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%matplotlib inline
from IPython.display import HTML,Image,SVG,YouTubeVideo
%matplotlib inline
from IPython.display import HTML,Image,SVG,YouTubeVideo
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from skimage import data
import numpy as np
from skimage.morphology import disk
import skimage.filters.rank as skr
from skimage.measure import label
from skimage.morphology import watershed
from skimage.io import imread
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
from skimage import data
import numpy as np
from skimage.morphology import disk
import skimage.filters.rank as skr
from skimage.measure import label
from skimage.morphology import watershed
from skimage.io import imread
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
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# segment the coins
im = data.coins()
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# segment the coins
im = data.coins()
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# detect the eyes / nose
im = data.chelsea()
plt.imshow(im);
# detect the eyes / nose
im = data.chelsea()
plt.imshow(im);
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# counting the galaxies
im = data.hubble_deep_field()
plt.imshow(im);
# counting the galaxies
im = data.hubble_deep_field()
plt.imshow(im);
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im = data.page()
bg = skr.median(im, disk(10))
res = (1.*im/bg) < .8
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(bg,cmap=plt.cm.gray);
plt.colorbar()
plt.figure()
plt.imshow(res.astype(np.uint8),cmap=plt.cm.gray);
plt.colorbar();
im = data.page()
bg = skr.median(im, disk(10))
res = (1.*im/bg) < .8
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(bg,cmap=plt.cm.gray);
plt.colorbar()
plt.figure()
plt.imshow(res.astype(np.uint8),cmap=plt.cm.gray);
plt.colorbar();
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# segment the cells
im = imread('../data/dh_phase.png')
th = im>150
th1 = im>100
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(1.*th+th1,cmap=plt.cm.gray)
plt.colorbar();
# segment the cells
im = imread('../data/dh_phase.png')
th = im>150
th1 = im>100
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(1.*th+th1,cmap=plt.cm.gray)
plt.colorbar();
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from skimage.feature import canny
ca = canny(im)
plt.figure(figsize=[10,10])
plt.imshow(ca,cmap=plt.cm.gray);
from skimage.feature import canny
ca = canny(im)
plt.figure(figsize=[10,10])
plt.imshow(ca,cmap=plt.cm.gray);
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from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
lab,n_lab = label(th,return_num=True)
bg = th1==0
lab[bg] = n_lab+1
#med = skr.median(im,disk(5))
#gr = skr.gradient(med,disk(3))
ws = watershed(255-im,lab)
plt.imshow(mark_boundaries(im,ws))
from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
lab,n_lab = label(th,return_num=True)
bg = th1==0
lab[bg] = n_lab+1
#med = skr.median(im,disk(5))
#gr = skr.gradient(med,disk(3))
ws = watershed(255-im,lab)
plt.imshow(mark_boundaries(im,ws))
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
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<matplotlib.image.AxesImage at 0x7f31b3da2450>
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im = imread('../data/exp0001.jpg')
plt.figure(figsize=[20,20])
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
im = imread('../data/exp0001.jpg')
plt.figure(figsize=[20,20])
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# count red and yellow flowers
im = imread('../data/flowers.jpg')
plt.imshow(im)
plt.colorbar();
# count red and yellow flowers
im = imread('../data/flowers.jpg')
plt.imshow(im)
plt.colorbar();
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# find the fiber orientation
im = imread('../data/image4.png')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# find the fiber orientation
im = imread('../data/image4.png')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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from IPython.display import YouTubeVideo
YouTubeVideo('PUcz11MLxUk', start=0, autoplay=1, theme="light", color="blue",)
from IPython.display import YouTubeVideo
YouTubeVideo('PUcz11MLxUk', start=0, autoplay=1, theme="light", color="blue",)
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# detect stroma
im = imread('../data/Rp042826d.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# detect stroma
im = imread('../data/Rp042826d.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# segment the flowers
im = imread('../data/KaneFlowers.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# segment the flowers
im = imread('../data/KaneFlowers.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
gr = skr.gradient(im,disk(3))
local_min = im <= skr.minimum(im,disk(5))
lab = label(local_min)
#med = skr.median(im,disk(5))
ws = watershed(gr,lab)
plt.figure(figsize=[10,10])
plt.imshow(mark_boundaries(im,ws))
#plt.imshow(local_min)
from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
gr = skr.gradient(im,disk(3))
local_min = im <= skr.minimum(im,disk(5))
lab = label(local_min)
#med = skr.median(im,disk(5))
ws = watershed(gr,lab)
plt.figure(figsize=[10,10])
plt.imshow(mark_boundaries(im,ws))
#plt.imshow(local_min)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
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<matplotlib.image.AxesImage at 0x7f31b13565d0>
