datasets/raise/split_and_compare_prnus_of_subgroups.py

@@ -18,41 +18,41 @@ NUMBER_OF_SUBGROUPS = 2
 DENOISER = 'wavelet'
 IMAGES_FOLDER = 'flat-field/TIF'
 
-setting = IMAGES_FOLDER.replace('/', '_') + f'_{DENOISER}'
-
 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)
 
-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)
+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)
         imageNpArray = img_as_float(np.array(imagePil))
         imagePrnuEstimateNpArray = imageNpArray - denoise(imageNpArray, DENOISER)
-        subgroupIterativeMean = subgroupsIterativeMean[subgroupIndex]
         subgroupIterativeMean.add(imagePrnuEstimateNpArray)
-        if subgroupIndex == NUMBER_OF_SUBGROUPS - 1:
-            assert NUMBER_OF_SUBGROUPS == 2
-            rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - subgroupIndex].mean)
-            rmss += [rms]
+        subgroupPrnuEstimateNpArray += [subgroupIterativeMean.mean]
 
-mostImagesSubgroupPrnuEstimatesNpArray = [subgroupIterativeMean.mean for subgroupIterativeMean in subgroupsIterativeMean]
+    subgroupsPrnuEstimatesNpArray += [subgroupPrnuEstimateNpArray]
+
+mostImagesSubgroupPrnuEstimatesNpArray = [subgroupPrnuEstimatesNpArray[-1] for subgroupPrnuEstimatesNpArray in subgroupsPrnuEstimateNpArray]
 minimum = np.min(mostImagesSubgroupPrnuEstimatesNpArray)
 maximum = np.max(mostImagesSubgroupPrnuEstimatesNpArray)
 
 for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
-    plt.imsave(f'{setting}_estimated_prnu_subgroup_{subgroupIndex}.png', (subgroupsIterativeMean[subgroupIndex].mean - minimum) / (maximum - minimum))
+    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.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')
 plt.ylabel('RMS between both subgroups estimated PRNUs')
 plt.plot(rmss)
-plt.savefig(f'{setting}_rms_between_estimated_prnu_of_2_subgroups.svg')
-#plt.show()
+plt.show()