Macros
#define	area_centered_grey_value_quantile(imgIn, val, quantilePercent, xc, yc, cdx, cdy, ch) area_grey_value_quantile(imgIn, val, quantilePercent, (xc)-((cdx)/2), (yc)-((cdy)/2), (cdx), (cdy), ch)
	Get Grey Value Quantile of an image area based around center (Macro). More...

#define	image_grey_value_quantile(imgIn, val, quantilePercent, ch) area_grey_value_quantile(imgIn, val, quantilePercent, 0, 0, (imgIn)->dx, (imgIn)->dy, ch)
	Get Grey Value Quantile of an image (Macro). More...

Functions
U32	arx (image *a, I32 thr)
	Count of Image Pixels above Threshold. More...

U32	arx2 (image *a, I32 th1, I32 th2)
	Count of Image Pixels between Two Thresholds. More...

I32	mean (image *a)
	Mean of Image's Grey Values. More...

U32	variance (image *a)
	Variance of Image's Grey Values. More...

I32	contrast (image *a)
	Contrast of Image's Grey Values. More...

U32	focus (image *a, I32 sh)
	Focal Value of an Image. More...

U32	SumImage (image *Src)
	Sum of all Image Pixel Values. More...

U40	ProdImage (image Src1, image Src2)
	Inner Product of Two Images. More...

I32	histo (image *a, U32 hist[])
	Histogram over Image's Grey Values. More...

I32	histo_at_rlc (image img, U32 hist[256], U16 rlc, U16 rlcCol)
	Histogram over RLC's Image Grey Values. More...

I32	histo_grey_value_quantile (U32 hist[256], U8 *val, I32 quantilePercent)
	Get Grey Value Quantile of a histo()-gram. More...

I32	histo_U32_value_quantile (U32 hist, I32 entries, U32 pos, I32 quantilePercent)

void	projh (image *a, U32 result[])
	Horizontal Projection of an Image. More...

void	projv (image *a, U32 result[])
	Vertical Projection of an Image. More...

I32	area_grey_value_quantile (image imgIn, U32 val, I32 quantilePercent, I32 x0, I32 y0, I32 dx, I32 dy, I32 ch)
	Get Grey Value Quantile of an image area. More...

I32	NormalizedCorrelationI32 (image SearchX, image Pattern, float MinContr, I32 Corr, float Contr, I32 PosX, I32 PosY)
	Normalized Correlation Returning I32 Correlation Value. More...

I32	contrastF (image src, F32 avg, F32 *contrast)
	Average Grey Value and Contrast Calculation. More...

Detailed Description

Functions in this group use a larger number of pixels of the source image in order to calculate a value or a number of values. Examples are projections and average calculations.

Macro Definition Documentation

◆ area_centered_grey_value_quantile

#define area_centered_grey_value_quantile	(	imgIn,
		val,
		quantilePercent,
		xc,
		yc,
		cdx,
		cdy,
		ch
	)	area_grey_value_quantile(imgIn, val, quantilePercent, (xc)-((cdx)/2), (yc)-((cdy)/2), (cdx), (cdy), ch)

The macro calculates the grey value which is equal to or smaller than the given grey value quantile of the histogram, but always at least 0.

More information can be found at the called function area_grey_value_quantile().

◆ image_grey_value_quantile

#define image_grey_value_quantile	(	imgIn,
		val,
		quantilePercent,
		ch
	)	area_grey_value_quantile(imgIn, val, quantilePercent, 0, 0, (imgIn)->dx, (imgIn)->dy, ch)

The macro calculates the grey value which is equal to or smaller than the given grey value quantile of the histogram, but always at least 0.

More information can be found at the called function area_grey_value_quantile().

Function Documentation

◆ arx()

U32 arx	(	image *	a,
		I32	thr
	)

The function calculates the count of pixels above (and not equals to) the threshold thr.

Memory Consumption: None.

See also: arx2().

◆ arx2()

U32 arx2	(	image *	a,
		I32	th1,
		I32	th2
	)

The function calculates the number of pixels between the thresholds th1 and th2.

