Learning OpenCV Lecture 3 (Counting the Pixels with Histograms)

时间：2014-07-22 23:14:42 阅读：462 评论：0 收藏：0 [点我收藏+]

In this chapter, we will cover:

Computing the image histogram
Applying look-up tables to modify image appearance
Equalizing the image histogram
Backprojecting a histogram to detect specific image content
Using the mean shift algorithm to find an object
Retrieving similar images using histogram comparison

Computing the image histogram

Using the cv::calcHist function.

Caculate 1D Histogram

Histogram1D.h

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

class Histogram1D {
private:

      int histSize[1]; // number of bins
      float hranges[2]; // min and max pixel value
      const float* ranges[1];
      int channels[1]; // only 1 channel used here

public:
     Histogram1D() {

            // Prepare arguments for 1D histogram
            histSize[0] = 256;
            hranges[0] = 0.0;
            hranges[1] = 255.0;
            ranges[0] = hranges;
            channels[0] = 0; // by default, we look at channel 0

     }

      // Computes the 1D histogram
     cv::MatND getHistogram(const cv::Mat &image);

      // Computes the 1D histogram and returns an image of it.
     cv::MatND getHistogramImage(const cv::Mat &image);

};

Histogram.cpp

#include "Histgram1D.h"

// Computes the 1D histogram
cv::MatND Histogram1D::getHistogram(const cv::Mat &image) {
     cv::MatND hist;

      // Compute histogram
     cv::calcHist(&image,
            1,               // histogram from 1 image only
            channels,  // the channel used
            cv::Mat(), // no mask is used
            hist,      // the resulting histogram
            1,               // it is a 1D histgram
            histSize,  // number of bins
            ranges           // pixel value range
     );

      return hist;
}

cv::Mat Histogram1D::getHistogramImage(const cv::Mat &image) {
     
      // Compute histogram first
     cv::MatND hist = getHistogram(image);

      // Get min and max bin values
      double maxVal = 0;
      double minVal = 0;
     cv::minMaxLoc(hist, &minVal, &maxVal, 0, 0);

      // Image on which to display histogram
     cv::Mat histImg(histSize[0], histSize[0], CV_8U, cv::Scalar(255));

      // Set highest point at 90% of nbins
      int hpt = static_cast <int >(0.9 * histSize[0]);

      // Draw a vertical line for each bin
      for ( int h = 0; h < histSize[0]; h++ ) {
            float binVal = hist.at<float>(h);
            int intensity = static_cast <int >(binVal * hpt / maxVal);

            // This function draws a line between 2 points
            cv::line(histImg, cv::Point(h, histSize[0]),
                                      cv::Point(h, histSize[0] - intensity),
                                      cv::Scalar::all(0));
     }

      return histImg;
}

　　main.cpp

#include <iostream>

#include "Histgram1D.h"

int main() {

      // Read input image
     cv::Mat image = cv::imread( "group.jpg", 0); // open in b&w

      // The histogram object
     Histogram1D h;

      // Compute the histogram
     cv::MatND histo = h.getHistogram(image);

      // Loop over each bin
      for (int i = 0; i < 256; i++) {
            std::cout << "Value " << i << " = " <<
                 histo.at<float >(i) << std::endl;
     }

      // Draw histogram image
     cv::Mat histoImage = h.getHistogramImage(image);
     cv::namedWindow( "histogram", CV_WINDOW_AUTOSIZE);
     cv::imshow( "histogram", histoImage);

      // threshold the image
     cv::Mat thresholded;
     cv::threshold(image, thresholded, 60, 255, cv::THRESH_BINARY);
     cv::namedWindow( "Binary image", CV_WINDOW_AUTOSIZE);
     cv::imshow( "Binary image", thresholded);

     cv::waitKey(0);

      return 0;
}

　　the result as follows:

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Caculate Color image histogram

ColoHistogram.h

#include <iostream>

#include "Histgram1D.h"

int main() {

      // Read input image
     cv::Mat image = cv::imread( "group.jpg", 0); // open in b&w

