moved public interface of hessian determinant to corner.py

skimage/feature/corner.py

@@ -7,6 +7,8 @@ from skimage.util import img_as_float, pad
 from skimage.feature import peak_local_max
 from skimage.feature.util import _prepare_grayscale_input_2D
 from skimage.feature.corner_cy import _corner_fast
+from ._hessian_det_appx import _hessian_matrix_det
+from ..transform import integral_image
 
 
 def _compute_derivatives(image, mode='constant', cval=0):
@@ -29,7 +31,7 @@ def _compute_derivatives(image, mode='constant', cval=0):
     imy : ndarray
         Derivative in y-direction.
 
-    """
+v    """
 
     imy = ndimage.sobel(image, axis=0, mode=mode, cval=cval)
     imx = ndimage.sobel(image, axis=1, mode=mode, cval=cval)
@@ -170,6 +172,52 @@ def hessian_matrix(image, sigma=1, mode='constant', cval=0):
     return Hxx, Hxy, Hyy
 
 
+def hessian_matrix_det(image, sigma, integral=True):
+    """Computes the approximate Hessian Determinant over an image.
+
+    This method uses box filters over integral images to compute the
+    approximate Hessian Determinant as described in [1]_.
+
+    Parameters
+    ----------
+    image : array
+        The integral image over which to compute Hessian Determinant.
+    sigma : float
+        Standard deviation used for the Gaussian kernel, used for the Hessian
+        matrix
+    integral : bool
+        If `False`, `image` is assumed to be integral and intergral image is
+        not computed. If `True` the integral image is computed for `image`
+        and used for finding the Hessian Determinant.
+
+
+    Returns
+    -------
+    out : array
+        The array of the Determinant of Hessians.
+
+    References
+    ----------
+    .. [1] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool,
+           "SURF: Speeded Up Robust Features"
+           ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf
+
+    Notes
+    -----
+    The running time of this method only depends on size of the image. It is
+    independent of `sigma` as one would expect. The downside is that the
+    result for `sigma` less than `3` is not accurate, i.e., not similar to
+    the result obtained if someone computed the Hessian and took it's
+    determinant.
+    """
+
+    image = img_as_float(image)
+    if(integral):
+        image = integral_image(image)
+
+    return np.array(_hessian_matrix_det(image, sigma))
+
+
 def _image_orthogonal_matrix22_eigvals(M00, M01, M11):
     l1 = (M00 + M11) / 2 + np.sqrt(4 * M01 ** 2 + (M00 - M11) ** 2) / 2
     l2 = (M00 + M11) / 2 - np.sqrt(4 * M01 ** 2 + (M00 - M11) ** 2) / 2