我使用Open来创建RGB到HSI，然后做一个直方图。然后进行傅里叶变换，再返回HSI到RGB。

Question

我无法调试这个程序。我将把RGB转换为HSI，然后在任何通道中放置一个直方图。在傅立叶之前和傅立叶之后。

#include "stdafx.h"
#include <opencv2/opencv.hpp>
#include <opencv\highgui.h>
#include <iostream>
// ass.cpp : Converts the given RGB image to HSI colour space then
//           performs Fourier filtering on a particular channel.
//

using namespace std;
using namespace cv;

// Declarations of 4 unfinished functions
Mat rgb2hsi(const Mat& rgb);  // converts RGB image to HSI space
Mat hsi2rgb(const Mat& hsi);  // converts HSI image to RGB space
Mat histogram(const Mat& im); // returns the histogram of the selected channel in HSI space
// void filter(Mat& im);//          // performs frequency-domain filtering on a single-channel image

int main(int argc, char* argv[])
{
    if (argc < 2) // check number of arguments
    {
        cerr << "feed me something!!" << endl; // no arguments passed
        return -1;
    }
    string path = argv[1];
    Mat im; // load an RGB image
    Mat hsi = rgb2hsi(im); // convert it to HSI space
    Mat slices[3]; // 3 channels of the converted HSI image
    im = imread(path); //try to load path
    if (im.empty()) // loaded Sucessfully
    {

        cerr << "I Cannot load the file : ";
            return -1;
    }
    imshow("BEFORE", im);

    split(hsi, slices); // split up the packed HSI image into an array of matrices

    Mat& h = slices[0];
    Mat& s = slices[1];
    Mat& i = slices[2]; // references to H, S, and I layers
    Mat hist1, hist2; // histogram of the selected channel before and after filtering

要应用直方图。可能是我错过了一些头球。没有抽签。

    Mat histogram(const Mat& im)
    {
        Mat hist;
        const float range[] = { 0, 255 };
        const int channels[] = { 0 };
        const int bins = range[1] - range[0];
        const int dims[] = { bins, 1 };
        const Size binSize(2, 240);
        const float* ranges[] = { range };

        // calculate the histogram
        calcHist(&im, 1, channels, Mat(), hist, 1, dims, ranges);

        Mat draw = Mat::zeros(binSize.height, binSize.width * bins, CV_8UC3);
        double maxVal;
        minMaxLoc(hist, NULL, &maxVal, 0, 0);

        for (int b = 0; b < bins; b++)
        {
            float val = hist.at<float>(b, 0);
            int x0 = binSize.width * b;
            int y0 = draw.rows - val / maxVal * binSize.height + 1;
            int x1 = binSize.width * (b + 1) - 1;
            int y1 = draw.rows - 1;

            rectangle(draw,0, cv::(Point(x0, y0), cv::Point(x1, y1)), Scalar::all(255), CV_FILLED);
        }

        return draw;
    }




imwrite("input-original.png", rgb); // write the input image
imwrite("hist-original.png", histogram(h)); // write the histogram of the selected channel
filter(h); // perform filtering
merge(slices, 3, hsi); // combine the separated H, S, and I layers to a big packed matrix
rgb = hsi2rgb(hsi); // convert HSI back to RGB colour space
imwrite("input-filtered.png", rgb); // write the filtered image
imwrite("hist-filtered.png", histogram(h)); // and the histogram of the filtered channel

return 0;
}



Mat rgb2hsi(const Mat& rgb)
{
    Mat slicesRGB[3];
    Mat slicesHSI[3];

    Mat &r = slicesRGB[0], &g = slicesRGB[1], &b = slicesRGB[2];
    Mat &h = slicesHSI[0], &s = slicesHSI[1], &i = slicesHSI[2];

    split(rgb, slicesRGB);

    //
    // TODO: implement colour conversion RGB => HSI
    //
    // begin of conversion code
    h = r * 1.0f;
    s = g * 1.0f;
    i = b * 1.0f;
    // end of conversion code

    Mat hsi;
    merge(slicesHSI, 3, hsi);

    return hsi;
}

Mat hsi2rgb(const Mat& hsi)
{
    Mat slicesRGB[3];
    Mat slicesHSI[3];

    Mat &r = slicesRGB[0], &g = slicesRGB[1], &b = slicesRGB[2];
    Mat &h = slicesHSI[0], &s = slicesHSI[1], &i = slicesHSI[2];

    split(hsi, slicesHSI);


