Fix keep_range and add test cases

skimage/transform/_geometric.py

@@ -4,8 +4,9 @@ import warnings
 import numpy as np
 from scipy import ndimage, spatial
 
-from skimage._shared.utils import get_bound_method_class, safe_as_int
-from skimage.util import img_as_float
+from .._shared.utils import get_bound_method_class, safe_as_int
+from ..util import img_as_float
+from ..exposure import rescale_intensity
 from ._warps_cy import _warp_fast
 
 
@@ -1148,7 +1149,10 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
     if keep_range:
         image = image.astype(np.double)
     else:
-        image = img_as_float(image)
+        if image.dtype == np.double:
+            image = rescale_intensity(image)
+        else:
+            image = img_as_float(image)
 
     input_shape = np.array(image.shape)
 

skimage/transform/_warps.py

@@ -1,9 +1,11 @@
 import numpy as np
 from scipy import ndimage
 
-from skimage.transform._geometric import (warp, SimilarityTransform,
-                                          AffineTransform, _clip_warp_output)
-from skimage.measure import block_reduce
+from ..util import img_as_float
+from ..exposure import rescale_intensity
+from ..measure import block_reduce
+from ._geometric import (warp, SimilarityTransform, AffineTransform,
+                         _clip_warp_output)
 
 
 def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
@@ -82,7 +84,10 @@ def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
         if keep_range:
             image = image.astype(np.double)
         else:
-            image = img_as_float(image)
+            if image.dtype == np.double:
+                image = rescale_intensity(image)
+            else:
+                image = img_as_float(image)
 
         out = ndimage.map_coordinates(image, coord_map, order=order,
                                       mode=mode, cval=cval)

skimage/transform/tests/test_warps.py

@@ -1,5 +1,5 @@
 from numpy.testing import (assert_almost_equal, run_module_suite,
-                           assert_array_equal, assert_raises)
+                           assert_equal, assert_raises)
 import numpy as np
 from scipy.ndimage import map_coordinates
 
@@ -236,13 +236,13 @@ def test_downscale_local_mean():
     out1 = downscale_local_mean(image1, (2, 3))
     expected1 = np.array([[  4.,   7.],
                           [ 16.,  19.]])
-    assert_array_equal(expected1, out1)
+    assert_equal(expected1, out1)
 
     image2 = np.arange(5 * 8).reshape(5, 8)
     out2 = downscale_local_mean(image2, (4, 5))
     expected2 = np.array([[ 14. ,  10.8],
                           [  8.5,   5.7]])
-    assert_array_equal(expected2, out2)
+    assert_equal(expected2, out2)
 
 
 def test_invalid():
@@ -255,7 +255,7 @@ def test_inverse():
     tform = SimilarityTransform(scale=0.5, rotation=0.1)
     inverse_tform = SimilarityTransform(matrix=np.linalg.inv(tform.params))
     image = np.arange(10 * 10).reshape(10, 10).astype(np.double)
-    assert_array_equal(warp(image, inverse_tform), warp(image, tform.inverse))
+    assert_equal(warp(image, inverse_tform), warp(image, tform.inverse))
 
 
 def test_slow_warp_nonint_oshape():
@@ -267,5 +267,18 @@ def test_slow_warp_nonint_oshape():
     warp(image, lambda xy: xy, output_shape=(13.0001, 19.9999))
 
 
+def test_keep_range():
+    image = np.linspace(0, 2, 25).reshape(5, 5)
+
+    out = rescale(image, 2, keep_range=False, clip=True, order=0)
+    assert out.min() == 0
+    assert out.max() == 1
+
+    out = rescale(image, 2, keep_range=True, clip=True, order=0)
+    assert out.min() == 0
+    assert out.max() == 2
+
+
+
 if __name__ == "__main__":
     run_module_suite()