From d4e13ed123bd182ab16e191b1ed0069a0d7e60a1 Mon Sep 17 00:00:00 2001
From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com>
Date: Tue, 16 Apr 2024 03:11:38 +0200
Subject: [PATCH] Compute automatically extreme values

---
 datasets/raise/extract_noise.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/datasets/raise/extract_noise.py b/datasets/raise/extract_noise.py
index 72a80e0..edf04e5 100755
--- a/datasets/raise/extract_noise.py
+++ b/datasets/raise/extract_noise.py
@@ -33,10 +33,11 @@ if requiresRaiseFiltering:
 
     imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{imagesFolderPath}/{imageFileName}').size == (4946, 3278)]
 
-minGreen = 308#None
+minColor = None
+maxColor = None
 
-def treatImage(imageFileName):
-    global mean, numberOfImagesInMean, minGreen
+def treatImage(imageFileName, computeExtremes = False):
+    global mean, numberOfImagesInMean, minColor, maxColor
     imageFilePath = f'{imagesFolderPath}/{imageFileName}'
     if imageFileName.endswith('.NEF'):
         with rawpy.imread(imageFilePath) as raw:
@@ -45,22 +46,22 @@ def treatImage(imageFileName):
             assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
             rawImageVisible = raw.raw_image_visible.copy()
             greenRawImageVisible = rawImageVisible[1::2, ::2]
-            '''
-            greenRawImageVisibleMin = greenRawImageVisible.min()
-            if minGreen is None or greenRawImageVisibleMin < minGreen:
-                minGreen = greenRawImageVisibleMin
-                print(minGreen)
-            '''
-            imageNpArray = (greenRawImageVisible - minGreen) / (4908 - minGreen)
+            if computeExtremes:
+                greenRawImageVisibleMin = greenRawImageVisible.min()
+                greenRawImageVisibleMax = greenRawImageVisible.max()
+                if minColor is None or greenRawImageVisibleMin < minColor:
+                    minColor = greenRawImageVisibleMin
+                if maxColor is None or greenRawImageVisibleMax > maxColor:
+                    maxColor = greenRawImageVisibleMax
+                return
+            imageNpArray = (greenRawImageVisible - minGreen) / (maxGreen - minGreen)
             # 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.
-            #exit(1)
     else:
         imagePil = Image.open(imageFilePath)
         imageNpArray = img_as_float(np.array(imagePil))
     # As the arguments differ from a denoiser to the other should use a match instead.
     imageDenoisedNpArray = denoise(imageNpArray, rescale_sigma=True)
-    #print(imageNpArray, imageDenoisedNpArray)
     imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
     if mean is None:
         mean = imageNoiseNpArray
@@ -68,6 +69,9 @@ def treatImage(imageFileName):
         mean = ((mean * numberOfImagesInMean) + imageNoiseNpArray) / (numberOfImagesInMean + 1)
     numberOfImagesInMean += 1
 
+for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
+    treatImage(imageFileName, computeExtremes = True)
+
 for imageFileName in tqdm(imagesFileNames, 'Denoising images'):
     treatImage(imageFileName)