From 50058d5d2efa60dd5b3998c61619ea14bd244038 Mon Sep 17 00:00:00 2001
From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com>
Date: Mon, 13 May 2024 15:55:59 +0200
Subject: [PATCH] #72: Verify pointer before actually implementing the correct
 second choice

---
 datasets/raise/attribute_source_camera.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index 9690f62..41e063f 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -139,7 +139,15 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
             imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
             cameraIterativeMean = camerasIterativeMean[camera]
-            cameraIterativeMean.add(imagePrnuEstimateNpArray)
+            if DENOISER != Denoiser.MEAN or PREDICT_ONLY_ON_WHOLE_TRAINING_SET:
+                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
+                print(f'{cameraIterativeMean.mean = }')
+                print(f'{camerasIterativeMean[camera].mean = }')
+                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