Memory Consumption: None.

See also: arx().

◆ mean()

I32 mean ( image * a )

The function calculates the mean value of an image variable. The value is rounded and returned to the calling function. There is no restriction for the format of the image.

The mean is the sum of all pixel values in the image divided by the image area.

Memory Consumption: None.

See also: variance(), histo(), projh(), projv().

◆ variance()

U32 variance ( image * a )

The function calculates the statistical variance of an image variable. The value is rounded and returned to the calling function. There is no restriction for the format of the image like for earlier VCLIB.

The variance is the sum of all pixel values squared divided by the image area.

The variance can be used to measure the contrast, e.g the presence or absence of high contrast structures like printing, etc.

Memory Consumption: None.

See also: mean(), histo(), projh(), projv().

◆ contrast()

I32 contrast ( image * a )

The function calculates the average contrast in image variable a.

Contrast is defined as the standard deviation of the grey values of all pixels in the image variable. The maximum allowed size of the image variable is 65536 pixels (e.g. 256×256)

See also: vc_corr0().

◆ contrastF()

I32 contrastF	(	image *	src,
		float *	avg,
		float *	contrast
	)

The function calculates the average grey value and the average contrast in image variable src.

Contrast is defined as the standard deviation of the grey values of all pixels in the image variable. The maximum allowed size of the image variable is 4096×4096-1 pixels.

See also: contrast(), mean().

◆ focus()

U32 focus	(	image *	a,
		I32	sh
	)

The function calculates the relative focus measure of the image variable a. sh is a shift value to suppress noise. sh=0 means no shift, sh=-1 is equivalent to dividing by 2, sh=-2 is equivalent to dividing by 4, etc. The focal value is calculated according to the following procedure:

a1	a2
b1	b2

a1 and a2 are adjacent Pixels in the upper line, b1 and b2 are adjacent pixels in the lower line. We have:

$focus := \sum\left(\left( | a1 - a2 | + | a1 - b1 | \right) \gg (-sh) \right)$

The summation is performed for all ((dx-1)*(dy-1)) pixels of the image variable.

Note: The relative focus measure calculated by the above formula depends upon the mean brightness of the image field used. Thus, the formula is only recommended for evaluating the focus if the individual images used for the comparison have similar values for the mean brightness.

Memory Consumption: None.

◆ SumImage()

U32 SumImage ( image * Src )

This function sums up all the pixels in an image. The result is U32, therefore (2^24-1) pixels may be in the image, e.g. almost 4096×4096 pixels.

Parameters

Src	Image with Pixels to Sum Up.

Return values

Sum	Equals to $\sum_{px}(Src_{px})$ .

◆ ProdImage()

U40 ProdImage	(	image *	Src1,
		image *	Src2
	)

This function calculates the cross-correlation or inner product between two images. Line summation is done with U32, therefore the maximum line width is (2^16-1) = 65535. The line summation is done with U40, so that the maximum image size is (2^24-1), e.g. almost 4096×4096 pixels.

Parameters

Src1	First Image.
Src2	Second Image.

Return values

Inner Product Equals to $\sum_{px}(Src1_{px} \cdot Src2_{px})$ .

◆ histo()

I32 histo	(	image *	a,
		U32	hist[]
	)

The function calculates the histogram of the image variable a. The histogram is the absolute frequency of the 256 different gray scales in an image/image window. In addition to the image variable a, a pointer to an array with 256 values is passed to the function. After calling the function, the result can be taken from this array.

Parameters

a	Source Image
hist	Target Histogram

Memory Consumption: None.

See also: mean(), variance().

◆ histo_at_rlc()

I32 histo_at_rlc	(	image *	img,
		U32	hist[256],
		U16 *	rlc,
		U16	rlcCol
	)

The function calculates the histogram of all pixels of the image variable a where a given run length code rlc has a specified color rlcCol. The histogram is the absolute frequency of the 256 different gray scales in an image/image window. In addition to the image variable a, a pointer to an array with 256 values is passed to the function. After calling the function, the result can be taken from this array.

