def LeeFilter(image, kernelLength, show=False):
import numpy as np
import cv2
from medImUtils import imUtils
image = imUtils.im2double(image)
rect = cv2.selectROI(image)
cv2.waitKey(0)
cv2.destroyAllWindows()
c1, c2 = rect[0], rect[0] + rect[2]
l1, l2 = rect[1], rect[0] + rect[3]
stdRect = np.std(image[l1:l2, c1:c2])
newImage = np.zeros(image.shape)
w = np.zeros((kernelLength, kernelLength), dtype=int)
w_center = int((w.shape[0]) / 2)
for indexrow, frow in enumerate(image[:-(w_center * 2)]):
for indexcolumn, fcolumn in enumerate(frow[:-(w_center * 2)]):
maskLocal = image[indexrow:1 + indexrow + w_center * 2,
indexcolumn:1 + indexcolumn + w_center * 2]
meanMask = np.mean(maskLocal + w)
k = np.clip(1 - (stdRect / (0.001 + maskLocal.std())), 0, 1)
newImage[indexrow + w_center,
indexcolumn + w_center] = meanMask + k * (
image[indexrow + w_center, indexcolumn + w_center] -
meanMask)
if show:
imUtils.showImageStyle(1, 2, {
'I1': imUtils.im2uint8(image),
'Imean': imUtils.im2uint8(newImage)
}, ['Original', 'Lee Filter'])
return newImage
def sobel(image):
"""
Filtro Sobel
INPUTS:
image: imagem a ser filtrada
OUTPUTS:
gradient_sobel: gradiente de Sobel resultante das derivadas x e y
"""
import numpy as np
from medImUtils import imUtils
from scipy import ndimage
wx = np.asarray([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
wy = np.asarray([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])
image_norm = imUtils.im2double(image)
dx = ndimage.convolve(image_norm, wx)
dy = ndimage.convolve(image_norm, wy)
gradient_sobel = np.sqrt(dx**2 + dy**2)
return gradient_sobel
def priwitt(image):
"""
Filtro Priwitt
INPUTS:
image: imagem a ser filtrada
OUTPUTS:
gradient_priwitt: gradiente de Priwitt resultante das derivadas x e y
"""
import numpy as np
from medImUtils import imUtils
from scipy import ndimage
wx = np.asarray([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]])
wy = np.asarray([[-1, -1, -1], [0, 0, 0], [1, 1, 1]])
image_norm = imUtils.im2double(image)
dx = ndimage.convolve(image_norm, wx)
dy = ndimage.convolve(image_norm, wy)
gradient_priwitt = np.sqrt(dx**2 + dy**2)
return gradient_priwitt
def laplace(image):
"""
Filtros Laplacianos
INPUTS:
image: imagem a ser filtrada
OUTPUTS:
dL1: Laplaciano resultante da máscara [[0,1,0],[1,-4,1],[0,1,0]]
dL2: Laplaciano resultante da máscara [[1,1,1],[1,-8,1],[1,1,1]]
"""
import numpy as np
from medImUtils import imUtils
from scipy import ndimage
L1 = np.asarray([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
L2 = np.asarray([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
image_norm = imUtils.im2double(image)
dL1 = ndimage.convolve(image_norm, L1)
dL2 = ndimage.convolve(image_norm, L2)
return dL1, dL2
kernelLength = 7
newImage = np.zeros(image.shape)
w = np.zeros((kernelLength, kernelLength), dtype=int)
w_center = int((w.shape[0]) / 2)
for indexrow, frow in enumerate(image[:-(w_center * 2)]):
for indexcolumn, fcolumn in enumerate(frow[:-(w_center * 2)]):
maskLocal = image[indexrow:1 + indexrow + w_center * 2,
indexcolumn:1 + indexcolumn + w_center * 2]
meanMask = np.mean(maskLocal + w)
k = np.clip(1 - (stdRect / (0.001 + maskLocal.std())), 0,
1) # minimo é zero e maximo é 1
newImage[
indexrow + w_center, indexcolumn + w_center] = meanMask + k * (
image[indexrow + w_center, indexcolumn + w_center] - meanMask)
imUtils.showImageStyle(1, 2, {
'I1': imUtils.im2uint8(I1),
'newImage': imUtils.im2uint8(newImage)
}, ['Original', 'Image Filtered'])
#%% Função zero bala Filtro de Lee (esse é rapidaooooo)
newImage = imUtils.LeeFilter(I1, 9, True)
#%% DESAFIO
sobel_Image = imUtils.im2double(imUtils.sobel(I1))
meanI1 = imUtils.im2double(imUtils.meanFilterFast(I1, 11))
challenge = meanI1 + sobel_Image * (I1 - meanI1)
imUtils.showImageStyle(1, 2, {
'I1': imUtils.im2uint8(I1),
'challenge': imUtils.im2uint8(challenge)
}, ['Original', 'Challenge'])