def test_binary():
img = np.random.randn(100, 100)
img[25:75, 25:75] += 2
img -= 1
# !!! Be careful when doing this concatenation, it seems 'c_' does not create a copy
#u = np.c_[img.flatten().copy(), -img.flatten().copy()]
u = np.c_[img.reshape(10000, 1), -img.reshape(10000, 1)].copy()
plt.ion()
plt.figure()
plt.imshow(u[:, 0].reshape(100, 100), cmap='gray', interpolation='nearest')
plt.figure()
plt.imshow(u[:, 1].reshape(100, 100), cmap='gray', interpolation='nearest')
import imgtools.pairwise as pw
edges, edge_weights = pw.get_uniform_smoothness_pw_single_image(img.shape)
pw_cost = 1 - np.eye(2)
# y = pygco.cut_general_graph(edges, edge_weights, u, pw_cost*2, n_iter=-1, algorithm='expansion')
unary = np.tile(img[:, :, np.newaxis], [1, 1, 2])
unary[:, :, 0] = img
unary[:, :, 1] = -img
unary_new = u.reshape((100, 100, 2))
print np.abs(unary - unary_new).max()
print(unary != unary_new).any()
#import ipdb
#ipdb.set_trace()
#plt.figure(); plt.imshow(unary[:,:,0], cmap='gray', interpolation='nearest')
#plt.figure(); plt.imshow(unary[:,:,1], cmap='gray', interpolation='nearest')
#y = pygco.cut_grid_graph_simple(unary, pw_cost*0, n_iter=-1)
#y_new = pygco.cut_grid_graph_simple(unary_new + np.random.randn(unary.shape[0], unary.shape[1], unary.shape[2])*0, pw_cost*0, n_iter=-1)
y_new = pygco.cut_grid_graph_simple(unary_new + np.zeros(unary_new.shape),
pw_cost * 1,
n_iter=-1)
y_new2 = pygco.cut_grid_graph_simple(unary_new, pw_cost * 0, n_iter=-1)
print unary_new.dtype
print(unary_new + np.zeros(unary_new.shape)).dtype
#plt.figure(); plt.imshow(y.reshape(100,100), cmap='gray', interpolation='nearest')
plt.figure()
plt.imshow(y_new.reshape(100, 100), cmap='gray', interpolation='nearest')
plt.figure()
plt.imshow(y_new2.reshape(100, 100), cmap='gray', interpolation='nearest')
t = raw_input('[Press any key]')
def test_grid():
x = np.zeros((100, 100))
x[:,50:] = 2
x[25:75, 25:75] = 1
y = x + np.random.randn(100, 100)
unary = np.tile(y[:,:,np.newaxis], [1,1,3])
im = unary[:,:,1]
im = im - 1
im[x == 0] *= -1
unary[:,:,1] = im
unary[:,:,2] = 2 - unary[:,:,2]
plt.ion()
plt.figure(); plt.imshow(unary[:,:,0], cmap="gray", interpolation="nearest")
plt.figure(); plt.imshow(unary[:,:,1], cmap="gray", interpolation="nearest")
plt.figure(); plt.imshow(unary[:,:,2], cmap="gray", interpolation="nearest")
pairwise = 1 - np.eye(3)
z = pygco.cut_grid_graph_simple(unary, pairwise * 2, n_iter=-1)
plt.figure(); plt.imshow(z.reshape(100, 100), cmap="gray", interpolation="nearest")
t = raw_input('[Press any key]')
def test_grid():
""" """
annot = np.zeros((100, 100))
annot[:, 50:] = 2
annot[25:75, 25:75] = 1
noise = annot + np.random.randn(100, 100)
unary = np.tile(noise[:, :, np.newaxis], [1, 1, 3])
tmp = unary[:, :, 1]
tmp = (tmp - 1)
tmp[annot == 0] *= -1
unary[:, :, 1] = tmp
unary[:, :, 2] = 2 - unary[:, :, 2]
fig = plt.figure(figsize=(unary.shape[-1] * 4, 4))
for i in range(unary.shape[-1]):
plt.subplot(1, unary.shape[-1], i + 1)
plt.imshow(unary[:, :, i], cmap="gray", interpolation="nearest")
fig.tight_layout(), fig.savefig('./images/grid_unary.png')
pairwise = (1 - np.eye(3)) * 10
labels = pygco.cut_grid_graph_simple(unary, pairwise, n_iter=-1)
