datasets/raise/extract_noise.py

@@ -14,16 +14,15 @@ imagesFolderPathFileName = imagesFolderPath.replace('/', '_')
 denoiser = 'wavelet'
 npArrayFilePath = f'mean_{imagesFolderPathFileName}_{denoiser}.npy'
 
-raiseNotFlatFields = False
-
 denoise = getattr(skimage.restoration, f'denoise_{denoiser}')
 
 mean = None
 numberOfImagesInMean = 0
 
-imagesFileNames = os.listdir(imagesFolderPath + ('/png' if raiseNotFlatFields else ''))
+imagesFileNames = os.listdir(imagesFolderPath + '/png')
 
-if raiseNotFlatFields:
+requiresRaiseFiltering = True
+if requiresRaiseFiltering:
     files = {}
 
     with open('RAISE_all.csv') as csvfile:
@@ -33,47 +32,31 @@ if raiseNotFlatFields:
             files[file] = row
 
     imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{imagesFolderPath}/png/{imageFileName}').size == (4946, 3278)]
-
-SKY = range(2_699, 2_807)
-WALL = range(2_807, 2_912)
-#imagesFileNames = [f'DSC0{imageIndex}.ARW' for imageIndex in SKY]
+#imagesFileNames = [f'DSC0{imageIndex}.ARW' for imageIndex in range(2807, 2911)]
 
 minColor = None
 maxColor = None
 
 def treatImage(imageFileName, computeExtremes = False):
     global mean, numberOfImagesInMean, minColor, maxColor
-    if raiseNotFlatFields:
-        imageFileName = imageFileName.replace('.png', '.NEF')
-        imageFilePath = f'{imagesFolderPath}/nef/{imageFileName}'
-    else:
-        imageFilePath = f'{imagesFolderPath}/{imageFileName}'
+    imageFileName = imageFileName.replace('.png', '.NEF')
+    imageFilePath = f'{imagesFolderPath}/nef/{imageFileName}'
     if imageFileName.endswith('.NEF') or imageFileName.endswith('.ARW'):
         with rawpy.imread(imageFilePath) as raw:
             colorDesc = raw.color_desc.decode('ascii')
             assert colorDesc == 'RGBG'
             assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
-            # RG
-            # GB
             rawImageVisible = raw.raw_image_visible.copy()
-
-            redRawImageVisible = rawImageVisible[::2, ::2]
-            greenRightRawImageVisible = rawImageVisible[::2, 1::2]
-
-            greenBottomRawImageVisible = rawImageVisible[1::2, ::2]
-            blueRawImageVisible = rawImageVisible[1::2, 1::2]
-
-            # Actual color to estimate the PRNU with.
-            colorRawImageVisible = greenBottomRawImageVisible
+            greenRawImageVisible = rawImageVisible[1::2, ::2]
             if computeExtremes:
-                colorRawImageVisibleMin = colorRawImageVisible.min()
-                colorRawImageVisibleMax = colorRawImageVisible.max()
-                if minColor is None or colorRawImageVisibleMin < minColor:
-                    minColor = colorRawImageVisibleMin
-                if maxColor is None or colorRawImageVisibleMax > maxColor:
-                    maxColor = colorRawImageVisibleMax
+                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 = (colorRawImageVisible - minColor) / (maxColor - minColor)
+            imageNpArray = (greenRawImageVisible - minColor) / (maxColor - minColor)
             # 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.
     else: