From 6ed57bc477d3afa1ea1c656a0ff42ff57aed3eb8 Mon Sep 17 00:00:00 2001 From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com> Date: Mon, 13 May 2024 03:13:58 +0200 Subject: [PATCH] Precise purpose of a part of code --- datasets/raise/attribute_source_camera.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py index 3a1170a..13d2f35 100755 --- a/datasets/raise/attribute_source_camera.py +++ b/datasets/raise/attribute_source_camera.py @@ -96,8 +96,7 @@ returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxC # - the second one is about estimating better and better the PRNU of each camera, as consider more and more training images and measuring the resulting attribution of cameras for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'): rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser - # As the second loop firstly compute noises of testing images. - # What about `MEAN` denoiser condition? + # As the second loop firstly if the denoiser is not `MEAN` or if `MEAN` predicts only on the whole training set, then compute noises of testing images. if not computeExtremes: print(f'{minColor=} {maxColor=}') if DENOISER != Denoiser.MEAN or PREDICT_ONLY_ON_WHOLE_TRAINING_SET: