def LeeFilter(image, kernelLength, show=False):
import numpy as np
import cv2
from ImageMedicalLib import changeFormat, info
if image.dtype == 'uint8':
image = changeFormat.uint2double(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:
info.showImageStyle(
1, 2, {
'I1': changeFormat.im2uint8(image),
'Imean': changeFormat.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 ImageMedicalLib import changeFormat
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]])
if image.dtype == 'uint8':
image = changeFormat.uint2double(image)
image_norm = 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 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 ImageMedicalLib import changeFormat
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]])
if image.dtype == 'uint8':
image = changeFormat.uint2double(image)
image_norm = image
dL1 = ndimage.convolve(image_norm, L1)
dL2 = ndimage.convolve(image_norm, L2)
return dL1, dL2
@author: Andre
"""
#%% Importando módulos
import numpy as np
import imageio
from ImageMedicalLib import changeFormat, info, filters
import cv2
import os
#%% 1 - Ultrassom bebe
cf = os.getcwd() # current folder
I1_path = os.path.join(cf, 'ImagensAulas',
'Ultrassom.pgm') # endereco da imagem
I1 = changeFormat.uint2double(imageio.imread(I1_path))
rect = cv2.selectROI(I1) # selecionando região de interesse com openCV
# Selecionar a região e dar enter 2x
cv2.waitKey(0)
cv2.destroyAllWindows()
c1, c2 = rect[0], rect[0] + rect[2] # coluna minima e coluna maxima
l1, l2 = rect[1], rect[0] + rect[3] # linha minima e linha maxima
meanRect = np.mean(I1[l1:l2, c1:c2])
stdRect = np.std(I1[l1:l2, c1:c2])
#%% Filtro de Lee (essa demora)
image = I1
kernelLength = 7
newImage = np.zeros(image.shape)
w = np.zeros((kernelLength, kernelLength), dtype=int)
#%% 8 - Maximo, minimo e media da imagem
maximum = I1.max()
minimum = I1.min()
meanI1 = int(I1.mean())
#%% 9 - Exibindo imagem
plt.figure()
plt.imshow(I1)
plt.set_cmap('gray')
plt.axis('off')
plt.title('Raio X Torax')
plt.colorbar()
plt.show()
#%% 10 - Convertendo para double
I1double = changeFormat.uint2double(I1)
newMax = I1double.max()
newMin = I1double.min()
newMean = I1double.mean()
newStd = I1double.std()
newMedian = np.median(I1double)
plt.figure()
plt.imshow(I1double)
plt.set_cmap('gray')
plt.axis('off')
plt.title('Raio X Torax double')
plt.colorbar()
plt.show()
Idouble50x50 = I1double[50, 50]
info.showImageStyle(1,
1, {'mamoConv': mamoConv}, ['Mamo Gaussian Filter'],
title='Mamo Gaussian Filter')
#%% Função mascara gaussiana
w_Gauss2D = filters.kernelGauss(10, 5, A=1)
info.showImageStyle(1, 1, {'w_Gauss2D': changeFormat.im2uint8(w_Gauss2D)},
['w_Gauss2D'])
#%% Convolucao com mascara gaussiana gerada no item acima
mamoConv = ndimage.convolve(mamo, w_Gauss2D)
info.showImageStyle(1, 1, {'mamoConv': mamoConv}, ['mamoConv'])
#%% 4 -Afiamento de bordas
imri = imageio.imread(r'ImagensAulas\TransversalMRI2.pgm')
imri = changeFormat.uint2double(
imri) # Conversão para double para não dar ruim
mask = filters.kernelGauss(8, 3, A=1)
f_blur = ndimage.convolve(imri, mask)
g = imri - f_blur
f_sharpened = (imri + g).clip(0, 1)
images = {
'imri': changeFormat.im2uint8(imri),
'f_blur': changeFormat.im2uint8(f_blur),
'f_sharpened': changeFormat.im2uint8(f_sharpened)
}
info.showImageStyle(1, 3, images, ['imri', 'f_blur', 'f_sharpened'])
#%% 5 - Gradientes x e y
dx = np.asarray([[1, -1]])
dy = np.asarray([[1], [-1]])
stent = imageio.imread(r'ImagensAulas\Stent.pgm')