diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index 41e063f..c03a339 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -32,7 +32,7 @@ for camera in IMAGES_CAMERAS_FOLDER:
     random.shuffle(imagesCamerasFileNames[camera])
 
 # Limit number of images per camera with the one having the less images.
-minimumNumberOfImagesCameras = min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
+minimumNumberOfImagesCameras = 4#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
 for camera in IMAGES_CAMERAS_FOLDER:
     imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
     print(camera, imagesCamerasFileNames[camera])
@@ -143,11 +143,11 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 cameraIterativeMean.add(imagePrnuEstimateNpArray)
             else:
                 # Still use `cameraIterativeMean` to simplify the implementation.
-                # TODO: cameraIterativeMean.mean = mean([image_training_j_camera - cameraIterativeMean.mean for j in range(l)])
-                cameraIterativeMean.mean = 42
+                cameraIterativeMean.mean = np.mean([cameraTrainingImage - mergeSingleColorChannelImagesAccordingToBayerFilter({color: cameraColorMeans[camera][color].mean for color in Color}) for cameraTrainingImage in [multipleColorsImage]], axis = 0)#[:cameraTrainingImageIndex + 1]])
                 print(f'{cameraIterativeMean.mean = }')
                 print(f'{camerasIterativeMean[camera].mean = }')
-                exit(1)
+                if cameraIterativeMean.mean[0, 0] != 0:
+                    exit(1)
             # If we are considering the last camera and (not `PREDICT_ONLY_ON_WHOLE_TRAINING_SET` or we are considering the last training image), then we proceeded an additional image for all cameras and we can predict the accuracy at this learning step.
             if cameraIndex == numberOfCameras - 1 and (not PREDICT_ONLY_ON_WHOLE_TRAINING_SET or cameraTrainingImageIndex == numberOfTrainingImages - 1):
                 numberOfTrainingImagesAccuracy = 0