made hessian_det_appx public

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']

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

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,