fig = plt.figure(figsize=(2 * 4, 4))
plt.subplot(1, 2, 1), plt.title('original annotation')
plt.imshow(annot, interpolation="nearest")
plt.subplot(1, 2, 2), plt.title('resulting labeling')
plt.imshow(labels.reshape(100, 100), interpolation="nearest")
fig.tight_layout(), fig.savefig('./images/grid_labels.png')
def test_grid():
""" """
annot = np.zeros((100, 100))
annot[:, 50:] = 2
annot[25:75, 25:75] = 1
noise = annot + np.random.randn(100, 100)
unary = np.tile(noise[:, :, np.newaxis], [1, 1, 3])
tmp = unary[:, :, 1]
tmp = (tmp - 1)
tmp[annot == 0] *= -1
unary[:, :, 1] = tmp
unary[:, :, 2] = 2 - unary[:, :, 2]
fig = plt.figure(figsize=(unary.shape[-1] * 4, 4))
for i in range(unary.shape[-1]):
plt.subplot(1, unary.shape[-1], i + 1)
plt.imshow(unary[:, :, i], cmap="gray", interpolation="nearest")
fig.tight_layout(), fig.savefig('./images/grid_unary.png')
pairwise = (1 - np.eye(3)) * 10
labels = pygco.cut_grid_graph_simple(unary, pairwise, n_iter=-1)
fig = plt.figure(figsize=(2 * 4, 4))
plt.subplot(1, 2, 1), plt.title('original annotation')
plt.imshow(annot, interpolation="nearest")
plt.subplot(1, 2, 2), plt.title('resulting labeling')
plt.imshow(labels.reshape(100, 100), interpolation="nearest")
fig.tight_layout(), fig.savefig('./images/grid_labels.png')
def test_grid():
x = np.zeros((100, 100))
x[:, 50:] = 2
x[25:75, 25:75] = 1
y = x + np.random.randn(100, 100)
unary = np.tile(y[:, :, np.newaxis], [1, 1, 3])
im = unary[:, :, 1]
im = im - 1
im[x == 0] *= -1
unary[:, :, 1] = im
unary[:, :, 2] = 2 - unary[:, :, 2]
plt.ion()
plt.figure()
plt.imshow(unary[:, :, 0], cmap="gray", interpolation="nearest")
plt.figure()
plt.imshow(unary[:, :, 1], cmap="gray", interpolation="nearest")
plt.figure()
plt.imshow(unary[:, :, 2], cmap="gray", interpolation="nearest")
pairwise = 1 - np.eye(3)
z = pygco.cut_grid_graph_simple(unary, pairwise * 2, n_iter=-1)
plt.figure()
plt.imshow(z.reshape(100, 100), cmap="gray", interpolation="nearest")
t = raw_input('[Press any key]')
def test_binary():
img = np.random.randn(100, 100)
img[25:75,25:75] += 2
img -= 1
# !!! Be careful when doing this concatenation, it seems 'c_' does not create a copy
#u = np.c_[img.flatten().copy(), -img.flatten().copy()]
u = np.c_[img.reshape(10000,1), -img.reshape(10000,1)].copy()
plt.ion()
plt.figure(); plt.imshow(u[:,0].reshape(100,100), cmap='gray', interpolation='nearest')
plt.figure(); plt.imshow(u[:,1].reshape(100,100), cmap='gray', interpolation='nearest')
import imgtools.pairwise as pw
edges, edge_weights = pw.get_uniform_smoothness_pw_single_image(img.shape)
pw_cost = 1 - np.eye(2)
# y = pygco.cut_general_graph(edges, edge_weights, u, pw_cost*2, n_iter=-1, algorithm='expansion')
unary = np.tile(img[:,:,np.newaxis], [1,1,2])
unary[:,:,0] = img
unary[:,:,1] = -img
unary_new = u.reshape((100,100,2))
print np.abs(unary - unary_new).max()
print (unary != unary_new).any()
#import ipdb
#ipdb.set_trace()
#plt.figure(); plt.imshow(unary[:,:,0], cmap='gray', interpolation='nearest')
#plt.figure(); plt.imshow(unary[:,:,1], cmap='gray', interpolation='nearest')
#y = pygco.cut_grid_graph_simple(unary, pw_cost*0, n_iter=-1)
