def _region_features_for(histone, dna, region):
pixels0 = histone[region].ravel()
pixels1 = dna[region].ravel()
bin0 = pixels0 > histone.mean()
bin1 = pixels1 > dna.mean()
overlap = [np.corrcoef(pixels0, pixels1)[0, 1], (bin0 & bin1).mean(), (bin0 | bin1).mean()]
spi = mh.sobel(histone, just_filter=1)
sp = spi[mh.erode(region)]
sdi = mh.sobel(dna, just_filter=1)
sd = sdi[mh.erode(region)]
sobels = [
np.dot(sp, sp) / len(sp),
np.abs(sp).mean(),
np.dot(sd, sd) / len(sd),
np.abs(sd).mean(),
np.corrcoef(sp, sd)[0, 1],
np.corrcoef(sp, sd)[0, 1] ** 2,
sp.std(),
sd.std(),
]
return np.concatenate(
[
[region.sum()],
haralick(histone * region, ignore_zeros=True).mean(0),
haralick(dna * region, ignore_zeros=True).mean(0),
overlap,
sobels,
haralick(mh.stretch(sdi * region), ignore_zeros=True).mean(0),
haralick(mh.stretch(spi * region), ignore_zeros=True).mean(0),
]
)
def _region_features_for(histone, dna, region):
pixels0 = histone[region].ravel()
pixels1 = dna[region].ravel()
bin0 = pixels0 > histone.mean()
bin1 = pixels1 > dna.mean()
overlap = [
np.corrcoef(pixels0, pixels1)[0, 1],
(bin0 & bin1).mean(),
(bin0 | bin1).mean(),
]
spi = mh.sobel(histone, just_filter=1)
sp = spi[mh.erode(region)]
sdi = mh.sobel(dna, just_filter=1)
sd = sdi[mh.erode(region)]
sobels = [
np.dot(sp, sp) / len(sp),
np.abs(sp).mean(),
np.dot(sd, sd) / len(sd),
np.abs(sd).mean(),
np.corrcoef(sp, sd)[0, 1],
np.corrcoef(sp, sd)[0, 1]**2,
sp.std(),
sd.std(),
]
return np.concatenate([
[region.sum()],
haralick(histone * region, ignore_zeros=True).mean(0),
haralick(dna * region, ignore_zeros=True).mean(0),
overlap,
sobels,
haralick(mh.stretch(sdi * region), ignore_zeros=True).mean(0),
haralick(mh.stretch(spi * region), ignore_zeros=True).mean(0),
])
def get_focus(movie, direction):
x = []
s = movie.shape
if direction == 0:
for i in range(s[0]):
x.append(mh.sobel(movie[i, :, :], just_filter=True))
return np.array(x)
elif direction == 1:
for i in range(s[1]):
x.append(mh.sobel(movie[:, i, :], just_filter=True))
return np.array(x)
elif direction == 2:
for i in range(s[2]):
x.append(mh.sobel(movie[:, :, i], just_filter=True))
return np.array(x)
def filter_channel(im):
'''Compute filtered versions of an image
Parameters
----------
im : ndarray
input image (2D)
Returns
-------
fs : ndarray
filtered version of the image (3D array)
'''
import numpy as np
import mahotas as mh
Tg = im > im.mean()
T2g = im > (im.mean() + 2*im.std())
zim = im-im.mean()
zim /= im.std()
z8 = mh.gaussian_filter(zim, 8)
z4 = mh.gaussian_filter(zim, 4)
z12 = mh.gaussian_filter(zim, 12)
z16 = mh.gaussian_filter(zim, 16)
zdiff = z16 - z8
sob = mh.sobel(im, just_filter=True)
return np.dstack([
Tg, T2g, zim, z4, z8, z12, z16, zdiff, sob])
def filter_channel(im):
'''Compute filtered versions of an image
Parameters
----------
im : ndarray
input image (2D)
Returns
-------
fs : ndarray
filtered version of the image (3D array)
'''
import numpy as np
import mahotas as mh
Tg = im > im.mean()
T2g = im > (im.mean() + 2 * im.std())
zim = im - im.mean()
zim /= im.std()
z8 = mh.gaussian_filter(zim, 8)
z4 = mh.gaussian_filter(zim, 4)
z12 = mh.gaussian_filter(zim, 12)
z16 = mh.gaussian_filter(zim, 16)
zdiff = z16 - z8
sob = mh.sobel(im, just_filter=True)
return np.dstack([Tg, T2g, zim, z4, z8, z12, z16, zdiff, sob])
def edginess_sobel(image):
'''
edgi = edginess_sobel(image)
Measure the "edginess" of an image
'''
edges = mh.sobel(image, just_filter=True)
