midrange_imagemidrange_imageMidrangeImageMidrangeImagemidrange_image (Operator)
Name
midrange_imagemidrange_imageMidrangeImageMidrangeImagemidrange_image
— Calculate the average of maximum and minimum inside any mask.
Signature
def midrange_image(image: HObject, mask: HObject, margin: Union[int, float, str]) -> HObject
Description
The operator midrange_imagemidrange_imageMidrangeImageMidrangeImageMidrangeImagemidrange_image
forms the average of maximum and
minimum inside the indicated mask in the whole image. Several
border treatments (MarginMarginMarginMarginmarginmargin
) can be chosen for filtering:
gray value Pixels outside of the image edges
are assumed to be constant (with the
indicated gray value).
'continued' Continuation of edge pixels.
'cyclic' Cyclic continuation of image edges.
'mirrored' Reflection of pixels at the image edges.
The indicated mask (= region of the mask image) is put over the
image to be filtered in such a way that the center of the mask
touches all pixels once.
For an explanation of the concept of smoothing filters see the introduction
of chapter Filters / Smoothing.
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
- 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
ImageImageImageImageimageimage
(input_object) (multichannel-)image(-array) →
objectHImageHObjectHImageHobject (byte / int2 / uint2 / int4 / real)
Image to be filtered.
MaskMaskMaskMaskmaskmask
(input_object) region →
objectHRegionHObjectHRegionHobject
Filter mask.
ImageMidrangeImageMidrangeImageMidrangeImageMidrangeimageMidrangeimage_midrange
(output_object) (multichannel-)image(-array) →
objectHImageHObjectHImageHobject * (byte / int2 / uint2 / int4 / real)
Filtered image.
MarginMarginMarginMarginmarginmargin
(input_control) string →
HTupleUnion[int, float, str]HTupleHtuple (string / integer / real) (string / int / long / double) (HString / Hlong / double) (char* / Hlong / double)
Border treatment.
Default value:
'mirrored'
"mirrored"
"mirrored"
"mirrored"
"mirrored"
"mirrored"
Suggested values: 'mirrored'"mirrored""mirrored""mirrored""mirrored""mirrored", 'cyclic'"cyclic""cyclic""cyclic""cyclic""cyclic", 'continued'"continued""continued""continued""continued""continued", 0, 30, 60, 90, 120, 150, 180, 210, 240, 255
Example (HDevelop)
read_image(Image,'fabrik')
draw_region(Region,WindowHandle)
midrange_image(Image,Region,Midrange,'mirrored')
dev_display(Midrange)
Example (C)
read_image(&Image,"fabrik");
draw_region(&Region,WindowHandle);
midrange_image(Image,Region,&Midrange,"mirrored");
disp_image(Midrange,WindowHandle);
Example (HDevelop)
read_image(Image,'fabrik')
draw_region(Region,WindowHandle)
midrange_image(Image,Region,Midrange,'mirrored')
dev_display(Midrange)
Example (HDevelop)
read_image(Image,'fabrik')
draw_region(Region,WindowHandle)
midrange_image(Image,Region,Midrange,'mirrored')
dev_display(Midrange)
Example (HDevelop)
read_image(Image,'fabrik')
draw_region(Region,WindowHandle)
midrange_image(Image,Region,Midrange,'mirrored')
dev_display(Midrange)
Complexity
For each pixel: O(sqrt(F) * 5) with F = area of MaskMaskMaskMaskmaskmask
.
Result
If the parameter values are correct the operator
midrange_imagemidrange_imageMidrangeImageMidrangeImageMidrangeImagemidrange_image
returns the value TRUE. The behavior in case
of empty input (no input images available) is set via the operator
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
read_imageread_imageReadImageReadImageReadImageread_image
,
draw_regiondraw_regionDrawRegionDrawRegionDrawRegiondraw_region
,
gen_circlegen_circleGenCircleGenCircleGenCirclegen_circle
,
gen_rectangle1gen_rectangle1GenRectangle1GenRectangle1GenRectangle1gen_rectangle1
Possible Successors
thresholdthresholdThresholdThresholdThresholdthreshold
,
dyn_thresholddyn_thresholdDynThresholdDynThresholdDynThresholddyn_threshold
,
regiongrowingregiongrowingRegiongrowingRegiongrowingRegiongrowingregiongrowing
Alternatives
sigma_imagesigma_imageSigmaImageSigmaImageSigmaImagesigma_image
See also
gen_circlegen_circleGenCircleGenCircleGenCirclegen_circle
,
gen_rectangle1gen_rectangle1GenRectangle1GenRectangle1GenRectangle1gen_rectangle1
,
gray_erosion_rectgray_erosion_rectGrayErosionRectGrayErosionRectGrayErosionRectgray_erosion_rect
,
gray_dilation_rectgray_dilation_rectGrayDilationRectGrayDilationRectGrayDilationRectgray_dilation_rect
,
gray_range_rectgray_range_rectGrayRangeRectGrayRangeRectGrayRangeRectgray_range_rect
References
R. Haralick, L. Shapiro; “Computer and Robot Vision”;
Addison-Wesley, 1992, Seite 319
Module
Foundation