Refactor geometric transforms.

2026-08-12 12:30:16 +08:00 · 2012-07-20 17:57:15 -04:00
parent 8e8e2b99a0
commit a130b8d2d9
6 changed files with 830 additions and 914 deletions
@@ -0,0 +1,79 @@
+from numpy.testing import assert_array_almost_equal, run_module_suite
+import numpy as np
+
+from skimage.transform import (warp, homography, fast_homography,
+                               SimilarityTransform)
+from skimage import transform as tf, data, img_as_float
+from skimage.color import rgb2gray
+
+
+def test_warp():
+    x = np.zeros((5, 5), dtype=np.uint8)
+    x[2, 2] = 255
+    x = img_as_float(x)
+    theta = -np.pi/2
+    tform = SimilarityTransform(1, theta, (0, 4))
+
+    x90 = warp(x, tform, order=1)
+    assert_array_almost_equal(x90, np.rot90(x))
+
+    x90 = warp(x, tform.inverse, order=1)
+    assert_array_almost_equal(x90, np.rot90(x))
+
+
+def test_homography():
+    x = np.zeros((5, 5), dtype=np.uint8)
+    x[1, 1] = 255
+    x = img_as_float(x)
+    theta = -np.pi/2
+    M = np.array([[np.cos(theta),-np.sin(theta),0],
+                  [np.sin(theta), np.cos(theta),4],
+                  [0,             0,            1]])
+    x90 = homography(x, M, order=1)
+    assert_array_almost_equal(x90, np.rot90(x))
+
+
+def test_fast_homography():
+    img = rgb2gray(data.lena()).astype(np.uint8)
+    img = img[:, :100]
+
+    theta = np.deg2rad(30)
+    scale = 0.5
+    tx, ty = 50, 50
+
+    H = np.eye(3)
+    S = scale * np.sin(theta)
+    C = scale * np.cos(theta)
+
+    H[:2, :2] = [[C, -S], [S, C]]
+    H[:2, 2] = [tx, ty]
+
+    for mode in ('constant', 'mirror', 'wrap'):
+        p0 = homography(img, H, mode=mode, order=1)
+        p1 = fast_homography(img, H, mode=mode)
+        p1 = np.round(p1)
+
+        ## import matplotlib.pyplot as plt
+        ## f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
+        ## ax0.imshow(img)
+        ## ax1.imshow(p0, cmap=plt.cm.gray)
+        ## ax2.imshow(p1, cmap=plt.cm.gray)
+        ## ax3.imshow(np.abs(p0 - p1), cmap=plt.cm.gray)
+        ## plt.show()
+
+        d = np.mean(np.abs(p0 - p1))
+        assert d < 0.2
+
+
+def test_swirl():
+    image = img_as_float(data.checkerboard())
+
+    swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}
+    swirled = tf.swirl(image, strength=10, **swirl_params)
+    unswirled = tf.swirl(swirled, strength=-10, **swirl_params)
+
+    assert np.mean(np.abs(image - unswirled)) < 0.01
+
+
+if __name__ == "__main__":
+    run_module_suite()