OpenCV
4.0.1dev
Open Source Computer Vision

Functions  
void  cv::adaptiveThreshold (InputArray src, OutputArray dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C) 
Applies an adaptive threshold to an array. More...  
void  cv::blendLinear (InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst) 
void  cv::distanceTransform (InputArray src, OutputArray dst, OutputArray labels, int distanceType, int maskSize, int labelType=DIST_LABEL_CCOMP) 
Calculates the distance to the closest zero pixel for each pixel of the source image. More...  
void  cv::distanceTransform (InputArray src, OutputArray dst, int distanceType, int maskSize, int dstType=CV_32F) 
int  cv::floodFill (InputOutputArray image, Point seedPoint, Scalar newVal, Rect *rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4) 
int  cv::floodFill (InputOutputArray image, InputOutputArray mask, Point seedPoint, Scalar newVal, Rect *rect=0, Scalar loDiff=Scalar(), Scalar upDiff=Scalar(), int flags=4) 
Fills a connected component with the given color. More...  
void  cv::grabCut (InputArray img, InputOutputArray mask, Rect rect, InputOutputArray bgdModel, InputOutputArray fgdModel, int iterCount, int mode=GC_EVAL) 
Runs the GrabCut algorithm. More...  
void  cv::integral (InputArray src, OutputArray sum, int sdepth=1) 
void  cv::integral (InputArray src, OutputArray sum, OutputArray sqsum, int sdepth=1, int sqdepth=1) 
void  cv::integral (InputArray src, OutputArray sum, OutputArray sqsum, OutputArray tilted, int sdepth=1, int sqdepth=1) 
Calculates the integral of an image. More...  
double  cv::threshold (InputArray src, OutputArray dst, double thresh, double maxval, int type) 
Applies a fixedlevel threshold to each array element. More...  
void  cv::watershed (InputArray image, InputOutputArray markers) 
Performs a markerbased image segmentation using the watershed algorithm. More...  
adaptive threshold algorithm
Enumerator  

ADAPTIVE_THRESH_MEAN_C Python: cv.ADAPTIVE_THRESH_MEAN_C  the threshold value \(T(x,y)\) is a mean of the \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood of \((x, y)\) minus C 
ADAPTIVE_THRESH_GAUSSIAN_C Python: cv.ADAPTIVE_THRESH_GAUSSIAN_C  the threshold value \(T(x, y)\) is a weighted sum (crosscorrelation with a Gaussian window) of the \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood of \((x, y)\) minus C . The default sigma (standard deviation) is used for the specified blockSize . See getGaussianKernel 
distanceTransform algorithm flags
enum cv::DistanceTypes 
Distance types for Distance Transform and Mestimators
enum cv::FloodFillFlags 
floodfill algorithm flags
enum cv::GrabCutClasses 
enum cv::GrabCutModes 
GrabCut algorithm flags.
enum cv::ThresholdTypes 
type of the threshold operation
void cv::adaptiveThreshold  (  InputArray  src, 
OutputArray  dst,  
double  maxValue,  
int  adaptiveMethod,  
int  thresholdType,  
int  blockSize,  
double  C  
) 
Python:  

dst  =  cv.adaptiveThreshold(  src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]  ) 
Applies an adaptive threshold to an array.
The function transforms a grayscale image to a binary image according to the formulae:
\[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\]
\[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\]
where \(T(x,y)\) is a threshold calculated individually for each pixel (see adaptiveMethod parameter).The function can process the image inplace.
src  Source 8bit singlechannel image. 
dst  Destination image of the same size and the same type as src. 
maxValue  Nonzero value assigned to the pixels for which the condition is satisfied 
adaptiveMethod  Adaptive thresholding algorithm to use, see AdaptiveThresholdTypes. The BORDER_REPLICATE  BORDER_ISOLATED is used to process boundaries. 
thresholdType  Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV, see 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. 
void cv::blendLinear  (  InputArray  src1, 
InputArray  src2,  
InputArray  weights1,  
InputArray  weights2,  
OutputArray  dst  
) 
Performs linear blending of two images:
\[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \]
src1  It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer. 
src2  It has the same type and size as src1. 
weights1  It has a type of CV_32FC1 and the same size with src1. 
weights2  It has a type of CV_32FC1 and the same size with src1. 
dst  It is created if it does not have the same size and type with src1. 
void cv::distanceTransform  (  InputArray  src, 
OutputArray  dst,  
OutputArray  labels,  
int  distanceType,  
int  maskSize,  
int  labelType = DIST_LABEL_CCOMP 

