Miscellaneous Image Transformations
[Image processing]

adaptive threshold algorithm see cv::adaptiveThreshold

Enumerator:

ADAPTIVE_THRESH_MEAN_C	the threshold value is a mean of the neighborhood of minus C
ADAPTIVE_THRESH_GAUSSIAN_C	the threshold value is a weighted sum (cross-correlation with a Gaussian window) of the neighborhood of minus C . The default sigma (standard deviation) is used for the specified blockSize . See cv::getGaussianKernel

Definition at line 323 of file imgproc.hpp.

the color conversion code

See also:

imgproc_color_conversions

Enumerator:

COLOR_BGR2BGRA	add alpha channel to RGB or BGR image
COLOR_BGRA2BGR	remove alpha channel from RGB or BGR image
COLOR_BGR2RGBA	convert between RGB and BGR color spaces (with or without alpha channel)
COLOR_BGR2GRAY	convert between RGB/BGR and grayscale, color conversions
COLOR_BGR2BGR565	convert between RGB/BGR and BGR565 (16-bit images)
COLOR_GRAY2BGR565	convert between grayscale to BGR565 (16-bit images)
COLOR_BGR2BGR555	convert between RGB/BGR and BGR555 (16-bit images)
COLOR_GRAY2BGR555	convert between grayscale and BGR555 (16-bit images)
COLOR_BGR2XYZ	convert RGB/BGR to CIE XYZ, color conversions
COLOR_BGR2YCrCb	convert RGB/BGR to luma-chroma (aka YCC), color conversions
COLOR_BGR2HSV	convert RGB/BGR to HSV (hue saturation value), color conversions
COLOR_BGR2Lab	convert RGB/BGR to CIE Lab, color conversions
COLOR_BGR2Luv	convert RGB/BGR to CIE Luv, color conversions
COLOR_BGR2HLS	convert RGB/BGR to HLS (hue lightness saturation), color conversions
COLOR_HSV2BGR	backward conversions to RGB/BGR
COLOR_BGR2YUV	convert between RGB/BGR and YUV
COLOR_YUV2RGB_NV12	YUV 4:2:0 family to RGB.
COLOR_YUV2RGB_UYVY	YUV 4:2:2 family to RGB.
COLOR_RGBA2mRGBA	alpha premultiplication
COLOR_RGB2YUV_I420	RGB to YUV 4:2:0 family.
COLOR_BayerBG2BGR	Demosaicing.
COLOR_BayerBG2BGR_VNG	Demosaicing using Variable Number of Gradients.
COLOR_BayerBG2BGR_EA	Edge-Aware Demosaicing.

Definition at line 494 of file imgproc.hpp.

distanceTransform algorithm flags

Enumerator:

DIST_LABEL_CCOMP	each connected component of zeros in src (as well as all the non-zero pixels closest to the connected component) will be assigned the same label
DIST_LABEL_PIXEL	each zero pixel (and all the non-zero pixels closest to it) gets its own label.

Definition at line 362 of file imgproc.hpp.

Mask size for distance transform.

Enumerator:

DIST_MASK_3	mask=3
DIST_MASK_5	mask=5

Definition at line 302 of file imgproc.hpp.

Distance types for Distance Transform and M-estimators.

See also:

cv::distanceTransform, cv::fitLine

Enumerator:

DIST_USER	User defined distance.
DIST_L1	distance = |x1-x2| + |y1-y2|
DIST_L2	the simple euclidean distance
DIST_C	distance = max(|x1-x2|,|y1-y2|)
DIST_L12	L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1))
DIST_FAIR	distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998
DIST_WELSCH	distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846
DIST_HUBER	distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345

Definition at line 290 of file imgproc.hpp.

floodfill algorithm flags

Enumerator:

FLOODFILL_FIXED_RANGE	If set, the difference between the current pixel and seed pixel is considered. Otherwise, the difference between neighbor pixels is considered (that is, the range is floating).
FLOODFILL_MASK_ONLY	If set, the function does not change the image ( newVal is ignored), and only fills the mask with the value specified in bits 8-16 of flags as described above. This option only make sense in function variants that have the mask parameter.

Definition at line 371 of file imgproc.hpp.

class of the pixel in GrabCut algorithm

Enumerator:

GC_BGD	an obvious background pixels
GC_FGD	an obvious foreground (object) pixel
GC_PR_BGD	a possible background pixel
GC_PR_FGD	a possible foreground pixel

Definition at line 341 of file imgproc.hpp.

