightmaskHeightmask_height and returns the filtered image in ImageRankImageRankImageRankimageRankimage_rank.

Conceptually, the rank filter sorts all gray values within the mask in ascending order and then selects the gray value with rank RankRankRankrankrank. The rank 1 corresponds to the smallest gray value and the rank MaskWidthMaskWidthMaskWidthmaskWidthmask_width * MaskHeightMaskHeightMaskHeightmaskHeightmask_height corresponds to the largest gray value within the mask. For RankRankRankrankrank = (MaskWidthMaskWidthMaskWidthmaskWidthmask_width * MaskHeightMaskHeightMaskHeightmaskHeightmask_height - 1) / 2 + 1, rank_rectrank_rectRankRectRankRectrank_rect returns the median gray value (see median_rectmedian_rectMedianRectMedianRectmedian_rect). For RankRankRankrankrank = 1, rank_rectrank_rectRankRectRankRectrank_rect performs a gray value erosion (see gray_erosion_rectgray_erosion_rectGrayErosionRectGrayErosionRectgray_erosion_rect, gray_erosion_shapegray_erosion_shapeGrayErosionShapeGrayErosionShapegray_erosion_shape, and gray_erosiongray_erosionGrayErosionGrayErosiongray_erosion), while for RankRankRankrankrank = MaskWidthMaskWidthMaskWidthmaskWidthmask_width * MaskHeightMaskHeightMaskHeightmaskHeightmask_height rank_rectrank_rectRankRectRankRectrank_rect performs a gray value dilation (see gray_dilation_rectgray_dilation_rectGrayDilationRectGrayDilationRectgray_dilation_rect, gray_dilation_shapegray_dilation_shapeGrayDilationShapeGrayDilationShapegray_dilation_shape, and gray_dilationgray_dilationGrayDilationGrayDilationgray_dilation).

rank_rectrank_rectRankRectRankRectrank_rect can be used, for example, to suppress noise or to suppress unwanted objects that are smaller than the mask. Furthermore, rank_rectrank_rectRankRectRankRectrank_rect is less sensitive to noise than the corresponding gray value morphology operators. Therefore, to obtain a more robust version of the gray value morphology, instead of using 1 or MaskWidthMaskWidthMaskWidthmaskWidthmask_width * MaskHeightMaskHeightMaskHeightmaskHeightmask_height, slightly larger or smaller values should be selected for RankRankRankrankrank.

For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.

Attention

If even values instead of odd values are passed in MaskHeightMaskHeightMaskHeightmaskHeightmask_height or MaskWidthMaskWidthMaskWidthmaskWidthmask_width, rank_rectrank_rectRankRectRankRectrank_rect uses the next larger odd values instead.

rank_rectrank_rectRankRectRankRectrank_rect uses an algorithm with constant runtime per pixel, i.e., the runtime only depends on the size of the input image and not on the mask size. Therefore, for large mask sizes rank_rectrank_rectRankRectRankRectrank_rect is the fastest implementation of the rank filter in HALCON. Depending on the computer architecture (processor type, availability of SIMD instructions like SSE2 or MMX, cache size and throughput, memory throughput), for small mask sizes the implementation used in rank_imagerank_imageRankImageRankImagerank_image is faster than rank_rectrank_rectRankRectRankRectrank_rect. Typically, this is the case for MaskHeightMaskHeightMaskHeightmaskHeightmask_height 15, but can also happen for larger mask sizes, e.g., if SIMD instructions are unavailable and memory throughput is low.

