Add support for matrix as inverse_map

skimage/transform/tests/test_warps.py

@@ -11,10 +11,9 @@ 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)
+def test_warp_tform():
+    x = np.zeros((5, 5), dtype=np.double)
+    x[2, 2] = 1
     theta = - np.pi / 2
     tform = SimilarityTransform(scale=1, rotation=theta, translation=(0, 4))
 
@@ -25,10 +24,36 @@ def test_warp():
     assert_array_almost_equal(x90, np.rot90(x))
 
 
+def test_warp_callable():
+    x = np.zeros((5, 5), dtype=np.double)
+    x[2, 2] = 1
+    refx = np.zeros((5, 5), dtype=np.double)
+    refx[1, 1] = 1
+
+    shift = lambda xy: xy + 1
+
+    outx = warp(x, shift, order=1)
+    assert_array_almost_equal(outx, refx)
+
+
+def test_warp_matrix():
+    x = np.zeros((5, 5), dtype=np.double)
+    x[2, 2] = 1
+    refx = np.zeros((5, 5), dtype=np.double)
+    refx[1, 1] = 1
+
+    matrix = np.array([[1, 0, 1], [0, 1, 1], [0, 0, 1]])
+
+    # _warp_fast
+    outx = warp(x, matrix, order=1)
+    assert_array_almost_equal(outx, refx)
+    # check for ndimage.map_coordinates
+    outx = warp(x, matrix, order=5)
+
+
 def test_homography():
-    x = np.zeros((5, 5), dtype=np.uint8)
-    x[1, 1] = 255
-    x = img_as_float(x)
+    x = np.zeros((5, 5), dtype=np.double)
+    x[1, 1] = 1
     theta = -np.pi / 2
     M = np.array([[np.cos(theta), - np.sin(theta), 0],
                   [np.sin(theta),   np.cos(theta), 4],