Add dense DAISY feature description.

2026-08-12 12:30:16 +08:00 · 2012-12-27 20:42:00 +01:00
parent eff906bfb3
commit 4173d16639
3 changed files with 256 additions and 0 deletions
@@ -0,0 +1,88 @@
+import numpy as np
+from numpy.testing import assert_raises, assert_almost_equal
+from numpy import sqrt, ceil
+
+from skimage import data
+from skimage import img_as_float
+from skimage.feature import daisy
+
+
+def test_daisy_color_image_unsupported_error():
+    img = np.zeros((20, 20, 3))
+    assert_raises(ValueError, daisy, img)
+
+
+def test_daisy_desc_dims():
+    img = img_as_float(data.lena()[:128, :128].mean(axis=2))
+    rings = 2
+    histograms = 4
+    orientations = 3
+    descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations)
+    assert(descs.shape[2] == (rings*histograms + 1)*orientations)
+
+    rings = 4
+    histograms = 5
+    orientations = 13
+    descs = daisy(img, rings=rings, histograms=histograms, orientations=orientations)
+    assert(descs.shape[2] == (rings*histograms + 1)*orientations)
+
+
+def test_descs_shape():
+    img = img_as_float(data.lena()[:256, :256].mean(axis=2))
+    radius = 20
+    step = 8
+    descs = daisy(img, radius=radius, step=step)
+    assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step)))
+    assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step)))
+
+    img = img[:-1,:-2]
+    radius = 5
+    step = 3
+    descs = daisy(img, radius=radius, step=step)
+    assert(descs.shape[0] == ceil((img.shape[0]-radius*2)/float(step)))
+    assert(descs.shape[1] == ceil((img.shape[1]-radius*2)/float(step)))
+
+
+def test_daisy_incompatible_sigmas_and_radii():
+    img = img_as_float(data.lena()[:128, :128].mean(axis=2))
+    sigmas = [1, 2]
+    radii = [1, 2]
+    assert_raises(ValueError, daisy, img, sigmas=sigmas, ring_radii=radii)
+
+
+def test_daisy_normalization():
+    img = img_as_float(data.lena()[:64, :64].mean(axis=2))
+
+    descs = daisy(img, normalization='l1')
+    for i in range(descs.shape[0]):
+        for j in range(descs.shape[1]):
+            assert_almost_equal(np.sum(descs[i,j,:]), 1)
+    descs_ = daisy(img)
+    assert_almost_equal(descs, descs_)
+
+    descs = daisy(img, normalization='l2')
+    for i in range(descs.shape[0]):
+        for j in range(descs.shape[1]):
+            assert_almost_equal(sqrt(np.sum(descs[i,j,:]**2)), 1)
+
+    orientations = 8
+    descs = daisy(img, orientations=orientations, normalization='daisy')
+    desc_dims = descs.shape[2]
+    for i in range(descs.shape[0]):
+        for j in range(descs.shape[1]):
+            for k in range(0, desc_dims, orientations):
+                assert_almost_equal(sqrt(np.sum(
+                        descs[i,j,k:k+orientations]**2)), 1)
+
+    img = np.zeros((50, 50))
+    descs = daisy(img, normalization='off')
+    for i in range(descs.shape[0]):
+        for j in range(descs.shape[1]):
+            assert_almost_equal(np.sum(descs[i,j,:]), 0)
+
+    assert_raises(ValueError, daisy, img, normalization='does_not_exist')
+
+
+if __name__ == '__main__':
+    from numpy import testing
+    testing.run_module_suite()