GrabCut algorithm flags.

Enumerator:

GC_INIT_WITH_RECT	The function initializes the state and the mask using the provided rectangle. After that it runs iterCount iterations of the algorithm.
GC_INIT_WITH_MASK	The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are automatically initialized with GC_BGD .
GC_EVAL	The value means that the algorithm should just resume.

Definition at line 349 of file imgproc.hpp.

type of the threshold operation 

Enumerator:

THRESH_BINARY
THRESH_BINARY_INV
THRESH_TRUNC
THRESH_TOZERO
THRESH_TOZERO_INV
THRESH_OTSU	flag, use Otsu algorithm to choose the optimal threshold value
THRESH_TRIANGLE	flag, use Triangle algorithm to choose the optimal threshold value

Definition at line 310 of file imgproc.hpp.

cv::undistort mode

Definition at line 335 of file imgproc.hpp.

Applies an adaptive threshold to an array.

The function transforms a grayscale image to a binary image according to the formulae:

• **THRESH_BINARY**

  

• **THRESH_BINARY_INV**

  

  where is a threshold calculated individually for each pixel (see adaptiveMethod parameter).

The function can process the image in-place.

Parameters:

src	Source 8-bit single-channel image.
dst	Destination image of the same size and the same type as src.
maxValue	Non-zero value assigned to the pixels for which the condition is satisfied
adaptiveMethod	Adaptive thresholding algorithm to use, see cv::AdaptiveThresholdTypes
thresholdType	Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV, see cv::ThresholdTypes.
blockSize	Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
C	Constant subtracted from the mean or weighted mean (see the details below). Normally, it is positive but may be zero or negative as well.

See also:

threshold, blur, GaussianBlur

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Calculates the distance to the closest zero pixel for each pixel of the source image.

The functions distanceTransform calculate the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.

When maskSize == DIST_MASK_PRECISE and distanceType == DIST_L2 , the function runs the algorithm described in Felzenszwalb04 . This algorithm is parallelized with the TBB library.

In other cases, the algorithm Borgefors86 is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight's move (the latest is available for a mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the same cost (denoted as `c`). For the cv::DIST_C and cv::DIST_L1 types, the distance is calculated precisely, whereas for cv::DIST_L2 (Euclidean distance) the distance can be calculated only with a relative error (a mask gives more accurate results). For `a`,`b`, and `c`, OpenCV uses the values suggested in the original paper:

• DIST_L1: `a = 1, b = 2`
• DIST_L2:
  • `3 x 3`: `a=0.955, b=1.3693`
  • `5 x 5`: `a=1, b=1.4, c=2.1969`
• DIST_C: `a = 1, b = 1`

Typically, for a fast, coarse distance estimation DIST_L2, a mask is used. For a more accurate distance estimation DIST_L2, a mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.

This variant of the function does not only compute the minimum distance for each pixel but also identifies the nearest connected component consisting of zero pixels (labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the component/pixel is stored in `labels(x, y)`. When labelType==DIST_LABEL_CCOMP, the function automatically finds connected components of zero pixels in the input image and marks them with distinct labels. When labelType==DIST_LABEL_CCOMP, the function scans through the input image and marks all the zero pixels with distinct labels.

In this mode, the complexity is still linear. That is, the function provides a very fast way to compute the Voronoi diagram for a binary image. Currently, the second variant can use only the approximate distance transform algorithm, i.e. maskSize=DIST_MASK_PRECISE is not supported yet.

Parameters:

src	8-bit, single-channel (binary) source image.
dst	Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src.
labels	Output 2D array of labels (the discrete Voronoi diagram). It has the type CV_32SC1 and the same size as src.
distanceType	Type of distance, see cv::DistanceTypes
maskSize	Size of the distance transform mask, see cv::DistanceTransformMasks. DIST_MASK_PRECISE is not supported by this variant. In case of the DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a mask gives the same result as or any larger aperture.
labelType	Type of the label array to build, see cv::DistanceTransformLabelTypes.

Calculates the integral of an image.

The functions calculate one or more integral images for the source image as follows:

Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:

It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently.

As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted .


Parameters:

src	input image as , 8-bit or floating-point (32f or 64f).
sum	integral image as , 32-bit integer or floating-point (32f or 64f).
sqsum	integral image for squared pixel values; it is , double-precision floating-point (64f) array.
tilted	integral for the image rotated by 45 degrees; it is array with the same data type as sum.
sdepth	desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F.
sqdepth	desired depth of the integral image of squared pixel values, CV_32F or CV_64F.

