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Minor changes in the wrapper #7

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Minor changes in the wrapper #7

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9938e0f
1) generalized energy type in C++ wrapper
Jan 23, 2013
e19467a
Just testing authentification
shapovalov Jan 24, 2013
39ccd76
Edge-dependent pairwise potentials now work for double energy type.
shapovalov Jan 24, 2013
e03ba71
Functions return energy value. Memory leak fixed.
shapovalov Jan 31, 2013
5a27564
changed check of dimension to check of length
fgregg May 15, 2013
bc726ff
implemented energy_assignment function:
fgregg May 15, 2013
a7a2bed
Merge pull request #1 from fgregg/master
shapovalov May 22, 2013
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14 changes: 11 additions & 3 deletions example.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import matplotlib.pyplot as plt
import numpy as np
from pygco import cut_simple, cut_from_graph
from pygco import cut_simple, cut_simple_gen_potts, cut_from_graph, cut_from_graph_gen_potts


def example_binary():
Expand All @@ -16,9 +16,15 @@ def example_binary():
unaries = (10 * np.dstack([unaries, -unaries]).copy("C")).astype(np.int32)
# create potts pairwise
pairwise = -10 * np.eye(2, dtype=np.int32)

# do simple cut
result = cut_simple(unaries, pairwise)

# generalized Potts potentials
pix_nums = np.r_[:10*10].reshape(10,10)
pairwise_cost = dict([(tuple(sorted(pair)), 30) for pair in zip(pix_nums[:,:-1].flatten(), pix_nums[:,1:].flatten())] +
[(tuple(sorted(pair)),0) for pair in zip(pix_nums[:-1,:].flatten(), pix_nums[1:,:].flatten())])
result_gp = cut_simple_gen_potts(unaries, pairwise_cost)

# use the gerneral graph algorithm
# first, we construct the grid graph
Expand All @@ -29,6 +35,9 @@ def example_binary():

# we flatten the unaries
result_graph = cut_from_graph(edges, unaries.reshape(-1, 2), pairwise)

# generalized Potts potentials
result_graph_gp = cut_from_graph_gen_potts(unaries.reshape(-1, 2), pairwise_cost)

# plot results
plt.subplot(231, title="original")
Expand Down Expand Up @@ -78,6 +87,5 @@ def example_multinomial():
plt.imshow(result_1d, interpolation="nearest")
plt.show()


example_binary()
example_multinomial()
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