From ec4657eda81f0fa448cadc3e09b10437d499da85 Mon Sep 17 00:00:00 2001
From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com>
Date: Wed, 15 May 2024 18:21:11 +0200
Subject: [PATCH] Clean low pass denoiser

---
 datasets/raise/utils.py | 24 +++++++++---------------
 1 file changed, 9 insertions(+), 15 deletions(-)

diff --git a/datasets/raise/utils.py b/datasets/raise/utils.py
index a8f8110..6c699a0 100644
--- a/datasets/raise/utils.py
+++ b/datasets/raise/utils.py
@@ -60,28 +60,22 @@ def denoise(imageNpArray, denoiser):
             # fft of image
             fft1 = fftpack.fftshift(fftpack.fft2(imageNpArray))
 
+            # Padding as follows may not be perfectly correct but seems fine enough.
             GAUSSIAN_SIGMA = 50
-            #low_pass_np = np.zeros(image1_np.shape, dtype = np.uint8)
 
-            low_pass_np_size = min(image1_np.shape)
-            low_pass_np = getGaussianKernel(low_pass_np_size, GAUSSIAN_SIGMA)
+            imageNpArrayShape = imageNpArray.shape
+            lowPassNpArraySize = min(imageNpArrayShape)
+            lowPassNpArray = getGaussianKernel(lowPassNpArraySize, GAUSSIAN_SIGMA)
 
-            #rectangle_low_pass_np = np.zeros(fft1.shape)
-            #rectangle_low_pass_np[]
-
-            requiredH, requiredW = image1_np.shape
-            vert_pad_size = (requiredH - low_pass_np_size) // 2
-            hor_pad_size = (requiredW - low_pass_np_size) // 2
-            rectangle_low_pass_np = np.pad(low_pass_np, [(vert_pad_size, vert_pad_size), (hor_pad_size, hor_pad_size)])
+            wantedHeight, wantedWidth = imageNpArrayShape
+            verticalPadSize, horizontalPadSize = [(wantedDimension - lowPassNpArraySize) // 2 for wantedDimension in [wantedHeight, wantedWidth]]
+            rectangleLowPassNpArray = np.pad(lowPassNpArray, [(verticalPadSize, verticalPadSize), (horizontalPadSize, horizontalPadSize)])
 
             # Unsure that doing such *normalization* is appropriate.
-            rectangle_low_pass_np /= rectangle_low_pass_np.max()
-
-            #plt.imshow(rectangle_low_pass_np)
-            #plt.show()
+            rectangleLowPassNpArray /= rectangleLowPassNpArray.max()
 
             # multiply both the images
-            filtered = np.multiply(fft1, lowPassNp)
+            filtered = np.multiply(fft1, rectangleLowPassNpArray)
 
             # inverse fft
             ifft2 = np.real(fftpack.ifft2(fftpack.ifftshift(filtered)))