Add and use getImagePrnuEstimatedNpArray

datasets/raise/attribute_source_camera.py

@@ -58,6 +58,21 @@ def getImageFilePath(camera, cameraImageIndex):
     imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
     return imageFilePath
 
+def getSingleColorChannelImages(camera, cameraImageIndex):
+    imageFilePath = getImageFilePath(camera, cameraImageIndex)
+    singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
+    return singleColorChannelImages
+
+def getMultipleColorsImage(singleColorChannelImages):
+    multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
+    return multipleColorsImage
+
+def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage):
+    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
+    multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
+    imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
+    return imagePrnuEstimateNpArray
+
 from utils import silentTqdm
 #tqdm = silentTqdm
 
@@ -70,31 +85,25 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
         print('Extracting noise of testing images')
         for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
             for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
-                imageFilePath = getImageFilePath(camera, numberOfTrainingImages + cameraTestingImageIndex)
-                print(f'{imageFilePath=}')
+                print(f'{camera=} {numberOfTrainingImages + cameraTestingImageIndex=}')
 
-                # Should make a function
-                singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
-                multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
-                singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
-                multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
-                imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
+                singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+                multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
+
+                imagePrnuEstimateNpArray = getImagePrnuEstimatedNpArray(singleColorChannelImages, multipleColorsImage)
 
                 cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [imagePrnuEstimateNpArray]
     for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
         for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
-            imageFilePath = getImageFilePath(camera, cameraTrainingImageIndex)
-            singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
-            multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
+            singleColorChannelImages = getSingleColorChannelImages(camera, cameraTrainingImageIndex)
+            multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
 
             if computeExtremes:
                 minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
                 continue
 
-            singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
-            multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
+            imagePrnuEstimateNpArray = getImagePrnuEstimatedNpArray(singleColorChannelImages, multipleColorsImage)
 
-            imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
             cameraIterativeMean = camerasIterativeMean[camera]
             cameraIterativeMean.add(imagePrnuEstimateNpArray)
             if cameraIndex == numberOfCameras - 1: