def test_denoise_bilateral_3d():
img = lena
# add some random noise
img += 0.5 * img.std() * np.random.random(img.shape)
img = np.clip(img, 0, 1)
out1 = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20)
out2 = filter.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30)
# make sure noise is reduced
assert img.std() > out1.std()
assert out1.std() > out2.std()
def test_denoise_bilateral_3d():
img = lena
# add some random noise
img += 0.5 * img.std() * np.random.random(img.shape)
img = np.clip(img, 0, 1)
out1 = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20)
out2 = filter.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30)
# make sure noise is reduced
assert img.std() > out1.std()
assert out1.std() > out2.std()
def smoothing_bilateral(data, sigma_space=15, sigma_color=0.05, pseudo_3D='True', sliceId=2):
if data.ndim == 3 and pseudo_3D:
if sliceId == 2:
for idx in range(data.shape[2]):
temp = skifil.denoise_bilateral(data[:, :, idx], sigma_range=sigma_color, sigma_spatial=sigma_space)
data[idx, :, :] = (255 * temp).astype(np.uint8)
elif sliceId == 0:
for idx in range(data.shape[0]):
temp = skifil.denoise_bilateral(data[idx, :, :], sigma_range=sigma_color, sigma_spatial=sigma_space)
data[idx, :, :] = (255 * temp).astype(np.uint8)
else:
data = skifil.denoise_bilateral(data, sigma_range=sigma_color, sigma_spatial=sigma_space)
data = (255 * data).astype(np.uint8)
return data
def loop(imgFiles):
for f in imgFiles:
img = img_as_float(data.load(os.path.join(inputDir,f)))
startTime = time.time()
img = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=3)
io.imsave(os.path.join(outputDirg,f), img)
print("Took %f seconds for %s" %(time.time() - startTime, f))
def bilateral_blur_gray_image_nz(image_nz, image_shape, mask_nz, sigma_range, sigma_spatial):
""" Blur a masked grayscale image """
# deal with the mask -- set the unmasked entries to the average
orig_mean = np.mean(image_nz)
image = np.empty(image_shape[0:2])
image.fill(orig_mean)
image[mask_nz] = image_nz
blurred = denoise_bilateral(
image,
sigma_range=sigma_range,
sigma_spatial=sigma_spatial,
win_size=max(int(sigma_spatial * 2), 3),
mode='reflect',
)
blurred_nz = blurred[mask_nz]
# adjust to keep the mean the same
new_mean = np.mean(blurred_nz)
blurred_nz *= orig_mean / new_mean
return blurred_nz
lena = lena[220:300, 220:320]
noisy = lena + 0.6 * lena.std() * np.random.random(lena.shape)
noisy = np.clip(noisy, 0, 1)
fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
plt.gray()
ax[0, 0].imshow(noisy)
ax[0, 0].axis('off')
ax[0, 0].set_title('noisy')
ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, multichannel=True))
ax[0, 1].axis('off')
ax[0, 1].set_title('TV')
ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15))
ax[0, 2].axis('off')
ax[0, 2].set_title('Bilateral')
ax[1, 0].imshow(denoise_tv_chambolle(noisy, weight=0.2, multichannel=True))
ax[1, 0].axis('off')
ax[1, 0].set_title('(more) TV')
ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.1, sigma_spatial=15))
ax[1, 1].axis('off')
ax[1, 1].set_title('(more) Bilateral')
ax[1, 2].imshow(lena)
ax[1, 2].axis('off')
ax[1, 2].set_title('original')
fig.subplots_adjust(wspace=0.02, hspace=0.2,
top=0.9, bottom=0.05, left=0, right=1)
lena = lena[220:300, 220:320]
noisy = lena + 0.6 * lena.std() * np.random.random(lena.shape)
noisy = np.clip(noisy, 0, 1)
fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
plt.gray()
ax[0, 0].imshow(noisy)
ax[0, 0].axis('off')
ax[0, 0].set_title('noisy')
ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, multichannel=True))
ax[0, 1].axis('off')
ax[0, 1].set_title('TV')
ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.05, sigma_spatial=15))
ax[0, 2].axis('off')
ax[0, 2].set_title('Bilateral')
ax[1, 0].imshow(denoise_tv_chambolle(noisy, weight=0.2, multichannel=True))
ax[1, 0].axis('off')
ax[1, 0].set_title('(more) TV')
ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.1, sigma_spatial=15))
ax[1, 1].axis('off')
ax[1, 1].set_title('(more) Bilateral')
ax[1, 2].imshow(lena)
ax[1, 2].axis('off')
ax[1, 2].set_title('original')
fig.subplots_adjust(wspace=0.02, hspace=0.2,
top=0.9, bottom=0.05, left=0, right=1)
import os, sys
from skimage import io
from skimage import filter
kidney_image = io.imread("color_img.bmp")
print kidney_image.shape
# estimate the noise in the image
# io.imshow (kidney_image) #have to set display to make this work
# do a test denosing using a total variation filter
kidney_image_denoised_tv = filter.denoise_tv_chambolle(kidney_image, weight=0.1)
io.imsave("kidney_image_denoised_tv.bmp", test_image_denoised_tv)
# do a test denosing using a lateral filter to preserve edges
kidney_image_denoised_lateral = filter.denoise_bilateral(kidney_image, sigma_range=0.05, sigma_spatial=15)
# save the denoised image
io.imsave("kidney_image_denoised_lateral.bmp", test_image_denoised_lateral)
def bilateral(ubyte_frame):
return img_as_ubyte(denoise_bilateral(img_as_float(ubyte_frame), sigma_range=0.3, sigma_spatial=15, win_size=10))