edges = edges.ravel()
return np.sqrt(np.dot(edges, edges))
def edginess_sobel(image):
'''
edgi = edginess_sobel(image)
Measure the "edginess" of an image
'''
edges = mh.sobel(image, just_filter=True)
edges = edges.ravel()
return np.sqrt(np.dot(edges, edges))
def stack(image):
stack,h,w = image.shape
focus = np.array([mh.sobel(t, just_filter=True) for t in image])
best = np.argmax(focus, 0)
image = image.reshape((stack,-1))
image = image.transpose()
r = image[np.arange(len(image)), best.ravel()]
r = r.reshape((h,w))
return r
def test_overlay():
im = mh.demos.load('luispedro', as_grey=1)
im = mh.stretch(im)
assert np.all(mh.overlay(im).max(2) == im)
edges = mh.sobel(im)
im3 = mh.overlay(im, green=edges)
assert np.all(im3[:, :, 0] == im)
assert np.all(im3[:, :, 2] == im)
assert np.all(im3[:, :, 1] >= im)
def test_overlay():
im = mh.demos.load('luispedro', as_grey=1)
im = mh.stretch(im)
assert np.all(mh.overlay(im).max(2) == im)
edges = mh.sobel(im)
im3 = mh.overlay(im, green=edges)
assert np.all(im3[:,:,0] == im)
assert np.all(im3[:,:,2] == im)
assert np.all(im3[:,:,1] >= im )
def edginess_sobel(image):
"""
Computes edges of the image using Sobel's algorithm
and lighter areas appear more edgier. Computed edge points
are squared and added to give one global feature per image.
"""
edges = mh.sobel(image,just_filter=True)
edges = edges.ravel()
return np.sqrt(np.dot(edges,edges))
def edginess_sobel(image):
'''Measure the "edginess" of an image
image should be a 2d numpy array (an image)
Returns a floating point value which is higher the "edgier" the image is.
'''
edges = mh.sobel(image, just_filter=True)
edges = edges.ravel()
return np.sqrt(np.dot(edges, edges))
def edginess_sobel(image):
'''Measure the "edginess" of an image
image should be a 2d numpy array (an image)
Returns a floating point value which is higher the "edgier" the image is.
'''
edges = mh.sobel(image, just_filter=True)
edges = edges.ravel()
return np.sqrt(np.dot(edges, edges))
def sobel(i):
#what does a sobel look like?
x = np.zeros((45, 384, 3))
for j in range(3):
slc = load_in_movie(path_to_movie)[i, :, :, j]
s = mh.sobel(slc, just_filter=True)
s = ((s - np.min(s)) / (np.max(s) - np.min(s))) * 255
x[..., j] = 255 - s
arr = Image.fromarray(x.astype("uint8"))
arr.save("/Users/loganjaeger/Desktop/aerogel/sobel/{}.png".format(
str(i + 1)))
def stack_per_channel(movie):
stack, h, w = movie.shape
focus = np.array(
[mh.sobel(t, just_filter=True) for t in movie]
) #Using sobel to determine "infocusness of each pixel". This measure is somewhat arbitrary, and I tried it using laplacian filter and entropy as well
best = np.argmax(focus, 0)
image = movie
image = image.reshape((stack, -1)) # image is now (stack, nr_pixels)
image = image.transpose() # image is now (nr_pixels, stack)
r = image[np.arange(len(image)),
best.ravel()] # Select the right pixel at each location
r = r.reshape((h, w)) # reshape to get final result
return r * 255
def stack_one_channel(channel, save=False):
movie = load_in_movie(path_to_movie)[:, :, :, channel]
d, h, w = movie.shape
focus = np.array([mh.sobel(t, just_filter=True) for t in movie])
print(focus.shape)
best = np.argmax(focus, 0)
movie = movie.reshape((d, -1)) # movie is now (d, nr_pixels)
movie = movie.transpose() # movie is now (nr_pixels, d)
r = movie[np.arange(len(movie)),
best.ravel()] # Select the right pixel at each location
r = r.reshape((h, w)) # reshape to get final result
if save:
