diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index cd35dfa..3ecc75b 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -43,11 +43,11 @@ for camera in IMAGES_CAMERAS_FOLDER:
     imageFileName = imagesCamerasFileNames[camera][0]
     imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
     singleColorChannelImagesShape = getColorChannel(imageFilePath, Color.RED).shape
-    #print(singleColorChannelImagesShape)
     if minimalColorChannelCameraResolution is None or singleColorChannelImagesShape < minimalColorChannelCameraResolution:
         minimalColorChannelCameraResolution = singleColorChannelImagesShape
-#print(minimalColorChannelCameraResolution)
-#exit(1)
+
+minColor = 13#None
+maxColor = 7497#None
 
 accuracy = []
 numberOfTrainingImages = int(minimumNumberOfImagesCameras * TRAINING_PORTION)
@@ -58,6 +58,23 @@ returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxC
 
 for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
     rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
+    if not computeExtremes:
+        print(f'{minColor=} {maxColor=}')
+        print('Extracting noise of testing images')
+        for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
+            for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
+                # Should make a function
+                imageFileName = imagesCamerasFileNames[camera][numberOfTrainingImages + cameraTestingImageIndex]
+                imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
+
+                # 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
+
+                cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [multipleColorsDenoisedImage]
     for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
         for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
             imageFileName = imagesCamerasFileNames[camera][cameraTrainingImageIndex]
@@ -92,23 +109,6 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                         if cameraPredicted == actualCamera:
                             numberOfTrainingImagesAccuracy += 1
                 accuracy += [numberOfTrainingImagesAccuracy / (numberOfTestingImages * numberOfCameras)]
-    if computeExtremes:
-        print(f'{minColor=} {maxColor=}')
-        print('Extracting noise of testing images')
-        for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
-            for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
-                # Should make a function
-                imageFileName = imagesCamerasFileNames[camera][numberOfTrainingImages + cameraTestingImageIndex]
-                imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
-                
-                # 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
-
-                cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [multipleColorsDenoisedImage]
 
 for camera in IMAGES_CAMERAS_FOLDER:
     plt.imsave(f'{setting}_estimated_prnu_subgroup_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))