def skeleton_image(folder, image_file, threshold=50, area_thresh=50, figsize=(10, 10), show=False):
"""
Skeletonizes the image and returns properties of each skeleton.
Parameters
----------
Returns
-------
Examples
--------
"""
# Median filtered image.
fname = '{}/{}'.format(folder, image_file)
image0 = sio.imread(fname)
image0 = np.ceil(255* (image0[:, :, 1] / image0[:, :, 1].max())).astype(int)
image0 = skimage.filters.median(image0)
filt = 'filt_{}.png'.format(image_file.split('.')[0])
sio.imsave(folder+'/'+filt, image0)
#threshold the image
binary0 = binary_image(folder, filt, threshold=threshold, close=True, show=False)
clean = 'clean_{}'.format(filt)
#label image
short_image, props = label_image(folder, clean, area_thresh=area_thresh, show=False)
short = 'short_{}'.format(clean)
short_image = short_image > 1
# Skeletonize
skeleton0 = skeletonize(short_image)
branch_data = csr.summarise(skeleton0)
branch_data_short = branch_data
#Remove small branches
mglia = branch_data['skeleton-id'].max()
nbranches = []
ncount = 0
for i in range(1, mglia+1):
bcount = branch_data[branch_data['skeleton-id']==i]['skeleton-id'].count()
if bcount > 0:
ids = branch_data.index[branch_data['skeleton-id']==i].tolist()
nbranches.append(bcount)
for j in range(0, len(ids)):
branch_data_short.drop([ids[j]])
ncount = ncount + 1
if show:
fig, ax = plt.subplots(figsize=(10, 10))
draw.overlay_euclidean_skeleton_2d(image0, branch_data_short,
skeleton_color_source='branch-type', axes=ax)
plt.savefig('{}/skel_{}'.format(folder, short))
return skeleton0, branch_data_short, nbranches, short_image, props
def skan(self, n):
blur = cv2.blur(self.mask, (5, 5))
binary = blur > filters.threshold_otsu(blur)
skeleton = morphology.skeletonize(binary)
ax = plt.subplot(2, 2, 2 * n - 1)
draw.overlay_skeleton_2d(blur, skeleton, dilate=1, axes=ax)
branch_data = summarize(Skeleton(skeleton))
ax = plt.subplot(2, 2, 2 * n)
draw.overlay_euclidean_skeleton_2d(blur,
branch_data,
skeleton_color_source='branch-type',
axes=ax)
df1 = branch_data.loc[branch_data['branch-type'] == 1]
return df1
def test_overlay_euclidean_skeleton(test_image, test_stats):
draw.overlay_euclidean_skeleton_2d(test_image, test_stats)
draw.overlay_euclidean_skeleton_2d(test_image, test_stats,
skeleton_color_source='branch-distance')
shape_skeleton = skeleton_image * shape
from skan import summarise
data = summarise(shape_skeleton)
data.head()
from skan import draw
draw.overlay_skeleton_2d(im, shape_skeleton, dilate=1)
def filtered(values):
return np.digitize(values, [0.125, 0.625])
data['filtered'] = filtered(data['mean pixel value'])
draw.overlay_euclidean_skeleton_2d(im, data,
skeleton_color_source='filtered')
from skan import Skeleton
skeleton = Skeleton(shape_skeleton, source_image=im)
# second time is much faster thanks to Numba JIT
skeleton = Skeleton(shape_skeleton, source_image=im)
def filtered2(skeleton):
values = skeleton.path_means()
return filtered(values)
ax, cvals = draw.overlay_skeleton_2d_class(skeleton,
skeleton_color_source=filtered2)
#h, w = imgb.shape[:2]
#imgb = cv2.resize(imgb,(h,h), interpolation=cv2.INTER_CUBIC)
pbef = Process(imgb)
pbef.lowp = np.array([150, 60, 100])
pbef.highp = np.array([170, 255, 255])
pbef.loww = np.array([90, 15, 150])
pbef.highw = np.array([115, 255, 255])
maskb = pbef.mask(kernel)
resb = pbef.res(maskb)
maskb = cv2.blur(maskb, (5, 5))
binaryb = maskb > filters.threshold_otsu(maskb)
skeletonb = morphology.skeletonize(binaryb)
fig, ax = plt.subplots()
draw.overlay_skeleton_2d(maskb, skeletonb, dilate=1, axes=ax)
#graphb = csgraph_from_masked(binaryb)
#plt.imshow(graphb)
gb, cb, db = skeleton_to_csgraph(skeletonb)
draw.overlay_skeleton_networkx(gb, cb, image=maskb)
branch_datab = summarize(Skeleton(skeletonb))
dfb = branch_datab.loc[branch_datab['branch-type'] == 1]
#dfb.to_csv(r'./before.csv')
draw.overlay_euclidean_skeleton_2d(maskb,
branch_datab,
skeleton_color_source='branch-type')
plt.show()
cv2.waitKey(0)
cv2.destroyAllWindows()
def skeleton_image(folder, image_file, threshold=50, area_thresh=50,
tofilt=True, ajar=True, close=True, figsize=(10, 10),
show=False, channel=0, disp_binary=True, imname=None):
"""Skeletonizes the image and returns properties of each skeleton.
