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Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/scipy/ndimage/measurements.py

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Fixed database typo and removed unnecessary class identifier.
2020-10-14 14:10:37 +00:00
# Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy
import numpy as np
from . import _ni_support
from . import _ni_label
from . import _nd_image
from . import morphology
__all__ = ['label', 'find_objects', 'labeled_comprehension', 'sum', 'mean',
'variance', 'standard_deviation', 'minimum', 'maximum', 'median',
'minimum_position', 'maximum_position', 'extrema', 'center_of_mass',
'histogram', 'watershed_ift']
def label(input, structure=None, output=None):
"""
Label features in an array.
Parameters
----------
input : array_like
An array-like object to be labeled. Any non-zero values in `input` are
counted as features and zero values are considered the background.
structure : array_like, optional
A structuring element that defines feature connections.
`structure` must be centrosymmetric
(see Notes).
If no structuring element is provided,
one is automatically generated with a squared connectivity equal to
one. That is, for a 2-D `input` array, the default structuring element
is::
[[0,1,0],
[1,1,1],
[0,1,0]]
output : (None, data-type, array_like), optional
If `output` is a data type, it specifies the type of the resulting
labeled feature array.
If `output` is an array-like object, then `output` will be updated
with the labeled features from this function. This function can
operate in-place, by passing output=input.
Note that the output must be able to store the largest label, or this
function will raise an Exception.
Returns
-------
label : ndarray or int
An integer ndarray where each unique feature in `input` has a unique
label in the returned array.
num_features : int
How many objects were found.
If `output` is None, this function returns a tuple of
(`labeled_array`, `num_features`).
If `output` is a ndarray, then it will be updated with values in
`labeled_array` and only `num_features` will be returned by this
function.
See Also
--------
find_objects : generate a list of slices for the labeled features (or
objects); useful for finding features' position or
dimensions
Notes
-----
A centrosymmetric matrix is a matrix that is symmetric about the center.
See [1]_ for more information.
The `structure` matrix must be centrosymmetric to ensure
two-way connections.
For instance, if the `structure` matrix is not centrosymmetric
and is defined as::
[[0,1,0],
[1,1,0],
[0,0,0]]
and the `input` is::
[[1,2],
[0,3]]
then the structure matrix would indicate the
entry 2 in the input is connected to 1,
but 1 is not connected to 2.
Examples
--------
Create an image with some features, then label it using the default
(cross-shaped) structuring element:
>>> from scipy.ndimage import label, generate_binary_structure
>>> a = np.array([[0,0,1,1,0,0],
... [0,0,0,1,0,0],
... [1,1,0,0,1,0],
... [0,0,0,1,0,0]])
>>> labeled_array, num_features = label(a)
Each of the 4 features are labeled with a different integer:
>>> num_features
4
>>> labeled_array
array([[0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 0],
[2, 2, 0, 0, 3, 0],
[0, 0, 0, 4, 0, 0]])
Generate a structuring element that will consider features connected even
if they touch diagonally:
>>> s = generate_binary_structure(2,2)
or,
>>> s = [[1,1,1],
... [1,1,1],
... [1,1,1]]
Label the image using the new structuring element:
>>> labeled_array, num_features = label(a, structure=s)
Show the 2 labeled features (note that features 1, 3, and 4 from above are
now considered a single feature):
>>> num_features
2
>>> labeled_array
array([[0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 0],
[2, 2, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0]])
References
----------
.. [1] James R. Weaver, "Centrosymmetric (cross-symmetric)
matrices, their basic properties, eigenvalues, and
eigenvectors." The American Mathematical Monthly 92.10
(1985): 711-717.
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
if structure is None:
structure = morphology.generate_binary_structure(input.ndim, 1)
structure = numpy.asarray(structure, dtype=bool)
if structure.ndim != input.ndim:
raise RuntimeError('structure and input must have equal rank')
for ii in structure.shape:
if ii != 3:
raise ValueError('structure dimensions must be equal to 3')
