def _normalization(image):
assert image.__class__ == numpy.ndarray
row, col = image.shape
#stdImag standardized image
stdImg = numpy.zeros((row, col))
"""
What image.sum() do is the same as the following code
but more faster than this.
for i in xrange(self.Row):
for j in xrange(self.Col):
sigma += image[i][j]
"""
#sigma = image.sum()
meanVal = image.mean()
stdValue = image.std()
if stdValue == 0:
stdValue = 1
stdImg = (image - meanVal) / stdValue
return stdImg
def _normalization(image):
assert image.__class__ == numpy.ndarray
row, col = image.shape
#stdImag standardized image
stdImg = numpy.zeros((row, col))
"""
What image.sum() do is the same as the following code
but more faster than this.
for i in xrange(self.Row):
for j in xrange(self.Col):
sigma += image[i][j]
"""
#sigma = image.sum()
meanVal = image.mean()
stdValue = image.std()
if stdValue == 0:
stdValue = 1
stdImg = (image - meanVal) / stdValue
return stdImg
def Normimg(image):
row, col = image.shape
img_new = np.zeros((row, col))
meanVal = image.mean()
stdValue = image.std()
if stdValue == 0:
stdValue = 1
img_new = (image - meanVal) / stdValue
return img_new
def _normalization(image):
assert image.__class__ == numpy.ndarray
row, col = image.shape
stdImg = numpy.zeros((row, col))
meanVal = image.mean()
stdValue = image.std()
if stdValue == 0:
stdValue = 1
stdImg = (image - meanVal)/stdValue
return stdImg