From 4bea5e3a97023dde9a34de1f6bbface567cf14fe Mon Sep 17 00:00:00 2001
From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com>
Date: Fri, 3 May 2024 02:32:05 +0200
Subject: [PATCH] Move `getImageNpArray` to `utils.py`

---
 datasets/raise/extract_noise.py | 20 +++-----------------
 datasets/raise/utils.py         | 14 ++++++++++++++
 2 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/datasets/raise/extract_noise.py b/datasets/raise/extract_noise.py
index 220540d..264d14a 100755
--- a/datasets/raise/extract_noise.py
+++ b/datasets/raise/extract_noise.py
@@ -4,7 +4,7 @@ import numpy as np
 import os
 from tqdm import tqdm
 import csv
-from utils import Color, denoise, iterativeMean, isARawImage, escapeFilePath, getColorChannel, saveNpArray, rescaleRawImageForDenoiser, updateExtremes, getColorMeans
+from utils import Color, denoise, iterativeMean, escapeFilePath, saveNpArray, getColorMeans, getImageNpArray
 import matplotlib.pyplot as plt
 
 imagesFolderPath = 'rafael/arw'
@@ -54,25 +54,11 @@ def getImageFilePath(imageFileName):
         imageFilePath = f'{imagesFolderPath}/{imageFileName}'
     return imageFilePath
 
-def getImageNpArray(imageFileName, computeExtremes, color):
-    global minColor, maxColor
-    imageFilePath = getImageFilePath(imageFileName)
-    imageNpArray = getColorChannel(imageFilePath, color)
-
-    if computeExtremes:
-        minColor, maxColor = updateExtremes(imageNpArray, minColor, maxColor)
-        return
-
-    if isARawImage(imageFileName) and denoiser != 'mean':
-        imageNpArray = rescaleRawImageForDenoiser(imageNpArray, minColor, maxColor)
-        # Pay attention to range of values expected by the denoiser.
-        # Indeed if provide the thousands valued raw image, then the denoiser only returns values between 0 and 1 and making the difference between both look pointless.
-    return imageNpArray
-
 # `color` is the actual color to estimate PRNU with.
 def treatImage(imageFileName, computeExtremes = False, color = None):
     global estimatedPrnuIterativeMean
-    imageNpArray = getImageNpArray(imageFileName, computeExtremes, color)
+    imageFilePath = getImageFilePath(imageFileName)
+    imageNpArray = getImageNpArray(imageFilePath, computeExtremes, color)
     if imageNpArray is None:
         return
     if denoiser != 'mean':
diff --git a/datasets/raise/utils.py b/datasets/raise/utils.py
index 7314baa..4b33340 100644
--- a/datasets/raise/utils.py
+++ b/datasets/raise/utils.py
@@ -142,3 +142,17 @@ def getColorMeans(imagesFileNames, colors = Color):
             colorIterativeMean.add(imageNpArray)
         colorMeans[color] = colorIterativeMean.mean
     return colorMeans
+
+def getImageNpArray(imageFilePath, computeExtremes, color):
+    global minColor, maxColor
+    imageNpArray = getColorChannel(imageFilePath, color)
+
+    if computeExtremes:
+        minColor, maxColor = updateExtremes(imageNpArray, minColor, maxColor)
+        return
+
+    if isARawImage(imageFilePath) and denoiser != 'mean':
+        imageNpArray = rescaleRawImageForDenoiser(imageNpArray, minColor, maxColor)
+        # Pay attention to range of values expected by the denoiser.
+        # Indeed if provide the thousands valued raw image, then the denoiser only returns values between 0 and 1 and making the difference between both look pointless.
+    return imageNpArray