Parameters

a	Source Image of type IMAGE_GREY.
hist	Target Histogram
rlc,rlcCol	Regions at `rlc` (unlabelled RLC) with color `rlcCol` will be selected for histogram building

Memory Consumption: None.

See also: histo(), mean(), variance().

◆ histo_grey_value_quantile()

I32 histo_grey_value_quantile	(	U32	hist[256],
		U8 *	val,
		I32	quantilePercent
	)

The function calculates the grey value which is equal to or smaller than the given grey value quantile of the histogram, but always at least 0.

Parameters

val	grey value, where at minimum (100% - quantilePercent) pixels of the area have a grey value which is bigger than this one.

Memory Consumption: None.

See also: image_grey_value_quantile(), areaCentered_grey_value_quantile().; mean(), variance(), histo().

◆ histo_U32_value_quantile()

I32 histo_U32_value_quantile	(	U32 *	hist,
		I32	entries,
		U32 *	pos,
		I32	quantilePercent
	)

Return values

1	if grey value is 0, but quantile is lower.

◆ projh()

void projh	(	image *	a,
		U32	x[]
	)

The function calculates the horizontal projection of an image variable. Here, projection means the sum of all pixels in one line. The result is stored in the array result[dy]. result[0] is the projection of the first line, result[1] the projection of the second line, etc.

Memory Consumption: None.

See also: also projv().

◆ projv()

void projv	(	image *	a,
		U32	x[]
	)

The function calculates the vertical projection of an image variable. Here, projection means the sum of all pixels in one column. The result is stored in the array result[dx]. result[0] is the projection of the first column, result[1] is the projection of the second column, etc.

Memory Consumption: None.

See also: projh().

◆ area_grey_value_quantile()

I32 area_grey_value_quantile	(	image *	imgIn,
		U32 *	val,
		I32	quantilePercent,
		I32	x0,
		I32	y0,
		I32	dx,
		I32	dy,
		I32	ch
	)

The function calculates the grey value which is equal to or smaller than the given grey value quantile of the histogram, but always at least 0.

Parameters

imgIn	image keeping the area to be processed.
val	grey value, where at minimum (100% - quantilePercent) pixels of the area have a grey value which is bigger than this one.

Memory Consumption: None.

See also: image_grey_value_quantile(), areaCentered_grey_value_quantile().; mean(), variance(), histo().

Return values

-1	iff region is empty.
1	iff region is smaller than the requested region.
-2	iff Image Type is not IMAGE_GREY.
-3	iff selected channel is not (yet) supported.
1	if grey value is 0, but quantile is lower.

◆ NormalizedCorrelationI32()

I32 NormalizedCorrelationI32	(	image *	SearchX,
		image *	Pattern,
		float	MinContr,
		I32 *	Corr,
		float *	Contr,
		I32 *	PosX,
		I32 *	PosY
	)

This function calls the function NormalizedCorrelation(). It has been kept for compatibility reasons. In case of doubt we recommend using NormalizedCorrelation() directly.

Parameters

SearchX	The Image to be Searched In.
Pattern	The Reference Pattern.
MinContr	The Minimum Contrast Requirement, can speed up the Search.
Corr	Correlation Value Output, Set to NULL, if not Required.
Contr	Contrast Value Output, Set to NULL, if not Required.
PosX	x-Position Output, Set to NULL, if not Required.
PosY	y-Position Output, Set to NULL, if not Required.

Multiple Pixels to Values Mappings

Macros

Functions

Detailed Description

Macro Definition Documentation

◆ area_centered_grey_value_quantile

◆ image_grey_value_quantile

Function Documentation

◆ arx()

◆ arx2()

◆ mean()

◆ variance()

◆ contrast()

◆ contrastF()

◆ focus()

◆ SumImage()

◆ ProdImage()

◆ histo()

◆ histo_at_rlc()

◆ histo_grey_value_quantile()

◆ histo_U32_value_quantile()

◆ projh()

◆ projv()

◆ area_grey_value_quantile()

◆ NormalizedCorrelationI32()