# Use 32 bits if it's large enough for this image.
# _ni_label.label() needs two entries for background and
# foreground tracking
need_64bits = input.size >= (2**31 - 2)
if isinstance(output, numpy.ndarray):
if output.shape != input.shape:
raise ValueError("output shape not correct")
caller_provided_output = True
else:
caller_provided_output = False
if output is None:
output = np.empty(input.shape, np.intp if need_64bits else np.int32)
else:
output = np.empty(input.shape, output)
# handle scalars, 0-D arrays
if input.ndim == 0 or input.size == 0:
if input.ndim == 0:
# scalar
maxlabel = 1 if (input != 0) else 0
output[...] = maxlabel
else:
# 0-D
maxlabel = 0
if caller_provided_output:
return maxlabel
else:
return output, maxlabel
try:
max_label = _ni_label._label(input, structure, output)
except _ni_label.NeedMoreBits:
# Make another attempt with enough bits, then try to cast to the
# new type.
tmp_output = np.empty(input.shape, np.intp if need_64bits else np.int32)
max_label = _ni_label._label(input, structure, tmp_output)
output[...] = tmp_output[...]
if not np.all(output == tmp_output):
# refuse to return bad results
raise RuntimeError("insufficient bit-depth in requested output type")
if caller_provided_output:
# result was written in-place
return max_label
else:
return output, max_label
def find_objects(input, max_label=0):
"""
Find objects in a labeled array.
Parameters
----------
input : ndarray of ints
Array containing objects defined by different labels. Labels with
value 0 are ignored.
max_label : int, optional
Maximum label to be searched for in `input`. If max_label is not
given, the positions of all objects are returned.
Returns
-------
object_slices : list of tuples
A list of tuples, with each tuple containing N slices (with N the
dimension of the input array). Slices correspond to the minimal
parallelepiped that contains the object. If a number is missing,
None is returned instead of a slice.
See Also
--------
label, center_of_mass
Notes
-----
This function is very useful for isolating a volume of interest inside
a 3-D array, that cannot be "seen through".
Examples
--------
>>> from scipy import ndimage
>>> a = np.zeros((6,6), dtype=int)
>>> a[2:4, 2:4] = 1
>>> a[4, 4] = 1
>>> a[:2, :3] = 2
>>> a[0, 5] = 3
>>> a
array([[2, 2, 2, 0, 0, 3],
[2, 2, 2, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0]])
>>> ndimage.find_objects(a)
[(slice(2, 5, None), slice(2, 5, None)), (slice(0, 2, None), slice(0, 3, None)), (slice(0, 1, None), slice(5, 6, None))]
>>> ndimage.find_objects(a, max_label=2)
[(slice(2, 5, None), slice(2, 5, None)), (slice(0, 2, None), slice(0, 3, None))]
>>> ndimage.find_objects(a == 1, max_label=2)
[(slice(2, 5, None), slice(2, 5, None)), None]
>>> loc = ndimage.find_objects(a)[0]
>>> a[loc]
array([[1, 1, 0],
[1, 1, 0],
[0, 0, 1]])
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
if max_label < 1:
max_label = input.max()
return _nd_image.find_objects(input, max_label)
def labeled_comprehension(input, labels, index, func, out_dtype, default, pass_positions=False):
"""
Roughly equivalent to [func(input[labels == i]) for i in index].
Sequentially applies an arbitrary function (that works on array_like input)
to subsets of an N-D image array specified by `labels` and `index`.
The option exists to provide the function with positional parameters as the
second argument.
Parameters
----------
input : array_like
Data from which to select `labels` to process.
labels : array_like or None
Labels to objects in `input`.
If not None, array must be same shape as `input`.
If None, `func` is applied to raveled `input`.
index : int, sequence of ints or None
Subset of `labels` to which to apply `func`.
If a scalar, a single value is returned.
If None, `func` is applied to all non-zero values of `labels`.
func : callable
Python function to apply to `labels` from `input`.
out_dtype : dtype
Dtype to use for `result`.
default : int, float or None
Default return value when a element of `index` does not exist
in `labels`.
pass_positions : bool, optional
If True, pass linear indices to `func` as a second argument.
Default is False.
Returns
-------
result : ndarray
Result of applying `func` to each of `labels` to `input` in `index`.
Examples
--------
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> from scipy import ndimage
>>> lbl, nlbl = ndimage.label(a)
>>> lbls = np.arange(1, nlbl+1)
>>> ndimage.labeled_comprehension(a, lbl, lbls, np.mean, float, 0)
array([ 2.75, 5.5 , 6. ])
Falling back to `default`:
>>> lbls = np.arange(1, nlbl+2)
>>> ndimage.labeled_comprehension(a, lbl, lbls, np.mean, float, -1)
array([ 2.75, 5.5 , 6. , -1. ])
Passing positions:
>>> def fn(val, pos):
... print("fn says: %s : %s" % (val, pos))
... return (val.sum()) if (pos.sum() % 2 == 0) else (-val.sum())
...
