Applies an adaptive threshold to an array.
Parameters: 


The function transforms a grayscale image to a binary image according to the formulas:
CV_THRESH_BINARY
CV_THRESH_BINARY_INV
where is a threshold calculated individually for each pixel.
For the method CV_ADAPTIVE_THRESH_MEAN_C it is the mean of a pixel neighborhood, minus param1 .
For the method CV_ADAPTIVE_THRESH_GAUSSIAN_C it is the weighted sum (gaussian) of a pixel neighborhood, minus param1 .
Converts an image from one color space to another.
Parameters: 


The function converts the input image from one color space to another. The function ignores the colorModel and channelSeq fields of the IplImage header, so the source image color space should be specified correctly (including order of the channels in the case of RGB space. For example, BGR means 24bit format with layout whereas RGB means 24format with layout).
The conventional range for R,G,B channel values is:
Of course, in the case of linear transformations the range can be specific, but in order to get correct results in the case of nonlinear transformations, the input image should be scaled.
The function can do the following transformations:
Transformations within RGB space like adding/removing the alpha channel, reversing the channel order, conversion to/from 16bit RGB color (R5:G6:B5 or R5:G5:B5), as well as conversion to/from grayscale using:
and
The conversion from a RGB image to gray is done with:
cvCvtColor(src ,bwsrc, CV_RGB2GRAY)
RGB CIE XYZ.Rec 709 with D65 white point ( CV_BGR2XYZ, CV_RGB2XYZ, CV_XYZ2BGR, CV_XYZ2RGB ):
, and cover the whole value range (in the case of floatingpoint images may exceed 1).
RGB YCrCb JPEG (a.k.a. YCC) ( CV_BGR2YCrCb, CV_RGB2YCrCb, CV_YCrCb2BGR, CV_YCrCb2RGB )
where
Y, Cr and Cb cover the whole value range.
RGB HSV ( CV_BGR2HSV, CV_RGB2HSV, CV_HSV2BGR, CV_HSV2RGB ) in the case of 8bit and 16bit images R, G and B are converted to floatingpoint format and scaled to fit the 0 to 1 range
if then On output , , .
The values are then converted to the destination data type:
8bit images
16bit images (currently not supported)
H, S, V are left as is
RGB HLS ( CV_BGR2HLS, CV_RGB2HLS, CV_HLS2BGR, CV_HLS2RGB ). in the case of 8bit and 16bit images R, G and B are converted to floatingpoint format and scaled to fit the 0 to 1 range.
if then On output , , .
The values are then converted to the destination data type:
8bit images
16bit images (currently not supported)
H, S, V are left as is
RGB CIE L*a*b* ( CV_BGR2Lab, CV_RGB2Lab, CV_Lab2BGR, CV_Lab2RGB ) in the case of 8bit and 16bit images R, G and B are converted to floatingpoint format and scaled to fit the 0 to 1 range
where
and
On output , , The values are then converted to the destination data type:
8bit images
currently not supported
L, a, b are left as is
RGB CIE L*u*v* ( CV_BGR2Luv, CV_RGB2Luv, CV_Luv2BGR, CV_Luv2RGB ) in the case of 8bit and 16bit images R, G and B are converted to floatingpoint format and scaled to fit 0 to 1 range
On output , , .
The values are then converted to the destination data type:
8bit images
currently not supported
L, u, v are left as is
The above formulas for converting RGB to/from various color spaces have been taken from multiple sources on Web, primarily from the Ford98 at the Charles Poynton site.
Bayer RGB ( CV_BayerBG2BGR, CV_BayerGB2BGR, CV_BayerRG2BGR, CV_BayerGR2BGR, CV_BayerBG2RGB, CV_BayerGB2RGB, CV_BayerRG2RGB, CV_BayerGR2RGB ) The Bayer pattern is widely used in CCD and CMOS cameras. It allows one to get color pictures from a single plane where R,G and B pixels (sensors of a particular component) are interleaved like this:
The output RGB components of a pixel are interpolated from 1, 2 or 4 neighbors of the pixel having the same color. There are several modifications of the above pattern that can be achieved by shifting the pattern one pixel left and/or one pixel up. The two letters and in the conversion constants CV_Bayer 2BGR and CV_Bayer 2RGB indicate the particular pattern type  these are components from the second row, second and third columns, respectively. For example, the above pattern has very popular “BG” type.
Calculates the distance to the closest zero pixel for all nonzero pixels of the source image.
Parameters: 


The function calculates the approximated distance from every binary image pixel to the nearest zero pixel. For zero pixels the function sets the zero distance, for others it finds the shortest path 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. Because the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (that is denoted as a ), all the diagonal shifts must have the same cost (denoted b ), and all knight’s moves must have the same cost (denoted c ). For CV_DIST_C and CV_DIST_L1 types the distance is calculated precisely, whereas for CV_DIST_L2 (Euclidian distance) the distance can be calculated only with some relative error (a mask gives more accurate results), OpenCV uses the values suggested in [Borgefors86] :
CV_DIST_C  a = 1, b = 1  

