mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 03:04:42 +08:00
JDWarner
c54d8d31f3
img_as_float() had a legacy note stating conversion would force negative values to the positive domain; this no longer described its functionality. Note changed to reflect the retention of negative values when converting from signed datatypes. When submitting my multichannel improvement to the random walker segmentation algorithm, I neglected to append my name to the CONTRIBUTORS file. Now fixed.
368 lines
11 KiB
Python
368 lines
11 KiB
Python
from __future__ import division
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import numpy as np
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__all__ = ['img_as_float', 'img_as_int', 'img_as_uint', 'img_as_ubyte',
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'img_as_bool']
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from .. import get_log
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log = get_log('dtype_converter')
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dtype_range = {np.bool_: (False, True),
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np.bool8: (False, True),
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np.uint8: (0, 255),
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np.uint16: (0, 65535),
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np.int8: (-128, 127),
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np.int16: (-32768, 32767),
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np.float32: (-1, 1),
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np.float64: (-1, 1)}
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integer_types = (np.uint8, np.uint16, np.int8, np.int16)
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_supported_types = (np.bool_, np.bool8,
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np.uint8, np.uint16, np.uint32,
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np.int8, np.int16, np.int32,
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np.float32, np.float64)
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if np.__version__ >= "1.6.0":
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dtype_range[np.float16] = (-1, 1)
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_supported_types += (np.float16, )
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def convert(image, dtype, force_copy=False, uniform=False):
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"""
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Convert an image to the requested data-type.
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Warnings are issued in case of precision loss, or when negative values
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are clipped during conversion to unsigned integer types (sign loss).
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Floating point values are expected to be normalized and will be clipped
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to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
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signed integers respectively.
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Numbers are not shifted to the negative side when converting from
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unsigned to signed integer types. Negative values will be clipped when
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converting to unsigned integers.
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Parameters
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----------
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image : ndarray
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Input image.
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dtype : dtype
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Target data-type.
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force_copy : bool
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Force a copy of the data, irrespective of its current dtype.
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uniform : bool
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Uniformly quantize the floating point range to the integer range.
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By default (uniform=False) floating point values are scaled and
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rounded to the nearest integers, which minimizes back and forth
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conversion errors.
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References
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----------
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(1) DirectX data conversion rules.
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http://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
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(2) Data Conversions.
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In "OpenGL ES 2.0 Specification v2.0.25", pp 7-8. Khronos Group, 2010.
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(3) Proper treatment of pixels as integers. A.W. Paeth.
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In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
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(4) Dirty Pixels. J. Blinn.
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In "Jim Blinn's corner: Dirty Pixels", pp 47-57. Morgan Kaufmann, 1998.
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"""
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image = np.asarray(image)
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dtypeobj = np.dtype(dtype)
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dtypeobj_in = image.dtype
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dtype = dtypeobj.type
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dtype_in = dtypeobj_in.type
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if dtype_in == dtype:
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if force_copy:
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image = image.copy()
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return image
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if not (dtype_in in _supported_types and dtype in _supported_types):
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raise ValueError("can not convert %s to %s." % (dtypeobj_in, dtypeobj))
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def sign_loss():
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log.warn("Possible sign loss when converting negative image of type "
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"%s to positive image of type %s." % (dtypeobj_in, dtypeobj))
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def prec_loss():
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log.warn("Possible precision loss when converting from "
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"%s to %s" % (dtypeobj_in, dtypeobj))
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def _dtype(itemsize, *dtypes):
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# Return first of `dtypes` with itemsize greater than `itemsize`
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return next(dt for dt in dtypes if itemsize < np.dtype(dt).itemsize)
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def _dtype2(kind, bits, itemsize=1):
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# Return dtype of `kind` that can store a `bits` wide unsigned int
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c = lambda x, y: x <= y if kind == 'u' else x < y
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s = next(i for i in (itemsize, ) + (2, 4, 8) if c(bits, i * 8))
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return np.dtype(kind + str(s))
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def _scale(a, n, m, copy=True):
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# Scale unsigned/positive integers from n to m bits
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# Numbers can be represented exactly only if m is a multiple of n
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# Output array is of same kind as input.
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kind = a.dtype.kind
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if n == m:
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return a.copy() if copy else a
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elif n > m:
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# downscale with precision loss
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prec_loss()
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if copy:
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b = np.empty(a.shape, _dtype2(kind, m))
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np.floor_divide(a, 2**(n - m), out=b, dtype=a.dtype,
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casting='unsafe')
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return b
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else:
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a //= 2**(n - m)
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return a
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elif m % n == 0:
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# exact upscale to a multiple of n bits
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if copy:
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b = np.empty(a.shape, _dtype2(kind, m))
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np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
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return b
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else:
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a = np.array(a, _dtype2(kind, m, a.dtype.itemsize), copy=False)
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a *= (2**m - 1) // (2**n - 1)
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return a
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else:
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# upscale to a multiple of n bits,
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# then downscale with precision loss
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prec_loss()
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o = (m // n + 1) * n
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if copy:
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b = np.empty(a.shape, _dtype2(kind, o))
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np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
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b //= 2**(o - m)
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return b
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else:
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a = np.array(a, _dtype2(kind, o, a.dtype.itemsize), copy=False)
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a *= (2**o - 1) // (2**n - 1)
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a //= 2**(o - m)
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return a
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kind = dtypeobj.kind
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kind_in = dtypeobj_in.kind
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itemsize = dtypeobj.itemsize
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itemsize_in = dtypeobj_in.itemsize
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if kind == 'b':
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# to binary image
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if kind_in in "fi":
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sign_loss()
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prec_loss()
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return image > dtype_in(dtype_range[dtype_in][1] / 2)
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if kind_in == 'b':
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# from binary image, to float and to integer
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result = dtype(image)
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if kind != 'f':
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result *= dtype(dtype_range[dtype][1])
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return result
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if kind in 'ui':
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imin = np.iinfo(dtype).min
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imax = np.iinfo(dtype).max
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if kind_in in 'ui':
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imin_in = np.iinfo(dtype_in).min
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imax_in = np.iinfo(dtype_in).max
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if kind_in == 'f':
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if np.min(image) < -1.0 or np.max(image) > 1.0:
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raise ValueError("Images of type float must be between -1 and 1.")
