From 313cdf2cb4a14722ad3071d347a08f7e3d28ea7c Mon Sep 17 00:00:00 2001
From: Gael varoquaux <gael.varoquaux@normalesup.org>
Date: Wed, 1 Dec 2010 20:00:47 +0100
Subject: [PATCH] ENH: dtype is img_to_graph defaults to input dtype
---
scikits/learn/feature_extraction/image.py | 9 ++++++---
1 file changed, 6 insertions(+), 3 deletions(-)
diff --git a/scikits/learn/feature_extraction/image.py b/scikits/learn/feature_extraction/image.py
index 027e631e18..cd5fb0865c 100644
--- a/scikits/learn/feature_extraction/image.py
+++ b/scikits/learn/feature_extraction/image.py
@@ -62,7 +62,7 @@ def _mask_edges_weights(mask, edges, weights):
def img_to_graph(img, mask=None,
- return_as=sparse.coo_matrix, dtype=np.float):
+ return_as=sparse.coo_matrix, dtype=None):
""" Create a graph of the pixel-to-pixel connections with the
gradient of the image as a the edge value.
@@ -75,10 +75,13 @@ def img_to_graph(img, mask=None,
pixels.
return_as: np.ndarray or a sparse matrix class, optional
The class to use to build the returned adjacency matrix.
- dtype: dtype, optional
- The data of the returned sparse matrix
+ dtype: None or dtype, optional
+ The data of the returned sparse matrix. By default it is the
+ dtype of img
"""
img = np.atleast_3d(img)
+ if dtype is None:
+ dtype = img.dtype
n_x, n_y, n_z = img.shape
edges = _make_edges_3d(n_x, n_y, n_z)
weights = _compute_gradient_3d(edges, img)
--
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