CV_DIST_L1  a = 1, b = 2  
CV_DIST_L2  a=0.955, b=1.3693  
CV_DIST_L2  a=1, b=1.4, c=2.1969 
And below are samples of the distance field (black (0) pixel is in the middle of white square) in the case of a userdefined distance:
Userdefined mask (a=1, b=1.5)
4.5  4  3.5  3  3.5  4  4.5 

4  3  2.5  2  2.5  3  4 
3.5  2.5  1.5  1  1.5  2.5  3.5 
3  2  1  1  2  3  
3.5  2.5  1.5  1  1.5  2.5  3.5 
4  3  2.5  2  2.5  3  4 
4.5  4  3.5  3  3.5  4  4.5 
Userdefined mask (a=1, b=1.5, c=2)
4.5  3.5  3  3  3  3.5  4.5 

3.5  3  2  2  2  3  3.5 
3  2  1.5  1  1.5  2  3 
3  2  1  1  2  3  
3  2  1.5  1  1.5  2  3 
3.5  3  2  2  2  3  3.5 
4  3.5  3  3  3  3.5  4 
Typically, for a fast, coarse distance estimation CV_DIST_L2 , a mask is used, and for a more accurate distance estimation CV_DIST_L2 , a mask is used.
When the output parameter labels is not NULL , for every nonzero pixel the function also finds the nearest connected component consisting of zero pixels. The connected components themselves are found as contours in the beginning of the function.
In this mode the processing time is still O(N), where N is the number of pixels. Thus, the function provides a very fast way to compute approximate Voronoi diagram for the binary image.
Connected component, represented as a tuple (area, value, rect), where area is the area of the component as a float, value is the average color as a CvScalar , and rect is the ROI of the component, as a CvRect .
Fills a connected component with the given color.
Parameters: 


The function fills a connected component starting from the seed point with the specified color. The connectivity is determined by the closeness of pixel values. The pixel at is considered to belong to the repainted domain if:
grayscale image, floating range
grayscale image, fixed range
color image, floating range
color image, fixed range
where is the value of one of pixel neighbors. That is, to be added to the connected component, a pixel’s color/brightness should be close enough to the:
Inpaints the selected region in the image.
Parameters: 


The function reconstructs the selected image area from the pixel near the area boundary. The function may be used to remove dust and scratches from a scanned photo, or to remove undesirable objects from still images or video.
Calculates the integral of an image.
Parameters: 


The function calculates one or more integral images for the source image as following:
Using these integral images, one may calculate sum, mean and standard deviation over a specific upright 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 variable window size, for example. In the case of multichannel images, sums for each channel are accumulated independently.
Does meanshift image segmentation
Parameters: 


The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered “posterized” image with color gradients and finegrain texture flattened. At every pixel of the input image (or downsized input image, see below) the function executes meanshift iterations, that is, the pixel neighborhood in the joint spacecolor hyperspace is considered:
where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y) , respectively (though, the algorithm does not depend on the color space used, so any 3component color space can be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector (R',G',B') are found and they act as the neighborhood center on the next iteration:
After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration):
Then , the gaussian pyramid of levels is built, and the above procedure is run on the smallest layer. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ much ( ) from the lowerresolution layer, that is, the boundaries of the color regions are clarified. Note, that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when ).
Implements image segmentation by pyramids.
Parameters: 


The function implements image segmentation by pyramids. The pyramid builds up to the level level . The links between any pixel a on level i and its candidate father pixel b on the adjacent level are established if . After the connected components are defined, they are joined into several clusters. Any two segments A and B belong to the same cluster, if . If the input image has only one channel, then . If the input image has three channels (red, green and blue), then
There may be more than one connected component per a cluster. The images src and dst should be 8bit singlechannel or 3channel images or equal size.
Applies a fixedlevel threshold to array elements.
Parameters: 


The function applies fixedlevel thresholding to a singlechannel array. The function is typically used to get a bilevel (binary) image out of a grayscale image ( CmpS could be also used for this purpose) or for removing a noise, i.e. filtering out pixels with too small or too large values. There are several types of thresholding that the function supports that are determined by thresholdType :
CV_THRESH_BINARY
CV_THRESH_BINARY_INV
CV_THRESH_TRUNC
CV_THRESH_TOZERO
CV_THRESH_TOZERO_INV
Also, the special value CV_THRESH_OTSU may be combined with one of the above values. In this case the function determines the optimal threshold value using Otsu’s algorithm and uses it instead of the specified thresh . The function returns the computed threshold value. Currently, Otsu’s method is implemented only for 8bit images.