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scikit-image/skimage/util/dtype.py
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Stefan van der Walt 31b6adb778 Bump numpy requirement to 1.7.2
2016-01-28 11:24:29 -08:00

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from __future__ import division
import numpy as np
from warnings import warn
__all__ = ['img_as_float', 'img_as_int', 'img_as_uint', 'img_as_ubyte',
'img_as_bool', 'dtype_limits']
dtype_range = {np.bool_: (False, True),
np.bool8: (False, True),
np.uint8: (0, 255),
np.uint16: (0, 65535),
np.int8: (-128, 127),
np.int16: (-32768, 32767),
np.int64: (-2**63, 2**63 - 1),
np.uint64: (0, 2**64 - 1),
np.int32: (-2**31, 2**31 - 1),
np.uint32: (0, 2**32 - 1),
np.float32: (-1, 1),
np.float64: (-1, 1)}
integer_types = (np.uint8, np.uint16, np.int8, np.int16)
_supported_types = (np.bool_, np.bool8,
np.uint8, np.uint16, np.uint32, np.uint64,
np.int8, np.int16, np.int32, np.int64,
np.float32, np.float64)
dtype_range[np.float16] = (-1, 1)
_supported_types += (np.float16, )
def dtype_limits(image, clip_negative=True):
"""Return intensity limits, i.e. (min, max) tuple, of the image's dtype.
Parameters
----------
image : ndarray
Input image.
clip_negative : bool
If True, clip the negative range (i.e. return 0 for min intensity)
even if the image dtype allows negative values.
"""
imin, imax = dtype_range[image.dtype.type]
if clip_negative:
imin = 0
return imin, imax
def convert(image, dtype, force_copy=False, uniform=False):
"""
Convert an image to the requested data-type.
Warnings are issued in case of precision loss, or when negative values
are clipped during conversion to unsigned integer types (sign loss).
Floating point values are expected to be normalized and will be clipped
to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
signed integers respectively.
Numbers are not shifted to the negative side when converting from
unsigned to signed integer types. Negative values will be clipped when
converting to unsigned integers.
Parameters
----------
image : ndarray
Input image.
dtype : dtype
Target data-type.
force_copy : bool
Force a copy of the data, irrespective of its current dtype.
uniform : bool
Uniformly quantize the floating point range to the integer range.
By default (uniform=False) floating point values are scaled and
rounded to the nearest integers, which minimizes back and forth
conversion errors.
References
----------
(1) DirectX data conversion rules.
http://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
(2) Data Conversions.
In "OpenGL ES 2.0 Specification v2.0.25", pp 7-8. Khronos Group, 2010.
(3) Proper treatment of pixels as integers. A.W. Paeth.
In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
(4) Dirty Pixels. J. Blinn.
In "Jim Blinn's corner: Dirty Pixels", pp 47-57. Morgan Kaufmann, 1998.
"""
image = np.asarray(image)
dtypeobj = np.dtype(dtype)
dtypeobj_in = image.dtype
dtype = dtypeobj.type
dtype_in = dtypeobj_in.type
if dtype_in == dtype:
if force_copy:
image = image.copy()
return image
if not (dtype_in in _supported_types and dtype in _supported_types):
raise ValueError("can not convert %s to %s." % (dtypeobj_in, dtypeobj))
def sign_loss():
warn("Possible sign loss when converting negative image of type "
"%s to positive image of type %s." % (dtypeobj_in, dtypeobj))
def prec_loss():
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)
def _dtype2(kind, bits, itemsize=1):
# Return dtype of `kind` that can store a `bits` wide unsigned int
c = lambda x, y: x <= y if kind == 'u' else x < y
s = next(i for i in (itemsize, ) + (2, 4, 8) if c(bits, i * 8))
return np.dtype(kind + str(s))
def _scale(a, n, m, copy=True):
# Scale unsigned/positive integers from n to m bits
# 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 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
prec_loss()
if copy:
b = np.empty(a.shape, _dtype2(kind, m))
np.floor_divide(a, 2**(n - m), out=b, dtype=a.dtype,
casting='unsafe')
return b
else:
a //= 2**(n - m)
return a
elif m % n == 0:
# exact upscale to a multiple of n bits
if copy:
b = np.empty(a.shape, _dtype2(kind, m))
np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
return b
else:
a = np.array(a, _dtype2(kind, m, a.dtype.itemsize), copy=False)
a *= (2**m - 1) // (2**n - 1)
return a
else:
# upscale to a multiple of n bits,
# then downscale with precision loss
prec_loss()
o = (m // n + 1) * n
if copy:
b = np.empty(a.shape, _dtype2(kind, o))
np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
b //= 2**(o - m)
return b
else:
a = np.array(a, _dtype2(kind, o, a.dtype.itemsize), copy=False)
a *= (2**o - 1) // (2**n - 1)
a //= 2**(o - m)
return a
kind = dtypeobj.kind
kind_in = dtypeobj_in.kind
itemsize = dtypeobj.itemsize
itemsize_in = dtypeobj_in.itemsize
if kind == 'b':
# to binary image
if kind_in in "fi":
sign_loss()
prec_loss()
return image > dtype_in(dtype_range[dtype_in][1] / 2)
if kind_in == 'b':
# from binary image, to float and to integer
result = image.astype(dtype)
if kind != 'f':
result *= dtype(dtype_range[dtype][1])
return result
if kind in 'ui':
imin = np.iinfo(dtype).min
imax = np.iinfo(dtype).max
if kind_in in 'ui':
imin_in = np.iinfo(dtype_in).min
imax_in = np.iinfo(dtype_in).max
if kind_in == 'f':
if np.min(image) < -1.0 or np.max(image) > 1.0:
raise ValueError("Images of type float must be between -1 and 1.")
