Added tests

doc/examples/plot_harris.py

@@ -6,11 +6,12 @@ Harris Corner detector
 The Harris corner filter detects interest points using edge detection in many
 direction.
 """
+
 from matplotlib import pyplot as plt
 from matplotlib import cm
 
 from skimage import data
-from skimage.filter import harris
+from skimage.filter import harris_corner_detector
 
 
 def plot_harris_points(image, filtered_coords):
@@ -26,5 +27,5 @@ def plot_harris_points(image, filtered_coords):
 
 
 im = data.lena().astype(float)
-filtered_coords = harris.harris_corner_detector(im, 6)
+filtered_coords = harris_corner_detector(im, 6)
 plot_harris_points(im, filtered_coords)

skimage/filter/harris.py

@@ -1,6 +1,7 @@
 #
 # Harris detector
 #
+# Inspired from Solem's implementation
 # http://www.janeriksolem.net/2009/01/harris-corner-detector-in-python.html
 
 import numpy as np
@@ -56,7 +57,7 @@ def harris_corner_detector(image, min_distance=10, threshold=0.1, eps=1e-6):
 
     params
     -------
-    harrisim: ndarray
+    harrisim: ndarray of floats
 
     min_distance: int, optional, default: 10
         minimum number of pixels separating corners and image boundary

skimage/filter/tests/test_harris.py

@@ -0,0 +1,23 @@
+import numpy as np
+
+import unittest
+
+from skimage.filter import harris_corner_detector
+
+
+class TestHarris(unittest.TestCase):
+
+    def test_square_image(self):
+        im = np.zeros((50, 50)).astype(float)
+        im[:25, :25] = 1.
+        results = harris_corner_detector(im)
+        self.assertTrue(results.any())
+        self.assertTrue(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_corner_detector(im)
+        assert results.any()
+        assert len(results) == 1