diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index f1bd461..50a00e9 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -25,32 +25,30 @@ imagesCamerasFileNames = {camera: os.listdir(imageCameraFolder) for camera, imag
 random.seed(0)
 # To not have a bias (chronological for instance) when split to make training and testing sets.
 for camera in IMAGES_CAMERAS_FOLDER:
-    #print(imagesCamerasFileNames[camera][:3])
     random.shuffle(imagesCamerasFileNames[camera])
-    #print(imagesCamerasFileNames[camera][:3])
-    #exit(1)
 
 minimumNumberOfImagesCameras = 10#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
-#print(minimumNumberOfImagesCameras)
-#exit(1)
 for camera in IMAGES_CAMERAS_FOLDER:
     IMAGES_CAMERAS_FOLDER[camera] = IMAGES_CAMERAS_FOLDER[camera][:minimumNumberOfImagesCameras]
 
 numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
-camerasIterativeMean = [iterativeMean() for _ in range(numberOfCameras)]
+camerasIterativeMean = {camera: iterativeMean() for camera in IMAGES_CAMERAS_FOLDER}
 
 minColor = None
 maxColor = None
 
 accuracy = []
+numberOfTrainingImages = int(minimumNumberOfImagesCameras * TRAINING_PORTION)
+numberOfTestingImages = minimumNumberOfImagesCameras - int(minimumNumberOfImagesCameras * TRAINING_PORTION)
+cameraTestingImagesNoise = {}#{camera: [] for camera in IMAGES_CAMERAS_FOLDER}
 
 returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
 
 for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
     rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
-    for cameraImageIndex in tqdm(range(minimumNumberOfImagesCameras * TRAINING_PORTION), 'Camera image index'):
-        for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
-            imageFileName = imagesCamerasFileNames[camera][cameraImageIndex]
+    for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
+        for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
+            imageFileName = imagesCamerasFileNames[camera][cameraTrainingImageIndex]
             imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
             singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
             multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
@@ -63,16 +61,33 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
             multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
 
             imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
-            cameraIterativeMean = subgroupsIterativeMean[cameraIndex]
+            cameraIterativeMean = camerasIterativeMean[camera]
             cameraIterativeMean.add(imagePrnuEstimateNpArray)
             if cameraIndex == numberOfCameras - 1:
-                rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - cameraIndex].mean)
-                rmss += [rms]
+                for cameraTestingImageIndex in tqdm(range(numberOfTrainingImages), 'Camera testing image index'):
+                    singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
+                    multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
+                    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
+                    multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
+                    rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - cameraIndex].mean)
+                accuracy += [rms]
     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'):
+                imageFilePath = 
+                singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), 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 range(IMAGES_CAMERAS_FOLDER):
-    plt.imsave(f'{setting}_estimated_prnu_subgroup_{escapeFilePath(camera)}.png', (subgroupsIterativeMean[cameraIndex].mean))
+    plt.imsave(f'{setting}_estimated_prnu_subgroup_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))
 
 plt.title(f'Accuracy of camera source attribution thanks to a given number of images to estimate PRNUs with {DENOISER} denoiser')
 plt.xlabel('Number of images to estimate PRNU')