if kind == 'f':
# floating point -> floating point
if itemsize_in > itemsize:
prec_loss()
return image.astype(dtype)
# floating point -> integer
prec_loss()
# use float type that can represent output integer type
image = np.array(image, _dtype(itemsize, dtype_in,
np.float32, np.float64))
if not uniform:
if kind == 'u':
image *= imax
else:
image *= imax - imin
image -= 1.0
image /= 2.0
np.rint(image, out=image)
np.clip(image, imin, imax, out=image)
elif kind == 'u':
image *= imax + 1
np.clip(image, 0, imax, out=image)
else:
image *= (imax - imin + 1.0) / 2.0
np.floor(image, out=image)
np.clip(image, imin, imax, out=image)
return image.astype(dtype)
if kind == 'f':
# integer -> floating point
if itemsize_in >= itemsize:
prec_loss()
# use float type that can exactly represent input integers
image = np.array(image, _dtype(itemsize_in, dtype,
np.float32, np.float64))
if kind_in == 'u':
image /= imax_in
# DirectX uses this conversion also for signed ints
#if imin_in:
# np.maximum(image, -1.0, out=image)
else:
image *= 2.0
image += 1.0
image /= imax_in - imin_in
return image.astype(dtype)
if kind_in == 'u':
if kind == 'i':
# unsigned integer -> signed integer
image = _scale(image, 8 * itemsize_in, 8 * itemsize - 1)
return image.view(dtype)
else:
# unsigned integer -> unsigned integer
return _scale(image, 8 * itemsize_in, 8 * itemsize)
if kind == 'u':
# signed integer -> unsigned integer
sign_loss()
image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize)
result = np.empty(image.shape, dtype)
np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
return result
# signed integer -> signed integer
if itemsize_in > itemsize:
return _scale(image, 8 * itemsize_in - 1, 8 * itemsize - 1)
image = image.astype(_dtype2('i', itemsize * 8))
image -= imin_in
image = _scale(image, 8 * itemsize_in, 8 * itemsize, copy=False)
image += imin
return image.astype(dtype)
def img_as_float(image, force_copy=False):
"""Convert an image to double-precision floating point format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of float64
Output image.
Notes
-----
The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
converting from unsigned or signed datatypes, respectively.
"""
return convert(image, np.float64, force_copy)
def img_as_uint(image, force_copy=False):
"""Convert an image to 16-bit unsigned integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of uint16
Output image.
Notes
-----
Negative input values will be clipped.
Positive values are scaled between 0 and 65535.
"""
return convert(image, np.uint16, force_copy)
def img_as_int(image, force_copy=False):
"""Convert an image to 16-bit signed integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of uint16
Output image.
Notes
-----
The values are scaled between -32768 and 32767.
If the input data-type is positive-only (e.g., uint8), then
the output image will still only have positive values.
"""
return convert(image, np.int16, force_copy)
def img_as_ubyte(image, force_copy=False):
"""Convert an image to 8-bit unsigned integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of ubyte (uint8)
Output image.
Notes
-----
Negative input values will be clipped.
Positive values are scaled between 0 and 255.
"""
return convert(image, np.uint8, force_copy)
def img_as_bool(image, force_copy=False):
"""Convert an image to boolean format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of bool (`bool_`)
Output image.
Notes
-----
The upper half of the input dtype's positive range is True, and the lower
half is False. All negative values (if present) are False.
"""
return convert(image, np.bool_, force_copy)
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