Converts an image from one color space to another.

The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.

The conventional ranges for R, G, and B channel values are:

• 0 to 255 for CV_8U images
• 0 to 65535 for CV_16U images
• 0 to 1 for CV_32F images

In case of linear transformations, the range does not matter. But in case of a non-linear transformation, an input RGB image should be normalized to the proper value range to get the correct results, for example, for RGB L\*u\*v\* transformation. For example, if you have a 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will have the 0..255 value range instead of 0..1 assumed by the function. So, before calling cvtColor , you need first to scale the image down:

    img *= 1./255;
    cvtColor(img, img, COLOR_BGR2Luv);

If you use cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.

If conversion adds the alpha channel, its value will set to the maximum of corresponding channel range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.

Parameters:

src	input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision floating-point.
dst	output image of the same size and depth as src.
code	color space conversion code (see cv::ColorConversionCodes).
dstCn	number of channels in the destination image; if the parameter is 0, the number of the channels is derived automatically from src and code.

See also:

imgproc_color_conversions

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters:

src	8-bit, single-channel (binary) source image.
dst	Output image with calculated distances. It is a 8-bit or 32-bit floating-point, single-channel image of the same size as src .
distanceType	Type of distance, see cv::DistanceTypes
maskSize	Size of the distance transform mask, see cv::DistanceTransformMasks. In case of the DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a mask gives the same result as or any larger aperture.
dstType	Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for the first variant of the function and distanceType == DIST_L1.

Fills a connected component with the given color.

The functions floodFill fill a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at is considered to belong to the repainted domain if:

• in case of a grayscale image and floating range

  

• in case of a grayscale image and fixed range

  

• in case of a color image and floating range

  

  

  and

  

• in case of a color image and fixed range

  

  

  and

  

where is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

• Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
• Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Parameters:

image	Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask	Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller than image. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags as described below. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap.
seedPoint	Starting point.
newVal	New value of the repainted domain pixels.
loDiff	Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff	Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
rect	Optional output parameter set by the function to the minimum bounding rectangle of the repainted domain.
flags	Operation flags. The first 8 bits contain a connectivity value. The default value of 4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see cv::FloodFillFlags.

Note:

Since the mask is larger than the filled image, a pixel in image corresponds to the pixel in the mask .

See also:

findContours

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. variant without `mask` parameter.

Runs the GrabCut algorithm.

The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).

Parameters:

img	Input 8-bit 3-channel image.
mask	Input/output 8-bit single-channel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the cv::GrabCutClasses.
rect	ROI containing a segmented object. The pixels outside of the ROI are marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
bgdModel	Temporary array for the background model. Do not modify it while you are processing the same image.
fgdModel	Temporary arrays for the foreground model. Do not modify it while you are processing the same image.
iterCount	Number of iterations the algorithm should make before returning the result. Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .
mode	Operation mode that could be one of the cv::GrabCutModes

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Applies a fixed-level threshold to each array element.

The function applies fixed-level thresholding to a single-channel array. The function is typically used to get a bi-level (binary) image out of a grayscale image ( cv::compare could be also used for this purpose) or for removing a noise, that is, filtering out pixels with too small or too large values. There are several types of thresholding supported by the function. They are determined by type parameter.

Also, the special values cv::THRESH_OTSU or cv::THRESH_TRIANGLE may be combined with one of the above values. In these cases, the function determines the optimal threshold value using the Otsu's or Triangle algorithm and uses it instead of the specified thresh . The function returns the computed threshold value. Currently, the Otsu's and Triangle methods are implemented only for 8-bit images.

Parameters:

src	input array (single-channel, 8-bit or 32-bit floating point).
dst	output array of the same size and type as src.
thresh	threshold value.
maxval	maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
type	thresholding type (see the cv::ThresholdTypes).

See also:

adaptiveThreshold, findContours, compare, min, max

Performs a marker-based image segmentation using the watershed algorithm.

The function implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in Meyer92 .

Before passing the image to the function, you have to roughly outline the desired regions in the image markers with positive (>0) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using findContours and drawContours (see the watershed.cpp demo). The markers are "seeds" of the future image regions. All the other pixels in markers , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the regions.

Note:

Any two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, they can touch each other in the initial marker image passed to the function.

Parameters:

image	Input 8-bit 3-channel image.
markers	Input/output 32-bit single-channel image (map) of markers. It should have the same size as image .

See also:

findContours