doc: add new tutorial motion deblur filter (#12215)
* doc: add new tutorial motion deblur filter * Update motion_deblur_filter.markdown a few minor changes
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Vlad Karpushin
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Vadim Pisarevsky
Vadim Pisarevsky
parent
f36790d521
commit
7d3a1657c9
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/**
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* @brief You will learn how to recover an image with motion blur distortion using a Wiener filter
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* @author Karpushin Vladislav, karpushin@ngs.ru, https://github.com/VladKarpushin
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*/
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#include <iostream>
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#include "opencv2/imgproc.hpp"
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#include "opencv2/imgcodecs.hpp"
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using namespace cv;
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using namespace std;
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void help();
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void calcPSF(Mat& outputImg, Size filterSize, int len, double theta);
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void fftshift(const Mat& inputImg, Mat& outputImg);
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void filter2DFreq(const Mat& inputImg, Mat& outputImg, const Mat& H);
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void calcWnrFilter(const Mat& input_h_PSF, Mat& output_G, double nsr);
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void edgetaper(const Mat& inputImg, Mat& outputImg, double gamma = 5.0, double beta = 0.2);
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const String keys =
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"{help h usage ? | | print this message }"
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"{image |input.png | input image name }"
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"{LEN |125 | length of a motion }"
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"{THETA |0 | angle of a motion in degrees }"
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"{SNR |700 | signal to noise ratio }"
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;
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int main(int argc, char *argv[])
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{
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help();
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CommandLineParser parser(argc, argv, keys);
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if (parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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int LEN = parser.get<int>("LEN");
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double THETA = parser.get<double>("THETA");
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int snr = parser.get<int>("SNR");
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string strInFileName = parser.get<String>("image");
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if (!parser.check())
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{
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parser.printErrors();
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return 0;
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}
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Mat imgIn;
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imgIn = imread(strInFileName, IMREAD_GRAYSCALE);
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if (imgIn.empty()) //check whether the image is loaded or not
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{
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cout << "ERROR : Image cannot be loaded..!!" << endl;
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return -1;
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}
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Mat imgOut;
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//! [main]
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// it needs to process even image only
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Rect roi = Rect(0, 0, imgIn.cols & -2, imgIn.rows & -2);
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//Hw calculation (start)
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Mat Hw, h;
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calcPSF(h, roi.size(), LEN, THETA);
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calcWnrFilter(h, Hw, 1.0 / double(snr));
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//Hw calculation (stop)
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imgIn.convertTo(imgIn, CV_32F);
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edgetaper(imgIn, imgIn);
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// filtering (start)
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filter2DFreq(imgIn(roi), imgOut, Hw);
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// filtering (stop)
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//! [main]
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imgOut.convertTo(imgOut, CV_8U);
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normalize(imgOut, imgOut, 0, 255, NORM_MINMAX);
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imwrite("result.jpg", imgOut);
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return 0;
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}
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void help()
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{
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cout << "2018-08-14" << endl;
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cout << "Motion_deblur_v2" << endl;
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cout << "You will learn how to recover an image with motion blur distortion using a Wiener filter" << endl;
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}
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//! [calcPSF]
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void calcPSF(Mat& outputImg, Size filterSize, int len, double theta)
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{
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Mat h(filterSize, CV_32F, Scalar(0));
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Point point(filterSize.width / 2, filterSize.height / 2);
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ellipse(h, point, Size(0, cvRound(float(len) / 2.0)), 90.0 - theta, 0, 360, Scalar(255), FILLED);
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Scalar summa = sum(h);
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outputImg = h / summa[0];
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}
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//! [calcPSF]
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//! [fftshift]
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void fftshift(const Mat& inputImg, Mat& outputImg)
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{
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outputImg = inputImg.clone();
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int cx = outputImg.cols / 2;
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int cy = outputImg.rows / 2;
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Mat q0(outputImg, Rect(0, 0, cx, cy));
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Mat q1(outputImg, Rect(cx, 0, cx, cy));
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Mat q2(outputImg, Rect(0, cy, cx, cy));
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Mat q3(outputImg, Rect(cx, cy, cx, cy));
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Mat tmp;
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q0.copyTo(tmp);
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q3.copyTo(q0);
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tmp.copyTo(q3);
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q1.copyTo(tmp);
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q2.copyTo(q1);
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tmp.copyTo(q2);
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}
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//! [fftshift]
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//! [filter2DFreq]
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void filter2DFreq(const Mat& inputImg, Mat& outputImg, const Mat& H)
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{
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Mat planes[2] = { Mat_<float>(inputImg.clone()), Mat::zeros(inputImg.size(), CV_32F) };
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Mat complexI;
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merge(planes, 2, complexI);
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dft(complexI, complexI, DFT_SCALE);
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Mat planesH[2] = { Mat_<float>(H.clone()), Mat::zeros(H.size(), CV_32F) };
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Mat complexH;
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merge(planesH, 2, complexH);
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Mat complexIH;
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mulSpectrums(complexI, complexH, complexIH, 0);
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idft(complexIH, complexIH);
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split(complexIH, planes);
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outputImg = planes[0];
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}
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//! [filter2DFreq]
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//! [calcWnrFilter]
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void calcWnrFilter(const Mat& input_h_PSF, Mat& output_G, double nsr)
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{
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Mat h_PSF_shifted;
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fftshift(input_h_PSF, h_PSF_shifted);
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Mat planes[2] = { Mat_<float>(h_PSF_shifted.clone()), Mat::zeros(h_PSF_shifted.size(), CV_32F) };
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Mat complexI;
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merge(planes, 2, complexI);
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dft(complexI, complexI);
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split(complexI, planes);
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Mat denom;
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pow(abs(planes[0]), 2, denom);
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denom += nsr;
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divide(planes[0], denom, output_G);
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}
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//! [calcWnrFilter]
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//! [edgetaper]
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void edgetaper(const Mat& inputImg, Mat& outputImg, double gamma, double beta)
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{
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int Nx = inputImg.cols;
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int Ny = inputImg.rows;
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Mat w1(1, Nx, CV_32F, Scalar(0));
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Mat w2(Ny, 1, CV_32F, Scalar(0));
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float* p1 = w1.ptr<float>(0);
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float* p2 = w2.ptr<float>(0);
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float dx = float(2.0 * CV_PI / Nx);
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float x = float(-CV_PI);
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for (int i = 0; i < Nx; i++)
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{
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p1[i] = float(0.5 * (tanh((x + gamma / 2) / beta) - tanh((x - gamma / 2) / beta)));
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x += dx;
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}
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float dy = float(2.0 * CV_PI / Ny);
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float y = float(-CV_PI);
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for (int i = 0; i < Ny; i++)
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{
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p2[i] = float(0.5 * (tanh((y + gamma / 2) / beta) - tanh((y - gamma / 2) / beta)));
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y += dy;
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}
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Mat w = w2 * w1;
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multiply(inputImg, w, outputImg);
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}
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//! [edgetaper]
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