) 
Python:  

dst  =  cv.distanceTransform(  src, distanceType, maskSize[, dst[, dstType]]  )  
dst, labels  =  cv.distanceTransformWithLabels(  src, distanceType, maskSize[, dst[, labels[, labelType]]]  ) 
Calculates the distance to the closest zero pixel for each pixel of the source image.
The function cv::distanceTransform calculates 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 [58] . This algorithm is parallelized with the TBB library.
In other cases, the algorithm [20] 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 \(5\times 5\) 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 DIST_C and DIST_L1 types, the distance is calculated precisely, whereas for DIST_L2 (Euclidean distance) the distance can be calculated only with a relative error (a \(5\times 5\) mask gives more accurate results). For a
,b
, and c
, OpenCV uses the values suggested in the original paper:
a = 1, b = 2
3 x 3
: a=0.955, b=1.3693
5 x 5
: a=1, b=1.4, c=2.1969
a = 1, b = 1
Typically, for a fast, coarse distance estimation DIST_L2, a \(3\times 3\) mask is used. For a more accurate distance estimation DIST_L2, a \(5\times 5\) 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 \((x, y)\) 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.
src  8bit, singlechannel (binary) source image. 
dst  Output image with calculated distances. It is a 8bit or 32bit floatingpoint, singlechannel 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 DistanceTypes 
maskSize  Size of the distance transform mask, see 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 \(3\times 3\) mask gives the same result as \(5\times 5\) or any larger aperture. 
labelType  Type of the label array to build, see DistanceTransformLabelTypes. 
void cv::distanceTransform  (  InputArray  src, 
OutputArray  dst,  
int  distanceType,  
int  maskSize,  
int  dstType = CV_32F 

) 
Python:  

dst  =  cv.distanceTransform(  src, distanceType, maskSize[, dst[, dstType]]  )  
dst, labels  =  cv.distanceTransformWithLabels(  src, distanceType, maskSize[, dst[, labels[, labelType]]]  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
src  8bit, singlechannel (binary) source image. 
dst  Output image with calculated distances. It is a 8bit or 32bit floatingpoint, singlechannel image of the same size as src . 
distanceType  Type of distance, see DistanceTypes 
maskSize  Size of the distance transform mask, see DistanceTransformMasks. In case of the DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times 5\) 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. 
int cv::floodFill  (  InputOutputArray  image, 
Point  seedPoint,  
Scalar  newVal,  
Rect *  rect = 0 , 

Scalar  loDiff = Scalar() , 

Scalar  upDiff = Scalar() , 

int  flags = 4 

) 
Python:  

retval, image, mask, rect  =  cv.floodFill(  image, mask, seedPoint, newVal[, loDiff[, upDiff[, flags]]]  ) 
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
int cv::floodFill  (  InputOutputArray  image, 
InputOutputArray  mask,  
Point  seedPoint,  
Scalar  newVal,  
Rect *  rect = 0 , 

Scalar  loDiff = Scalar() , 

Scalar  upDiff = Scalar() , 

int  flags = 4 

) 
Python:  

retval, image, mask, rect  =  cv.floodFill(  image, mask, seedPoint, newVal[, loDiff[, upDiff[, flags]]]  ) 
Fills a connected component with the given color.
The function cv::floodFill fills 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 \((x,y)\) is considered to belong to the repainted domain if:
\[\texttt{src} (x',y') \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\]
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y) \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\]
\[\texttt{src} (x',y')_r \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\]
\[\texttt{src} (x',y')_g \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\]
and\[\texttt{src} (x',y')_b \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\]
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\]
\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\]
and\[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\]
where \(src(x',y')\) 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:
Use these functions to either mark a connected component with the specified color inplace, or build a mask and then extract the contour, or copy the region to another image, and so on.
image  Input/output 1 or 3channel, 8bit, or floatingpoint 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 singlechannel 8bit 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. Floodfilling cannot go across nonzero 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. Additionally, the function fills the border of the mask with ones to simplify internal processing. 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 (816) 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 bitwise or (), see FloodFillFlags. 
void cv::grabCut  (  InputArray  img, 
InputOutputArray  mask,  
Rect  rect,  
InputOutputArray  bgdModel,  
InputOutputArray  fgdModel,  
int  iterCount,  
int  mode = GC_EVAL 

