From 61a8a78657bd6fd996747fea9664bc7e9a80c8ea Mon Sep 17 00:00:00 2001
From: Vighnesh Birodkar <vighneshbirodkar@gmail.com>
Date: Sat, 15 Mar 2014 20:02:09 +0530
Subject: [PATCH] made hessian_det_appx public

---
 skimage/feature/__init__.py           | 2 ++
 skimage/feature/_hessian_det_appx.pyx | 2 +-
 skimage/feature/blob.py               | 4 ++--
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py
index 49167a36..7d1d9add 100644
--- a/skimage/feature/__init__.py
+++ b/skimage/feature/__init__.py
@@ -15,6 +15,7 @@ from .orb import ORB
 from .match import match_descriptors
 from .util import plot_matches
 from .blob import blob_dog, blob_log, blob_doh
+from ._hessian_det_appx import hessian_det_appx
 
 
 __all__ = ['daisy',
@@ -44,4 +45,5 @@ __all__ = ['daisy',
            'plot_matches',
            'blob_dog',
            'blob_doh',
+           'hessian_det_appx',
            'blob_log']
diff --git a/skimage/feature/_hessian_det_appx.pyx b/skimage/feature/_hessian_det_appx.pyx
index a8ca2734..5d3f7511 100644
--- a/skimage/feature/_hessian_det_appx.pyx
+++ b/skimage/feature/_hessian_det_appx.pyx
@@ -78,7 +78,7 @@ cdef inline cnp.double_t _integ(
     return ans
 
 
-def _hessian_det_appx(cnp.double_t[:, ::1] img, float sigma):
+def hessian_det_appx(cnp.double_t[:, ::1] img, float sigma):
     """Computes the approximate Hessian Determinant over an image.
 
     This method uses box filters over integral images to compute the
diff --git a/skimage/feature/blob.py b/skimage/feature/blob.py
index 96de1d65..57818a98 100644
--- a/skimage/feature/blob.py
+++ b/skimage/feature/blob.py
@@ -6,7 +6,7 @@ from math import sqrt, hypot, log
 from numpy import arccos
 from skimage.util import img_as_float
 from .peak import peak_local_max
-from ._hessian_det_appx import _hessian_det_appx
+from ._hessian_det_appx import hessian_det_appx
 from skimage.transform import integral_image
 
 
@@ -398,7 +398,7 @@ def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=0.01,
     else:
         sigma_list = np.linspace(min_sigma, max_sigma, num_sigma)
 
-    hessian_images = [_hessian_det_appx(image, s) for s in sigma_list]
+    hessian_images = [hessian_det_appx(image, s) for s in sigma_list]
     image_cube = np.dstack(hessian_images)
 
     local_maxima = peak_local_max(image_cube, threshold_abs=threshold,