Minor modifications to Harris corner detector

* Add some references.
* Make keyword argument explicit in example.
* Remove test class in favor of functions since no setup was required.
* Clean up docstrings.

doc/examples/plot_harris.py

@@ -3,8 +3,11 @@
 Harris Corner detector
 ===============================================================================
 
-The Harris corner filter detects interest points using edge detection in
-multiple direction.
+The Harris corner filter [1]_ detects "interest points" [2]_ using edge
+detection in multiple directions.
+
+.. [1] http://en.wikipedia.org/wiki/Corner_detection
+.. [2] http://en.wikipedia.org/wiki/Interest_point_detection
 """
 
 from matplotlib import pyplot as plt
@@ -26,5 +29,6 @@ def plot_harris_points(image, filtered_coords):
 
 
 im = img_as_float(data.lena())
-filtered_coords = harris(im, 6)
+filtered_coords = harris(im, min_distance=6)
 plot_harris_points(im, filtered_coords)
+

skimage/filter/harris.py

@@ -1,8 +1,9 @@
-#
-# Harris detector
-#
-# Inspired from Solem's implementation
-# http://www.janeriksolem.net/2009/01/harris-corner-detector-in-python.html
+"""
+Harris corner detector
+
+Inspired from Solem's implementation
+http://www.janeriksolem.net/2009/01/harris-corner-detector-in-python.html
+"""
 
 import numpy as np
 from scipy import ndimage
@@ -14,18 +15,18 @@ def _compute_harris_response(image, eps=1e-6, gaussian_deviation=1):
 
     Parameters
     ----------
-    image: ndarray of floats
-        Input image
+    image : ndarray of floats
+        Input image.
 
-    eps: float, optional
-        Normalisation factor
+    eps : float, optional
+        Normalisation factor.
 
-    gaussian_deviation: integer, optional
-        Standard deviation used for the Gaussian kernel
+    gaussian_deviation : integer, optional
+        Standard deviation used for the Gaussian kernel.
 
     Returns
     --------
-    image: (M, N) ndarray
+    image : (M, N) ndarray
         Harris image response
     """
     if len(image.shape) == 3:
@@ -43,6 +44,8 @@ def _compute_harris_response(image, eps=1e-6, gaussian_deviation=1):
     # determinant and trace
     Wdet = Wxx * Wyy - Wxy ** 2
     Wtr = Wxx + Wyy
+    # Alternate formula for Harris response.
+    # Alison Noble, "Descriptions of Image Surfaces", PhD thesis (1989)
     harris = Wdet / (Wtr + eps)
 
     # Non maximum filter of size 3
@@ -65,24 +68,25 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
 
     Parameters
     ----------
-    image: ndarray of floats
-        Input image
+    image : ndarray of floats
+        Input image.
 
-    min_distance: int, optional
-        Minimum number of pixels separating interest points and image boundary
+    min_distance : int, optional
+        Minimum number of pixels separating interest points and image boundary.
 
-    threshold: float, optional
+    threshold : float, optional
         Relative threshold impacting the number of interest points.
 
-    eps: float, optional
-        Normalisation factor
+    eps : float, optional
+        Normalisation factor.
 
-    gaussian_deviation: integer, optional
-        Standard deviation used for the Gaussian kernel
+    gaussian_deviation : integer, optional
+        Standard deviation used for the Gaussian kernel.
 
-    returns:
-    --------
-    array: coordinates of interest points
+    Returns
+    -------
+    coordinates : (N, 2) array
+        (row, column) coordinates of interest points.
     """
     harrisim = _compute_harris_response(image, eps=eps,
                     gaussian_deviation=gaussian_deviation)
@@ -116,3 +120,4 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
               (coords[i][1] - min_distance):(coords[i][1] + min_distance)] = 0
 
     return np.array(filtered_coords)
+

skimage/filter/tests/test_harris.py

@@ -6,37 +6,42 @@ from skimage import img_as_float
 from skimage.filter import harris
 
 
-class TestHarris():
-    def test_square_image(self):
-        im = np.zeros((50, 50)).astype(float)
-        im[:25, :25] = 1.
-        results = harris(im)
-        assert results.any()
-        assert len(results) == 1
+def test_square_image():
+    im = np.zeros((50, 50)).astype(float)
+    im[:25, :25] = 1.
+    results = harris(im)
+    assert results.any()
+    assert len(results) == 1
 
-    def test_noisy_square_image(self):
-        im = np.zeros((50, 50)).astype(float)
-        im[:25, :25] = 1.
-        im = im + np.random.uniform(size=im.shape) * .5
-        results = harris(im)
-        assert results.any()
-        assert len(results) == 1
+def test_noisy_square_image():
+    im = np.zeros((50, 50)).astype(float)
+    im[:25, :25] = 1.
+    im = im + np.random.uniform(size=im.shape) * .5
+    results = harris(im)
+    assert results.any()
+    assert len(results) == 1
 
-    def test_squared_dot(self):
-        im = np.zeros((50, 50))
-        im[4:8, 4:8] = 1
-        im = img_as_float(im)
-        results = harris(im, min_distance=3)
-        print results
-        assert (results == np.array([[6, 6]])).all()
+def test_squared_dot():
+    im = np.zeros((50, 50))
+    im[4:8, 4:8] = 1
+    im = img_as_float(im)
+    results = harris(im, min_distance=3)
+    print results
+    assert (results == np.array([[6, 6]])).all()
+
+def test_rotated_lena():
+    """
+    The harris filter should yield the same results with an image and it's
+    rotation.
+    """
+    im = img_as_float(data.lena().mean(axis=2))
+    results = harris(im)
+    im_rotated = im.T
+    results_rotated = harris(im_rotated)
+    assert (results[:, 0] == results_rotated[:, 1]).all()
+
+
+if __name__ == '__main__':
+    from numpy import testing
+    testing.run_module_suite()
 
-    def test_rotated_lena(self):
-        """
-        The harris filter should yield the same results with an image and it's
-        rotation.
-        """
-        im = img_as_float(data.lena().mean(axis=2))
-        results = harris(im)
-        im_rotated = im.T
-        results_rotated = harris(im_rotated)
-        assert (results[:, 0] == results_rotated[:, 1]).all()