Foundation (CVFoundation.dll) 15.0
Enumerations

Enumerations

enum  TBlobFeatureType { FBLOB_BLACK_TO_FEATURE = 0 , FBLOB_WHITE_TO_FEATURE = 1 }
 Feature range to be recognized as an object (blob). More...
 
enum  TBlobSortDirection { FBLOB_SORT_RISING = 1 , FBLOB_SORT_FALLING = 0 }
 Sort order of blob objects. More...
 
enum  TBlobSortMode {
  FBLOB_SORT_UNSORTED = 0 , FBLOB_SORT_SIZE = 1 , FBLOB_SORT_POSX = 2 , FBLOB_SORT_POSY = 3 ,
  FBLOB_SORT_DISTANCE = 4
}
 Mode for sorting blob objects. More...
 
enum  TBlobTouchBorderFilter {
  FBLOB_BORDER_NONE = 0 , FBLOB_BORDER_RIGHT = 1 , FBLOB_BORDER_TOP = 2 , FBLOB_BORDER_LEFT = 4 ,
  FBLOB_BORDER_BOTTOM = 8 , FBLOB_BORDER_ALL = 15
}
 Blob object border mask. More...
 
enum  TCannyEdgeFilter { CE_Scharr = 0 , CE_Sobel = 1 , CE_Sobel2nd = 2 }
 To apply the Canny Edge detector, the function FilterCanny internally performs a derivative-operation. More...
 
enum  TDistanceNorm { DN_LInf = 0 , DN_L1 = 1 , DN_L2 = 2 }
 Indicates the distance norm to be used for the distance transformation (see DistanceTransformation). More...
 
enum  TFilterMask { FM_3x3 = 0 , FM_5x5 = 1 , FM_7x7 = 2 }
 Kernel/Mask size. More...
 
enum  TInterpolationMode {
  IP_NearestNeighbour = 0 , IP_Linear = 1 , IP_Cubic = 2 , IP_Lanczos = 3 ,
  IP_Supersample = 4
}
 Mode for sorting blob objects. More...
 
enum  TMomentsCalculation { MC_FavorNone = 0 , MC_FavorSpeed = 1 , MC_FavorAccuracy = 2 }
 Specify the preferences for moment calculations. More...
 
enum  TMorphologyMask {
  MM_Custom = 0 , MM_Square = 1 , MM_Rect = 2 , MM_Cross = 3 ,
  MM_Circle = 4 , MM_Ellipse = 5
}
 Defines different types of masks that may be used with the morphological functions ErodeImage, DilateImage, OpeningImage, ClosingImage. More...
 
enum  TSegmentationBorder { SEGMENTATION_BORDER_NONE = 0 , SEGMENTATION_BORDER_4 = 1 , SEGMENTATION_BORDER_8 = 2 }
 Specifies the border of the segments. More...
 
enum  TSegmentationNorm { SEGMENTATION_NORM_INF = 0 , SEGMENTATION_NORM_L1 = 1 }
 Indicates the norm to form the mask for marker propagation. More...
 
enum  TThresholdComparison {
  TH_Less = 0 , TH_LessOrEqual = 1 , TH_Equal = 2 , TH_GreaterOrEqual = 3 ,
  TH_Greater = 4
}
 Specify the different comparison modes supported by the thresholding functions. More...
 
enum  TWatershedNorm { WS_NORM_INF = 0 , WS_NORM_L1 = 1 , WS_NORM_L2 = 2 , WS_NORM_FM = 3 }
 Indicates the norm to form the mask for marker propagation. More...
 
enum  TWatershedSegmentation { WS_SEGMENT_QUEUE = 0 , WS_SEGMENT_DISTANCE = 1 }
 Specifies the algorithm how the watershed function is performed. More...
 
enum  TWaveletFilter {
  WT_HAAR_FILTER = 0 , WT_DAUB2_FILTER = 1 , WT_DAUB3_FILTER = 2 , WT_DAUB4_FILTER = 3 ,
  WT_BI_1_3 = 4 , WT_BI_1_5 = 5 , WT_CUSTOM = 20
}
 Wavelet filter to be applied on the input image. More...
 

Detailed Description

Enumeration Type Documentation

◆ TBlobFeatureType

Feature range to be recognized as an object (blob).

Supported platforms:
Win32
Win64
Related Topics:
FBlobSetObjectFeature
Enumerator
FBLOB_BLACK_TO_FEATURE 

The feature is displayed in black.

FBLOB_WHITE_TO_FEATURE 

The feature is displayed in white.

◆ TBlobSortDirection

Sort order of blob objects.

Supported platforms:
Win32
Win64
Related Topics:
FBlobSetSortMode, FBlobSetSortParameter
Enumerator
FBLOB_SORT_RISING 

Sorted in ascending order.

FBLOB_SORT_FALLING 

Sorted in descending order.

◆ TBlobSortMode

Mode for sorting blob objects.

Supported platforms:
Win32
Win64
Related Topics:
FBlobSetSortMode, FBlobSetSortParameter
Enumerator
FBLOB_SORT_UNSORTED 

Unsorted.

FBLOB_SORT_SIZE 

Sorted by size.

FBLOB_SORT_POSX 

Sorted by X-position.

FBLOB_SORT_POSY 

Sorted by Y-position.

FBLOB_SORT_DISTANCE 

Sorted by distance to point.

◆ TBlobTouchBorderFilter

Blob object border mask.

Supported platforms:
Win32
Win64
Related Topics:
FBlobGetObjectTouchBorder
Enumerator
FBLOB_BORDER_NONE 

Turns the filter off.

FBLOB_BORDER_RIGHT 

Filter for objects which touch the right border.

FBLOB_BORDER_TOP 

Filter for objects which touch the top border.

FBLOB_BORDER_LEFT 

Filter for objects which touch the left border.

FBLOB_BORDER_BOTTOM 

Filter for objects which touch the bottom border.

FBLOB_BORDER_ALL 

Filter for objects which touch any border.

◆ TCannyEdgeFilter

To apply the Canny Edge detector, the function FilterCanny internally performs a derivative-operation.

There are three ways currently implemented how FilterCanny may do this:
Scharr, Sobel and 2nd order Sobel
Which of the derivative operators is applied depends on the TCannyEdgeFilter value passed to the function FilterCanny.

Supported platforms:
Win32
Win64
Related Topics:
FilterCanny
Enumerator
CE_Scharr 

Scharr Edge detection operator (see FilterScharrHorizontal and FilterScharrVertical).

CE_Sobel 

The 1st Order Sobel Filter (see FilterSobelHorizontal and FilterSobelVertical).

CE_Sobel2nd 

The 2nd Order Sobel Filter (see FilterSobelHorizontal2ndOrder and FilterSobelVertical2ndOrder).

◆ TDistanceNorm

Indicates the distance norm to be used for the distance transformation (see DistanceTransformation).

Supported platforms:
Win32
Win64
Enumerator
DN_LInf 

Infinity norm.

DN_L1 

L1 norm.

DN_L2 

L2 norm.

◆ TFilterMask

Kernel/Mask size.

The finite impulse response filters implemented in the CVFoundation.dll require that an operation mask is specified either directly or implicitly through the kernel size. There are two ways in which this is done:

  • Some functions (such as the statistical filters FilterBoxMin, FilterBoxMax, FilterBoxMean, FilterBoxMedian) directly accept the parameters MaskWidth, MaskHeight, MaskOffsetX and MaskOffsetY. These allow the specification of the rectangular mask on which the filter is operating (MaskWidth, MaskHeight), and the location of that mask relative to the pixel that is currently being processed (MaskOffsetX, MaskOffsetY specify the location of that "anchor cell" inside the mask).
  • Other functions accept a parameter of type TFilterMask, which enumerates three square mask sizes that are frequently used. If a function takes a TFilterMask parameter, then the filter mask is always square, then the filter mask is always square, and the anchor cell is always the center cell of the mask/kernel.

Of course it is necessary for the filter functions to access image data beyond the image boundaries. For example a filter function working with a mask of 7x7 pixels, needs to access three pixel coordinates beyond the border of the "real" image data (image e.g. the anchor cell to be located in the top left pixel of the image; only the bottom right corner of the mask covers "real" image data, three quarters are in undefined regions). To resolve this problem, Common Vision Blox always expands the images to be processed by the necessary amount by simply reproducing the image data in the outermost pixel rows and columns.

Enumerator
FM_3x3 

3x3 Kernel/Filter mask.

FM_5x5 

5x5 Kernel/Filter mask.

FM_7x7 

7x7 Kernel/Filter mask.

◆ TInterpolationMode

Mode for sorting blob objects.

Supported platforms:
Win32
Win64
Related Topics:
FBlobSetSortMode, FBlobSetSortParameter
Enumerator
IP_NearestNeighbour 

Nearest neighbour interpolation.

