From 9d8e5ddbec3b5c7cc2e77c99172e11d8bed1c88c Mon Sep 17 00:00:00 2001
From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com>
Date: Sat, 27 Apr 2024 19:24:12 +0200
Subject: [PATCH] Make `split_and_compare_prnus_of_subgroups.py` only use 2
 images of RAM quantity

---
 .../split_and_compare_prnus_of_subgroups.py   | 37 +++++++++----------
 1 file changed, 17 insertions(+), 20 deletions(-)

diff --git a/datasets/raise/split_and_compare_prnus_of_subgroups.py b/datasets/raise/split_and_compare_prnus_of_subgroups.py
index 91c5527..68ef827 100755
--- a/datasets/raise/split_and_compare_prnus_of_subgroups.py
+++ b/datasets/raise/split_and_compare_prnus_of_subgroups.py
@@ -21,35 +21,32 @@ IMAGES_FOLDER = 'flat-field/TIF'
 imagesFileNames = os.listdir(IMAGES_FOLDER)
 # To not have a bias (chronological for instance) when split to make subgroups.
 shuffle(imagesFileNames)
-numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
-numberOfImagesThresholds = range(numberOfImagesPerSubgroup, numberOfImagesPerSubgroup + 1)
 
-subgroupsPrnuEstimatesNpArray = []
-for subgroupIndex in tqdm(range(NUMBER_OF_SUBGROUPS), 'Subgroup'):
-    subgroupPrnuEstimateNpArray = []
-    subgroupIterativeMean = iterativeMean()
-    subgroupImagesFileNames = imagesFileNames[numberOfImagesPerSubgroup * subgroupIndex : numberOfImagesPerSubgroup * (subgroupIndex + 1)]
-    for imageFileName in tqdm(subgroupImagesFileNames, f'Image of subgroup {subgroupIndex}'):
-        imagePath = f'{IMAGES_FOLDER}/{imageFileName}'
-        imagePil = Image.open(imagePath)
+numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
+
+subgroupsIterativeMean = [iterativeMean() for _ in range(NUMBER_OF_SUBGROUPS)]
+rmss = []
+for subgroupImageIndex in tqdm(range(numberOfImagesPerSubgroup), 'Subgroup image index'):
+    for subgroupIndex in tqdm(range(NUMBER_OF_SUBGROUPS), 'Subgroup'):
+        imageIndex = (subgroupIndex * NUMBER_OF_SUBGROUPS) + subgroupImageIndex
+        imageFileName = imagesFileNames[imageIndex]
+        imageFilePath = f'{IMAGES_FOLDER}/{imageFileName}'
+        imagePil = Image.open(imageFilePath)
         imageNpArray = img_as_float(np.array(imagePil))
         imagePrnuEstimateNpArray = imageNpArray - denoise(imageNpArray, DENOISER)
+        subgroupIterativeMean = subgroupsIterativeMean[subgroupIndex]
         subgroupIterativeMean.add(imagePrnuEstimateNpArray)
-        subgroupPrnuEstimateNpArray += [subgroupIterativeMean.mean]
+        if subgroupIndex == NUMBER_OF_SUBGROUPS - 1:
+            assert NUMBER_OF_SUBGROUPS == 2
+            rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - subgroupIndex].mean)
+            rmss += [rms]
 
-    subgroupsPrnuEstimatesNpArray += [subgroupPrnuEstimateNpArray]
-
-mostImagesSubgroupPrnuEstimatesNpArray = [subgroupPrnuEstimatesNpArray[-1] for subgroupPrnuEstimatesNpArray in subgroupsPrnuEstimateNpArray]
+mostImagesSubgroupPrnuEstimatesNpArray = [subgroupIterativeMean.mean for subgroupIterativeMean in subgroupsIterativeMean]
 minimum = np.min(mostImagesSubgroupPrnuEstimatesNpArray)
 maximum = np.max(mostImagesSubgroupPrnuEstimatesNpArray)
 
 for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
-    plt.imsave(f'prnu_subgroup_{subgroupIndex}.png', (subgroupsPrnuEstimateNpArray[subgroupIndex][-1] - minimum) / (maximum - minimum))
-
-rmss = []
-for numberOfImagesIndex in range(len(numberOfImagesThresholds)):
-    rms = rmsDiffNumpy(subgroupsPrnuEstimatesNpArray[0][numberOfImagesIndex], subgroupsPrnuEstimatesNpArray[1][numberOfImagesIndex])
-    rmss += [rms]
+    plt.imsave(f'prnu_subgroup_{subgroupIndex}.png', (subgroupsIterativeMean[subgroupIndex].mean - minimum) / (maximum - minimum))
 
 plt.title(f'RMS between both subgroups estimated PRNUs with {DENOISER} denoiser for a given number of images among them')
 plt.xlabel('Number of images of each subgroup')