      // The histogram object
     Histogram1D h;

      // Compute the histogram
     cv::MatND histo = h.getHistogram(image);

      // Loop over each bin
      for (int i = 0; i < 256; i++) {
            std::cout << "Value " << i << " = " <<
                 histo.at<float >(i) << std::endl;
     }

      // Draw histogram image
     cv::Mat histoImage = h.getHistogramImage(image);
     cv::namedWindow( "histogram", CV_WINDOW_AUTOSIZE);
     cv::imshow( "histogram", histoImage);

      // threshold the image
     cv::Mat thresholded;
     cv::threshold(image, thresholded, 60, 255, cv::THRESH_BINARY);
     cv::namedWindow( "Binary image", CV_WINDOW_AUTOSIZE);
     cv::imshow( "Binary image", thresholded);

     cv::waitKey(0);

      return 0;
}

　　ColorHistogram.cpp

#include "ColorHistogram.h"

// Computes the 1D histogram
cv::MatND ColorHistogram::getHistogram(const cv::Mat &image) {
     cv::MatND hist;

      // Compute histogram
     cv::calcHist(&image,
            1,               // histogram from 1 image only
            channels,  // the channel used
            cv::Mat(), // no mask is used
            hist,      // the resulting histogram
            3,               // it is a 3D histgram
            histSize,  // number of bins
            ranges           // pixel value range
            );

      return hist;
}

cv::SparseMat ColorHistogram::getSpareHistogram(const cv::Mat &image) {

      // Compute histogram first
     cv::SparseMat hist(3, histSize, CV_32F);

      // Compute histogram
     cv::calcHist(&image,
            1,               // histogram from 1 image only
            channels,  // the channel used
            cv::Mat(), // no mask is used
            hist,      // the resulting histogram
            3,               // it is a 3D histgram
            histSize,  // number of bins
            ranges           // pixel value range
            );

      return hist;
}

Applying look-up tables to modify image appearance

A look-up table is a simple one-to-one (or many-to-one) function that defines how pixel values are transformed into new values. It is a 1D array with, in the case of regular gray-level images, 256 entries. Entry i of the table gives the new intensity value of the corresponding gray level, that is:

newIntensity= lookup[oldIntensity];

Function cv::LUT in OpenCV applies a look-up table to an image in order to produce a new image. We can add this function to our Histogram1D class:

cv::Mat Histogram1D::applyLookUp(const cv::Mat& image,  // input image
      const cv::Mat& lookup) { // 1*256 uchar matrix

      // the output image
     cv::Mat result;

      // apply the lookup table
     cv::LUT(image, lookup, result);

      return result;
}
cv::Mat Histogram1D::strech(const cv::Mat &image, int minValue /* = 0 */) {

      // Compute histogram first
     cv::MatND hist = getHistogram(image);
      // find left extremity of the histogram
      int imin = 0;
      for ( ; imin < histSize[0]; imin ++) {
            std::cout << hist.at<float>(imin) << std::endl;
            if (hist.at<float >(imin) > minValue) {
                 break;
            }

     }

      // find right extremity of the histogram
      int imax = histSize[0] - 1;
      for ( ; imax >= 0; imax --) {
            if (hist.at<float >(imax) > minValue)
                 break;
     }

      // Create lookup table
      int dim(256);
     cv::Mat lookup(1,     // 1 dimension
            &dim,            // 256 entries
            CV_8U            // uchar
            );

      // Build lookup table
      for (int i = 0; i < 256; i++) {

            // stretch between imin and imax
            if (i < imin) lookup.at<uchar>(i) = 0;
            else if (i > imax) lookup.at<uchar>(i) = 255;
            //linear mapping
            else lookup.at<uchar>(i) = static_cast <uchar>(255.0 * (i - imin) / (imax - imin) + 0.5);
     }

      // Apply lookup table
     cv::Mat result;
     result = applyLookUp(image, lookup);
      return result;
}

