wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-06-28 21:47:10 +08:00
Code Issues Packages Projects Releases Wiki Activity

Restore numpy 1.5 compatibility

Browse Source
This commit is contained in:
cgohlke
2012-02-12 19:57:41 -08:00
parent 917f345d61
commit 8045e1f623
1 changed files with 23 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/util/dtype.py
+23 -12
View File
@@ -17,7 +17,11 @@ integer_types = (np.uint8, np.uint16, np.int8, np.int16)
_supported_types = (np.uint8, np.uint16, np.uint32,
np.int8, np.int16, np.int32,
np.float16, np.float32, np.float64)
np.float32, np.float64)
if np.__version__ >= "1.6.0":
dtype_range[np.float16] = (0, 1)
_supported_types += (np.float16, )
def convert(image, dtype):
@@ -62,6 +66,10 @@ def convert(image, dtype):
log.warn("Possible precision loss when converting from "
"%s to %s" % (dtypeobj_in, dtypeobj))
def _dtype(itemsize, *dtypes):
# Return first of `dtypes` with itemsize greater than `itemsize`
return next(dt for dt in dtypes if itemsize < np.dtype(dt).itemsize)
if kind_in == 'f':
if np.min(image) < 0 or np.max(image) > 1:
raise ValueError("Images of type float must be between 0 and 1")
@@ -72,7 +80,9 @@ def convert(image, dtype):
return dtype(image)
# floating point -> integer
prec_loss()
image = np.array(image, dtype=np.promote_types(dtype_in, dtype))
# use float type that can represent output integer type
image = np.array(image, _dtype(itemsize, dtype_in,
np.float32, np.float64))
image *= np.iinfo(dtype).max + 1
np.clip(image, 0, np.iinfo(dtype).max, out=image)
return dtype(image)
@@ -80,7 +90,9 @@ def convert(image, dtype):
# integer -> floating point
if itemsize_in >= itemsize:
prec_loss()
image = np.array(image, dtype=np.promote_types(dtype_in, dtype))
# use float type that can represent input integers
image = np.array(image, _dtype(itemsize_in, dtype,
np.float32, np.float64))
if np.iinfo(dtype_in).min:
sign_loss()
image -= np.iinfo(dtype_in).min
@@ -108,7 +120,9 @@ def convert(image, dtype):
if kind == 'u':
# signed integer -> unsigned integer
sign_loss()
image = np.array(image, dtype=np.promote_types(dtype_in, dtype))
# use next higher precision signed integer type
image = np.array(image, _dtype(itemsize_in,
np.int16, np.int32, np.int64))
image -= np.iinfo(dtype_in).min
if itemsize_in == itemsize:
return dtype(image)
@@ -129,15 +143,12 @@ def convert(image, dtype):
if itemsize_in > itemsize:
prec_loss()
return dtype(image // 2**(8 * (itemsize_in - itemsize)))
# upcast to next higher precision signed integer type
dt = next(dt for dt in (np.int16, np.int32, np.int64)
if image.itemsize < np.dtype(dt).itemsize)
image = np.array(image, dtype=dt)
# use next higher precision signed integer type
image = np.array(image, _dtype(itemsize_in,
np.int16, np.int32, np.int64))
image -= np.iinfo(dtype_in).min
# upcast to next higher precision signed integer type
dt = next(dt for dt in (np.int32, np.int64)
if image.itemsize < np.dtype(dt).itemsize)
result = np.array(image, dtype=dt)
# use next higher precision signed integer type
result = np.array(image, _dtype(image.itemsize, np.int32, np.int64))
result *= 2**(8 * (itemsize - itemsize_in))
if itemsize - itemsize_in == 3:
# int8 -> int32