r = Image.fromarray(r)
r.save(os.path.join(save_path, "stacked_img.png"))
return r
def sobel(dirs):
# Create acutance feature:
features = [] # store local feature descriptors
for idir in range(len(dirs)):
files = [name for name in os.listdir(dirs[idir]) if os.path.isfile(os.path.join(dirs[idir], name))]
for ifile in range(len(files)):
if files[ifile][-3:] != "jpg": # ignore non-image files
continue
image = mh.imread(dirs[idir] + files[ifile]).astype(np.uint8) # read image
if len(image.shape) == 3: # convert to gray if colored
image = mh.colors.rgb2gray(image, dtype=np.uint8)
# Calculate the object edges
edges = mh.sobel(image, just_filter=True)
edges = edges.ravel() # flatten the array
features.append(np.sqrt(np.dot(edges, edges)))
features = np.array(features)
return features
def edginess_sobel(im):
edges = mh.sobel(im, just_filter=True)
edges = edges.ravel()
return np.sqrt(np.dot(edges, edges))
import mahotas
import numpy as np
from pylab import imshow, gray, show, subplot
from os import path
lena_image = path.join(
path.dirname(path.abspath(__file__)),
'data',
'lena.jpg')
photo = mahotas.imread(lena_image, as_grey=True)
photo = photo.astype(np.uint8)
gray()
subplot(131)
imshow(photo)
edge_sobel = mahotas.sobel(photo)
subplot(132)
imshow(edge_sobel)
edge_dog = mahotas.dog(photo)
subplot(133)
imshow(edge_dog)
show()
# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
from matplotlib import pyplot as plt
import numpy as np
import mahotas as mh
image = mh.imread('../1400OS_10_01.jpeg')
image = mh.colors.rgb2gray(image, dtype=np.uint8)
image = image[::4, ::4]
thresh = mh.sobel(image)
filtered = mh.sobel(image, just_filter=True)
thresh = mh.dilate(thresh, np.ones((7, 7)))
filtered = mh.dilate(mh.stretch(filtered), np.ones((7, 7)))
h, w = thresh.shape
canvas = 255 * np.ones((h, w * 2 + 64), np.uint8)
canvas[:, :w] = thresh * 255
canvas[:, -w:] = filtered
mh.imsave('../1400OS_10_09+.jpg', canvas)
# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
from matplotlib import pyplot as plt
import numpy as np
import mahotas as mh
image = mh.imread('../1400OS_10_01.jpeg')
image = mh.colors.rgb2gray(image, dtype=np.uint8)
image = image[::4, ::4]
thresh = mh.sobel(image)
filtered = mh.sobel(image, just_filter=True)
thresh = mh.dilate(thresh, np.ones((7, 7)))
filtered = mh.dilate(mh.stretch(filtered), np.ones((7, 7)))
h, w = thresh.shape
canvas = 255 * np.ones((h, w * 2 + 64), np.uint8)
canvas[:, :w] = thresh * 255
canvas[:, -w:] = filtered
mh.imsave('../1400OS_10_09+.jpg', canvas)
def test_sobel_pure():
f = np.random.random((64, 128))
f2 = f.copy()
_ = mh.sobel(f)
assert np.all(f == f2)
def test_find_edge():
f = np.zeros((32,48))
f[:,16:] = 1
f = mh.gaussian_filter(f,4)
fs = mh.sobel(f)
assert np.sum(fs[:,15:17] > 0) >= 32
def test_find_edge():
f = np.zeros((32,48))
f[:,16:] = 1
f = mh.gaussian_filter(f,4)
fs = mh.sobel(f)
assert np.all(fs[:,15] > 0)
import mahotas
import numpy as np
from pylab import imshow, gray, show, subplot
from os import path
lena_image = path.join(path.dirname(path.abspath(__file__)), 'data',
'lena.jpg')
photo = mahotas.imread(lena_image, as_grey=True)
photo = photo.astype(np.uint8)
gray()
subplot(131)
imshow(photo)
edge_sobel = mahotas.sobel(photo)
subplot(132)
imshow(edge_sobel)
edge_dog = mahotas.dog(photo)
subplot(133)
imshow(edge_dog)
show()
def test_sobel_pure():
f = np.random.random((64, 128))
f2 = f.copy()
_ = mh.sobel(f)
assert np.all(f == f2)