　　Using the function as follows:

    
     cv::Mat streched = h.strech(image, 100);
     cv::namedWindow( "streched image", CV_WINDOW_AUTOSIZE);
     cv::imshow( "streched image", streched);
     cv::Mat strechedHistoImage = h.getHistogramImage(streched);
     cv::namedWindow( "strechedHistoImage", CV_WINDOW_AUTOSIZE);
     cv::imshow( "strechedHistoImage", strechedHistoImage);

　　results as follows:

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Equalizing the image histogram

OpenCV offers an easy-to-use function that performs histogram equalization. It can be called as follows:

cv::Mat Histogram1D::equalize(const cv::Mat &image) {
     cv::Mat result;
     cv::equalizeHist(image, result);
      return result;
}

　　result as follows：

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Backprojecting a histogram to detect specific image content

ContentFinder.h

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

class ContentFinder {
private:

      float hranges[2];
      const float* ranges[3];
      int channels[3];

      float threshold;
     cv::MatND histogram;

public:
     ContentFinder() : threshold(-1.0f) {
            ranges[0] = hranges; // all channels have same range
            ranges[1] = hranges;
            ranges[2] = hranges;
     }

      // Sets the threshold on histogram values [0, 1]
      void setThreshold(float t) {
            threshold = t;
     }
      // Gets the threshold
      float getThreshold() {
            return threshold;
     }

      // Sets the reference histogram
      void setHistogram(const cv::MatND &h) {
            histogram = h;
            cv::normalize(histogram, histogram, 1.0);
     }

     cv::Mat find(const cv::Mat &image, float minValue, float maxValue, int *channels, int dim);
};

　　ContentFinder.cpp

#include "ContentFinder.h"

cv::Mat ContentFinder::find(const cv::Mat &image,
      float minValue,
      float maxValue,
      int *channels,
      int dim) {

     cv::Mat result;
     hranges[0] = minValue;
     hranges[1] = maxValue;
      for (int i = 0; i < dim; i++) {
            this->channels[i] = channels[i];
     }

     cv::calcBackProject(&image, 1,   // input image
            channels,                        // list of channels used
            histogram,                       // the histogram we are using
            result,                               // the resulting backprojection
            ranges,                               // the range of values
            255.0                            // the scaling factor
            );

      // Threshold back projection to obtain a binary image
      if (threshold > 0.0)
            cv::threshold(result, result, 255 * threshold, 255, cv::THRESH_BINARY);
      return result;
}

Let‘s now use a BGR histogram on the color version of the image we used above. This time, we will try to detect the blue sky area. We will first load the color image, reduce the number of color using the color reduction function of Chapter 2, and define the region of interest: ColorHistogram hc;

ColorHistogram hc;

      // load color image

     cv::Mat color = cv::imread( "waves.jpg");

      //reduce colors

     color = hc.colorReduce(color, 32);

      // blue sky area

     cv::Mat imageROI = color(cv::Rect(0, 0, 165, 75));

Next, you compute the histogram and use the findmethod to detect the sky portion
of the image:

cv::MatND hist = hc.getHistogram(imageROI);

     ContentFinder finder;

     finder.setHistogram(hist);

     finder.setThreshold(0.05f);

      //Get back-projection of color histogram

     cv::Mat result = finder.find(color);

     cv::namedWindow( "original image", CV_WINDOW_AUTOSIZE);

     cv::imshow( "original image", color);

     cv::namedWindow( "color back projection result", CV_WINDOW_AUTOSIZE);

     cv::imshow( "color back projection result", result);

The result of the detection on the color version of the image, of the previous section is seen
here:

Using the mean shift algorithm to find an object

colorhistogram.h

#if !defined COLHISTOGRAM
#define COLHISTOGRAM

#include <opencv2\core\core.hpp>
#include <opencv2\imgproc\imgproc.hpp>

class ColorHistogram {

  private:

    int histSize[3];
      float hranges[2];
    const float* ranges[3];
    int channels[3];

  public:

