def dft(args):
'''Compute and show DFT of image'''
X = basic_image_ip(IMAGE_PATH, args)
if args.verbose:
print (X.shape)
fX = dip.fft2(X)
fX = dip.fftshift(fX)
fX = np.log(np.abs(fX))
if args.verbose:
print ("Done with FFT")
dip.imshow(fX)
dip.show()
#(c) Reading an image X = dip.im_read(picture_link) #(d) Converting the image to normalized floating point space X = dip.im_to_float(X) X *= 255 #(e) Adding Constant to Image Y = X + 75 #(f) Renormalize the image and covert to integer Y = dip.float_to_im(Y/255) #(g) Writing an image to disk dip.im_write(Y, save_link_1) #(h) Square Intenstiy and write image to disk Z = X**2 Z = dip.float_to_im(Z/255) Z = dip.im_write(Z, save_link_2) #(i) Compute FFT of X fX = dip.fft2(X) fX = dip.fftshift(fX) fX = np.log(np.abs(fX)) #(j) Save and show the resulting spectrum dip.imshow(fX) dip.show()
scan_id, frame_num, band = find_image(coordinateMetadata, utc)
params = find_image(coordinateMetadata, utc)
image = download_image(OBJECT, params)
print(image)
w = wcs.WCS(image)
x, y = w.wcs_world2pix(asteroidMetadata.ra[element],
asteroidMetadata.dec[element], 0)
X = display_fits(image)
X = filter(X)
plt.imshow(np.log(np.abs(X)))
plt.show()
fx = dip.fftshift(dip.fft2(X))
radius = 30
center = len(fx) / 2
f = np.log(np.abs(fx))
for a in range(len(f)):
for b in range(len(f[a])):
if np.square(center - a) + np.square(center - b) < np.square(radius):
f[a, b] = 0
plt.imshow(np.log(np.abs(dip.ifft2(fx))))
plt.show()
import dippykit as dip
import numpy as np
# Part (a)
I1 = dip.im_read('/home/harshbhate/Pictures/lena.png') # Specify your image here
I1 = dip.im_to_float(I1) #Converting image to float
I1 *= 255 #Normalizing
# Part (b)
# Take the Fourier transform of the image
H1 = dip.fft2(I1) # Fourier transform of I1
H1 = dip.fftshift(H1)
H1 = np.log(np.abs(H1))
print ("Shape of I1:"+str(I1.shape))
print("Shape of H1"+str(H1.shape))
# Part (c)
# Downsample the image by 2 in both directions (and take its Fourier transform)
x_scaling = 2
y_scaling = 2
sampling_matrix = np.array([[x_scaling, 0],[0, y_scaling]])
I2 = dip.sampling.resample(I1, sampling_matrix) # Downsampled I1
H2 = dip.fft2(I2) # Fourier transform of I2
# Part (d)
# Pad the downsampled image's spectrum (H2) with zeros and then take its
# inverse Fourier transform
H3 = np.pad(H2,(128, 128), 'constant', constant_values = (0,0)) # Zero-padded H2
I3 = np.abs(dip.ifft2(H3)) # Interpolated image
I3 = I3/(np.amax(I3))*255 #Normalizing
#Converting everything back to int and normalizing
I1 = dip.float_to_im(I1/255)
I2 = dip.float_to_im(I2/255)
def parser():
'''Parsing the input Argument'''
parser = argparse.ArgumentParser()
parser.add_argument("-v","--verbose", help="increase output verbosity",
action="store_true")
args = parser.parse_args()
return args
if __name__ == "__main__":
'''Main Function'''
#pdb.set_trace()
args = parser()
dft(args)
M = np.array([[0, 1],
[1, 0]])
if args.verbose:
print ("The M matrix:\n"+str(M))
L = L_matrix(M, args)
if args.verbose:
print ("The L matrix:\n"+str(L))
Xd = downsample(M, args, 'm_5.jpg')
Xt = upsample(Xd, L, args, None, 'm_5.jpg')
Xt = upsample(Xd, L, args, 'lin', 'm_5_lin.jpg')
#FFT of image rotated by 90
fX = dip.fft2(Xd)
fX = dip.fftshift(fX)
fX = np.log(np.abs(fX))
dip.imshow(fX)
dip.show()
PSNR (Xt, args)
# Proof of concept low pass filter
dim_filter = 800
h = np.zeros((dim_filter, dim_filter))
for u in range(dim_filter):
for v in range(dim_filter):
h[u][v] = exp(-(u + v) / (dim_filter * 0.3))
# Loading image
im = dip.im_read('images/UW_400.png')
im = dip.im_to_float(im)
if 2 < im.ndim:
im = np.mean(im, axis=2)
F = dip.fft2(im)
print(h * F)
#Plot results
#Original spectra
dip.figure(1)
dip.subplot(2, 2, 1)
dip.imshow(im, 'gray')
dip.title('Original Image')
dip.subplot(2, 2, 2)
dip.imshow(np.real(dip.ifft2(F * h)), 'gray')
dip.title('Modified image')
dip.subplot(2, 2, 3)