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rgb = imread('../data/4colors.JPG')
plt.figure(figsize=[20,20])
plt.imshow(rgb)
plt.colorbar();
rgb = imread('../data/4colors.JPG')
plt.figure(figsize=[20,20])
plt.imshow(rgb)
plt.colorbar();
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r = skr.median(rgb[:,:,0],disk(1))
plt.imshow(r,cmap=plt.cm.gray)
r = skr.median(rgb[:,:,0],disk(1))
plt.imshow(r,cmap=plt.cm.gray)
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<matplotlib.image.AxesImage at 0x7f31b1d95510>
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s = rgb.sum(axis=2)
th = s > 100
#post-processing
pth = skr.minimum(th.astype(np.uint8),disk(1))
plt.figure(figsize=[20,20])
plt.imshow(pth,cmap=plt.cm.gray)
plt.colorbar()
s = rgb.sum(axis=2)
th = s > 100
#post-processing
pth = skr.minimum(th.astype(np.uint8),disk(1))
plt.figure(figsize=[20,20])
plt.imshow(pth,cmap=plt.cm.gray)
plt.colorbar()
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<matplotlib.colorbar.Colorbar at 0x7f31b1fc8d10>
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lab = label(pth)
lut = np.arange(0,np.max(lab)+1)
plt.imshow(lab)
plt.colorbar()
mask = lab == 20
plt.imshow(mask)
lab = label(pth)
lut = np.arange(0,np.max(lab)+1)
plt.imshow(lab)
plt.colorbar()
mask = lab == 20
plt.imshow(mask)
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<matplotlib.image.AxesImage at 0x7f31b22cf310>
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from random import shuffle
shuffle(lut)
from random import shuffle
shuffle(lut)
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shuffle(lut)
plt.imshow(lut[lab])
plt.colorbar()
shuffle(lut)
plt.imshow(lut[lab])
plt.colorbar()
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<matplotlib.colorbar.Colorbar at 0x7f31b3e5af50>
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# segment the cell
im = imread('../data/exp0001crop.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# segment the cell
im = imread('../data/exp0001crop.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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m = skr.median(im,disk(5))
plt.imshow(m,cmap=plt.cm.gray)
plt.colorbar()
m = skr.median(im,disk(5))
plt.imshow(m,cmap=plt.cm.gray)
plt.colorbar()
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<matplotlib.colorbar.Colorbar at 0x7f31b1f866d0>
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th1 = m < 90
th2 = np.bitwise_and(110 > m,m < 130)
plt.imshow(th2)
th1 = m < 90
th2 = np.bitwise_and(110 > m,m < 130)
plt.imshow(th2)
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<matplotlib.image.AxesImage at 0x7f31b21dda10>
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markers = label(th2)
plt.imshow(markers)
plt.colorbar()
markers = label(th2)
plt.imshow(markers)
plt.colorbar()
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<matplotlib.colorbar.Colorbar at 0x7f31b2498350>
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markers[markers==3] = 2
ws = watershed(im,markers)
markers[markers==3] = 2
ws = watershed(im,markers)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
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plt.imshow(ws)
plt.imshow(mark_boundaries(im,ws))
plt.imshow(ws)
plt.imshow(mark_boundaries(im,ws))
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<matplotlib.image.AxesImage at 0x7f31b238f290>
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# segment the cell
im = imread('../data/brain.jpg')[:,:,0]
plt.figure(figsize=(10,10))
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
# segment the cell
im = imread('../data/brain.jpg')[:,:,0]
plt.figure(figsize=(10,10))
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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plt.hist(im.flatten(),255);
plt.hist(im.flatten(),255);
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from skimage.filters import threshold_otsu
t_otsu = threshold_otsu(im)
t_otsu
from skimage.filters import threshold_otsu
t_otsu = threshold_otsu(im)
t_otsu
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36
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th = im > t_otsu
plt.figure(figsize=(10,10))
plt.imshow(th)
th = im > t_otsu
plt.figure(figsize=(10,10))
plt.imshow(th)
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<matplotlib.image.AxesImage at 0x7f31b1c7c790>
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lab = label(th,connectivity=1)
plt.imshow(lab)
lab = label(th,connectivity=1)
plt.imshow(lab)
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<matplotlib.image.AxesImage at 0x7f31b1b29fd0>
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from skimage.measure import regionprops
from skimage.measure import regionprops
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props = regionprops(lab)
brain = (lab==7).astype(np.uint8)
pp = skr.maximum(brain,disk(3))
pp = skr.minimum(pp,disk(3))
plt.imshow(pp)
props = regionprops(lab)
brain = (lab==7).astype(np.uint8)
pp = skr.maximum(brain,disk(3))
pp = skr.minimum(pp,disk(3))
plt.imshow(pp)
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<matplotlib.image.AxesImage at 0x7f31b1aa9fd0>
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for p in props:
print(p.area, p.label)
for p in props:
print(p.area, p.label)
1459 1 5 2 1 3 3 4 1 5 16 6 6323 7 1 8 2 9 1 10 1 11 1 12 1 13 16 14 1 15 1 16 1 17 2 18 2 19 2 20 2 21 30 22 1 23 1 24 1 25 1 26 2 27 2 28 5 29 1 30 1 31 2 32 1 33 13 34
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