     ColorHistogram() {

            // Prepare arguments for a color histogram
            histSize[0]= histSize[1]= histSize[2]= 256;
            hranges[0]= 0.0;     // BRG range
            hranges[1]= 255.0;
            ranges[0]= hranges; // all channels have the same range
            ranges[1]= hranges;
            ranges[2]= hranges;
            channels[0]= 0;       // the three channels
            channels[1]= 1;
            channels[2]= 2;
     }

      // Computes the histogram.
     cv::MatND getHistogram(const cv::Mat &image) {

            cv::MatND hist;

            // BGR color histogram
            hranges[0]= 0.0;     // BRG range
            hranges[1]= 255.0;
            channels[0]= 0;       // the three channels
            channels[1]= 1;
            channels[2]= 2;

            // Compute histogram
            cv::calcHist(&image,
                 1,               // histogram of 1 image only
                 channels,  // the channel used
                 cv::Mat(), // no mask is used
                 hist,      // the resulting histogram
                 3,               // it is a 3D histogram
                 histSize,  // number of bins
                 ranges           // pixel value range
            );

            return hist;
     }

      // Computes the histogram.
     cv::SparseMat getSparseHistogram(const cv::Mat &image) {

            cv::SparseMat hist(3,histSize,CV_32F);

            // BGR color histogram
            hranges[0]= 0.0;     // BRG range
            hranges[1]= 255.0;
            channels[0]= 0;       // the three channels
            channels[1]= 1;
            channels[2]= 2;

            // Compute histogram
            cv::calcHist(&image,
                 1,               // histogram of 1 image only
                 channels,  // the channel used
                 cv::Mat(), // no mask is used
                 hist,      // the resulting histogram
                 3,               // it is a 3D histogram
                 histSize,  // number of bins
                 ranges           // pixel value range
            );

            return hist;
     }

      // Computes the 2D ab histogram.
      // BGR source image is converted to Lab
     cv::MatND getabHistogram(const cv::Mat &image) {

            cv::MatND hist;

            // Convert to Lab color space
            cv::Mat lab;
            cv::cvtColor(image, lab, CV_BGR2Lab);

            // Prepare arguments for a 2D color histogram
            hranges[0]= -128.0;
            hranges[1]= 127.0;
            channels[0]= 1; // the two channels used are ab
            channels[1]= 2;

            // Compute histogram
            cv::calcHist(&lab,
                 1,               // histogram of 1 image only
                 channels,  // the channel used
                 cv::Mat(), // no mask is used
                 hist,      // the resulting histogram
                 2,               // it is a 2D histogram
                 histSize,  // number of bins
                 ranges           // pixel value range
            );

            return hist;
     }

      // Computes the 1D Hue histogram with a mask.
      // BGR source image is converted to HSV
     cv::MatND getHueHistogram(const cv::Mat &image) {

            cv::MatND hist;

            // Convert to Lab color space
            cv::Mat hue;
            cv::cvtColor(image, hue, CV_BGR2HSV);

            // Prepare arguments for a 1D hue histogram
            hranges[0]= 0.0;
            hranges[1]= 180.0;
            channels[0]= 0; // the hue channel

            // Compute histogram
            cv::calcHist(&hue,
                 1,               // histogram of 1 image only
                 channels,  // the channel used
                 cv::Mat(), // no mask is used
                 hist,      // the resulting histogram
                 1,               // it is a 1D histogram
                 histSize,  // number of bins
                 ranges           // pixel value range
            );

            return hist;
     }

     cv::Mat colorReduce(const cv::Mat &image, int div=64) {

       int n= static_cast<int >(log(static_cast <double >(div))/log(2.0));
       // mask used to round the pixel value
       uchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0

       cv::Mat_<cv::Vec3b>::const_iterator it= image.begin<cv::Vec3b>();
       cv::Mat_<cv::Vec3b>::const_iterator itend= image.end<cv::Vec3b>();

