OpenCV
3.1.0
Open Source Computer Vision

Enumerations  
enum  cv::HistCompMethods { cv::HISTCMP_CORREL = 0, cv::HISTCMP_CHISQR = 1, cv::HISTCMP_INTERSECT = 2, cv::HISTCMP_BHATTACHARYYA = 3, cv::HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA, cv::HISTCMP_CHISQR_ALT = 4, cv::HISTCMP_KL_DIV = 5 } 
Functions  
void  cv::calcBackProject (const Mat *images, int nimages, const int *channels, InputArray hist, OutputArray backProject, const float **ranges, double scale=1, bool uniform=true) 
Calculates the back projection of a histogram. More...  
void  cv::calcBackProject (const Mat *images, int nimages, const int *channels, const SparseMat &hist, OutputArray backProject, const float **ranges, double scale=1, bool uniform=true) 
void  cv::calcBackProject (InputArrayOfArrays images, const std::vector< int > &channels, InputArray hist, OutputArray dst, const std::vector< float > &ranges, double scale) 
void  cv::calcHist (const Mat *images, int nimages, const int *channels, InputArray mask, OutputArray hist, int dims, const int *histSize, const float **ranges, bool uniform=true, bool accumulate=false) 
Calculates a histogram of a set of arrays. More...  
void  cv::calcHist (const Mat *images, int nimages, const int *channels, InputArray mask, SparseMat &hist, int dims, const int *histSize, const float **ranges, bool uniform=true, bool accumulate=false) 
void  cv::calcHist (InputArrayOfArrays images, const std::vector< int > &channels, InputArray mask, OutputArray hist, const std::vector< int > &histSize, const std::vector< float > &ranges, bool accumulate=false) 
double  cv::compareHist (InputArray H1, InputArray H2, int method) 
Compares two histograms. More...  
double  cv::compareHist (const SparseMat &H1, const SparseMat &H2, int method) 
float  cv::EMD (InputArray signature1, InputArray signature2, int distType, InputArray cost=noArray(), float *lowerBound=0, OutputArray flow=noArray()) 
Computes the "minimal work" distance between two weighted point configurations. More...  
void  cv::equalizeHist (InputArray src, OutputArray dst) 
Equalizes the histogram of a grayscale image. More...  
enum cv::HistCompMethods 
Histogram comparison methods
Enumerator  

HISTCMP_CORREL 
Correlation \[d(H_1,H_2) = \frac{\sum_I (H_1(I)  \bar{H_1}) (H_2(I)  \bar{H_2})}{\sqrt{\sum_I(H_1(I)  \bar{H_1})^2 \sum_I(H_2(I)  \bar{H_2})^2}}\] where \[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\] and \(N\) is a total number of histogram bins. 
HISTCMP_CHISQR 
ChiSquare \[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)H_2(I)\right)^2}{H_1(I)}\] 
HISTCMP_INTERSECT 
Intersection \[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\] 
HISTCMP_BHATTACHARYYA 
Bhattacharyya distance (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.) \[d(H_1,H_2) = \sqrt{1  \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\] 
HISTCMP_HELLINGER 
Synonym for HISTCMP_BHATTACHARYYA. 
HISTCMP_CHISQR_ALT 
Alternative ChiSquare \[d(H_1,H_2) = 2 * \sum _I \frac{\left(H_1(I)H_2(I)\right)^2}{H_1(I)+H_2(I)}\] This alternative formula is regularly used for texture comparison. See e.g. [114] 
HISTCMP_KL_DIV 
KullbackLeibler divergence \[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\] 
void cv::calcBackProject  (  const Mat *  images, 
int  nimages,  
const int *  channels,  
InputArray  hist,  
OutputArray  backProject,  
const float **  ranges,  
double  scale = 1 , 

bool  uniform = true 

) 
Calculates the back projection of a histogram.
The functions calcBackProject calculate the back project of the histogram. That is, similarly to cv::calcHist , at each location (x, y) the function collects the values from the selected channels in the input images and finds the corresponding histogram bin. But instead of incrementing it, the function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of statistics, the function computes probability of each element value in respect with the empirical probability distribution represented by the histogram. See how, for example, you can find and track a brightcolored object in a scene:
This is an approximate algorithm of the CamShift color object tracker.
images  Source arrays. They all should have the same depth, CV_8U or CV_32F , and the same size. Each of them can have an arbitrary number of channels. 
nimages  Number of source images. 
channels  The list of channels used to compute the back projection. The number of channels must match the histogram dimensionality. The first array channels are numerated from 0 to images[0].channels()1 , the second array channels are counted from images[0].channels() to images[0].channels() + images[1].channels()1, and so on. 
hist  Input histogram that can be dense or sparse. 
backProject  Destination back projection array that is a singlechannel array of the same size and depth as images[0] . 
ranges  Array of arrays of the histogram bin boundaries in each dimension. See calcHist . 
scale  Optional scale factor for the output back projection. 
uniform  Flag indicating whether the histogram is uniform or not (see above). 
void cv::calcBackProject  (  const Mat *  images, 
int  nimages,  
const int *  channels,  
const SparseMat &  hist,  
OutputArray  backProject,  
const float **  ranges,  
double  scale = 1 , 

