Update forgotten to commit scripts

datasets/raise/attribute_source_camera.py

@@ -111,6 +111,7 @@ returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxC
 
 # Assume to have `{min,max}Color` hardcoded.
 # Can just load to memory `getSingleColorChannelImages`, see [Robust_image_source_identification_on_modern_smartphones/issues/62#issuecomment-1861](https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/62#issuecomment-1861).
+'''
 rescaleIfNeeded = rescaleRawImageForDenoiser
 cameraTrainingImages = {}
 for cameraTrainingImageIndex in tqdm(range(numberOfTrainingImages), 'Load to memory camera training image'):
@@ -124,6 +125,7 @@ for camera in IMAGES_CAMERAS_FOLDER:
     for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Load to memory camera testing image'):
         singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
         singleColorChannelTestingImages[camera] += [singleColorChannelImages]
+'''
 
 # 2 loops:
 # - the first one is about computing `{min,max}Color`
@@ -139,7 +141,8 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
                     print(f'{camera=} {numberOfTrainingImages + cameraTestingImageIndex=}')
 
-                    singleColorChannelImages = singleColorChannelTestingImages[camera][cameraTestingImageIndex]#getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+                    #singleColorChannelImages = singleColorChannelTestingImages[camera][cameraTestingImageIndex]
+                    singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
                     multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
 
                     imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
@@ -156,7 +159,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
                 continue
 
-            if DENOISER == Denoiser.MEAN:
+            if DENOISER == Denoiser.MEAN and not PREDICT_ONLY_ON_WHOLE_TRAINING_SET:
                 for color in Color:
                     cameraColorMeans[camera][color].add(singleColorChannelImages[color])
             imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
@@ -182,7 +185,8 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                             if DENOISER != Denoiser.MEAN:
                                 cameraTestingImageNoise = cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex]
                             else:
-                                singleColorChannelImages = singleColorChannelTestingImages[camera][cameraTestingImageIndex]#getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+                                #singleColorChannelImages = singleColorChannelTestingImages[camera][cameraTestingImageIndex]
+                                singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
                                 multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
                                 cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 

datasets/raise/utils.py

@@ -205,7 +205,7 @@ def getColorMeans(imagesFileNames, colors, singleColorChannelCropResolution = No
     for color in colors:
         colorIterativeMean = iterativeMean()
         for imageFileName in tqdm(imagesFileNames, f'Computing mean of {str(color).replace("_", " ")} colored images'):
-            imageNpArray = getImageNpArray(imageFileName, False, color, Denoiser.MEAN)
+            imageNpArray, minColor_, maxColor_ = getImageNpArray(imageFileName, False, color, Denoiser.MEAN, None, None)
             if singleColorChannelCropResolution is not None:
                 imageNpArray = getImageCrop(imageNpArray, singleColorChannelCropResolution)
             imageNpArray = gaussian_filter(imageNpArray, sigma = 5)