Not really an interpolation in the strict sense: The value of the closest pixel in 2D space is used
for the target pixel of the interpolation. Normally the
result looks quite jagged - but no other mode is as fast. 
IP_Linear 

Linear interpolation between vertical and horizontal neighbour pixels.

IP_Cubic 

Cubic interpolation between vertical and horizontal neighbour pixels.

IP_Lanczos 

Interpolation using a 3-lobed Lanczos window function.

IP_Supersample 

Interpolation through super-sampling and averaging of the target position with sub-pixel positions.

◆ TMomentsCalculation

Specify the preferences for moment calculations.

Supported platforms:
Win32
Win64
Related Topics:
CreateImageMoments
Enumerator
MC_FavorNone 

Calculation type (fast or precise) is chosen automatically depending on the image size involved.

MC_FavorSpeed 

Fast calculation.

MC_FavorAccuracy 

Precise calculation.

◆ TMorphologyMask

Defines different types of masks that may be used with the morphological functions ErodeImage, DilateImage, OpeningImage, ClosingImage.

Depending on the mask type being used, different parameters of those functions will or will not be put to use.

Supported platforms:
Win32
Win64
Related Topics:
ErodeImage, DilateImage, OpeningImage, ClosingImage
Enumerator
MM_Custom 

Custom mask.

  • MaskWidth and MaskHeight parameter are ignored. Instead the mask width and mask height is determined from the mask image that must be provided (which must be no bigger than 256x256 pixels).
  • Mask offsets are tested against this width and height.
MM_Square 

Square mask.

  • MaskHeight parameter will be ignored; instead the MaskWidth parameter also applies to the MaskHeight.
  • Mask offsets are both tested against the MaskWidth .
  • CustomMask parameter is ignored.
MM_Rect 

Rectangular mask.

  • CustomMask parameter is ignored.
MM_Cross 

Cross mask.

  • CustomMask parameter is ignored.
MM_Circle 

Circular mask.

  • MaskHeight parameter will be ignored; instead the MaskWidth parameter also applies to the MaskHeight.
  • Mask offsets are both tested against the MaskWidth .
  • CustomMask parameter is ignored.
MM_Ellipse 

Ellipse mask.

  • CustomMask parameter is ignored.

◆ TSegmentationBorder

Specifies the border of the segments.

All pixel adjacent to the differently marked pixels are considered as border pixels, and their value is set to white (255 in SegmentWatershed and 254 in SegmentGradient).

Note
If a border scheme is selected the maximum value (255 or 254) should not be used to mark segments.
Supported platforms:
Win32
Win64
Enumerator
SEGMENTATION_BORDER_NONE 

The segments are formed without borders.

SEGMENTATION_BORDER_4 

Pixels of the 4-connectivity border between image segments are marked white (255 or 254).

SEGMENTATION_BORDER_8 

Pixels of the 8-connectivity border between image segments are marked white (255 or 254).

◆ TSegmentationNorm

Indicates the norm to form the mask for marker propagation.

This enumeration is used for the function LabelMarker.

Supported platforms:
Win32
Win64
Enumerator
SEGMENTATION_NORM_INF 

Infinity norm (8-connectivity).

SEGMENTATION_NORM_L1 

L1 norm (4-connectivity).

◆ TThresholdComparison

Specify the different comparison modes supported by the thresholding functions.

Supported platforms:
Win32
Win64
Related Topics:
CreateConstantThresholdImage CreateImageThresholdImage CreateTransparentThresholdImage
Enumerator
TH_Less 

Less comparison of the pixels.

TH_LessOrEqual 

Less or equal comparison of the pixels.

TH_Equal 

Equal comparison of the pixels.

TH_GreaterOrEqual 

Greater or equal comparison of the pixels.

TH_Greater 

Greater comparison of the pixels.

◆ TWatershedNorm

Indicates the norm to form the mask for marker propagation.

Supported platforms:
Win32
Win64
Enumerator
WS_NORM_INF 

Infinity norm (8-connectivity, 3x3 rectangular mask).

WS_NORM_L1 

L1 norm (4-connectivity, 3x3 cross mask).

WS_NORM_L2 

L2 norm (approximation of L2 norm, 8-connectivity, 3x3 mask).

WS_NORM_FM 

Fast marching distance.

◆ TWatershedSegmentation

Specifies the algorithm how the watershed function is performed.

Supported platforms:
Win32
Win64
Enumerator
WS_SEGMENT_QUEUE 

Specifies the classic watershed segmentation with priority queue.