       // Set output image (always 1-channel)
       cv::Mat result(image.rows,image.cols,image.type());
       cv::Mat_<cv::Vec3b>::iterator itr= result.begin<cv::Vec3b>();

       for ( ; it!= itend; ++it, ++itr) {
       
        (*itr)[0]= ((*it)[0]&mask) + div/2;
        (*itr)[1]= ((*it)[1]&mask) + div/2;
        (*itr)[2]= ((*it)[2]&mask) + div/2;
       }

       return result;
}

      // Computes the 1D Hue histogram with a mask.
      // BGR source image is converted to HSV
      // Pixels with low saturation are ignored
     cv::MatND getHueHistogram(const cv::Mat &image,
            int minSaturation=0) {
                 cv::MatND hist;
                 // Convert to HSV color space
                 cv::Mat hsv;
                 cv::cvtColor(image, hsv, CV_BGR2HSV);
                 // Mask to be used (or not)
                 cv::Mat mask;
                 if (minSaturation>0) {
                      // Spliting the 3 channels into 3 images
                      std::vector<cv::Mat> v;
                      cv::split(hsv,v);
                      // Mask out the low saturated pixels
                      cv::threshold(v[1],mask,minSaturation,255,
                            cv::THRESH_BINARY);
                 }
                 // Prepare arguments for a 1D hue histogram
                 hranges[0]= 0.0;
                 hranges[1]= 180.0;
                 channels[0]= 0; // the hue channel
                 // Compute histogram
                 cv::calcHist(&hsv,
                      1, // histogram of 1 image only
                      channels, // the channel used
                      mask, // binary mask
                      hist, // the resulting histogram
                      1, // it is a 1D histogram
                      histSize, // number of bins
                      ranges // pixel value range
                      );
                 return hist;
     }

};


#endif

　　bojectFinder.h

#if !defined OFINDER
#define OFINDER

#include <opencv2\core\core.hpp>
#include <opencv2\imgproc\imgproc.hpp>

class ObjectFinder {

  private:

      float hranges[2];
    const float* ranges[3];
    int channels[3];

      float threshold;
     cv::MatND histogram;
     cv::SparseMat shistogram;
      bool isSparse;

  public:

     ObjectFinder() : threshold(0.1f), isSparse(false) {

            ranges[0]= hranges; // all channels have the same range
            ranges[1]= hranges;
            ranges[2]= hranges;
     }
  
      // Sets the threshold on histogram values [0,1]
      void setThreshold(float t) {

            threshold= t;
     }

      // Gets the threshold
      float getThreshold() {

            return threshold;
     }

      // Sets the reference histogram
      void setHistogram(const cv::MatND& h) {

            isSparse= false;
            histogram= h;
            cv::normalize(histogram,histogram,1.0);
     }

      // Sets the reference histogram
      void setHistogram(const cv::SparseMat& h) {

            isSparse= true;
            shistogram= h;
            cv::normalize(shistogram,shistogram,1.0,cv::NORM_L2);
     }

      // Finds the pixels belonging to the histogram
     cv::Mat find(const cv::Mat& image) {

            cv::Mat result;

            hranges[0]= 0.0;      // range [0,255]
            hranges[1]= 255.0;
            channels[0]= 0;       // the three channels
            channels[1]= 1;
            channels[2]= 2;

            if (isSparse) { // call the right function based on histogram type

              cv::calcBackProject(&image,
                      1,            // one image
                      channels,     // vector specifying what histogram dimensions belong to what image channels
                      shistogram,   // the histogram we are using
                      result,       // the resulting back projection image
                      ranges,       // the range of values, for each dimension
                      255.0         // the scaling factor is chosen such that a histogram value of 1 maps to 255
              );

            } else {

              cv::calcBackProject(&image,
                      1,            // one image
                      channels,     // vector specifying what histogram dimensions belong to what image channels
                      histogram,    // the histogram we are using
                      result,       // the resulting back projection image
                      ranges,       // the range of values, for each dimension
                      255.0         // the scaling factor is chosen such that a histogram value of 1 maps to 255
              );
            }


