tribta
8 years ago
parent
13317bdfda
commit
954e2f9b9c
5 changed files with 382 additions and 93 deletions
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import org.opencv.core.*; |
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import org.opencv.highgui.HighGui; |
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import org.opencv.imgcodecs.Imgcodecs; |
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import java.util.List; |
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import java.util.*; |
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class DiscreteFourierTransformRun{ |
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private void help() { |
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System.out.println("" + |
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"This program demonstrated the use of the discrete Fourier transform (DFT). \n" + |
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"The dft of an image is taken and it's power spectrum is displayed.\n" + |
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"Usage:\n" + |
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"./DiscreteFourierTransform [image_name -- default ../data/lena.jpg]"); |
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} |
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public void run(String[] args){ |
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help(); |
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String filename = ((args.length > 0) ? args[0] : "../data/lena.jpg"); |
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Mat I = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE); |
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if( I.empty() ) { |
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System.out.println("Error opening image"); |
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System.exit(-1); |
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} |
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//! [expand]
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Mat padded = new Mat(); //expand input image to optimal size
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int m = Core.getOptimalDFTSize( I.rows() ); |
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int n = Core.getOptimalDFTSize( I.cols() ); // on the border add zero values
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Core.copyMakeBorder(I, padded, 0, m - I.rows(), 0, n - I.cols(), Core.BORDER_CONSTANT, Scalar.all(0)); |
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//! [expand]
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//! [complex_and_real]
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List<Mat> planes = new ArrayList<Mat>(); |
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padded.convertTo(padded, CvType.CV_32F); |
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planes.add(padded); |
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planes.add(Mat.zeros(padded.size(), CvType.CV_32F)); |
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Mat complexI = new Mat(); |
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Core.merge(planes, complexI); // Add to the expanded another plane with zeros
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//! [complex_and_real]
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//! [dft]
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Core.dft(complexI, complexI); // this way the result may fit in the source matrix
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//! [dft]
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// compute the magnitude and switch to logarithmic scale
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// => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
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//! [magnitude]
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Core.split(complexI, planes); // planes.get(0) = Re(DFT(I)
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// planes.get(1) = Im(DFT(I))
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Core.magnitude(planes.get(0), planes.get(1), planes.get(0));// planes.get(0) = magnitude
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Mat magI = planes.get(0); |
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//! [magnitude]
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//! [log]
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Mat matOfOnes = Mat.ones(magI.size(), magI.type()); |
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Core.add(matOfOnes, magI, magI); // switch to logarithmic scale
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Core.log(magI, magI); |
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//! [log]
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//! [crop_rearrange]
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// crop the spectrum, if it has an odd number of rows or columns
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magI = magI.submat(new Rect(0, 0, magI.cols() & -2, magI.rows() & -2)); |
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// rearrange the quadrants of Fourier image so that the origin is at the image center
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int cx = magI.cols()/2; |
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int cy = magI.rows()/2; |
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Mat q0 = new Mat(magI, new Rect(0, 0, cx, cy)); // Top-Left - Create a ROI per quadrant
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Mat q1 = new Mat(magI, new Rect(cx, 0, cx, cy)); // Top-Right
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Mat q2 = new Mat(magI, new Rect(0, cy, cx, cy)); // Bottom-Left
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Mat q3 = new Mat(magI, new Rect(cx, cy, cx, cy)); // Bottom-Right
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Mat tmp = new Mat(); // swap quadrants (Top-Left with Bottom-Right)
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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); // swap quadrant (Top-Right with Bottom-Left)
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q2.copyTo(q1); |
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tmp.copyTo(q2); |
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//! [crop_rearrange]
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magI.convertTo(magI, CvType.CV_8UC1); |
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//! [normalize]
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Core.normalize(magI, magI, 0, 255, Core.NORM_MINMAX, CvType.CV_8UC1); // Transform the matrix with float values
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// into a viewable image form (float between
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// values 0 and 255).