WS_SEGMENT_DISTANCE 

Specifies the watershed segmentation by calculating the topological distance for each pixel.

◆ TWaveletFilter

Wavelet filter to be applied on the input image.

Different standard filters are provided. The provided filters are either orthogonal (Haar, Daubechies 2, Daubechies 3, Daubechies 4) or bi-orthogonal (Bi-orthogonal 1.3, Bi-orthogonal 1.5 filter). The (bi-)orthogonal wavelet transform decomposes the source image into nearly disjoint frequency sub-bands. Using orthogonal wavelet bases the representation of the signal is non-redundant. This means, applying the forward and inverse wavelet transformation on the source images results in perfect reconstruction. All implemented filter coefficients are normalised. Due to perferct reconstruction the values for the reconstruction filters can be computed using the decomposition filter values (high-pass and low-pass filter values).

It is possible to use a different kind of filter. Therefore, specify WT_CUSTOM as input filter. In this case you need to specify the filter coefficients.

Note
The graphics below are taken from: F. Wasilewski. Wavelet Browser for PyWavelets, 2011. URL: http://wavelets.pybytes.com/.
Supported platforms:
Win32
Win64
Related Topics:
ForwardWaveletTransform, InverseWaveletTransform
Enumerator
WT_HAAR_FILTER 

Haar wavelet filter (2 taps - orthogonal wavelet filter).

Scaling Function Wavelet Function
Continous
Function
HaarScaling HaarWavelet
Decomposition
Filter
Values
0.70710678118654757
0.70710678118654757
-0.70710678118654757
0.70710678118654757
WT_DAUB2_FILTER 

Daubechies 2 wavelet filter (4 taps - orthogonal wavelet filter).

Scaling Function Wavelet Function
Continous
Function
Daub2Scaling Daub2Wavelet
Decomposition
Filter
Values
-0.12940952255092145
0.22414386804185735
0.83651630373746899
0.48296291314469025
-0.48296291314469025
0.83651630373746899
-0.22414386804185735
-0.12940952255092145
WT_DAUB3_FILTER 

Daubechies 3 wavelet filter (6 taps - orthogonal wavelet filter).

Scaling Function Wavelet Function
Continous
Function
Daub3Scaling Daub3Wavelet
Decomposition
Filter
Values
0.035226291882100656
-0.085441273882241486
-0.13501102001039084
0.45987750211933132
0.80689150931333875
0.33267055295095688
-0.33267055295095688
0.80689150931333875
-0.45987750211933132
-0.13501102001039084
0.085441273882241486
0.035226291882100656
WT_DAUB4_FILTER 

Daubechies 4 wavelet filter (8 taps - orthogonal wavelet filter).

Scaling Function Wavelet Function
Continous
Function
Daub4Scaling Daub4Wavelet
Decomposition
Filter
Values
-0.010597401784997278
0.032883011666982945
0.030841381835986965
-0.18703481171888114
-0.027983769416983849
0.63088076792959036
0.71484657055254153
0.23037781330885523
-0.23037781330885523
0.71484657055254153
-0.63088076792959036
-0.027983769416983849
0.18703481171888114
0.030841381835986965
-0.032883011666982945
-0.010597401784997278
WT_BI_1_3 

Bi-orthogonal 1.3 wavelet filter.

Scaling Function Wavelet Function
Continous
Decomposition
Function
Bi13ScalingDec Bi13WaveletDec
Continous
Reconstruction
Function
Bi13ScalingRec Bi13WaveletRec
Decomposition
Filter
Values
-0.088388347648318447
0.088388347648318447
0.70710678118654757
0.70710678118654757
0.088388347648318447
-0.088388347648318447
0.0
0.0
-0.70710678118654757
0.70710678118654757
0.0
0.0
WT_BI_1_5 

Bi-orthogonal 1.5 wavelet filter.

Scaling Function Wavelet Function
Continous
Decomposition
Function
Bi15ScalingDec Bi15WaveletDec
Continous
Reconstruction
Function
Bi15ScalingRec Bi15WaveletRec
Decomposition
Filter
Values
0.01657281518405971
-0.01657281518405971
-0.12153397801643787
0.12153397801643787
0.70710678118654757
0.70710678118654757
0.12153397801643787
-0.12153397801643787
-0.01657281518405971
0.01657281518405971
0.0
0.0
0.0
0.0
-0.70710678118654757
0.70710678118654757
0.0
0.0
0.0
0.0
WT_CUSTOM 

Custom specified wavelet filter.