Add and use getImageCrop

datasets/raise/utils.py

@@ -132,12 +132,14 @@ def updateExtremes(imageNpArray, minColor, maxColor):
 def print(*toPrint):
     __builtin__.print(datetime.now(), *toPrint)
 
-def getColorMeans(imagesFileNames, colors, denoiser):
+def getColorMeans(imagesFileNames, colors, denoiser, singleColorChannelCropResolution = None):
     colorMeans = {}
     for color in colors:
         colorIterativeMean = iterativeMean()
         for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} colored images'):
             imageNpArray = getImageNpArray(imageFileName, False, color, denoiser)
+            if singleColorChannelCropResolution is not None:
+                imageNpArray = getImageCrop(imageNpArray, singleColorChannelCropResolution)
             imageNpArray = gaussian_filter(imageNpArray, sigma = 5)
             colorIterativeMean.add(imageNpArray)
         colorMeans[color] = colorIterativeMean.mean
@@ -156,3 +158,7 @@ def getImageNpArray(imageFilePath, computeExtremes, color, denoiser):
         # 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.
     return imageNpArray
+
+def getImageCrop(image, cropResolution):
+    imageCrop = image[:cropResolution[0], :cropResolution[1]]
+    return imageCrop