def test_bilateral():
image = np.zeros((21, 21), dtype=np.uint16)
selem = np.ones((3, 3), dtype=np.uint8)
image[10, 10] = 1000
image[10, 11] = 1010
image[10, 9] = 900
assert rank.mean_bilateral(image, selem, s0=1, s1=1)[10, 10] == 1000
assert rank.pop_bilateral(image, selem, s0=1, s1=1)[10, 10] == 1
assert rank.mean_bilateral(image, selem, s0=11, s1=11)[10, 10] == 1005
assert rank.pop_bilateral(image, selem, s0=11, s1=11)[10, 10] == 2
def test_bilateral():
image = np.zeros((21, 21), dtype=np.uint16)
selem = np.ones((3, 3), dtype=np.uint8)
image[10, 10] = 1000
image[10, 11] = 1010
image[10, 9] = 900
assert rank.mean_bilateral(image, selem, s0=1, s1=1)[10, 10] == 1000
assert rank.pop_bilateral(image, selem, s0=1, s1=1)[10, 10] == 1
assert rank.mean_bilateral(image, selem, s0=11, s1=11)[10, 10] == 1005
assert rank.pop_bilateral(image, selem, s0=11, s1=11)[10, 10] == 2
(median filters already achieved this), here we use the **bilateral** filter
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 mean_bilateral
noisy_image = img_as_ubyte(data.camera())
bilat = mean_bilateral(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
fig, ax = plt.subplots(2, 2, figsize=(10, 7))
ax1, ax2, ax3, ax4 = ax.ravel()
ax1.imshow(noisy_image, cmap=plt.cm.gray)
ax1.set_title('Original')
ax1.axis('off')
ax2.imshow(bilat, cmap=plt.cm.gray)
ax2.set_title('Bilateral mean')
ax2.axis('off')
ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
ax3.axis('off')
def segmentation(sample):
samples = []
cores = []
images = []
cf = .9
entropy_ratio = .5
core_ratio = .07 #around 10% of image is core
eq_thresh = 10
csize = 300 #change it later
# for sample in samples:
# if image_number<5:
# image_number += 1
# continue
# image_number += 1
try:
gray_images = np.array([i for i in open_image(sample)])
except:
print 'can\'t open'
# continue
m, n = gray_images[0].shape
g_min = np.min(gray_images[1:], axis=0)
g_max = np.max(gray_images[1:], axis=0)
g_avg = np.average(gray_images[1:], axis=0)
g_mean = rank.mean_bilateral(g_max, disk(40))
images.append(g_max)
selem = disk(5)
diff = g_max-g_min
diff1 = g_avg-g_min
diff2 = g_max-g_avg
h2 = histogram(diff2)
'''
equalize image -> cores are white or black
'''
equalized = img_as_ubyte(exposure.equalize_hist(diff))
#equalized = img_as_ubyte(exposure.equalize_hist(g_min))#g_min
equalized = exposure.adjust_gamma(g_max,2)
##eq_mask = []
#equalized = img_as_ubyte(exposure.equalize_hist(mask))
#eq_mask = equalized<eq_thresh
'''
local otsu
'''
radius = 20
selem = disk(radius)
local_otsu = rank.otsu(equalized, selem)
# local_otsu = tmp<threshold_otsu(equalized)
bg = diff<=local_otsu
ent = rank.entropy(g_max*~bg, disk(35))
grad = rank.gradient(g_mean, disk(50))
tmp = ent*grad
core_mask = tmp>(np.min(tmp)+(np.max(tmp)-np.min(tmp))*entropy_ratio)
#
# h = histogram(local_otsu)
# cdf = 0
# t = g_min.shape[0]*g_min.shape[1]*core_ratio
#
# for i in range(len(h)):
# cdf += h[i]
# if cdf > t:
# maxi = i
# break
#
# core_mask = (local_otsu<maxi)
## imshow(core_mask)
# ##cores = np.logical_and(eq_mask, core_mask)
# ##imshow(eq_mask)
# #
# #
lbl, num_lbl = ndi.label(core_mask)
for i in range(1,num_lbl+1):
'''
lbl==0 is background
'''
c = np.where(np.max(lbl==i, axis=0)==True)[0]
left = c[0]
right = c[-1]
c = np.where(np.max(lbl==i, axis=1)==True)[0]
up = c[0]
down = c[-1]
# '''
# Don't consider edge cores
# '''
# if left<csize/2 or right>n-csize/2:
# continue
# if up<csize/2 or down>m-csize/2:
# continue
#
core = np.zeros((csize, csize))
h = down-up
w = right-left
middle_x = min(max((up+down)/2, csize/2),m-csize/2)
middle_y = min(max((left+right)/2, csize/2), n-csize/2)
# core = (core_mask*gray_images[0])[middle_x-csize/2:middle_x+csize/2, middle_y-csize/2:middle_y+csize/2]
core = gray_images[0][middle_x-csize/2:middle_x+csize/2, middle_y-csize/2:middle_y+csize/2]
core = exposure.adjust_gamma(core,.5)
cores.append(core)
return cores
