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PEP8: indentation in random_walker_segmentation

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This commit is contained in:
Emmanuelle Gouillart
2012-08-27 11:26:11 +02:00
parent 28161eaee6
commit 9d29d5df78
1 changed files with 9 additions and 9 deletions
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skimage/segmentation/random_walker_segmentation.py
+9 -9
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@@ -135,15 +135,15 @@ def _mask_edges_weights(edges, weights, mask):
corresponding weights of the edges.
"""
mask0 = np.hstack((mask[:, :, :-1].ravel(), mask[:, :-1].ravel(),
mask[:-1].ravel()))
mask[:-1].ravel()))
mask1 = np.hstack((mask[:, :, 1:].ravel(), mask[:, 1:].ravel(),
mask[1:].ravel()))
mask[1:].ravel()))
ind_mask = np.logical_and(mask0, mask1)
edges, weights = edges[:, ind_mask], weights[ind_mask]
max_node_index = edges.max()
# Reassign edges labels to 0, 1, ... edges_number - 1
order = np.searchsorted(np.unique(edges.ravel()),
np.arange(max_node_index + 1))
np.arange(max_node_index + 1))
edges = order[edges]
return edges, weights
@@ -163,7 +163,7 @@ def _build_laplacian(data, mask=None, beta=50):
def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
return_full_prob=False):
return_full_prob=False):
"""
Random walker algorithm for segmentation from markers.
@@ -306,7 +306,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
labels = np.copy(labels)
label_values = np.unique(labels)
# Reorder label values to have consecutive integers (no gaps)
if np.any(np.diff(label_values) > 1):
if np.any(np.diff(label_values) != 1):
mask = labels >= 0
labels[mask] = rank_order(labels[mask])[0].astype(labels.dtype)
labels = labels.astype(np.int32)
@@ -328,7 +328,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
# first at pixel j by anisotropic diffusion.
if mode == 'cg':
X = _solve_cg(lap_sparse, B, tol=tol,
return_full_prob=return_full_prob)
return_full_prob=return_full_prob)
if mode == 'cg_mg':
if not amg_loaded:
warnings.warn(
@@ -338,10 +338,10 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
X = _solve_cg(lap_sparse, B, tol=tol)
else:
X = _solve_cg_mg(lap_sparse, B, tol=tol,
return_full_prob=return_full_prob)
return_full_prob=return_full_prob)
if mode == 'bf':
X = _solve_bf(lap_sparse, B,
return_full_prob=return_full_prob)
return_full_prob=return_full_prob)
# Clean up results
data = np.squeeze(data)
if return_full_prob:
@@ -352,7 +352,7 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
mask_i = np.squeeze(labels == i)
X[i - 1, mask_i] = 1
X[np.setdiff1d(np.arange(0, labels.max(), dtype=np.int),
[i - 1]), mask_i] = 0
[i - 1]), mask_i] = 0
else:
X = _clean_labels_ar(X + 1, labels).reshape(data.shape)
return X