#y_new = pygco.cut_grid_graph_simple(unary_new + np.random.randn(unary.shape[0], unary.shape[1], unary.shape[2])*0, pw_cost*0, n_iter=-1)
y_new = pygco.cut_grid_graph_simple(unary_new + np.zeros(unary_new.shape), pw_cost*1, n_iter=-1)
y_new2 = pygco.cut_grid_graph_simple(unary_new, pw_cost*0, n_iter=-1)
print unary_new.dtype
print (unary_new + np.zeros(unary_new.shape)).dtype
#plt.figure(); plt.imshow(y.reshape(100,100), cmap='gray', interpolation='nearest')
plt.figure(); plt.imshow(y_new.reshape(100,100), cmap='gray', interpolation='nearest')
plt.figure(); plt.imshow(y_new2.reshape(100,100), cmap='gray', interpolation='nearest')
t = raw_input('[Press any key]')
def test_binary():
""" """
img = np.random.randn(100, 100)
img[25:75, 25:75] += 2
img -= 1
# !!! Be careful when doing this concatenation,
# it seems 'c_' does not create a copy
#u = np.c_[img.flatten().copy(), -img.flatten().copy()]
unary = np.c_[img.reshape(img.size, 1), -img.reshape(img.size, 1)].copy()
fig = plt.figure(figsize=(unary.shape[-1] * 4, 4))
for i in range(unary.shape[-1]):
plt.subplot(1, unary.shape[-1], i + 1)
plt.imshow(unary[:, i].reshape((100, 100)), cmap="gray", interpolation="nearest")
fig.tight_layout(), fig.savefig('./images/binary_unary.png')
# edges, edge_weights = get_uniform_smoothness_pw_single_image(img.shape)
smooth = 1 - np.eye(2)
unary = np.tile(img[:,:,np.newaxis], [1, 1, 2])
unary[:, :, 0] = img
unary[:, :, 1] = -img
unary_new = unary.reshape((100, 100, 2))
assert np.abs(unary - unary_new).max() == 0.
assert not (unary != unary_new).any()
# y = pygco.cut_grid_graph_simple(unary, pw_cost*0, n_iter=-1)
# labels = pygco.cut_grid_graph_simple(unary_new + np.random.
# randn(unary.shape[0], unary.shape[1], unary.shape[2])*0, pw_cost*0, n_iter=-1)
labels = pygco.cut_grid_graph_simple(unary_new + np.zeros(unary_new.shape),
smooth, n_iter=-1)
labels_0 = pygco.cut_grid_graph_simple(unary_new, smooth * 0, n_iter=-1)
fig = plt.figure(figsize=(3 * 4, 4))
plt.subplot(1, 3, 1), plt.title('image')
plt.imshow(img, interpolation="nearest")
plt.subplot(1, 3, 2), plt.title('labeling (smooth=1)')
plt.imshow(labels.reshape(100, 100), interpolation="nearest")
plt.subplot(1, 3, 3), plt.title('labeling (smooth=0)')
plt.imshow(labels_0.reshape(100, 100), interpolation="nearest")
fig.tight_layout(), fig.savefig('./images/binary_labels.png')
def test_binary():
""" """
img = np.random.randn(100, 100)
img[25:75, 25:75] += 2
img -= 1
# !!! Be careful when doing this concatenation,
# it seems 'c_' does not create a copy
#u = np.c_[img.flatten().copy(), -img.flatten().copy()]
unary = np.c_[img.reshape(img.size, 1), -img.reshape(img.size, 1)].copy()
fig = plt.figure(figsize=(unary.shape[-1] * 4, 4))
for i in range(unary.shape[-1]):
plt.subplot(1, unary.shape[-1], i + 1)
plt.imshow(unary[:, i].reshape((100, 100)), cmap="gray", interpolation="nearest")
fig.tight_layout(), fig.savefig('./images/binary_unary.png')
# edges, edge_weights = get_uniform_smoothness_pw_single_image(img.shape)
smooth = 1 - np.eye(2)
unary = np.tile(img[:,:,np.newaxis], [1, 1, 2])
unary[:, :, 0] = img
unary[:, :, 1] = -img
unary_new = unary.reshape((100, 100, 2))
assert np.abs(unary - unary_new).max() == 0.
