248 lines
8.1 KiB
Python
248 lines
8.1 KiB
Python
"""Vendored code from scikit-image in order to limit the number of dependencies
|
|
Extracted from scikit-image/skimage/exposure/exposure.py
|
|
"""
|
|
|
|
import numpy as np
|
|
|
|
from warnings import warn
|
|
|
|
_integer_types = (
|
|
np.byte,
|
|
np.ubyte, # 8 bits
|
|
np.short,
|
|
np.ushort, # 16 bits
|
|
np.intc,
|
|
np.uintc, # 16 or 32 or 64 bits
|
|
np.int_,
|
|
np.uint, # 32 or 64 bits
|
|
np.longlong,
|
|
np.ulonglong,
|
|
) # 64 bits
|
|
_integer_ranges = {t: (np.iinfo(t).min, np.iinfo(t).max) for t in _integer_types}
|
|
dtype_range = {
|
|
np.bool_: (False, True),
|
|
np.float16: (-1, 1),
|
|
np.float32: (-1, 1),
|
|
np.float64: (-1, 1),
|
|
}
|
|
dtype_range.update(_integer_ranges)
|
|
|
|
|
|
DTYPE_RANGE = dtype_range.copy()
|
|
DTYPE_RANGE.update((d.__name__, limits) for d, limits in dtype_range.items())
|
|
DTYPE_RANGE.update(
|
|
{
|
|
"uint10": (0, 2**10 - 1),
|
|
"uint12": (0, 2**12 - 1),
|
|
"uint14": (0, 2**14 - 1),
|
|
"bool": dtype_range[np.bool_],
|
|
"float": dtype_range[np.float64],
|
|
}
|
|
)
|
|
|
|
|
|
def intensity_range(image, range_values="image", clip_negative=False):
|
|
"""Return image intensity range (min, max) based on desired value type.
|
|
|
|
Parameters
|
|
----------
|
|
image : array
|
|
Input image.
|
|
range_values : str or 2-tuple, optional
|
|
The image intensity range is configured by this parameter.
|
|
The possible values for this parameter are enumerated below.
|
|
|
|
'image'
|
|
Return image min/max as the range.
|
|
'dtype'
|
|
Return min/max of the image's dtype as the range.
|
|
dtype-name
|
|
Return intensity range based on desired `dtype`. Must be valid key
|
|
in `DTYPE_RANGE`. Note: `image` is ignored for this range type.
|
|
2-tuple
|
|
Return `range_values` as min/max intensities. Note that there's no
|
|
reason to use this function if you just want to specify the
|
|
intensity range explicitly. This option is included for functions
|
|
that use `intensity_range` to support all desired range types.
|
|
|
|
clip_negative : bool, optional
|
|
If True, clip the negative range (i.e. return 0 for min intensity)
|
|
even if the image dtype allows negative values.
|
|
"""
|
|
if range_values == "dtype":
|
|
range_values = image.dtype.type
|
|
|
|
if range_values == "image":
|
|
i_min = np.min(image)
|
|
i_max = np.max(image)
|
|
elif range_values in DTYPE_RANGE:
|
|
i_min, i_max = DTYPE_RANGE[range_values]
|
|
if clip_negative:
|
|
i_min = 0
|
|
else:
|
|
i_min, i_max = range_values
|
|
return i_min, i_max
|
|
|
|
|
|
def _output_dtype(dtype_or_range):
|
|
"""Determine the output dtype for rescale_intensity.
|
|
|
|
The dtype is determined according to the following rules:
|
|
- if ``dtype_or_range`` is a dtype, that is the output dtype.
|
|
- if ``dtype_or_range`` is a dtype string, that is the dtype used, unless
|
|
it is not a NumPy data type (e.g. 'uint12' for 12-bit unsigned integers),
|
|
in which case the data type that can contain it will be used
|
|
(e.g. uint16 in this case).
|
|
- if ``dtype_or_range`` is a pair of values, the output data type will be
|
|
float.
|
|
|
|
Parameters
|
|
----------
|
|
dtype_or_range : type, string, or 2-tuple of int/float
|
|
The desired range for the output, expressed as either a NumPy dtype or
|
|
as a (min, max) pair of numbers.
|
|
|
|
Returns
|
|
-------
|
|
out_dtype : type
|
|
The data type appropriate for the desired output.
|
|
"""
|
|
if type(dtype_or_range) in [list, tuple, np.ndarray]:
|
|
# pair of values: always return float.
|
|
return np.float_
|
|
if type(dtype_or_range) == type:
|
|
# already a type: return it
|
|
return dtype_or_range
|
|
if dtype_or_range in DTYPE_RANGE:
|
|
# string key in DTYPE_RANGE dictionary
|
|
try:
|
|
# if it's a canonical numpy dtype, convert
|
|
return np.dtype(dtype_or_range).type
|
|
except TypeError: # uint10, uint12, uint14
|
|
# otherwise, return uint16
|
|
return np.uint16
|
|
else:
|
|
raise ValueError(
|
|
"Incorrect value for out_range, should be a valid image data "
|
|
"type or a pair of values, got %s." % str(dtype_or_range)
|
|
)
|
|
|
|
|
|
def rescale_intensity(image, in_range="image", out_range="dtype"):
|
|
"""Return image after stretching or shrinking its intensity levels.