) 
Python:  

mask, bgdModel, fgdModel  =  cv.grabCut(  img, mask, rect, bgdModel, fgdModel, iterCount[, mode]  ) 
Runs the GrabCut algorithm.
The function implements the GrabCut image segmentation algorithm.
img  Input 8bit 3channel image. 
mask  Input/output 8bit singlechannel mask. The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT. Its elements may have one of the 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 GrabCutModes 
void cv::integral  (  InputArray  src, 
OutputArray  sum,  
int  sdepth = 1 

) 
Python:  

sum  =  cv.integral(  src[, sum[, sdepth]]  )  
sum, sqsum  =  cv.integral2(  src[, sum[, sqsum[, sdepth[, sqdepth]]]]  )  
sum, sqsum, tilted  =  cv.integral3(  src[, sum[, sqsum[, tilted[, sdepth[, sqdepth]]]]]  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
void cv::integral  (  InputArray  src, 
OutputArray  sum,  
OutputArray  sqsum,  
int  sdepth = 1 , 

int  sqdepth = 1 

) 
Python:  

sum  =  cv.integral(  src[, sum[, sdepth]]  )  
sum, sqsum  =  cv.integral2(  src[, sum[, sqsum[, sdepth[, sqdepth]]]]  )  
sum, sqsum, tilted  =  cv.integral3(  src[, sum[, sqsum[, tilted[, sdepth[, sqdepth]]]]]  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
void cv::integral  (  InputArray  src, 
OutputArray  sum,  
OutputArray  sqsum,  
OutputArray  tilted,  
int  sdepth = 1 , 

int  sqdepth = 1 

) 
Python:  

sum  =  cv.integral(  src[, sum[, sdepth]]  )  
sum, sqsum  =  cv.integral2(  src[, sum[, sqsum[, sdepth[, sqdepth]]]]  )  
sum, sqsum, tilted  =  cv.integral3(  src[, sum[, sqsum[, tilted[, sdepth[, sqdepth]]]]]  ) 
Calculates the integral of an image.
The function calculates one or more integral images for the source image as follows:
\[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\]
\[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\]
\[\texttt{tilted} (X,Y) = \sum _{y<Y,abs(xX+1) \leq Yy1} \texttt{image} (x,y)\]
Using these integral images, you can calculate sum, mean, and standard deviation over a specific upright or rotated rectangular region of the image in a constant time, for example:
\[\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2) \texttt{sum} (x_1,y_2) \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\]
It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multichannel 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 .
src  input image as \(W \times H\), 8bit or floatingpoint (32f or 64f). 
sum  integral image as \((W+1)\times (H+1)\) , 32bit integer or floatingpoint (32f or 64f). 
sqsum  integral image for squared pixel values; it is \((W+1)\times (H+1)\), doubleprecision floatingpoint (64f) array. 
tilted  integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) 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. 
double cv::threshold  (  InputArray  src, 
OutputArray  dst,  
double  thresh,  
double  maxval,  
int  type  
) 
Python:  

retval, dst  =  cv.threshold(  src, thresh, maxval, type[, dst]  ) 
Applies a fixedlevel threshold to each array element.
The function applies fixedlevel thresholding to a multiplechannel array. The function is typically used to get a bilevel (binary) image out of a grayscale image ( 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 THRESH_OTSU or 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.
src  input array (multiplechannel, 8bit or 32bit floating point). 
dst  output array of the same size and type and the same number of channels as src. 
thresh  threshold value. 
maxval  maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding types. 
type  thresholding type (see ThresholdTypes). 
void cv::watershed  (  InputArray  image, 
InputOutputArray  markers  
) 
Python:  

markers  =  cv.watershed(  image, markers  ) 
Performs a markerbased image segmentation using the watershed algorithm.
The function implements one of the variants of watershed, nonparametric markerbased segmentation algorithm, described in [141] .
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.
image  Input 8bit 3channel image. 
markers  Input/output 32bit singlechannel image (map) of markers. It should have the same size as image . 