        // Threshold back projection to obtain a binary image
            if (threshold>0.0)
                 cv::threshold(result, result, 255*threshold, 255, cv::THRESH_BINARY);

            return result;
     }

     cv::Mat find(const cv::Mat& image, float minValue, float maxValue, int *channels, int dim) {

            cv::Mat result;

            hranges[0]= minValue;
            hranges[1]= maxValue;

            for (int i=0; i<dim; i++)
                 this->channels[i]= channels[i];

            if (isSparse) { // call the right function based on histogram type

              cv::calcBackProject(&image,
                      1,            // we only use one image at a time
                      channels,     // vector specifying what histogram dimensions belong to what image channels
                      shistogram,   // the histogram we are using
                      result,       // the resulting back projection image
                      ranges,       // the range of values, for each dimension
                      255.0         // the scaling factor is chosen such that a histogram value of 1 maps to 255
              );

            } else {

              cv::calcBackProject(&image,
                      1,            // we only use one image at a time
                      channels,     // vector specifying what histogram dimensions belong to what image channels
                      histogram,    // the histogram we are using
                      result,       // the resulting back projection image
                      ranges,       // the range of values, for each dimension
                      255.0         // the scaling factor is chosen such that a histogram value of 1 maps to 255
              );
            }

        // Threshold back projection to obtain a binary image
            if (threshold>0.0)
                 cv::threshold(result, result, 255*threshold, 255, cv::THRESH_BINARY);

            return result;
     }

};

#endif

　　finder.cpp

#include <iostream>
#include <vector>
using namespace std;

#include <opencv2\core\core.hpp>
#include <opencv2\highgui\highgui.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\video\tracking.hpp>

#include "objectFinder.h"
#include "colorhistogram.h"

int main()
{
      // Read reference image
     cv::Mat image= cv::imread("../baboon1.jpg" );
      if (!image.data)
            return 0;

      // Define ROI
     cv::Mat imageROI= image(cv::Rect(110,260,35,40));
     cv::rectangle(image, cv::Rect(110,260,35,40),cv::Scalar(0,0,255));

      // Display image
     cv::namedWindow( "Image");
     cv::imshow( "Image",image);

      // Get the Hue histogram
      int minSat=65;
     ColorHistogram hc;
     cv::MatND colorhist= hc.getHueHistogram(imageROI,minSat);

     ObjectFinder finder;
     finder.setHistogram(colorhist);
     finder.setThreshold(0.2f);

      // Convert to HSV space
     cv::Mat hsv;
     cv::cvtColor(image, hsv, CV_BGR2HSV);

      // Split the image
     vector<cv::Mat> v;
     cv::split(hsv,v);

      // Eliminate pixels with low saturation
     cv::threshold(v[1],v[1],minSat,255,cv::THRESH_BINARY);
     cv::namedWindow( "Saturation");
     cv::imshow( "Saturation",v[1]);

      // Get back-projection of hue histogram
      int ch[1]={0};
     cv::Mat result= finder.find(hsv,0.0f,180.0f,ch,1);

     cv::namedWindow( "Result Hue");
     cv::imshow( "Result Hue",result);

     cv::bitwise_and(result,v[1],result);
     cv::namedWindow( "Result Hue and");
     cv::imshow( "Result Hue and",result);

      // Second image
     image= cv::imread("../baboon3.jpg");

      // Display image
     cv::namedWindow( "Image 2");
     cv::imshow( "Image 2",image);

      // Convert to HSV space
     cv::cvtColor(image, hsv, CV_BGR2HSV);

      // Split the image
     cv::split(hsv,v);

      // Eliminate pixels with low saturation
     cv::threshold(v[1],v[1],minSat,255,cv::THRESH_BINARY);
     cv::namedWindow( "Saturation");
     cv::imshow( "Saturation",v[1]);

      // Get back-projection of hue histogram
     result= finder.find(hsv,0.0f,180.0f,ch,1);

     cv::namedWindow( "Result Hue");
     cv::imshow( "Result Hue",result);

