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Update remove_period_patterns.py

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Benjamin Loison 2024-05-15 15:39:11 +02:00
parent d68cd1f8d9
commit dac9ce9d50
Signed by: Benjamin_Loison
SSH Key Fingerprint: SHA256:BtnEgYTlHdOg1u+RmYcDE0mnfz1rhv5dSbQ2gyxW8B8
1 changed files with 29 additions and 20 deletions
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49
datasets/raise/fft/remove_period_patterns.py
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@ -24,35 +24,43 @@ image = multipleColorsImage
fft1 = fftpack.fftshift(fftpack.fft2(image))
originalFft1 = fft1.copy()
fft1 = abs(fft1)
# This example is intended to demonstrate how astropy.convolve and
# scipy.convolve handle missing data, so we start by setting the brightest
# pixels to NaN to simulate a "saturated" data set
height, width = fft1.shape
for x in range(width):
fft1[height // 2, x] = np.nan
def removePeriodicPatterns(fft1Part):
# This example is intended to demonstrate how astropy.convolve and
# scipy.convolve handle missing data, so we start by setting the brightest
# pixels to NaN to simulate a "saturated" data set
height, width = fft1Part.shape
for x in range(width):
fft1Part[height // 2, x] = np.nan
middleX = width // 2
RANGE = 1
for verticalLineX in [width // 4, width // 2, round(3 * width / 4)]:
for y in range(height):
for x in range(verticalLineX - RANGE, verticalLineX + RANGE + 1):
fft1[y, x] = np.nan
middleX = width // 2
RANGE = 1
for verticalLineX in [width // 4, width // 2, round(3 * width / 4)]:
for y in range(height):
for x in range(verticalLineX - RANGE, verticalLineX + RANGE + 1):
fft1Part[y, x] = np.nan
# We smooth with a Gaussian kernel
kernel = Gaussian2DKernel(x_stddev = X_STDDEV)
# We smooth with a Gaussian kernel
kernel = Gaussian2DKernel(x_stddev = X_STDDEV)
# create a "fixed" image with NaNs replaced by interpolated values
fixedImage = interpolate_replace_nans(fft1, kernel)
# create a "fixed" image with NaNs replaced by interpolated values
fixedImage = interpolate_replace_nans(fft1Part, kernel)
return fixedImage
realFixedImage = removePeriodicPatterns(np.real(fft1).copy())
imaginaryFixedImage = removePeriodicPatterns(np.imag(fft1).copy())
fixedImage = realFixedImage + 1j * imaginaryFixedImage
#fixedImage = realFixedImage
figure, axes = plt.subplots(1, 2, sharex = True, sharey = True)
plt.suptitle('Attenuating FFT significant lines')
axes[0].set_title('Original FFT')
#originalFft1Abs = abs(originalFft1)
#minValue = np.min()
axes[0].imshow(np.log10(abs(originalFft1)))
axes[1].set_title('FFT with significant lines attenuated')
axes[1].imshow(np.log10(fixedImage))
axes[1].imshow(np.log10(abs(fixedImage)))
plt.tight_layout()
plt.show()
#plt.imsave('fft.png', np.log10(fixedImage))
@ -66,8 +74,9 @@ def inverseFft(fft):
plt.suptitle('Rafael 23/04/24 PRNU mean denoiser with periodic patterns attenuated')
axes[0].set_title('Original PRNU')
axes[0].imshow(inverseFft(originalFft1))
invOriginalFft1 = inverseFft(originalFft1)
axes[0].imshow(invOriginalFft1)
axes[1].set_title('PRNU with periodic patterns attenuated')
axes[0].imshow(inverseFft(fixedImage))
axes[1].imshow(inverseFft(fixedImage), vmin = invOriginalFft1.min(), vmax = invOriginalFft1.max())
plt.tight_layout()
plt.show()