assert not (unary != unary_new).any()
# y = pygco.cut_grid_graph_simple(unary, pw_cost*0, n_iter=-1)
# labels = pygco.cut_grid_graph_simple(unary_new + np.random.
# randn(unary.shape[0], unary.shape[1], unary.shape[2])*0, pw_cost*0, n_iter=-1)
labels = pygco.cut_grid_graph_simple(unary_new + np.zeros(unary_new.shape),
smooth, n_iter=-1)
labels_0 = pygco.cut_grid_graph_simple(unary_new, smooth * 0, n_iter=-1)
fig = plt.figure(figsize=(3 * 4, 4))
plt.subplot(1, 3, 1), plt.title('image')
plt.imshow(img, interpolation="nearest")
plt.subplot(1, 3, 2), plt.title('labeling (smooth=1)')
plt.imshow(labels.reshape(100, 100), interpolation="nearest")
plt.subplot(1, 3, 3), plt.title('labeling (smooth=0)')
plt.imshow(labels_0.reshape(100, 100), interpolation="nearest")
fig.tight_layout(), fig.savefig('./images/binary_labels.png')
def BinaryMixtureCut(Foreground, Background, I=None, Sigma=None):
"""Performs a binary graph-cut optimization for region masking, given
foreground and background probabilities for image and
Parameters
----------
I : array_like
An intensity image used for calculating the continuity term. If not
provided, only the data term from the foreground/background model will
drive the graph-cut optimization. Default value = None.
Foreground : array_like
Intensity image the same size as 'I' with values in the range [0, 1]
representing probabilities that each pixel is in the foreground class.
Background : array_like
Intensity image the same size as 'I' with values in the range [0, 1]
representing probabilities that each pixel is in the background class.
Sigma : double
Parameter controling exponential decay rate of continuity term. Units
are intensity values. Recommended ~10% of dynamic range of image.
Defaults to 25 if 'I' is type uint8 (range [0, 255]), and 0.1 if type
is float (expected range [0, 1]). Default value = None.
Notes
-----
The graph cutting algorithm is sensitive to the magnitudes of weights in
the data variable 'D'. Small probabilities will be magnified by the
logarithm in formulating this cost, and so we add a small positive value
to each in order to scale them appropriately.
Returns
-------
Mask : array_like
A binary mask indicating the foreground regions.
See Also
--------
PoissonMixture
References
----------
.. [1] Y. Al-Kofahi et al "Improved Automatic Detection and Segmentation
of Cell Nuclei in Histopathology Images" in IEEE Transactions on Biomedical
Engineering,vol.57,no.4,pp.847-52, 2010.
"""
# generate a small number for conditioning calculations
Small = np.finfo(np.float).eps
# formulate unary data costs
D = np.stack((-np.log(Background + Small) / -np.log(Small),
-np.log(Foreground + Small) / -np.log(Small)),
axis=2)
# formulate pairwise label costs
Pairwise = 1 - np.eye(2)
# calculate edge weights between pixels if argument 'I' is provided
if I is not None:
if Sigma is None:
if issubclass(I.dtype.type, np.float):
Sigma = 0.1
elif I.dtype.type == np.uint8:
Sigma = 25
# formulate vertical and horizontal costs
H = np.exp(-(I[:, 0:-1].astype(np.float) -
I[:, 1:].astype(np.float))**2 / (2 * Sigma**2))
V = np.exp(-(I[0:-1, :].astype(np.float) -
I[1:, :].astype(np.float))**2 / (2 * Sigma**2))
# cut the graph with edge information from image
Mask = gc.cut_grid_graph(D, Pairwise, V, H, n_iter=-1,
algorithm='expansion')
else:
# cut the graph without edge information from image
Mask = gc.cut_grid_graph_simple(D, Pairwise, n_iter=-1,
algorithm='expansion')
# reshape solution to image size and return
return Mask.reshape(Foreground.shape[0], Foreground.shape[1])