ENH tests for Felzenszwalbs segmentation, fixed off-by-one error

skimage/segmentation/felzenszwalb.py

@@ -67,8 +67,8 @@ def felzenszwalb_segmentation(image, scale=1, sigma=0.8):
 
     # put pixels in same segment only if in the same segment in all images
     # we do this by combining the channels to one number
-    n0 = max(segmentations[0])
-    n1 = max(segmentations[1])
+    n0 = segmentations[0].max() + 1
+    n1 = segmentations[1].max() + 1
     hasher = np.array([n1 * n0, n0, 1])
     segmentations = np.dstack(segmentations).reshape(-1, n_channels)
     segmentation = np.dot(segmentations, hasher)

skimage/segmentation/tests/test_felzenszwalb.py

@@ -0,0 +1,39 @@
+import numpy as np
+from numpy.testing import assert_equal, assert_array_equal
+from nose.tools import assert_greater
+from skimage.segmentation import felzenszwalb_segmentation
+
+
+def test_grey():
+    # very weak tests. This algorithm is pretty unstable.
+    img = np.zeros((20, 20))
+    img[:10, 10:] = 0.2
+    img[10:, :10] = 0.4
+    img[10:, 10:] = 0.6
+    seg = felzenszwalb_segmentation(img, sigma=0)
+    # we expect 4 segments:
+    assert_equal(len(np.unique(seg)), 4)
+    # that mostly respect the 4 regions:
+    for i in xrange(4):
+        hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
+        assert_greater(hist[i], 40)
+
+
+def test_color():
+    # very weak tests. This algorithm is pretty unstable.
+    img = np.zeros((20, 20, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    seg = felzenszwalb_segmentation(img, sigma=0)
+    # we expect 4 segments:
+    assert_equal(len(np.unique(seg)), 4)
+    assert_array_equal(seg[:10, :10], 0)
+    assert_array_equal(seg[10:, :10], 3)
+    assert_array_equal(seg[:10, 10:], 1)
+    assert_array_equal(seg[10:, 10:], 2)
+
+
+if __name__ == '__main__':
+    from numpy import testing
+    testing.run_module_suite()