Update remove_period_patterns.py

datasets/raise/fft/remove_period_patterns.py

@@ -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()