def do_stuff():
image = get_image()
dft = cv2.dft(np.float32(image), flags=cv2.DFT_COMPLEX_OUTPUT)
# shift the zero-frequncy component to the center of the spectrum
dft_shift = np.fft.fftshift(dft)
# save image of the image in the fourier domain.
magnitude_spectrum = 20 * np.log(
cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))
# magnitude_spectrum = 20 * np.log(np.sqrt(dft_shift[:, :, 0] + dft_shift[:, :, 1]))
cv2.imwrite("magnitude_spectrum.png", magnitude_spectrum)
rows, cols = image.shape
crow, ccol = rows // 2, cols // 2
# create a mask first, center square is 1, remaining all zeros
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1
# mask -= 1
# mask = np.abs(mask)
# apply mask and inverse DFT
fshift = dft_shift * mask
f_ishift = np.fft.ifftshift(fshift)
img_back = cv2.idft(f_ishift)
img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
img_back = img_back / img_back.max() * 255
cv2.imwrite("filtered_image_low.png", img_back)
def LPK_OpenCv(mask, r, c, D0, dftShift):
mask[r - D0:r + D0, c - D0:c + D0] = 1
fshift = dftShift * mask
ishift = np.fft.ifftshift(fshift)
iimage = cv2.idft(ishift)
iimage = cv2.magnitude(iimage[:, :, 0], iimage[:, :, 1])
return iimage
def apply_mask(img, mask):
dft_shift = apply_fourier_transform(img)
fshift = dft_shift * mask
f_ishift = np.fft.ifftshift(fshift)
img_back = cv2.idft(f_ishift)
img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
return img_back
def DFT_IDFT_OpenCv(image):
dft = cv2.dft(np.float32(image), flags=cv2.DFT_COMPLEX_OUTPUT)
dftShift = np.fft.fftshift(dft)
fourier = 20 * np.log(cv2.magnitude(dftShift[:, :, 0], dftShift[:, :, 1]))
ishift = np.fft.ifftshift(dftShift)
iimage = cv2.idft(ishift)
iimage = cv2.magnitude(iimage[:, :, 0], iimage[:, :, 1])
Plt_Contrast(image, fourier, iimage)
def HPK_OpenCv(r, c, D0, dftShift):
dftShift[r - D0:r + D0, c - D0:c + D0] = 0
ishift = np.fft.ifftshift(dftShift)
iimage = cv2.idft(ishift)
iimage = cv2.magnitude(iimage[:, :, 0], iimage[:, :, 1])
return iimage
from matplotlib import pyplot as plt
import numpy as np
img = cv.imread('tree.jpg', 0)
dft = cv.dft(np.float32(img), flags=cv.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
magnitude_spectrum = 20 * \
np.log(cv.magnitude(dft_shift[:,:, 0], dft_shift[:,:, 1]))
plt.subplot(121), plt.imshow(img, cmap='gray')
plt.title('Source'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(magnitude_spectrum, cmap='gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()
rows, cols = img.shape
crow, ccol = int(rows / 2), int(cols / 2)
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1
fshift = dft_shift * mask
f_ishift = np.fft.ifftshift(fshift)
img_back = cv.idft(f_ishift)
plt.subplot(121), plt.imshow(img, cmap='gray')
plt.title('Source'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(img_back, cmap='gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.imshow([])