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if kind == 'f':
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# floating point -> floating point
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if itemsize_in > itemsize:
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prec_loss()
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return dtype(image)
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# floating point -> integer
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prec_loss()
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# use float type that can represent output integer type
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image = np.array(image, _dtype(itemsize, dtype_in,
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np.float32, np.float64))
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if not uniform:
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if kind == 'u':
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image *= imax
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else:
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image *= imax - imin
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image -= 1.0
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image /= 2.0
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np.rint(image, out=image)
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np.clip(image, imin, imax, out=image)
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elif kind == 'u':
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image *= imax + 1
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np.clip(image, 0, imax, out=image)
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else:
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image *= (imax - imin + 1.0) / 2.0
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np.floor(image, out=image)
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np.clip(image, imin, imax, out=image)
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return dtype(image)
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if kind == 'f':
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# integer -> floating point
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if itemsize_in >= itemsize:
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prec_loss()
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# use float type that can exactly represent input integers
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image = np.array(image, _dtype(itemsize_in, dtype,
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np.float32, np.float64))
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if kind_in == 'u':
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image /= imax_in
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# DirectX uses this conversion also for signed ints
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#if imin_in:
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# np.maximum(image, -1.0, out=image)
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else:
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image *= 2.0
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image += 1.0
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image /= imax_in - imin_in
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return dtype(image)
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if kind_in == 'u':
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if kind == 'i':
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# unsigned integer -> signed integer
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image = _scale(image, 8 * itemsize_in, 8 * itemsize - 1)
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return image.view(dtype)
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else:
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# unsigned integer -> unsigned integer
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return _scale(image, 8 * itemsize_in, 8 * itemsize)
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if kind == 'u':
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# signed integer -> unsigned integer
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sign_loss()
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image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize)
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result = np.empty(image.shape, dtype)
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np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
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return result
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# signed integer -> signed integer
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if itemsize_in > itemsize:
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return _scale(image, 8 * itemsize_in - 1, 8 * itemsize - 1)
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image = image.astype(_dtype2('i', itemsize * 8))
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image -= imin_in
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image = _scale(image, 8 * itemsize_in, 8 * itemsize, copy=False)
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image += imin
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return dtype(image)
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def img_as_float(image, force_copy=False):
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"""Convert an image to double-precision floating point format.
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Parameters
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----------
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image : ndarray
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Input image.
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force_copy : bool
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Force a copy of the data, irrespective of its current dtype.
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Returns
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-------
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out : ndarray of float64
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Output image.
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Notes
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-----
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The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
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converting from unsigned or signed datatypes, respectively.
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"""
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return convert(image, np.float64, force_copy)
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def img_as_uint(image, force_copy=False):
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"""Convert an image to 16-bit unsigned integer format.
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Parameters
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----------
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image : ndarray
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Input image.
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force_copy : bool
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Force a copy of the data, irrespective of its current dtype.
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Returns
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-------
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out : ndarray of uint16
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Output image.
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Notes
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-----
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Negative input values will be shifted to the positive domain.
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"""
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return convert(image, np.uint16, force_copy)
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def img_as_int(image, force_copy=False):
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"""Convert an image to 16-bit signed integer format.
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Parameters
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----------
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image : ndarray
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Input image.
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force_copy : bool
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Force a copy of the data, irrespective of its current dtype.
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Returns
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-------
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out : ndarray of uint16
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Output image.
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Notes
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-----
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If the input data-type is positive-only (e.g., uint8), then
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the output image will still only have positive values.
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"""
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return convert(image, np.int16, force_copy)
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def img_as_ubyte(image, force_copy=False):
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"""Convert an image to 8-bit unsigned integer format.
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Parameters
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----------
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image : ndarray
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Input image.
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force_copy : bool
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Force a copy of the data, irrespective of its current dtype.
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Returns
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-------
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out : ndarray of ubyte (uint8)
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Output image.
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Notes
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-----
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If the input data-type is positive-only (e.g., uint16), then
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the output image will still only have positive values.
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"""
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return convert(image, np.uint8, force_copy)
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def img_as_bool(image, force_copy=False):
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"""Convert an image to boolean format.
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Parameters
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----------
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image : ndarray
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Input image.
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force_copy : bool
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Force a copy of the data, irrespective of its current dtype.
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Returns
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-------
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out : ndarray of bool (`bool_`)
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Output image.
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Notes
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-----
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The upper half of the input dtype's positive range is True, and the lower
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half is False. All negative values (if present) are False.
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"""
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return convert(image, np.bool_, force_copy)
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