原文转自https://docs.opencv.org/2.4/modules/imgproc/doc/histograms.html

文档是英文的，应该不难看懂，就不给翻译了。

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Histograms

calcHist

Calculates a histogram of a set of arrays.

C++: void 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 )

C++: void 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 )

Python: cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]]) → hist

C: void cvCalcHist(IplImage** image, CvHistogram* hist, int accumulate=0, const CvArr* mask=NULL )

Python: cv.CalcHist(image, hist, accumulate=0, mask=None) → None

Parameters:

Parameters:	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 8-bit array of the same size as `images[i]` . The non-zero 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 0-th 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.

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 8-bit array of the same size as images[i] . The non-zero 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 0-th 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.

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 Hue-Saturation histogram for a color image.

#include <cv.h>
#include <highgui.h>using namespace cv;int main( int argc, char** argv )
{Mat src, hsv;if( argc != 2 || !(src=imread(argv[1], 1)).data )return -1;cvtColor(src, hsv, CV_BGR2HSV);// Quantize the hue to 30 levels// and the saturation to 32 levelsint hbins = 30, sbins = 32;int histSize[] = {hbins, sbins};// hue varies from 0 to 179, see cvtColorfloat hranges[] = { 0, 180 };// saturation varies from 0 (black-gray-white) to// 255 (pure spectrum color)float sranges[] = { 0, 256 };const float* ranges[] = { hranges, sranges };MatND hist;// we compute the histogram from the 0-th and 1-st channelsint channels[] = {0, 1};calcHist( &hsv, 1, channels, Mat(), // do not use maskhist, 2, histSize, ranges,true, // the histogram is uniformfalse );double maxVal=0;minMaxLoc(hist, 0, &maxVal, 0, 0);int scale = 10;Mat histImg = Mat::zeros(sbins*scale, hbins*scale, CV_8UC3);for( int h = 0; h < hbins; h++ )for( int s = 0; s < sbins; s++ ){float binVal = hist.at<float>(h, s);int intensity = cvRound(binVal*255/maxVal);rectangle( histImg, Point(h*scale, s*scale),Point( (h+1)*scale - 1, (s+1)*scale - 1),Scalar::all(intensity),CV_FILLED );}namedWindow( "Source", 1 );imshow( "Source", src );namedWindow( "H-S Histogram", 1 );imshow( "H-S Histogram", histImg );waitKey();
}

Note

An example for creating histograms of an image can be found at opencv_source_code/samples/cpp/demhist.cpp
(Python) An example for creating color histograms can be found at opencv_source/samples/python2/color_histogram.py
(Python) An example illustrating RGB and grayscale histogram plotting can be found at opencv_source/samples/python2/hist.py

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OpenCV直方图计算函数calcHist详解

Histograms

calcHist

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