def smoothing_tv(data, weight, pseudo_3D='True', multichannel=False, sliceId=2):
if data.ndim == 3 and pseudo_3D:
if sliceId == 2:
for idx in range(data.shape[2]):
temp = skifil.denoise_tv_chambolle(data[:, :, idx], weight=weight, multichannel=multichannel)
data[:, :, idx] = (255 * temp).astype(np.uint8)
elif sliceId == 0:
for idx in range(data.shape[0]):
temp = skifil.denoise_tv_chambolle(data[idx, :, :], weight=weight, multichannel=multichannel)
data[idx, :, :] = (255 * temp).astype(np.uint8)
else:
data = skifil.denoise_tv_chambolle(data, weight=weight, multichannel=False)
data = (255 * data).astype(np.uint8)
return data
def tvChambolle(image, weight, eps=0.001, maxIter=None, out=None):
# to restore min max ?
restore=False
if image.min() < 1.0 or image.max() > 1.0:
restore=True
gaussSmoothed = gaussianSmoothing(image, simga=1.0)
oldMin = gaussSmoothed.min()
oldMax = gaussSmoothed.max()
image -= image.min()
image /= image.max()
kwargs = dict(im=image, weight=weight, eps=eps, multichannel=False)
if maxIter is not None:
kwargs['n_iter_max'] = maxIter
res = denoise_tv_chambolle(**kwargs)
if restore:
res -= res.min()
res /= res.max()
res *= (oldMax-oldMin)
res += oldMin
if out is None:
return res
else:
out[:] = res[:]
return out
def _denoise(self, img, weight):
"""
use TV-denoise to remove noise
http://scipy-lectures.github.com/advanced/image_processing/
http://en.wikipedia.org/wiki/Total_variation_denoising
"""
from skimage.filters import denoise_tv_chambolle
img = denoise_tv_chambolle(img, weight=weight) * 255
return img.astype('uint8')
def _denoise(self, img, weight):
"""
use TV-denoise to remove noise
http://scipy-lectures.github.com/advanced/image_processing/
http://en.wikipedia.org/wiki/Total_variation_denoising
"""
from skimage.filters import denoise_tv_chambolle
img = denoise_tv_chambolle(img, weight=weight) * 255
return img.astype('uint8')
def smoothing_tv(data,
weight,
pseudo_3D='True',
multichannel=False,
sliceId=2):
if data.ndim == 3 and pseudo_3D:
if sliceId == 2:
for idx in range(data.shape[2]):
temp = skifil.denoise_tv_chambolle(data[:, :, idx],
weight=weight,
multichannel=multichannel)
data[:, :, idx] = (255 * temp).astype(np.uint8)
elif sliceId == 0:
for idx in range(data.shape[0]):
temp = skifil.denoise_tv_chambolle(data[idx, :, :],
weight=weight,
multichannel=multichannel)
data[idx, :, :] = (255 * temp).astype(np.uint8)
else:
data = skifil.denoise_tv_chambolle(data,
weight=weight,
multichannel=False)
data = (255 * data).astype(np.uint8)
return data
# FILTER COMPARISION
import matplotlib.pyplot as plt
from skimage import data
from skimage import filters
from scipy import ndimage
coins = data.coins()
gaussian_filter_coins = ndimage.gaussian_filter(coins, sigma=2)
med_filter_coins = ndimage.filters.median_filter(coins,size=2)
tv_filter_coins = filters.denoise_tv_chambolle(coins, weight=0.1)
plt.figure(figsize=(16, 4))
plt.subplot(141)
plt.imshow(coins[10:80, 300:370], cmap='gray', interpolation='nearest')
plt.axis('off')
plt.title('Image')
plt.subplot(142)
plt.imshow(gaussian_filter_coins[10:80, 300:370], cmap='gray',interpolation='nearest')
plt.axis('off')
plt.title('Gaussian filter')
plt.subplot(143)
plt.imshow(med_filter_coins[10:80, 300:370], cmap='gray',interpolation='nearest')
plt.axis('off')
plt.title('Median filter')
plt.subplot(144)
plt.imshow(tv_filter_coins[10:80, 300:370], cmap='gray',interpolation='nearest')
plt.axis('off')
plt.title('TV filter')
plt.show()