>>> ndimage.labeled_comprehension(a, lbl, lbls, fn, float, 0, True)
fn says: [1 2 5 3] : [0 1 4 5]
fn says: [4 7] : [ 7 11]
fn says: [9 3] : [12 13]
array([ 11., 11., -12., 0.])
"""
as_scalar = numpy.isscalar(index)
input = numpy.asarray(input)
if pass_positions:
positions = numpy.arange(input.size).reshape(input.shape)
if labels is None:
if index is not None:
raise ValueError("index without defined labels")
if not pass_positions:
return func(input.ravel())
else:
return func(input.ravel(), positions.ravel())
try:
input, labels = numpy.broadcast_arrays(input, labels)
except ValueError:
raise ValueError("input and labels must have the same shape "
"(excepting dimensions with width 1)")
if index is None:
if not pass_positions:
return func(input[labels > 0])
else:
return func(input[labels > 0], positions[labels > 0])
index = numpy.atleast_1d(index)
if np.any(index.astype(labels.dtype).astype(index.dtype) != index):
raise ValueError("Cannot convert index values from <%s> to <%s> "
"(labels' type) without loss of precision" %
(index.dtype, labels.dtype))
index = index.astype(labels.dtype)
# optimization: find min/max in index, and select those parts of labels, input, and positions
lo = index.min()
hi = index.max()
mask = (labels >= lo) & (labels <= hi)
# this also ravels the arrays
labels = labels[mask]
input = input[mask]
if pass_positions:
positions = positions[mask]
# sort everything by labels
label_order = labels.argsort()
labels = labels[label_order]
input = input[label_order]
if pass_positions:
positions = positions[label_order]
index_order = index.argsort()
sorted_index = index[index_order]
def do_map(inputs, output):
"""labels must be sorted"""
nidx = sorted_index.size
# Find boundaries for each stretch of constant labels
# This could be faster, but we already paid N log N to sort labels.
lo = numpy.searchsorted(labels, sorted_index, side='left')
hi = numpy.searchsorted(labels, sorted_index, side='right')
for i, l, h in zip(range(nidx), lo, hi):
if l == h:
continue
output[i] = func(*[inp[l:h] for inp in inputs])
temp = numpy.empty(index.shape, out_dtype)
temp[:] = default
if not pass_positions:
do_map([input], temp)
else:
do_map([input, positions], temp)
output = numpy.zeros(index.shape, out_dtype)
output[index_order] = temp
if as_scalar:
output = output[0]
return output
def _safely_castable_to_int(dt):
"""Test whether the NumPy data type `dt` can be safely cast to an int."""
int_size = np.dtype(int).itemsize
safe = ((np.issubdtype(dt, np.signedinteger) and dt.itemsize <= int_size) or
(np.issubdtype(dt, np.unsignedinteger) and dt.itemsize < int_size))
return safe
def _stats(input, labels=None, index=None, centered=False):
"""Count, sum, and optionally compute (sum - centre)^2 of input by label
Parameters
----------
input : array_like, N-D
The input data to be analyzed.
labels : array_like (N-D), optional
The labels of the data in `input`. This array must be broadcast
compatible with `input`; typically, it is the same shape as `input`.
If `labels` is None, all nonzero values in `input` are treated as
the single labeled group.
index : label or sequence of labels, optional
These are the labels of the groups for which the stats are computed.
If `index` is None, the stats are computed for the single group where
`labels` is greater than 0.
centered : bool, optional
If True, the centered sum of squares for each labeled group is
also returned. Default is False.
Returns
-------
counts : int or ndarray of ints
The number of elements in each labeled group.
sums : scalar or ndarray of scalars
The sums of the values in each labeled group.
sums_c : scalar or ndarray of scalars, optional
The sums of mean-centered squares of the values in each labeled group.
This is only returned if `centered` is True.
