Merge pull request #1818 from blink1073/improve_downcast

Add support for casting from 64bit integers and use smart downcast

skimage/io/tests/test_mpl_imshow.py

@@ -87,8 +87,7 @@ def test_outside_standard_range():
 
 def test_nonstandard_type():
     plt.figure()
-    with expected_warnings(["Non-standard image type",
-                            "Low image dynamic range"]):
+    with expected_warnings(["Low image dynamic range"]):
         ax_im = io.imshow(im64)
     assert ax_im.get_clim() == (im64.min(), im64.max())
     assert n_subplots(ax_im) == 2

skimage/util/dtype.py

@@ -21,8 +21,8 @@ dtype_range = {np.bool_: (False, True),
 integer_types = (np.uint8, np.uint16, np.int8, np.int16)
 
 _supported_types = (np.bool_, np.bool8,
-                    np.uint8, np.uint16, np.uint32,
-                    np.int8, np.int16, np.int32,
+                    np.uint8, np.uint16, np.uint32, np.uint64,
+                    np.int8, np.int16, np.int32, np.int64,
                     np.float32, np.float64)
 
 if np.__version__ >= "1.6.0":
@@ -125,7 +125,18 @@ def convert(image, dtype, force_copy=False, uniform=False):
         # Numbers can be represented exactly only if m is a multiple of n
         # Output array is of same kind as input.
         kind = a.dtype.kind
-        if n == m:
+        if n > m and a.max() < 2 ** m:
+            mnew = int(np.ceil(m / 2) * 2)
+            if mnew > m:
+                dtype = "int%s" % mnew
+            else:
+                dtype = "uint%s" % mnew
+            n = int(np.ceil(n / 2) * 2)
+            msg = ("Downcasting %s to %s without scaling because max "
+                   "value %s fits in %s" % (a.dtype, dtype, a.max(), dtype))
+            warn(msg)
+            return a.astype(_dtype2(kind, m))
+        elif n == m:
             return a.copy() if copy else a
         elif n > m:
             # downscale with precision loss

skimage/util/tests/test_dtype.py

@@ -29,7 +29,7 @@ def test_range():
                         (img_as_float, np.float64),
                         (img_as_uint, np.uint16),
                         (img_as_ubyte, np.ubyte)]:
-            
+
             with expected_warnings(['precision loss|sign loss|\A\Z']):
                 y = f(x)
 
@@ -62,7 +62,7 @@ def test_range_extra_dtypes():
     for dtype_in, dt in dtype_pairs:
         imin, imax = dtype_range_extra[dtype_in]
         x = np.linspace(imin, imax, 10).astype(dtype_in)
-        
+
         with expected_warnings(['precision loss|sign loss|\A\Z']):
             y = convert(x, dt)
 
@@ -72,9 +72,12 @@ def test_range_extra_dtypes():
                y, omin, omax, np.dtype(dt))
 
 
-def test_unsupported_dtype():
+def test_downcast():
     x = np.arange(10).astype(np.uint64)
-    assert_raises(ValueError, img_as_int, x)
+    with expected_warnings('Downcasting'):
+        y = img_as_int(x)
+    assert np.allclose(y, x.astype(np.int16))
+    assert y.dtype == np.int16, y.dtype
 
 
 def test_float_out_of_range():