Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/segmentation/tests/test_chan_vese.py

@@ -0,0 +1,90 @@
+import numpy as np
+from skimage.segmentation import chan_vese
+
+from skimage._shared import testing
+from skimage._shared.testing import assert_array_equal
+
+
+def test_chan_vese_flat_level_set():
+    # because the algorithm evolves the level set around the
+    # zero-level, it the level-set has no zero level, the algorithm
+    # will not produce results in theory. However, since a continuous
+    # approximation of the delta function is used, the algorithm
+    # still affects the entirety of the level-set. Therefore with
+    # infinite time, the segmentation will still converge.
+    img = np.zeros((10, 10))
+    img[3:6, 3:6] = np.ones((3, 3))
+    ls = np.full((10, 10), 1000)
+    result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=ls)
+    assert_array_equal(result.astype(np.float), np.ones((10, 10)))
+    result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=-ls)
+    assert_array_equal(result.astype(np.float), np.zeros((10, 10)))
+
+
+def test_chan_vese_small_disk_level_set():
+    img = np.zeros((10, 10))
+    img[3:6, 3:6] = np.ones((3, 3))
+    result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set="small disk")
+    assert_array_equal(result.astype(np.float), img)
+
+
+def test_chan_vese_simple_shape():
+    img = np.zeros((10, 10))
+    img[3:6, 3:6] = np.ones((3, 3))
+    result = chan_vese(img, mu=0.0, tol=1e-8).astype(np.float)
+    assert_array_equal(result, img)
+
+
+def test_chan_vese_extended_output():
+    img = np.zeros((10, 10))
+    img[3:6, 3:6] = np.ones((3, 3))
+    result = chan_vese(img, mu=0.0, tol=1e-8, extended_output=True)
+    assert_array_equal(len(result), 3)
+
+
+def test_chan_vese_remove_noise():
+    ref = np.zeros((10, 10))
+    ref[1:6, 1:6] = np.array([[0, 1, 1, 1, 0],
+                              [1, 1, 1, 1, 1],
+                              [1, 1, 1, 1, 1],
+                              [1, 1, 1, 1, 1],
+                              [0, 1, 1, 1, 0]])
+    img = ref.copy()
+    img[8, 3] = 1
+    result = chan_vese(img, mu=0.3, tol=1e-3, max_iter=100, dt=10,
+                       init_level_set="disk").astype(np.float)
+    assert_array_equal(result, ref)
+
+
+def test_chan_vese_incorrect_image_type():
+    img = np.zeros((10, 10, 3))
+    ls = np.zeros((10, 9))
+    with testing.raises(ValueError):
+        chan_vese(img, mu=0.0, init_level_set=ls)
+
+
+def test_chan_vese_gap_closing():
+    ref = np.zeros((20, 20))
+    ref[8:15, :] = np.ones((7, 20))
+    img = ref.copy()
+    img[:, 6] = np.zeros((20))
+    result = chan_vese(img, mu=0.7, tol=1e-3, max_iter=1000, dt=1000,
+                       init_level_set="disk").astype(np.float)
+    assert_array_equal(result, ref)
+
+
+def test_chan_vese_incorrect_level_set():
+    img = np.zeros((10, 10))
+    ls = np.zeros((10, 9))
+    with testing.raises(ValueError):
+        chan_vese(img, mu=0.0, init_level_set=ls)
+    with testing.raises(ValueError):
+        chan_vese(img, mu=0.0, init_level_set="a")
+
+
+def test_chan_vese_blank_image():
+    img = np.zeros((10, 10))
+    level_set = np.random.rand(10, 10)
+    ref = level_set > 0
+    result = chan_vese(img, mu=0.0, tol=0.0, init_level_set=level_set)
+    assert_array_equal(result, ref)