diff --git a/datasets/raise/extract_noise.py b/datasets/raise/extract_noise.py
index 2b20582..26cec21 100755
--- a/datasets/raise/extract_noise.py
+++ b/datasets/raise/extract_noise.py
@@ -21,29 +21,29 @@ imagesFileNames = os.listdir(imagesFolderPath)
 pbar = tqdm(total = len(imagesFileNames))
 
 def treatImage(imageFileName):
-	global mean, numberOfImagesInMean, pbar
-	imageFilePath = f'{imagesFolderPath}/{imageFileName}'
-	imagePil = Image.open(imageFilePath)
-	imageNpArray = img_as_float(np.array(imagePil))
-	print('Before mean computation')
-	imageNoiseNpArray = imageNpArray - denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1)
-	print('After mean computation')
-	with mutex:
-		print('Start mutex section')
-		if mean is None:
-			print('Inter A')
-			mean = imageNoiseNpArray
-		else:
-			print('Inter B')
-			mean = ((mean * numberOfImagesInMean) + imageNoiseNpArray) / (numberOfImagesInMean + 1)
-		print('Intermediary 0')
-		numberOfImagesInMean += 1
-		print('Intermediary 1')
-		pbar.update(numberOfImagesInMean)
-		print('End mutex section')
+    global mean, numberOfImagesInMean, pbar
+    imageFilePath = f'{imagesFolderPath}/{imageFileName}'
+    imagePil = Image.open(imageFilePath)
+    imageNpArray = img_as_float(np.array(imagePil))
+    print('Before mean computation')
+    imageNoiseNpArray = imageNpArray - denoise_tv_chambolle(imageNpArray, weight=0.2, channel_axis=-1)
+    print('After mean computation')
+    with mutex:
+        print('Start mutex section')
+        if mean is None:
+            print('Inter A')
+            mean = imageNoiseNpArray
+        else:
+            print('Inter B')
+            mean = ((mean * numberOfImagesInMean) + imageNoiseNpArray) / (numberOfImagesInMean + 1)
+        print('Intermediary 0')
+        numberOfImagesInMean += 1
+        print('Intermediary 1')
+        pbar.update(numberOfImagesInMean)
+        print('End mutex section')
 
 with Pool(5) as p:
-	p.map(treatImage, imagesFileNames)
+    p.map(treatImage, imagesFileNames)
 
 with open(npArrayFilePath, 'wb') as f:
-	np.save(f, mean)
+    np.save(f, mean)