|
|
|
|
The desired intensity range of the input and output, `in_range` and
|
|
`out_range` respectively, are used to stretch or shrink the intensity range
|
|
of the input image. See examples below.
|
|
|
|
Parameters
|
|
----------
|
|
image : array
|
|
Image array.
|
|
in_range, out_range : str or 2-tuple, optional
|
|
Min and max intensity values of input and output image.
|
|
The possible values for this parameter are enumerated below.
|
|
|
|
'image'
|
|
Use image min/max as the intensity range.
|
|
'dtype'
|
|
Use min/max of the image's dtype as the intensity range.
|
|
dtype-name
|
|
Use intensity range based on desired `dtype`. Must be valid key
|
|
in `DTYPE_RANGE`.
|
|
2-tuple
|
|
Use `range_values` as explicit min/max intensities.
|
|
|
|
Returns
|
|
-------
|
|
out : array
|
|
Image array after rescaling its intensity. This image is the same dtype
|
|
as the input image.
|
|
|
|
Notes
|
|
-----
|
|
.. versionchanged:: 0.17
|
|
The dtype of the output array has changed to match the output dtype, or
|
|
float if the output range is specified by a pair of floats.
|
|
|
|
See Also
|
|
--------
|
|
equalize_hist
|
|
|
|
Examples
|
|
--------
|
|
By default, the min/max intensities of the input image are stretched to
|
|
the limits allowed by the image's dtype, since `in_range` defaults to
|
|
'image' and `out_range` defaults to 'dtype':
|
|
|
|
>>> image = np.array([51, 102, 153], dtype=np.uint8)
|
|
>>> rescale_intensity(image)
|
|
array([ 0, 127, 255], dtype=uint8)
|
|
|
|
It's easy to accidentally convert an image dtype from uint8 to float:
|
|
|
|
>>> 1.0 * image
|
|
array([ 51., 102., 153.])
|
|
|
|
Use `rescale_intensity` to rescale to the proper range for float dtypes:
|
|
|
|
>>> image_float = 1.0 * image
|
|
>>> rescale_intensity(image_float)
|
|
array([0. , 0.5, 1. ])
|
|
|
|
To maintain the low contrast of the original, use the `in_range` parameter:
|
|
|
|
>>> rescale_intensity(image_float, in_range=(0, 255))
|
|
array([0.2, 0.4, 0.6])
|
|
|
|
If the min/max value of `in_range` is more/less than the min/max image
|
|
intensity, then the intensity levels are clipped:
|
|
|
|
>>> rescale_intensity(image_float, in_range=(0, 102))
|
|
array([0.5, 1. , 1. ])
|
|
|
|
If you have an image with signed integers but want to rescale the image to
|
|
just the positive range, use the `out_range` parameter. In that case, the
|
|
output dtype will be float:
|
|
|
|
>>> image = np.array([-10, 0, 10], dtype=np.int8)
|
|
>>> rescale_intensity(image, out_range=(0, 127))
|
|
array([ 0. , 63.5, 127. ])
|
|
|
|
To get the desired range with a specific dtype, use ``.astype()``:
|
|
|
|
>>> rescale_intensity(image, out_range=(0, 127)).astype(np.int8)
|
|
array([ 0, 63, 127], dtype=int8)
|
|
|
|
If the input image is constant, the output will be clipped directly to the
|
|
output range:
|
|
>>> image = np.array([130, 130, 130], dtype=np.int32)
|
|
>>> rescale_intensity(image, out_range=(0, 127)).astype(np.int32)
|
|
array([127, 127, 127], dtype=int32)
|
|
"""
|
|
if out_range in ["dtype", "image"]:
|
|
out_dtype = _output_dtype(image.dtype.type)
|
|
else:
|
|
out_dtype = _output_dtype(out_range)
|
|
|
|
imin, imax = map(float, intensity_range(image, in_range))
|
|
omin, omax = map(
|
|
float, intensity_range(image, out_range, clip_negative=(imin >= 0))
|
|
)
|
|
|
|
if np.any(np.isnan([imin, imax, omin, omax])):
|
|
warn(
|
|
"One or more intensity levels are NaN. Rescaling will broadcast "
|
|
"NaN to the full image. Provide intensity levels yourself to "
|
|
"avoid this. E.g. with np.nanmin(image), np.nanmax(image).",
|
|
stacklevel=2,
|
|
)
|
|
|
|
image = np.clip(image, imin, imax)
|
|
|
|
if imin != imax:
|
|
image = (image - imin) / (imax - imin)
|
|
return np.asarray(image * (omax - omin) + omin, dtype=out_dtype)
|
|
else:
|
|
return np.clip(image, omin, omax).astype(out_dtype)
|