Composite function of binary_image, clean_image and skeletonizing
functionality from skan.
Parameters
----------
folder : string
Directory containing image_file
image_file : string
Filename of image to be analyzed
threshold : int or float
Intensity threshold level for threshold.
area_thresh : int or float
Size cutoff level to remove small objects
figsize : tuple of int or float
Size out output figure
show : bool
If True, prints image to Jupyter notebook
channel : int
If multichannel is True, reads in image corresponding to this channel in
file.
disp_binary: bool
If True, prints binary image instead of raw image
imname : string
Output filename
Returns
-------
skeleton0 : numpy.ndarray
Skeletonized version of input image_file
branch_data_short : pandas.core.frame.DataFrame
Data associated with each branch found in input image
nbranches : list
Number of branches on each cell in branch_data_short
short_image : numpy.ndarray
Cleaned up binary image from image_file prior to skeletonization
props : skimage.object
Contains all properties of objects identified in image
Examples
--------
"""
# Median filtered image.
fname = '{}/{}'.format(folder, image_file)
image0 = sio.imread(fname)
if channel is None:
image0 = np.ceil(255 * (image0[:, :] / image0[:, :].max())).astype(int)
else:
image0 = np.ceil(255 * (image0[:, :, channel
] / image0[:, :, channel
].max())).astype(int)
if tofilt:
image0 = skimage.filters.median(image0)
image_file = 'filt_{}'.format(image_file)
sio.imsave(folder+'/'+image_file, image0)
# label image
short_image, props = clean_image(folder, image_file, threshold=threshold,
area_thresh=area_thresh, ajar=ajar,
close=close, imname='labelim.tif',
channel=None, show=False)
short_image = short_image > 1
# Skeletonize
skeleton0 = skeletonize(short_image)
branch_data = csr.summarise(skeleton0)
branch_data_short = branch_data
# Remove small branches
mglia = branch_data['skeleton-id'].max()
nbranches = []
ncount = 0
for i in range(1, mglia+1):
bcount = branch_data[branch_data['skeleton-id'
] == i]['skeleton-id'].count()
if bcount > 0:
ids = branch_data.index[branch_data['skeleton-id'] == i].tolist()
nbranches.append(bcount)
for j in range(0, len(ids)):
branch_data_short.drop([ids[j]])
ncount = ncount + 1
if show:
fig, ax = plt.subplots(figsize=figsize)
if disp_binary:
draw.overlay_euclidean_skeleton_2d(short_image, branch_data_short,
skeleton_color_source='branch-type',
axes=ax)
else:
draw.overlay_euclidean_skeleton_2d(image0, branch_data_short,
skeleton_color_source='branch-type',
axes=ax)
if imname is None:
output = "skel_{}".format(image_file)
else:
output = imname
plt.savefig('{}/{}'.format(folder, output))
skel = Bunch(im=skeleton0, branchdat=branch_data_short, nbran=nbranches,
shortim=short_image, props=props)
return skel
branch_data = summarize(Skeleton(image_skeleton))
print(branch_data.head())
fig = plt.plot()
branch_data.hist(column='branch-distance', by='branch-type', bins=100)
result_file = (save_path + base_name + '_hist.' + ext)
plt.savefig(result_file,
transparent=True,
bbox_inches='tight',
pad_inches=0)
fig = plt.plot()
draw.overlay_euclidean_skeleton_2d(source_image,
branch_data,
skeleton_color_source='branch-distance',
skeleton_colormap='hsv')
result_file = (save_path + base_name + '_overlay.' + ext)
plt.savefig(result_file,
transparent=True,
bbox_inches='tight',
pad_inches=0)
print(filename, abs_path)