Tutorial Discrete Fourier Transform

doc/tutorials/core/table_of_content_core.markdown

@@ -76,6 +76,8 @@ understanding how to manipulate the images on a pixel level.
 
 -   @subpage tutorial_discrete_fourier_transform
 
+    *Languages:* C++, Java, Python
+
     *Compatibility:* \> OpenCV 2.0
 
     *Author:* Bernát Gábor

samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp

@@ -8,45 +8,58 @@
 using namespace cv;
 using namespace std;
 
-static void help(char* progName)
+static void help(void)
 {
     cout << endl
         <<  "This program demonstrated the use of the discrete Fourier transform (DFT). " << endl
         <<  "The dft of an image is taken and it's power spectrum is displayed."          << endl
         <<  "Usage:"                                                                      << endl
-        << progName << " [image_name -- default ../data/lena.jpg] "               << endl << endl;
+        <<  "./discrete_fourier_transform [image_name -- default ../data/lena.jpg]"       << endl;
 }
 
 int main(int argc, char ** argv)
 {
-    help(argv[0]);
+    help();
 
     const char* filename = argc >=2 ? argv[1] : "../data/lena.jpg";
 
     Mat I = imread(filename, IMREAD_GRAYSCALE);
-    if( I.empty())
+    if( I.empty()){
+        cout << "Error opening image" << endl;
         return -1;
+    }
 
+//! [expand]
     Mat padded;                            //expand input image to optimal size
     int m = getOptimalDFTSize( I.rows );
     int n = getOptimalDFTSize( I.cols ); // on the border add zero values
     copyMakeBorder(I, padded, 0, m - I.rows, 0, n - I.cols, BORDER_CONSTANT, Scalar::all(0));
+//! [expand]
 
+//! [complex_and_real]
     Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};
     Mat complexI;
     merge(planes, 2, complexI);         // Add to the expanded another plane with zeros
+//! [complex_and_real]
 
+//! [dft]
     dft(complexI, complexI);            // this way the result may fit in the source matrix
+//! [dft]
 
     // compute the magnitude and switch to logarithmic scale
     // => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
+//! [magnitude]
     split(complexI, planes);                   // planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
     magnitude(planes[0], planes[1], planes[0]);// planes[0] = magnitude
     Mat magI = planes[0];
+//! [magnitude]
 
+//! [log]
     magI += Scalar::all(1);                    // switch to logarithmic scale
     log(magI, magI);
+//! [log]
 
+//! [crop_rearrange]
     // crop the spectrum, if it has an odd number of rows or columns
     magI = magI(Rect(0, 0, magI.cols & -2, magI.rows & -2));
 
@@ -67,9 +80,12 @@ int main(int argc, char ** argv)
     q1.copyTo(tmp);                    // swap quadrant (Top-Right with Bottom-Left)
     q2.copyTo(q1);
     tmp.copyTo(q2);
+//! [crop_rearrange]
 
+//! [normalize]
     normalize(magI, magI, 0, 1, NORM_MINMAX); // Transform the matrix with float values into a
                                             // viewable image form (float between values 0 and 1).
+//! [normalize]
 
     imshow("Input Image"       , I   );    // Show the result
     imshow("spectrum magnitude", magI);

samples/java/tutorial_code/core/discrete_fourier_transform/DiscreteFourierTransform.java

+import org.opencv.core.*;
+import org.opencv.highgui.HighGui;
+import org.opencv.imgcodecs.Imgcodecs;
+
+import java.util.List;
+import java.util.*;
+
+class DiscreteFourierTransformRun{
+    private void help() {
+        System.out.println("" +
+                "This program demonstrated the use of the discrete Fourier transform (DFT). \n" +
+                "The dft of an image is taken and it's power spectrum is displayed.\n" +
+                "Usage:\n" +
+                "./DiscreteFourierTransform [image_name -- default ../data/lena.jpg]");
+    }
+
+    public void run(String[] args){
+
+        help();
+
+        String filename = ((args.length > 0) ? args[0] : "../data/lena.jpg");
+
+        Mat I = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);
+        if( I.empty() ) {
+            System.out.println("Error opening image");
+            System.exit(-1);
+        }
+
+        //! [expand]
+        Mat padded = new Mat();                     //expand input image to optimal size
+        int m = Core.getOptimalDFTSize( I.rows() );
+        int n = Core.getOptimalDFTSize( I.cols() ); // on the border add zero values
+        Core.copyMakeBorder(I, padded, 0, m - I.rows(), 0, n - I.cols(), Core.BORDER_CONSTANT, Scalar.all(0));
+        //! [expand]
+
+        //! [complex_and_real]
+        List<Mat> planes = new ArrayList<Mat>();
+        padded.convertTo(padded, CvType.CV_32F);
+        planes.add(padded);
+        planes.add(Mat.zeros(padded.size(), CvType.CV_32F));
+        Mat complexI = new Mat();
+        Core.merge(planes, complexI);         // Add to the expanded another plane with zeros
+        //! [complex_and_real]
+
+        //! [dft]
+        Core.dft(complexI, complexI);         // this way the result may fit in the source matrix
+        //! [dft]
+
+        // compute the magnitude and switch to logarithmic scale
+        // => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
+        //! [magnitude]
+        Core.split(complexI, planes);                               // planes.get(0) = Re(DFT(I)
+                                                                    // planes.get(1) = Im(DFT(I))
+        Core.magnitude(planes.get(0), planes.get(1), planes.get(0));// planes.get(0) = magnitude
+        Mat magI = planes.get(0);
+        //! [magnitude]
+
+        //! [log]
+        Mat matOfOnes = Mat.ones(magI.size(), magI.type());
+        Core.add(matOfOnes, magI, magI);         // switch to logarithmic scale
+        Core.log(magI, magI);
+        //! [log]
+
+        //! [crop_rearrange]
+        // crop the spectrum, if it has an odd number of rows or columns
+        magI = magI.submat(new Rect(0, 0, magI.cols() & -2, magI.rows() & -2));
+
+        // rearrange the quadrants of Fourier image  so that the origin is at the image center
+        int cx = magI.cols()/2;
+        int cy = magI.rows()/2;
+
+        Mat q0 = new Mat(magI, new Rect(0, 0, cx, cy));   // Top-Left - Create a ROI per quadrant
+        Mat q1 = new Mat(magI, new Rect(cx, 0, cx, cy));  // Top-Right
+        Mat q2 = new Mat(magI, new Rect(0, cy, cx, cy));  // Bottom-Left
+        Mat q3 = new Mat(magI, new Rect(cx, cy, cx, cy)); // Bottom-Right
+
+        Mat tmp = new Mat();               // swap quadrants (Top-Left with Bottom-Right)
+        q0.copyTo(tmp);
+        q3.copyTo(q0);
+        tmp.copyTo(q3);
+
+        q1.copyTo(tmp);                    // swap quadrant (Top-Right with Bottom-Left)
+        q2.copyTo(q1);
+        tmp.copyTo(q2);
+        //! [crop_rearrange]
+
+        magI.convertTo(magI, CvType.CV_8UC1);
+        //! [normalize]
+        Core.normalize(magI, magI, 0, 255, Core.NORM_MINMAX, CvType.CV_8UC1); // Transform the matrix with float values
+                                                                            // into a viewable image form (float between
+                                                                            // values 0 and 255).
+        //! [normalize]
+
+        HighGui.imshow("Input Image"       , I   );    // Show the result
+        HighGui.imshow("Spectrum Magnitude", magI);
+        HighGui.waitKey();
+
+        System.exit(0);
+    }
+}
+
+
+public class DiscreteFourierTransform {
+    public static void main(String[] args) {
+        // Load the native library.
+        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
+        new DiscreteFourierTransformRun().run(args);
+    }
+}