      // Eliminate low stauration pixels
     cv::bitwise_and(result,v[1],result);
     cv::namedWindow( "Result Hue and");
     cv::imshow( "Result Hue and",result);

      // Get back-projection of hue histogram
     finder.setThreshold(-1.0f);
     result= finder.find(hsv,0.0f,180.0f,ch,1);
     cv::bitwise_and(result,v[1],result);
     cv::namedWindow( "Result Hue and raw");
     cv::imshow( "Result Hue and raw",result);

     cv::Rect rect(110,260,35,40);
     cv::rectangle(image, rect, cv::Scalar(0,0,255));

     cv::TermCriteria criteria(cv::TermCriteria::MAX_ITER,10,0.01);
     cout << "meanshift= " << cv::meanShift(result,rect,criteria) << endl;

     cv::rectangle(image, rect, cv::Scalar(0,255,0));

      // Display image
     cv::namedWindow( "Image 2 result");
     cv::imshow( "Image 2 result",image);

     cv::waitKey();
      return 0;
}

　　 results:

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Retrieving similar images using histogram comparison

imageComparator.h

#if !defined ICOMPARATOR
#define ICOMPARATOR

#include <opencv2\core\core.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include "colorhistogram.h"

class ImageComparator {

  private:

     cv::Mat reference;
     cv::Mat input;
     cv::MatND refH;
     cv::MatND inputH;

     ColorHistogram hist;
      int div;

  public:

     ImageComparator() : div(32) {

     }

      // Color reduction factor
      // The comparaison will be made on images with
      // color space reduced by this factor in each dimension
      void setColorReduction( int factor) {

            div= factor;
     }

      int getColorReduction() {

            return div;
     }

      void setReferenceImage(const cv::Mat& image) {

            reference= hist.colorReduce(image,div);
            refH= hist.getHistogram(reference);
     }

      double compare(const cv::Mat& image) {

            input= hist.colorReduce(image,div);
            inputH= hist.getHistogram(input);

            return cv::compareHist(refH,inputH,CV_COMP_INTERSECT);
     }
};

#endif

　　retrieve.cpp

#include <iostream>
using namespace std;

#include <opencv2\core\core.hpp>
#include <opencv2\highgui\highgui.hpp>

#include "imageComparator.h"

int main()
{
      // Read reference image
     cv::Mat image= cv::imread("../waves.jpg" );
      if (!image.data)
            return 0;

      // Display image
     cv::namedWindow( "Query Image");
     cv::imshow( "Query Image",image);

     ImageComparator c;
     c.setReferenceImage(image);

      // Read an image and compare it with reference
     cv::Mat input= cv::imread("../dog.jpg" );
     cout << "waves vs dog: " << c.compare(input) << endl;

      // Read an image and compare it with reference
     input= cv::imread("../marais.jpg");
     cout << "waves vs marais: " << c.compare(input) << endl;

      // Read an image and compare it with reference
     input= cv::imread("../bear.jpg");
     cout << "waves vs bear: " << c.compare(input) << endl;

      // Read an image and compare it with reference
     input= cv::imread("../beach.jpg");
     cout << "waves vs beach: " << c.compare(input) << endl;

      // Read an image and compare it with reference
     input= cv::imread("../polar.jpg");
     cout << "waves vs polar: " << c.compare(input) << endl;

      // Read an image and compare it with reference
     input= cv::imread("../moose.jpg");
     cout << "waves vs moose: " << c.compare(input) << endl;

      // Read an image and compare it with reference
     input= cv::imread("../lake.jpg");
     cout << "waves vs lake: " << c.compare(input) << endl;

      // Read an image and compare it with reference
     input= cv::imread("../fundy.jpg");
     cout << "waves vs fundy: " << c.compare(input) << endl;

     cv::waitKey();
      return 0;
}

　　results:

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Learning OpenCV Lecture 3 (Counting the Pixels with Histograms),布布扣,bubuko.com

Learning OpenCV Lecture 3 (Counting the Pixels with Histograms)

原文：http://www.cnblogs.com/starlitnext/p/3861364.html

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