# print 'image', image_number
#
#if __name__=='__main__':
# os.system('rm %s -R'%(output_dir))
# os.system('mkdir %s'%(output_dir))
# #os.system('mkdir %sres/021'%(input_dir))
# #os.system('mkdir %sres/041'%(input_dir))
# for i in range(len(cores)):
# image_name = '%s/%i.png'%(output_dir, i)
# imsave(image_name, cores[i])
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
from skimage.filter import rank
image = (data.coins()).astype(np.uint16) * 16
selem = disk(20)
percentile_result = rank.mean_percentile(image, selem=selem, p0=.1, p1=.9)
bilateral_result = rank.mean_bilateral(image, selem=selem, s0=500, s1=500)
normal_result = rank.mean(image, selem=selem)
fig, axes = plt.subplots(nrows=3, figsize=(8, 10))
ax0, ax1, ax2 = axes
ax0.imshow(np.hstack((image, percentile_result)))
ax0.set_title('Percentile mean')
ax0.axis('off')
ax1.imshow(np.hstack((image, bilateral_result)))
ax1.set_title('Bilateral mean')
ax1.axis('off')
ax2.imshow(np.hstack((image, normal_result)))
ax2.set_title('Local mean')
try:
image_gray= io.imread(filePath+baseName+str(i).zfill(6)+'-'+str(j).zfill(2)+'.pgm') # Load the image!
except:
loadedImage=False
if loadedImage: # (Do nothing otherwise)
# We run a median filter over the image to eliminate both isolated 'hot' and 'cold' pixels,
# due to the camera itself or random noise, without actually blurring the image. This adds
# slightly unpredictable distortion to the image, but because we only consider a tiny local
# neighborhood of each pixel, in practice it doesn't matter.
image_gray=median(image_gray, disk(1))
# The bilateral mean is effectively a selective Gaussian blur,
# smoothing the image without mixing across edges of substantially
# different structures.
image_gray= rank.mean_bilateral(image_gray,selem=disk10, s0=5, s1=5)
# Average and max brightness across entire image...
imAvBrightness=np.mean(image_gray)
imMaxBrightness=np.max(image_gray)
# Use DoG method to find 'blobs' (hypothetical cells in general size range of cells)
# in image, using slightly dynamic thresholding based on overall image brightness
blobs_dog = blob_dog(image_gray,min_sigma = 2, max_sigma=8, threshold=0.02+0.02*imMaxBrightness/255.0,overlap=0.9)
if len(blobs_dog)>0: # Proceed with cell analysis if we see any blobs
blobs_dog[:,2]=blobs_dog[:,2]*sqrt(2) # Convert column to approximate radius of blob
blobdata=[] # Various data about blobs
blobpos=[] # Positions of blobs
sentBack=False # Part of control code for labeling training sets; irrelevant
firstblob=True # Because the first blob forms the first row in a Numpy array
(median filters already achieved this), here we use the **bilateral** filter
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 mean_bilateral
noisy_image = img_as_ubyte(data.camera())
bilat = mean_bilateral(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
fig, ax = plt.subplots(2, 2, figsize=(10, 7))
ax1, ax2, ax3, ax4 = ax.ravel()
ax1.imshow(noisy_image, cmap=plt.cm.gray)
ax1.set_title('Original')
ax1.axis('off')
ax2.imshow(bilat, cmap=plt.cm.gray)
ax2.set_title('Bilateral mean')
ax2.axis('off')
ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
ax3.axis('off')
frequencies remain untouched.
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage import data
from skimage.morphology import disk
from skimage.filter import rank
image = (data.coins()).astype(np.uint16) * 16
selem = disk(20)
percentile_result = rank.mean_percentile(image, selem=selem, p0=.1, p1=.9)
bilateral_result = rank.mean_bilateral(image, selem=selem, s0=500, s1=500)
normal_result = rank.mean(image, selem=selem)
fig, axes = plt.subplots(nrows=3, figsize=(8, 10))
ax0, ax1, ax2 = axes
ax0.imshow(np.hstack((image, percentile_result)))
ax0.set_title('Percentile mean')
ax0.axis('off')
ax1.imshow(np.hstack((image, bilateral_result)))
ax1.set_title('Bilateral mean')
ax1.axis('off')
ax2.imshow(np.hstack((image, normal_result)))