Furthermore, it should be noted that rank_rectrank_rectRankRectRankRectrank_rect uses a recursive implementation, which internally computes the filter response on the smallest enclosing rectangle of the domain of the input image. Therefore, if the domain of the input image only covers a small fraction of the smallest enclosing rectangle, it can happen that rank_imagerank_imageRankImageRankImagerank_image is faster than rank_rectrank_rectRankRectRankRectrank_rect even for larger values of MaskHeightMaskHeightMaskHeightmaskHeightmask_height.

rank_rectrank_rectRankRectRankRectrank_rect should neither be used with RankRankRankrankrank = 1 to perform a gray value erosion nor with RankRankRankrankrank = MaskWidthMaskWidthMaskWidthmaskWidthmask_width * MaskHeightMaskHeightMaskHeightmaskHeightmask_height to perform a gray value dilation. In these cases, the operators gray_erosion_rectgray_erosion_rectGrayErosionRectGrayErosionRectgray_erosion_rect or gray_erosion_shapegray_erosion_shapeGrayErosionShapeGrayErosionShapegray_erosion_shape and gray_dilation_rectgray_dilation_rectGrayDilationRectGrayDilationRectgray_dilation_rect or gray_dilation_shapegray_dilation_shapeGrayDilationShapeGrayDilationShapegray_dilation_shape, respectively, are faster than rank_rectrank_rectRankRectRankRectrank_rect for almost all mask sizes.

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

  • 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)

Image to be filtered.

ImageRankImageRankImageRankimageRankimage_rank (output_object)  (multichannel-)image(-array) objectHImageHObjectHObjectHobject * (byte)

Filtered image.

MaskWidthMaskWidthMaskWidthmaskWidthmask_width (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Width of the filter mask.

Default: 15

Suggested values: 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 31, 49, 51, 61, 71, 81, 91, 101

Value range: 3 ≤ MaskWidth MaskWidth MaskWidth maskWidth mask_width ≤ 4095

Minimum increment: 2

Recommended increment: 2

MaskHeightMaskHeightMaskHeightmaskHeightmask_height (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Height of the filter mask.

Default: 15

Suggested values: 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 31, 49, 51, 61, 71, 81, 91, 101

Value range: 3 ≤ MaskHeight MaskHeight MaskHeight maskHeight mask_height ≤ 4095

Minimum increment: 2

Recommended increment: 2

RankRankRankrankrank (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Rank of the output gray value.

Default: 5

Suggested values: 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 31, 49, 51, 61, 71, 81, 91, 101

Minimum increment: 1

Recommended increment: 2

Restriction: 1 <= Rank && Rank <= MaskWidth * MaskHeight

Complexity

For each pixel: O(1).

Result

If the parameter values are correct the operator rank_rectrank_rectRankRectRankRectrank_rect returns the value 2 ( H_MSG_TRUE) . The behavior in case of empty input (no input images available) is 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

read_imageread_imageReadImageReadImageread_image

Possible Successors

thresholdthresholdThresholdThresholdthreshold, dyn_thresholddyn_thresholdDynThresholdDynThresholddyn_threshold, regiongrowingregiongrowingRegiongrowingRegiongrowingregiongrowing

Alternatives

rank_imagerank_imageRankImageRankImagerank_image, median_rectmedian_rectMedianRectMedianRectmedian_rect, median_imagemedian_imageMedianImageMedianImagemedian_image

See also

gray_erosion_rectgray_erosion_rectGrayErosionRectGrayErosionRectgray_erosion_rect, gray_dilation_rectgray_dilation_rectGrayDilationRectGrayDilationRectgray_dilation_rect, gray_erosion_shapegray_erosion_shapeGrayErosionShapeGrayErosionShapegray_erosion_shape, gray_dilation_shapegray_dilation_shapeGrayDilationShapeGrayDilationShapegray_dilation_shape, gray_erosiongray_erosionGrayErosionGrayErosiongray_erosion, gray_dilationgray_dilationGrayDilationGrayDilationgray_dilation

References

S. Perreault, P. Hébert; “Median Filtering in Constant Time”; IEEE Transactions on Image Processing, vol. 16, no. 9, pp. 2389-2394, 2007.
D. Cline, K.B. White, P.K. Egbert; “Fast 8-Bit Median Filtering Based On Separability”; International Conference on Image Processing, vol. V, pp. 281-284, 2007.

Module

Foundation