#49: Get PRNU by using RAW images

And got:

```
Denoising images:   0%|                                                                      | 0/100 [00:00<?, ?it/s]maxGreen=2275
Denoising images:   1%|▌                                                             | 1/100 [00:00<01:11,  1.38it/s]maxGreen=2377
Denoising images:   2%|█▏                                                            | 2/100 [00:01<01:18,  1.24it/s]maxGreen=3468
Denoising images:  19%|███████████▌                                                 | 19/100 [00:11<00:46,  1.75it/s]maxGreen=4908
Denoising images: 100%|████████████████████████████████████████████████████████████| 100/100 [00:57<00:00,  1.74it/s]
```

datasets/raise/extract_noise.py

@@ -33,35 +33,34 @@ 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
+
 def treatImage(imageFileName):
-    global mean, numberOfImagesInMean
+    global mean, numberOfImagesInMean, minGreen
     imageFilePath = f'{imagesFolderPath}/{imageFileName}'
     if imageFileName.endswith('.NEF'):
-        imageFileName = 'ra2c888f8t.NEF'
-        imageFilePath = f'{imagesFolderPath}/{imageFileName}'
-        print(imageFilePath)
         with rawpy.imread(imageFilePath) as raw:
             colorDesc = raw.color_desc.decode('ascii')
             assert colorDesc == 'RGBG'
-            assert np.array_equal(raw.raw_pattern, np.array([[3, 2], [0, 1]], dtype = np.uint8))
+            assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
             rawImageVisible = raw.raw_image_visible.copy()
-            centerY = 2263
-            centerX = 237
-            RANGE = 5
-            rawImageVisible = rawImageVisible.astype(np.float64)
-            rawImageVisibleShape = rawImageVisible.shape
-            for y in range(0, rawImageVisibleShape[0], 2):
-                for x in range(0, rawImageVisibleShape[1], 2):
-                    rawImageVisible[y, x] *= -1
-            print(rawImageVisible[centerY - RANGE:centerY + RANGE, centerX - RANGE:centerX + RANGE])
-            #greenRawImageVisible = raw.rawImageVisible
+            greenRawImageVisible = rawImageVisible[1::2, ::2]
+            '''
+            greenRawImageVisibleMin = greenRawImageVisible.min()
+            if minGreen is None or greenRawImageVisibleMin < minGreen:
+                minGreen = greenRawImageVisibleMin
+                print(minGreen)
+            '''
+            imageNpArray = (greenRawImageVisible - minGreen) / (4908 - minGreen)
             # Pay attention to range of values expected by the denoiser.
-            exit(1)
+            # 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, channel_axis=-1, convert2ycbcr=True, rescale_sigma=True)
+    imageDenoisedNpArray = denoise(imageNpArray, rescale_sigma=True)
+    #print(imageNpArray, imageDenoisedNpArray)
     imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
     if mean is None:
         mean = imageNoiseNpArray