diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index 50a00e9..cd35dfa 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -27,30 +27,42 @@ random.seed(0)
 for camera in IMAGES_CAMERAS_FOLDER:
     random.shuffle(imagesCamerasFileNames[camera])
 
-minimumNumberOfImagesCameras = 10#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:
-    IMAGES_CAMERAS_FOLDER[camera] = IMAGES_CAMERAS_FOLDER[camera][:minimumNumberOfImagesCameras]
+    imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
 
 numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
 camerasIterativeMean = {camera: iterativeMean() for camera in IMAGES_CAMERAS_FOLDER}
 
 minColor = None
 maxColor = None
+# Assume that for each camera, its images have the same resolution.
+# The following consider a given color channel resolution, assuming they all have the same resolution.
+minimalColorChannelCameraResolution = None
+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)
 
 accuracy = []
 numberOfTrainingImages = int(minimumNumberOfImagesCameras * TRAINING_PORTION)
 numberOfTestingImages = minimumNumberOfImagesCameras - int(minimumNumberOfImagesCameras * TRAINING_PORTION)
-cameraTestingImagesNoise = {}#{camera: [] for camera in IMAGES_CAMERAS_FOLDER}
+cameraTestingImagesNoise = {}
 
 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 cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
-        for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
+        for cameraIndex, camera in enumerate(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}
+            singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
             multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
 
             if computeExtremes:
@@ -64,29 +76,41 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
             cameraIterativeMean = camerasIterativeMean[camera]
             cameraIterativeMean.add(imagePrnuEstimateNpArray)
             if cameraIndex == numberOfCameras - 1:
-                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]
+                numberOfTrainingImagesAccuracy = 0
+                # Loop over each camera testing image folder.
+                for actualCamera in IMAGES_CAMERAS_FOLDER:
+                    for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
+                        cameraPredicted = None
+                        minimalDistance = None
+                        # Loop over each camera to compute closeness between the considered testing image noise and the estimated PRNUs of the various cameras.
+                        for camera in IMAGES_CAMERAS_FOLDER:
+                            distance = rmsDiffNumpy(cameraTestingImagesNoise[camera][cameraTestingImageIndex], camerasIterativeMean[camera].mean)
+                            print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
+                            if minimalDistance is None or distance < minimalDistance:
+                                minimalDistance = distance
+                                cameraPredicted = camera
+                        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'):
-                imageFilePath = 
-                singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
+                # 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 range(IMAGES_CAMERAS_FOLDER):
+for camera in IMAGES_CAMERAS_FOLDER:
     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')