    // begin of conversion code
    r = h * 1.0f;
    g = s * 1.0f;
    b = i * 1.0f;
    // end of conversion code

    Mat rgb;
    merge(slicesRGB, 3, rgb);

    return rgb;
}

Mat histogram(const Mat& im)
{
    Mat hist;
    const float range[] = { 0, 255 };
    const int channels[] = { 0 };
    const int bins = range[1] - range[0];
    const int dims[] = { bins, 1 };
    const Size binSize(2, 240);
    const float* ranges[] = { range };

    // calculate the histogram
    calcHist(&im, 1, channels, Mat(), hist, 1, dims, ranges);

    Mat draw = Mat::zeros(binSize.height, binSize.width * bins, CV_8UC3);
    double maxVal;
    minMaxLoc(hist, NULL, &maxVal, 0, 0);

    for (int b = 0; b < bins; b++)
    {
        float val = hist.at<float>(b, 0);
        int x0 = binSize.width * b;
        int y0 = draw.rows - val / maxVal * binSize.height + 1;
        int x1 = binSize.width * (b + 1) - 1;
        int y1 = draw.rows - 1;

        rectangle(draw, Point(x0, y0), Point(x1, y1), Scalar::all(255), CV_FILLED);
    }

    return draw;
}

void filter(Mat& im)
{
    int type = im.type();

    // Convert pixel data from unsigned 8-bit integers (0~255)
    //  to 32-bit floating numbers, as required by cv::dft
    if (type != CV_32F) im.convertTo(im, CV_32F);

    // Perform 2-D Discrete Fourier Transform
    Mat f;
    dft(im, f, DFT_COMPLEX_OUTPUT + DFT_SCALE); // do DFT

    // Separate the packed complex matrix to two matrices
    Mat complex[2];
    Mat& real = complex[0]; // the real part
    Mat& imag = complex[1]; // the imaginary part
    split(f, complex); // dft(im) => {real,imag}

    // Frequency domain filtering
    int xc = im.cols / 2; // find (xc,yc) the highest
    int yc = im.rows / 2; //  frequency component

    for (int y = 0; y < im.rows; y++) // go through each row..
    {
        for (int x = 0; x < im.cols; x++) // then through each column..
        {
            //
            // TODO: Design your formula here to decide if the component is
            //       discarded or kept.
            //
            if (false) // override this condition
            {
                real.at<float>(y, x) = 0;
                imag.at<float>(y, x) = 0;
            }
        }
    }

    // Pack the real and imaginary parts 
    //  back to the 2-channel matrix
    merge(complex, 2, f); // {real,imag} => f

    // Perform 2-D Inverse Discrete Fourier Transform
    idft(f, im, DFT_REAL_OUTPUT); // do iDFT

    // convert im back to it's original type
    im.convertTo(im, type);
}

错误列表

1 IntelliSense:预期a '；‘d:\709 Tutorial\Dibya_project\Dibya_project\Dibya_project.cpp 48 2 Dibya_project 2 IntelliSense:标识符“Dibya_project”是未定义的d:\709 Tutorial\Dibya_project\Dibya_project\Dibya_project.cpp 70 13 Dibya_project 3 IntelliSense:没有重载函数的实例“矩形”匹配参数列表参数类型为：(、int、cv::Scalar_、( int) d:\709 Tutorial\Dibya_project\Dibya_project\Dibya_project.cpp 72 4 Dibya_project 4 IntelliSense:期望标识符d:\709 Tutorial\Dibya_project\Dibya_project\Dibya_project.cpp 72 26 Dibya_project 5 IntelliSense:没有构造函数"cv::Point_::Point with _Tp=int“的实例与参数列表参数类型匹配如下：(，双Tutorial\Dibya_project\Dibya_project\Dibya_project.cpp (双_X) d:\709 __cdecl 72 27 Dibya_project

Answer 1

(在Mat直方图(.)中)：

rectangle(draw,0, cv::(Point(x0, y0), cv::Point(x1, y1)), Scalar::all(255), CV_FILLED);

应当是：

rectangle(draw,0, cv::Rect(Point(x0, y0), cv::Point(x1, y1)), Scalar::all(255), CV_FILLED);

或者：

rectangle(draw,0, Point(x0, y0), cv::Point(x1, y1), Scalar::all(255), CV_FILLED);

Answer 2

我认为包含highgui头文件有一个错误。