def thread_proc(): while True: try: bucket = v_queue.get_nowait() except: break beta_mat_block=beta[:,:,(z_division*s_portion)+bucket] alpha_mat_block=alpha[:,:,(z_division*s_portion)+bucket] correction_mat_pixel=scsp.diags(Dimg_org[((z_division*s_portion)+bucket)*r_org*c_org:((z_division*s_portion)+bucket+1)*r_org*c_org],0) correction_mat2_pixel=scsp.eye(r_org*c_org,r_org*c_org) correction_mat_pixel=scsp.hstack([correction_mat_pixel,correction_mat2_pixel]) del correction_mat2_pixel beta_mat_block=np.repeat(beta_mat_block,window_size_x,axis=0) beta_mat=np.repeat(beta_mat_block,window_size_y,axis=1) del beta_mat_block beta_mat=np.uint16((float(1)/float(accuracy1))*beta_mat) beta_changed=rank.bilateral_mean(beta_mat,selem=selem,s0=lower_val_beta,s1=upper_val_beta) beta_changed=(np.float32(beta_changed))*float(accuracy1) del beta_mat beta_vec=np.reshape(beta_changed,(r_org*c_org,1),order='F') alpha_mat_block=np.repeat(alpha_mat_block,window_size_x,axis=0) alpha_mat=np.repeat(alpha_mat_block,window_size_y,axis=1) del alpha_mat_block alpha_mat=alpha_mat+cnst alpha_mat=np.uint16((float(1)/float(accuracy2))*alpha_mat) alpha_changed=rank.bilateral_mean(alpha_mat,selem=selem,s0=lower_val_alpha,s1=upper_val_alpha) alpha_changed=((np.float32(alpha_changed))*float(accuracy2))-cnst del alpha_mat alpha_vec=np.reshape(alpha_changed,(r_org*c_org,1),order='F') param_vec=np.vstack([beta_vec,alpha_vec]) corrected_data_smoothed=correction_mat_pixel*param_vec corrected_data_smoothed=np.reshape(corrected_data_smoothed,(r_org,c_org),order='F') num_finished = 0 with finished_count.get_lock(): finished_count.value += 1 num_finished = finished_count.value #print ' Finished %d/%d' % (num_finished, buckets) with value_portion_arr.get_lock(): value_portion[...][:,:,bucket]=corrected_data_smoothed
that restricts the local neighborhood to pixel having a greylevel similar to
the central one.
.. note::
a different implementation is available for color images in
`skimage.filter.denoise_bilateral`.
"""
from skimage.filter.rank import bilateral_mean
ima = data.camera()
selem = disk(10)
bilat = bilateral_mean(ima.astype(np.uint16), disk(20), s0=10, s1=10)
# display results
fig = plt.figure(figsize=[10, 7])
plt.subplot(2, 2, 1)
plt.imshow(ima, cmap=plt.cm.gray)
plt.xlabel('original')
plt.subplot(2, 2, 3)
plt.imshow(bilat, cmap=plt.cm.gray)
plt.xlabel('bilateral mean')
plt.subplot(2, 2, 2)
plt.imshow(ima[200:350, 350:450], cmap=plt.cm.gray)
plt.subplot(2, 2, 4)
plt.imshow(bilat[200:350, 350:450], cmap=plt.cm.gray)
"""
that restricts the local neighborhood to pixel having a gray-level similar to
the central one.
.. note::
A different implementation is available for color images in
`skimage.filter.denoise_bilateral`.
"""
from skimage.filter.rank import bilateral_mean
noisy_image = img_as_ubyte(data.camera())
selem = disk(10)
bilat = bilateral_mean(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
fig = plt.figure(figsize=[10, 7])
plt.subplot(2, 2, 1)
plt.imshow(noisy_image, cmap=plt.cm.gray)
plt.title('Original')
plt.axis('off')
plt.subplot(2, 2, 3)
plt.imshow(bilat, cmap=plt.cm.gray)
plt.title('Bilateral mean')
plt.axis('off')
plt.subplot(2, 2, 2)
plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
that restricts the local neighborhood to pixel having a greylevel similar to
the central one.
.. note::
a different implementation is available for color images in
`skimage.filter.denoise_bilateral`.
"""
from skimage.filter.rank import bilateral_mean
ima = data.camera()
selem = disk(10)
bilat = bilateral_mean(ima.astype(np.uint16), disk(20), s0=10, s1=10)
# display results
fig = plt.figure(figsize=[10, 7])
plt.subplot(2, 2, 1)
plt.imshow(ima, cmap=plt.cm.gray)
plt.xlabel('original')
plt.subplot(2, 2, 3)
plt.imshow(bilat, cmap=plt.cm.gray)
plt.xlabel('bilateral mean')
plt.subplot(2, 2, 2)
plt.imshow(ima[200:350, 350:450], cmap=plt.cm.gray)
plt.subplot(2, 2, 4)
plt.imshow(bilat[200:350, 350:450], cmap=plt.cm.gray)
"""
that restricts the local neighborhood to pixel having a gray-level similar to
the central one.
.. note::
A different implementation is available for color images in
`skimage.filter.denoise_bilateral`.
"""
from skimage.filter.rank import bilateral_mean
noisy_image = img_as_ubyte(data.camera())
selem = disk(10)
bilat = bilateral_mean(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
fig = plt.figure(figsize=[10, 7])
plt.subplot(2, 2, 1)
plt.imshow(noisy_image, cmap=plt.cm.gray)
plt.title('Original')
plt.axis('off')
plt.subplot(2, 2, 3)
plt.imshow(bilat, cmap=plt.cm.gray)
plt.title('Bilateral mean')
plt.axis('off')
plt.subplot(2, 2, 2)
plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
complete image (background and details). Bilateral mean exhibits a high
filtering rate for continuous area (i.e. background) while higher image
frequencies remain untouched.
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage import data
from skimage.morphology import disk
import skimage.filter.rank as rank
a16 = (data.coins()).astype(np.uint16) * 16
selem = disk(20)
f1 = rank.percentile_mean(a16, selem=selem, p0=.1, p1=.9)
f2 = rank.bilateral_mean(a16, selem=selem, s0=500, s1=500)
f3 = rank.mean(a16, selem=selem)
# display results
fig, axes = plt.subplots(nrows=3, figsize=(15, 10))
ax0, ax1, ax2 = axes
ax0.imshow(np.hstack((a16, f1)))
ax0.set_title('percentile mean')
ax1.imshow(np.hstack((a16, f2)))
ax1.set_title('bilateral mean')
ax2.imshow(np.hstack((a16, f3)))
ax2.set_title('local mean')
plt.show()