Fixed database typo and removed unnecessary class identifier.

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

@@ -0,0 +1,82 @@
+import numpy as np
+from skimage import data
+from skimage.segmentation import felzenszwalb
+
+from skimage._shared import testing
+from skimage._shared.testing import (assert_greater, test_parallel,
+                                     assert_equal, assert_array_equal,
+                                     assert_warns, assert_no_warnings)
+
+
+@test_parallel()
+def test_grey():
+    # very weak tests.
+    img = np.zeros((20, 21))
+    img[:10, 10:] = 0.2
+    img[10:, :10] = 0.4
+    img[10:, 10:] = 0.6
+    seg = felzenszwalb(img, sigma=0)
+    # we expect 4 segments:
+    assert_equal(len(np.unique(seg)), 4)
+    # that mostly respect the 4 regions:
+    for i in range(4):
+        hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
+        assert_greater(hist[i], 40)
+
+
+def test_minsize():
+    # single-channel:
+    img = data.coins()[20:168, 0:128]
+    for min_size in np.arange(10, 100, 10):
+        segments = felzenszwalb(img, min_size=min_size, sigma=3)
+        counts = np.bincount(segments.ravel())
+        # actually want to test greater or equal.
+        assert_greater(counts.min() + 1, min_size)
+    # multi-channel:
+    coffee = data.coffee()[::4, ::4]
+    for min_size in np.arange(10, 100, 10):
+        segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
+        counts = np.bincount(segments.ravel())
+        # actually want to test greater or equal.
+        assert_greater(counts.min() + 1, min_size)
+
+
+def test_3D():
+    grey_img = np.zeros((10, 10))
+    rgb_img = np.zeros((10, 10, 3))
+    three_d_img = np.zeros((10, 10, 10))
+    with assert_no_warnings():
+        felzenszwalb(grey_img, multichannel=True)
+        felzenszwalb(grey_img, multichannel=False)
+        felzenszwalb(rgb_img, multichannel=True)
+    with assert_warns(RuntimeWarning):
+        felzenszwalb(three_d_img, multichannel=True)
+    with testing.raises(ValueError):
+        felzenszwalb(rgb_img, multichannel=False)
+        felzenszwalb(three_d_img, multichannel=False)
+
+
+def test_color():
+    # very weak tests.
+    img = np.zeros((20, 21, 3))
+    img[:10, :10, 0] = 1
+    img[10:, :10, 1] = 1
+    img[10:, 10:, 2] = 1
+    seg = felzenszwalb(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], 2)
+    assert_array_equal(seg[:10, 10:], 1)
+    assert_array_equal(seg[10:, 10:], 3)
+
+
+def test_merging():
+    # test region merging in the post-processing step
+    img = np.array([[0, 0.3], [0.7, 1]])
+    # With scale=0, only the post-processing is performed.
+    seg = felzenszwalb(img, scale=0, sigma=0, min_size=2)
+    # we expect 2 segments:
+    assert_equal(len(np.unique(seg)), 2)
+    assert_array_equal(seg[0, :], 0)
+    assert_array_equal(seg[1, :], 1)