datasets/raise/attribute_source_camera.py

@@ -3,7 +3,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
-from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, getColorMeans, getImageCrop, Denoiser
+from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, getColorMeans, getImageCrop
 import sys
 import os
 import random
@@ -12,7 +12,7 @@ sys.path.insert(0, '../../algorithms/distance/')
 
 from rms_diff import rmsDiffNumpy
 
-DENOISER = Denoiser.MEAN
+DENOISER = 'mean'
 IMAGES_CAMERAS_FOLDER = {
     'RAISE': 'flat-field/nef',
     'Rafael 23/04/24': 'rafael/230424',
@@ -68,13 +68,13 @@ def getMultipleColorsImage(singleColorChannelImages):
     return multipleColorsImage
 
 def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
-    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != Denoiser.MEAN else cameraColorMeans[camera][color] for color in Color}
+    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != 'mean' else cameraColorMeans[camera][color] for color in Color}
     multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
     imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
     return imagePrnuEstimateNpArray
 
 imagesCamerasFilePaths = {camera: [f'{IMAGES_CAMERAS_FOLDER[camera]}/{imagesCamerasFileName}' for imagesCamerasFileName in imagesCamerasFileNames[camera]] for camera in imagesCamerasFileNames}
-cameraColorMeans = {camera: getColorMeans(imagesCamerasFilePaths[camera], Color, minimalColorChannelCameraResolution) for camera in imagesCamerasFilePaths}
+cameraColorMeans = {camera: getColorMeans(imagesCamerasFilePaths[camera], Color, DENOISER, minimalColorChannelCameraResolution) for camera in imagesCamerasFilePaths}
 
 from utils import silentTqdm
 #tqdm = silentTqdm

datasets/raise/extract_noise.py

@@ -4,12 +4,14 @@ import numpy as np
 import os
 from tqdm import tqdm
 import csv
-from utils import Color, denoise, iterativeMean, escapeFilePath, saveNpArray, getColorMeans, getImageNpArray, Denoiser
+from utils import Color, denoise, iterativeMean, escapeFilePath, saveNpArray, getColorMeans, getImageNpArray
 import matplotlib.pyplot as plt
 
 IMAGES_FOLDER_PATH = 'rafael/230424'
 imagesFolderPathFileName = escapeFilePath(IMAGES_FOLDER_PATH)
-DENOISER = Denoiser.WAVELET
+# Among:
+# `denoise` possible denoisers and `mean`.
+DENOISER = 'mean'
 
 RAISE_NOT_FLAT_FIELDS = False
 # `[Color.RED, Color.GREEN_RIGHT, ...]` or `Color` or `[None]` for not raw images.
@@ -59,14 +61,14 @@ def treatImage(imageFileName, computeExtremes = False, color = None):
     imageNpArray = getImageNpArray(imageFilePath, computeExtremes, color, DENOISER)
     if imageNpArray is None:
         return
-    if DENOISER != Denoiser.MEAN:
+    if DENOISER != 'mean':
         imageDenoisedNpArray = denoise(imageNpArray, DENOISER)
     else:
         imageDenoisedNpArray = colorMeans[color]
     imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
     estimatedPrnuIterativeMean.add(imageNoiseNpArray)
 
-if (minColor is None or maxColor is None) and DENOISER != Denoiser.MEAN:
+if (minColor is None or maxColor is None) and DENOISER != 'mean':
     # Assuming same intensity scale across color channels.
     for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
         for color in COLORS:
@@ -77,8 +79,8 @@ if (minColor is None or maxColor is None) and DENOISER != Denoiser.MEAN:
     print(f'{minColor=}')
     print(f'{maxColor=}')
 
-if DENOISER == Denoiser.MEAN:
-    colorMeans = getColorMeans(imagesFileNames, COLORS)
+if DENOISER == 'mean':
+    colorMeans = getColorMeans(imagesFileNames, COLORS, DENOISER)
     for color in Color:
         colorMeans[color] = colorMeans[color]
         fileName = f'mean_{imagesFolderPathFileName}_{color}'

datasets/raise/split_and_compare_prnus_of_subgroups.py

@@ -3,7 +3,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
-from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, Denoiser
+from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray
 import sys
 import os
 import random
@@ -13,7 +13,7 @@ sys.path.insert(0, '../../algorithms/distance/')
 from rms_diff import rmsDiffNumpy
 
 NUMBER_OF_SUBGROUPS = 2
-DENOISER = Denoiser.WAVELET
+DENOISER = 'wavelet'
 IMAGES_FOLDER = 'flat-field/NEF'
 
 setting = escapeFilePath(IMAGES_FOLDER) + f'_{DENOISER}'

datasets/raise/utils.py

@@ -18,28 +18,19 @@ class Color(Enum):
     def __str__(self):
         return self.name.lower()
 
-class Denoiser(Enum):
-    WAVELET = auto()
-    BILATERAL = auto()
-    TV_CHAMBOLLE = auto()
-    MEAN = auto()
-
-    def __str__(self):
-        return self.name.lower()
-
 # Among:
 # - `wavelet`
 # - `bilateral`
 # - `tv_chambolle`
-def denoise(imageNpArray, denoiser):
-    skImageRestorationDenoise = getattr(skimage.restoration, f'denoise_{denoiser}')
+def denoise(imageNpArray, denoiserName):
+    skImageRestorationDenoise = getattr(skimage.restoration, f'denoise_{denoiserName}')
 
-    match denoiser:
-        case Denoiser.WAVELET:
+    match denoiserName:
+        case 'wavelet':
             imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, rescale_sigma=True)
-        case Denoiser.BILATERAL:
+        case 'bilateral':
             imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, sigma_color=0.05, sigma_spatial=15)
-        case Denoiser.TV_CHAMBOLLE:
+        case 'tv_chambolle':
             imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, weight=0.2)
     return imageDenoisedNpArray
 
@@ -141,12 +132,12 @@ def updateExtremes(imageNpArray, minColor, maxColor):
 def print(*toPrint):
     __builtin__.print(datetime.now(), *toPrint)
 
-def getColorMeans(imagesFileNames, colors, singleColorChannelCropResolution = None):
+def getColorMeans(imagesFileNames, colors, denoiser, singleColorChannelCropResolution = None):
     colorMeans = {}
     for color in colors:
         colorIterativeMean = iterativeMean()
         for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} colored images'):
-            imageNpArray = getImageNpArray(imageFileName, False, color, Denoiser.MEAN)
+            imageNpArray = getImageNpArray(imageFileName, False, color, denoiser)
             if singleColorChannelCropResolution is not None:
                 imageNpArray = getImageCrop(imageNpArray, singleColorChannelCropResolution)
             imageNpArray = gaussian_filter(imageNpArray, sigma = 5)
@@ -162,7 +153,7 @@ def getImageNpArray(imageFilePath, computeExtremes, color, denoiser):
         minColor, maxColor = updateExtremes(imageNpArray, minColor, maxColor)
         return
 
-    if isARawImage(imageFilePath) and denoiser != Denoiser.MEAN:
+    if isARawImage(imageFilePath) and denoiser != 'mean':
         imageNpArray = rescaleRawImageForDenoiser(imageNpArray, minColor, maxColor)
         # Pay attention to range of values expected by the denoiser.
         # Indeed if provide the thousands valued raw image, then the denoiser only returns values between 0 and 1 and making the difference between both look pointless.