bool  uniform = true 

) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
void cv::calcBackProject  (  InputArrayOfArrays  images, 
const std::vector< int > &  channels,  
InputArray  hist,  
OutputArray  dst,  
const std::vector< float > &  ranges,  
double  scale  
) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
void cv::calcHist  (  const Mat *  images, 
int  nimages,  
const int *  channels,  
InputArray  mask,  
OutputArray  hist,  
int  dims,  
const int *  histSize,  
const float **  ranges,  
bool  uniform = true , 

bool  accumulate = false 

) 
Calculates a histogram of a set of arrays.
The functions calcHist calculate the histogram of one or more arrays. The elements of a tuple used to increment a histogram bin are taken from the corresponding input arrays at the same location. The sample below shows how to compute a 2D HueSaturation histogram for a color image. :
images  Source arrays. They all should have the same depth, CV_8U or CV_32F , and the same size. Each of them can have an arbitrary number of channels. 
nimages  Number of source images. 
channels  List of the dims channels used to compute the histogram. The first array channels are numerated from 0 to images[0].channels()1 , the second array channels are counted from images[0].channels() to images[0].channels() + images[1].channels()1, and so on. 
mask  Optional mask. If the matrix is not empty, it must be an 8bit array of the same size as images[i] . The nonzero mask elements mark the array elements counted in the histogram. 
hist  Output histogram, which is a dense or sparse dims dimensional array. 
dims  Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS (equal to 32 in the current OpenCV version). 
histSize  Array of histogram sizes in each dimension. 
ranges  Array of the dims arrays of the histogram bin boundaries in each dimension. When the histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower (inclusive) boundary \(L_0\) of the 0th histogram bin and the upper (exclusive) boundary \(U_{\texttt{histSize}[i]1}\) for the last histogram bin histSize[i]1 . That is, in case of a uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform ( uniform=false ), then each of ranges[i] contains histSize[i]+1 elements: \(L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}2}=L_{\texttt{histSize[i]}1}, U_{\texttt{histSize[i]}1}\) . The array elements, that are not between \(L_0\) and \(U_{\texttt{histSize[i]}1}\) , are not counted in the histogram. 
uniform  Flag indicating whether the histogram is uniform or not (see above). 
accumulate  Accumulation flag. If it is set, the histogram is not cleared in the beginning when it is allocated. This feature enables you to compute a single histogram from several sets of arrays, or to update the histogram in time. 
void cv::calcHist  (  const Mat *  images, 
int  nimages,  
const int *  channels,  
InputArray  mask,  
SparseMat &  hist,  
int  dims,  
const int *  histSize,  
const float **  ranges,  
bool  uniform = true , 

bool  accumulate = false 

) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
this variant uses cv::SparseMat for output
void cv::calcHist  (  InputArrayOfArrays  images, 
const std::vector< int > &  channels,  
InputArray  mask,  
OutputArray  hist,  
const std::vector< int > &  histSize,  
const std::vector< float > &  ranges,  
bool  accumulate = false 

) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
double cv::compareHist  (  InputArray  H1, 
InputArray  H2,  
int  method  
) 
Compares two histograms.
The function compare two dense or two sparse histograms using the specified method.
The function returns \(d(H_1, H_2)\) .
While the function works well with 1, 2, 3dimensional dense histograms, it may not be suitable for highdimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of nonzero histogram bins can slightly shift. To compare such histograms or more general sparse configurations of weighted points, consider using the cv::EMD function.
H1  First compared histogram. 
H2  Second compared histogram of the same size as H1 . 
method  Comparison method, see cv::HistCompMethods 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
float cv::EMD  (  InputArray  signature1, 
InputArray  signature2,  
int  distType,  
InputArray  cost = noArray() , 

float *  lowerBound = 0 , 

OutputArray  flow = noArray() 

) 
Computes the "minimal work" distance between two weighted point configurations.
The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in [122], [123] is multidimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using lineartime algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.
signature1  First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floatingpoint matrix. Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the userdefined cost matrix is used. 
signature2  Second signature of the same format as signature1 , though the number of rows may be different. The total weights may be different. In this case an extra "dummy" point is added to either signature1 or signature2 . 
distType  Used metric. See cv::DistanceTypes. 
cost  Userdefined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrix is used, lower boundary lowerBound cannot be calculated because it needs a metric function. 
lowerBound  Optional input/output parameter: lower boundary of a distance between the two signatures that is a distance between mass centers. The lower boundary may not be calculated if the userdefined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You must** initialize *lowerBound . If the calculated distance between mass centers is greater or equal to *lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case *lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, *lowerBound should be set to 0. 
flow  Resultant \(\texttt{size1} \times \texttt{size2}\) flow matrix: \(\texttt{flow}_{i,j}\) is a flow from \(i\) th point of signature1 to \(j\) th point of signature2 . 
void cv::equalizeHist  (  InputArray  src, 
OutputArray  dst  
) 
Equalizes the histogram of a grayscale image.
The function equalizes the histogram of the input image using the following algorithm:
\[H'_i = \sum _{0 \le j < i} H(j)\]
The algorithm normalizes the brightness and increases the contrast of the image.
src  Source 8bit single channel image. 
dst  Destination image of the same size and type as src . 