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//! [normalize]
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HighGui.imshow("Input Image" , I ); // Show the result
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HighGui.imshow("Spectrum Magnitude", magI); |
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HighGui.waitKey(); |
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System.exit(0); |
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} |
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} |
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public class DiscreteFourierTransform { |
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public static void main(String[] args) { |
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// Load the native library.
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME); |
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new DiscreteFourierTransformRun().run(args); |
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} |
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} |
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from __future__ import print_function |
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import sys |
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import cv2 |
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import numpy as np |
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def print_help(): |
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print(''' |
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This program demonstrated the use of the discrete Fourier transform (DFT). |
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The dft of an image is taken and it's power spectrum is displayed. |
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Usage: |
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discrete_fourier_transform.py [image_name -- default ../../../../data/lena.jpg]''') |
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def main(argv): |
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print_help() |
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filename = argv[0] if len(argv) > 0 else "../../../../data/lena.jpg" |
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I = cv2.imread(filename, cv2.IMREAD_GRAYSCALE) |
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if I is None: |
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print('Error opening image') |
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return -1 |
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## [expand] |
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rows, cols = I.shape |
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m = cv2.getOptimalDFTSize( rows ) |
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n = cv2.getOptimalDFTSize( cols ) |
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padded = cv2.copyMakeBorder(I, 0, m - rows, 0, n - cols, cv2.BORDER_CONSTANT, value=[0, 0, 0]) |
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## [expand] |
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## [complex_and_real] |
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planes = [np.float32(padded), np.zeros(padded.shape, np.float32)] |
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complexI = cv2.merge(planes) # Add to the expanded another plane with zeros |
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## [complex_and_real] |
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## [dft] |
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cv2.dft(complexI, complexI) # this way the result may fit in the source matrix |
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## [dft] |
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# compute the magnitude and switch to logarithmic scale |
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# = > log(1 + sqrt(Re(DFT(I)) ^ 2 + Im(DFT(I)) ^ 2)) |
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## [magnitude] |
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cv2.split(complexI, planes) # planes[0] = Re(DFT(I), planes[1] = Im(DFT(I)) |
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cv2.magnitude(planes[0], planes[1], planes[0])# planes[0] = magnitude |
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magI = planes[0] |
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## [magnitude] |
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## [log] |
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matOfOnes = np.ones(magI.shape, dtype=magI.dtype) |
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cv2.add(matOfOnes, magI, magI) # switch to logarithmic scale |
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cv2.log(magI, magI) |
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## [log] |
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## [crop_rearrange] |
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magI_rows, magI_cols = magI.shape |
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# crop the spectrum, if it has an odd number of rows or columns |
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magI = magI[0:(magI_rows & -2), 0:(magI_cols & -2)] |
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cx = int(magI_rows/2) |
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cy = int(magI_cols/2) |
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q0 = magI[0:cx, 0:cy] # Top-Left - Create a ROI per quadrant |
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q1 = magI[cx:cx+cx, 0:cy] # Top-Right |
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q2 = magI[0:cx, cy:cy+cy] # Bottom-Left |
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q3 = magI[cx:cx+cx, cy:cy+cy] # Bottom-Right |
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tmp = np.copy(q0) # swap quadrants (Top-Left with Bottom-Right) |
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magI[0:cx, 0:cy] = q3 |
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magI[cx:cx + cx, cy:cy + cy] = tmp |
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tmp = np.copy(q1) # swap quadrant (Top-Right with Bottom-Left) |
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magI[cx:cx + cx, 0:cy] = q2 |
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magI[0:cx, cy:cy + cy] = tmp |
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## [crop_rearrange] |
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## [normalize] |
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cv2.normalize(magI, magI, 0, 1, cv2.NORM_MINMAX) # Transform the matrix with float values into a |
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## viewable image form(float between values 0 and 1). |
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## [normalize] |
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cv2.imshow("Input Image" , I ) # Show the result |
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cv2.imshow("spectrum magnitude", magI) |
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cv2.waitKey() |
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if __name__ == "__main__": |
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main(sys.argv[1:]) |
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