samples/python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py

+from __future__ import print_function
+import sys
+
+import cv2
+import numpy as np
+
+
+def print_help():
+    print('''
+    This program demonstrated the use of the discrete Fourier transform (DFT).
+    The dft of an image is taken and it's power spectrum is displayed.
+    Usage:
+    discrete_fourier_transform.py [image_name -- default ../../../../data/lena.jpg]''')
+
+
+def main(argv):
+
+    print_help()
+
+    filename = argv[0] if len(argv) > 0 else "../../../../data/lena.jpg"
+
+    I = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
+    if I is None:
+        print('Error opening image')
+        return -1
+    ## [expand]
+    rows, cols = I.shape
+    m = cv2.getOptimalDFTSize( rows )
+    n = cv2.getOptimalDFTSize( cols )
+    padded = cv2.copyMakeBorder(I, 0, m - rows, 0, n - cols, cv2.BORDER_CONSTANT, value=[0, 0, 0])
+    ## [expand]
+    ## [complex_and_real]
+    planes = [np.float32(padded), np.zeros(padded.shape, np.float32)]
+    complexI = cv2.merge(planes)         # Add to the expanded another plane with zeros
+    ## [complex_and_real]
+    ## [dft]
+    cv2.dft(complexI, complexI)         # this way the result may fit in the source matrix
+    ## [dft]
+    # compute the magnitude and switch to logarithmic scale
+    # = > log(1 + sqrt(Re(DFT(I)) ^ 2 + Im(DFT(I)) ^ 2))
+    ## [magnitude]
+    cv2.split(complexI, planes)                   # planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
+    cv2.magnitude(planes[0], planes[1], planes[0])# planes[0] = magnitude
+    magI = planes[0]
+    ## [magnitude]
+    ## [log]
+    matOfOnes = np.ones(magI.shape, dtype=magI.dtype)
+    cv2.add(matOfOnes, magI, magI) #  switch to logarithmic scale
+    cv2.log(magI, magI)
+    ## [log]
+    ## [crop_rearrange]
+    magI_rows, magI_cols = magI.shape
+    # crop the spectrum, if it has an odd number of rows or columns
+    magI = magI[0:(magI_rows & -2), 0:(magI_cols & -2)]
+    cx = int(magI_rows/2)
+    cy = int(magI_cols/2)
+
+    q0 = magI[0:cx, 0:cy]         # Top-Left - Create a ROI per quadrant
+    q1 = magI[cx:cx+cx, 0:cy]     # Top-Right
+    q2 = magI[0:cx, cy:cy+cy]     # Bottom-Left
+    q3 = magI[cx:cx+cx, cy:cy+cy] # Bottom-Right
+
+    tmp = np.copy(q0)               # swap quadrants (Top-Left with Bottom-Right)
+    magI[0:cx, 0:cy] = q3
+    magI[cx:cx + cx, cy:cy + cy] = tmp
+
+    tmp = np.copy(q1)               # swap quadrant (Top-Right with Bottom-Left)
+    magI[cx:cx + cx, 0:cy] = q2
+    magI[0:cx, cy:cy + cy] = tmp
+    ## [crop_rearrange]
+    ## [normalize]
+    cv2.normalize(magI, magI, 0, 1, cv2.NORM_MINMAX) # Transform the matrix with float values into a
+    ## viewable image form(float between values 0 and 1).
+    ## [normalize]
+    cv2.imshow("Input Image"       , I   )    # Show the result
+    cv2.imshow("spectrum magnitude", magI)
+    cv2.waitKey()
+
+if __name__ == "__main__":
+    main(sys.argv[1:])