How to use OpenCV to realize histogram calculation in C++

Today, I will talk to you about how to use OpenCV to achieve histogram calculation in C++, which may not be well understood by many people. In order to make you understand better, the editor has summarized the following for you. I hope you can get something according to this article.

Code demonstration

Create a new project opencv-0020, configure properties (VS2017 configure OpenCV common properties), and then write # include and main methods in the source file

Code for histogram calculation

Step-by-step instructions:

1. Split-channel display

This is to display the source image in different channels through the function of split. In imshow (img2,bgr_planes [0]), we change it to 0,1,2, respectively, and the display effect is as follows:

Cv::imshow (img2, bgr_planes [0])

Cv::imshow (img2, bgr_planes [1])

Cv::imshow (img2, bgr_planes [2])

two。 Set the number and value range of bin

3. Computational histogram

The parameters are described as follows:

& rgb_planes [0]: enter an array (or array set)

1: enter the number of arrays (here we use a single-channel image, we can also enter an array set)

0: the dim index is required. Here we only count the grayscale (and each array is a single channel), so just write 0.

Mat (): mask (0 means ignore the pixel), if not defined, do not use the mask

R_hist: the matrix that stores the histogram

1: histogram dimension

HistSize: number of bin per dimension

HistRange: the range of values for each dimension

Uniform and accumulate: bin of the same size, clear histogram traces

4. Create a histogram canvas

5. Normalization of histogram to range

Before drawing a histogram, use normalize to normalize the histogram so that the values in the histogram bin are scaled to the specified range.

This function accepts the following parameters:

R_hist: input array

R_hist: normalized output array (support in-place calculation)

0 and histImage.rows: here, they are the limits of values after normalized r_hist

NORM_MINMAX: normalization method (the method specified in the example scales the value to the specified range above)

-1: indicates that the normalized output array is of the same type as the input array

Mat (): optional mask

6. Draw a histogram on a histogram.

Here is an one-dimensional histogram, using the following expression:

R_hist.at (I)

: math: `i` indicates the dimension. If we want to access the 2D histogram, we need to use an expression like this:

R_hist.at (I, j)

7. Show histogram

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