diff --git a/skimage/morphology/tests/test_watershed.py b/skimage/morphology/tests/test_watershed.py index 46298ae5..660e9a9a 100644 --- a/skimage/morphology/tests/test_watershed.py +++ b/skimage/morphology/tests/test_watershed.py @@ -296,27 +296,27 @@ class TestWatershed(unittest.TestCase): def test_watershed07(self): "A regression test of a competitive case that failed" - data = np.array([[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255], - [255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255], - [255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255], - [255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255], - [255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255], - [255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255], - [255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255], - [255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255], - [255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255], - [255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255], - [255,255,255,255,255,204,153,103,103,153,204,255,255,255,255,255], - [255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255], - [255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255], - [255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255], - [255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255], - [255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255], - [255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255], - [255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255], - [255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255], - [255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255], - [255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]]) + data = np.array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255], + [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255], + [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255], + [255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255], + [255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255], + [255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255], + [255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255], + [255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 204, 153, 103, 103, 153, 204, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255], + [255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255], + [255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255], + [255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255], + [255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255], + [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255], + [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255], + [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]]) mask = (data != 255) markers = np.zeros(data.shape, int) markers[6, 7] = 1 @@ -332,27 +332,27 @@ class TestWatershed(unittest.TestCase): def test_watershed08(self): "The border pixels + an edge are all the same value" - data = np.array([[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255], - [255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255], - [255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255], - [255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255], - [255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255], - [255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255], - [255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255], - [255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255], - [255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255], - [255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255], - [255,255,255,255,255,204,153,141,141,153,204,255,255,255,255,255], - [255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255], - [255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255], - [255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255], - [255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255], - [255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255], - [255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255], - [255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255], - [255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255], - [255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255], - [255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]]) + data = np.array([[255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255], + [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255], + [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255], + [255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255], + [255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255], + [255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255], + [255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255], + [255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 204, 153, 141, 141, 153, 204, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 204, 183, 141, 94, 94, 141, 183, 204, 255, 255, 255, 255], + [255, 255, 255, 204, 183, 141, 111, 72, 72, 111, 141, 183, 204, 255, 255, 255], + [255, 255, 204, 183, 141, 111, 72, 39, 39, 72, 111, 141, 183, 204, 255, 255], + [255, 255, 204, 153, 111, 72, 39, 1, 1, 39, 72, 111, 153, 204, 255, 255], + [255, 255, 204, 153, 111, 94, 72, 52, 52, 72, 94, 111, 153, 204, 255, 255], + [255, 255, 204, 183, 153, 141, 111, 103, 103, 111, 141, 153, 183, 204, 255, 255], + [255, 255, 255, 204, 204, 183, 153, 153, 153, 153, 183, 204, 204, 255, 255, 255], + [255, 255, 255, 255, 255, 204, 204, 204, 204, 204, 204, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], + [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255]]) mask = (data != 255) markers = np.zeros(data.shape, int) markers[6, 7] = 1