def minProcess(image, view=False):
"""MinProcess using filter only shape [3,3]
Using the minimal value of neighborhood as the filting result
Args:
image: The original image
view: The flag to decided to show the result image
Set to False default
Returns:
rets: The list contains the filted image
"""
from hw2_filter import extending
extendingImage = extending(image, 3, 3)
height = extendingImage.shape[0]
width = extendingImage.shape[1]
filterHeight = 3
filterWidth = 3
correlateImage = np.zeros((height, width), dtype=int)
for x in range(height-(filterHeight-1)):
for y in range(width-(filterWidth-1)):
myList = [extendingImage[x+i][y+j] for i in range(filterHeight) for j in range(filterWidth)]
retList = sorted(myList)
correlateImage[x+int((filterHeight-1)/2)][y+int((filterWidth-1)/2)] = int(retList[0])
if view:
show(image, correlateImage, "minProcess")
return correlateImage
def harmonicProcess(sourceImage, view=False):
"""HarmonicProcess using filter only shape [3,3]
Args:
sourceImage: The original image
view: The flag to decided to show the result image
Set to False default
Returns:
rets: The list contains the filted image
"""
from hw2_filter import extending
filter_size = [3]
rets = []
for size in filter_size:
# extending
extendingImage = extending(sourceImage, size, size)
height, width = extendingImage.shape
import math
# calculating
correlateImage = np.zeros((height, width), dtype=extendingImage.dtype)
for x in range(height-(size-1)):
for y in range(width-(size-1)):
sum = 0
for j in range(size):
for k in range(size):
if extendingImage[x+j][y+k] == 0.0:
sum = sum + math.inf
else:
sum = sum + 1/(extendingImage[x+j][y+k])
correlateImage[x+int((size-1)/2)][y+int((size-1)/2)] = 1/sum
# cutting
tarImage = np.zeros(sourceImage.shape, dtype=float)
for x in range(tarImage.shape[0]):
for y in range(tarImage.shape[1]):
tarImage[x][y] = int((correlateImage[x+size-1][y+size-1]))
ret = np.zeros(sourceImage.shape, dtype=int)
for x in range(ret.shape[0]):
for y in range(ret.shape[1]):
if math.isinf(tarImage[x][y]):
ret[x][y] = 255
else:
ret[x][y] = int(size*size*tarImage[x][y])
rets.append(ret)
if view:
show4(sourceImage, rets, "harmonic")
return rets
def geometric_meanProcess(image, view=False):
"""Geometic_meanProcess using filter only shape [3,3]
Args:
image: The original image
view: The flag to decided to show the result image
Set to False default
Returns:
rets: The list contains the filted image
"""
from hw2_filter import extending
extendingImage = extending(image, 3, 3)
height = extendingImage.shape[0]
width = extendingImage.shape[1]
filterHeight = 3
filterWidth = 3
correlateImg = np.zeros((height, width), dtype=extendingImage.dtype)
for x in range(height-(filterHeight-1)):
for y in range(width-(filterWidth-1)):
sum = 1
for j in range(filterHeight):
for k in range(filterWidth):
sum = sum * extendingImage[x+j][y+k]
correlateImg[x+int((filterHeight-1)/2)][y+int((filterWidth-1)/2)] = sum
# import math
tarImage = np.zeros((image.shape[0], image.shape[1]), dtype=int)
for x in range(tarImage.shape[0]):
for y in range(tarImage.shape[1]):
tarImage[x][y] = int(pow(correlateImg[x+filterHeight-1][y+filterWidth-1], 1/9))
if view:
show(image, tarImage, "geometric_mean")
return tarImage