def MedianImageColor(InputImage, Ignore=None):
InputImage = InputImage.convert('L')
InputImagePixels = InputImage.load()
# flatten the image so that we can take the median
ColoredImage = PILToCV2(InputImage)
GreyImage = cv2.cvtColor(ColoredImage, cv2.COLOR_BGR2GRAY)
FlatImage = GreyImage.flatten()
if Ignore != None:
PrunnedImage = []
for Item in FlatImage:
if Item != Ignore:
PrunnedImage.append(Item)
return Statistics.Median(PrunnedImage)
else:
# take the median
return Statistics.Median(FlatImage)
def MedianArray(Array, SampleSize, FillerValue=255):
if SampleSize % 2 != 0:
raise ValueError("SampleSize must be even.")
ReturnArray = []
ValuesArray = []
for i in range(0, len(Array)):
ValuesArray.append([])
# we keep the array the same size, so sometimes the sample size will be smaller
HalfSampleSize = int(SampleSize / 2.0)
# +1 because of how range() works
for p in range(-HalfSampleSize, HalfSampleSize + 1):
if (i + p) < 0 or (i + p) >= len(Array):
ValuesArray[i].append(FillerValue)
else:
ValuesArray[i].append(Array[i + p])
for Items in ValuesArray:
ReturnArray.append(Statistics.Median(Items))
return ReturnArray
