datasets/raise/extract_noise.py

@@ -4,8 +4,9 @@ import numpy as np
 import os
 from tqdm import tqdm
 import csv
-from utils import Color, denoise, iterativeMean, isARawImage, escapeFilePath, getColorChannel, saveNpArray, rescaleRawImageForDenoiser, updateExtremes, getColorMeans
+from utils import Color, denoise, iterativeMean, isARawImage, escapeFilePath, getColorChannel, saveNpArray, rescaleRawImageForDenoiser, updateExtremes
 import matplotlib.pyplot as plt
+from scipy.ndimage import gaussian_filter
 
 imagesFolderPath = 'rafael/arw'
 imagesFolderPathFileName = escapeFilePath(imagesFolderPath)
@@ -74,7 +75,7 @@ def treatImage(imageFileName, computeExtremes = False, color = None):
     if denoiser != 'mean':
         imageDenoisedNpArray = denoise(imageNpArray, denoiser)
     else:
-        imageDenoisedNpArray = colorMeans[color]
+        imageDenoisedNpArray = means[color]
     imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
     estimatedPrnuIterativeMean.add(imageNoiseNpArray)
 
@@ -90,12 +91,17 @@ if (minColor is None or maxColor is None) and denoiser != 'mean':
     print(f'{maxColor=}')
 
 if denoiser == 'mean':
-    colorMeans = getColorMeans(imagesFileNames, colors)
-    for color in Color:
-        colorMeans[color] = colorMeans[color]
+    means = {}
+    for color in colors:
+        colorIterativeMean = iterativeMean()
+        for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} colored images'):
+            imageNpArray = getImageNpArray(imageFileName, False, color)
+            imageNpArray = gaussian_filter(imageNpArray, sigma = 5)
+            colorIterativeMean.add(imageNpArray)
+        means[color] = colorIterativeMean.mean
         fileName = f'mean_{imagesFolderPathFileName}_{color}'
         # Then use `merge_single_color_channel_images_according_to_bayer_filter.py` to consider all color channels, instead of saving this single color channel as an image.
-        saveNpArray(fileName, colorMeans[color])
+        saveNpArray(fileName, colorIterativeMean.mean)
 
 for color in colors:
     estimatedPrnuIterativeMean = iterativeMean()

datasets/raise/utils.py

@@ -7,7 +7,6 @@ from PIL import Image
 from skimage import img_as_float
 from datetime import datetime
 import builtins as __builtin__
-from scipy.ndimage import gaussian_filter
 
 class Color(Enum):
     RED = auto()
@@ -131,14 +130,3 @@ def updateExtremes(imageNpArray, minColor, maxColor):
 
 def print(*toPrint):
     __builtin__.print(datetime.now(), *toPrint)
-
-def getColorMeans(imagesFileNames, colors = Color):
-    colorMeans = {}
-    for color in colors:
-        colorIterativeMean = iterativeMean()
-        for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} colored images'):
-            imageNpArray = getImageNpArray(imageFileName, False, color)
-            imageNpArray = gaussian_filter(imageNpArray, sigma = 5)
-            colorIterativeMean.add(imageNpArray)
-        colorMeans[color] = colorIterativeMean.mean
-    return colorMeans