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3dcc8c38b4
Removed obsolete python samples #25268 Clean Samples #25006 This PR removes 36 obsolete python samples from the project, as part of an effort to keep the codebase clean and focused on current best practices. Some of these samples will be updated with latest algorithms or will be combined with other existing samples. Removed Samples: > browse.py camshift.py coherence.py color_histogram.py contours.py deconvolution.py dft.py dis_opt_flow.py distrans.py edge.py feature_homography.py find_obj.py fitline.py gabor_threads.py hist.py houghcircles.py houghlines.py inpaint.py kalman.py kmeans.py laplace.py lk_homography.py lk_track.py logpolar.py mosse.py mser.py opt_flow.py plane_ar.py squares.py stitching.py text_skewness_correction.py texture_flow.py turing.py video_threaded.py video_v4l2.py watershed.py These changes aim to improve the repository's clarity and usability by removing examples that are no longer relevant or have been superseded by more up-to-date techniques.
121 lines
2.8 KiB
Python
Executable File
121 lines
2.8 KiB
Python
Executable File
#!/usr/bin/env python
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'''
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sample for discrete fourier transform (dft)
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USAGE:
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dft.py <image_file>
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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import sys
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def shift_dft(src, dst=None):
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'''
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Rearrange the quadrants of Fourier image so that the origin is at
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the image center. Swaps quadrant 1 with 3, and 2 with 4.
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src and dst arrays must be equal size & type
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'''
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if dst is None:
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dst = np.empty(src.shape, src.dtype)
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elif src.shape != dst.shape:
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raise ValueError("src and dst must have equal sizes")
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elif src.dtype != dst.dtype:
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raise TypeError("src and dst must have equal types")
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if src is dst:
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ret = np.empty(src.shape, src.dtype)
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else:
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ret = dst
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h, w = src.shape[:2]
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cx1 = cx2 = w // 2
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cy1 = cy2 = h // 2
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# if the size is odd, then adjust the bottom/right quadrants
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if w % 2 != 0:
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cx2 += 1
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if h % 2 != 0:
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cy2 += 1
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# swap quadrants
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# swap q1 and q3
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ret[h-cy1:, w-cx1:] = src[0:cy1 , 0:cx1 ] # q1 -> q3
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ret[0:cy2 , 0:cx2 ] = src[h-cy2:, w-cx2:] # q3 -> q1
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# swap q2 and q4
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ret[0:cy2 , w-cx2:] = src[h-cy2:, 0:cx2 ] # q2 -> q4
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ret[h-cy1:, 0:cx1 ] = src[0:cy1 , w-cx1:] # q4 -> q2
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if src is dst:
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dst[:,:] = ret
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return dst
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def main():
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if len(sys.argv) > 1:
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fname = sys.argv[1]
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else:
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fname = 'baboon.jpg'
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print("usage : python dft.py <image_file>")
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im = cv.imread(cv.samples.findFile(fname))
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# convert to grayscale
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im = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
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h, w = im.shape[:2]
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realInput = im.astype(np.float64)
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# perform an optimally sized dft
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dft_M = cv.getOptimalDFTSize(w)
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dft_N = cv.getOptimalDFTSize(h)
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# copy A to dft_A and pad dft_A with zeros
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dft_A = np.zeros((dft_N, dft_M, 2), dtype=np.float64)
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dft_A[:h, :w, 0] = realInput
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# no need to pad bottom part of dft_A with zeros because of
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# use of nonzeroRows parameter in cv.dft()
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cv.dft(dft_A, dst=dft_A, nonzeroRows=h)
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cv.imshow("win", im)
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# Split fourier into real and imaginary parts
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image_Re, image_Im = cv.split(dft_A)
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# Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
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magnitude = cv.sqrt(image_Re**2.0 + image_Im**2.0)
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# Compute log(1 + Mag)
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log_spectrum = cv.log(1.0 + magnitude)
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# Rearrange the quadrants of Fourier image so that the origin is at
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# the image center
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shift_dft(log_spectrum, log_spectrum)
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# normalize and display the results as rgb
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cv.normalize(log_spectrum, log_spectrum, 0.0, 1.0, cv.NORM_MINMAX)
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cv.imshow("magnitude", log_spectrum)
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cv.waitKey(0)
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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