Move denoise from extract_noise.py to utils.py

See next commit leveraging it in `split_and_compare_prnus_of_subgroups.py`

datasets/raise/extract_noise.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python
 
-import skimage.restoration
 from skimage import img_as_float
 import numpy as np
 from PIL import Image
@@ -8,26 +7,20 @@ import os
 from tqdm import tqdm
 import csv
 import rawpy
-from utils import Color
+from utils import Color, denoise
 import matplotlib.pyplot as plt
 from scipy.ndimage import gaussian_filter
 
 imagesFolderPath = 'rafael/arw'
 imagesFolderPathFileName = imagesFolderPath.replace('/', '_')
 # Among:
-# - `wavelet`
-# - `bilateral`
-# - `tv_chambolle`
-# - `mean`
+# `denoise` possible denoisers and `mean`.
 denoiser = 'mean'
 
 raiseNotFlatFields = False
 # `[Color.RED, Color.GREEN_RIGHT, ...]` or `Color` or `[None]` for not raw images.
 colors = [None]
 
-if denoiser != 'mean':
-    denoise = getattr(skimage.restoration, f'denoise_{denoiser}')
-
 imagesFileNames = os.listdir(imagesFolderPath + ('/png' if raiseNotFlatFields else ''))
 
 if raiseNotFlatFields:
@@ -113,15 +106,10 @@ def treatImage(imageFileName, computeExtremes = False, color = None):
     imageNpArray = getImageNpArray(imageFileName, computeExtremes, color)
     if imageNpArray is None:
         return
-    match denoiser:
-        case 'wavelet':
-            imageDenoisedNpArray = denoise(imageNpArray, rescale_sigma=True)
-        case 'bilateral':
-            imageDenoisedNpArray = denoise(imageNpArray, sigma_color=0.05, sigma_spatial=15)
-        case 'tv_chambolle':
-            imageDenoisedNpArray = denoise(imageNpArray, weight=0.2)
-        case 'mean':
-            imageDenoisedNpArray = means[color]
+    if denoiser != 'mean':
+        imageDenoisedNpArray = denoise(imageNpArray, denoiser)
+    else:
+        imageDenoisedNpArray = means[color]
     imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
     if mean is None:
         mean = imageNoiseNpArray

datasets/raise/utils.py

@@ -1,4 +1,5 @@
 from enum import Enum, auto
+import skimage.restoration
 
 class Color(Enum):
     RED = auto()
@@ -8,3 +9,19 @@ class Color(Enum):
 
     def __str__(self):
         return self.name.lower()
+
+# Among:
+# - `wavelet`
+# - `bilateral`
+# - `tv_chambolle`
+def denoise(imageNpArray, denoiserName):
+    skImageRestorationDenoise = getattr(skimage.restoration, f'denoise_{denoiserName}')
+
+    match denoiserName:
+        case 'wavelet':
+            imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, rescale_sigma=True)
+        case 'bilateral':
+            imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, sigma_color=0.05, sigma_spatial=15)
+        case 'tv_chambolle':
+            imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, weight=0.2)
+    return imageDenoisedNpArray