"""
def single_group(vals):
if centered:
vals_c = vals - vals.mean()
return vals.size, vals.sum(), (vals_c * vals_c.conjugate()).sum()
else:
return vals.size, vals.sum()
if labels is None:
return single_group(input)
# ensure input and labels match sizes
input, labels = numpy.broadcast_arrays(input, labels)
if index is None:
return single_group(input[labels > 0])
if numpy.isscalar(index):
return single_group(input[labels == index])
def _sum_centered(labels):
# `labels` is expected to be an ndarray with the same shape as `input`.
# It must contain the label indices (which are not necessarily the labels
# themselves).
means = sums / counts
centered_input = input - means[labels]
# bincount expects 1-D inputs, so we ravel the arguments.
bc = numpy.bincount(labels.ravel(),
weights=(centered_input *
centered_input.conjugate()).ravel())
return bc
# Remap labels to unique integers if necessary, or if the largest
# label is larger than the number of values.
if (not _safely_castable_to_int(labels.dtype) or
labels.min() < 0 or labels.max() > labels.size):
# Use numpy.unique to generate the label indices. `new_labels` will
# be 1-D, but it should be interpreted as the flattened N-D array of
# label indices.
unique_labels, new_labels = numpy.unique(labels, return_inverse=True)
counts = numpy.bincount(new_labels)
sums = numpy.bincount(new_labels, weights=input.ravel())
if centered:
# Compute the sum of the mean-centered squares.
# We must reshape new_labels to the N-D shape of `input` before
# passing it _sum_centered.
sums_c = _sum_centered(new_labels.reshape(labels.shape))
idxs = numpy.searchsorted(unique_labels, index)
# make all of idxs valid
idxs[idxs >= unique_labels.size] = 0
found = (unique_labels[idxs] == index)
else:
# labels are an integer type allowed by bincount, and there aren't too
# many, so call bincount directly.
counts = numpy.bincount(labels.ravel())
sums = numpy.bincount(labels.ravel(), weights=input.ravel())
if centered:
sums_c = _sum_centered(labels)
# make sure all index values are valid
idxs = numpy.asanyarray(index, numpy.int_).copy()
found = (idxs >= 0) & (idxs < counts.size)
idxs[~found] = 0
counts = counts[idxs]
counts[~found] = 0
sums = sums[idxs]
sums[~found] = 0
if not centered:
return (counts, sums)
else:
sums_c = sums_c[idxs]
sums_c[~found] = 0
return (counts, sums, sums_c)
def sum(input, labels=None, index=None):
"""
Calculate the sum of the values of the array.
Parameters
----------
input : array_like
Values of `input` inside the regions defined by `labels`
are summed together.
labels : array_like of ints, optional
Assign labels to the values of the array. Has to have the same shape as
`input`.
index : array_like, optional
A single label number or a sequence of label numbers of
the objects to be measured.
Returns
-------
sum : ndarray or scalar
An array of the sums of values of `input` inside the regions defined
by `labels` with the same shape as `index`. If 'index' is None or scalar,
a scalar is returned.
See also
--------
mean, median
Examples
--------
>>> from scipy import ndimage
>>> input = [0,1,2,3]
>>> labels = [1,1,2,2]
>>> ndimage.sum(input, labels, index=[1,2])
[1.0, 5.0]
>>> ndimage.sum(input, labels, index=1)
1
>>> ndimage.sum(input, labels)
6
"""
count, sum = _stats(input, labels, index)
return sum
def mean(input, labels=None, index=None):
"""
Calculate the mean of the values of an array at labels.
Parameters
----------
input : array_like
Array on which to compute the mean of elements over distinct
regions.
labels : array_like, optional
Array of labels of same shape, or broadcastable to the same shape as
`input`. All elements sharing the same label form one region over
which the mean of the elements is computed.
index : int or sequence of ints, optional
Labels of the objects over which the mean is to be computed.
Default is None, in which case the mean for all values where label is
greater than 0 is calculated.
Returns
-------
out : list
Sequence of same length as `index`, with the mean of the different
regions labeled by the labels in `index`.
See also
--------
variance, standard_deviation, minimum, maximum, sum, label
Examples
--------
>>> from scipy import ndimage
>>> a = np.arange(25).reshape((5,5))
>>> labels = np.zeros_like(a)
>>> labels[3:5,3:5] = 1
>>> index = np.unique(labels)
>>> labels
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 1],
[0, 0, 0, 1, 1]])
>>> index
array([0, 1])
>>> ndimage.mean(a, labels=labels, index=index)
[10.285714285714286, 21.0]
"""
count, sum = _stats(input, labels, index)
return sum / numpy.asanyarray(count).astype(numpy.float64)
def variance(input, labels=None, index=None):
"""
Calculate the variance of the values of an N-D image array, optionally at
specified sub-regions.
Parameters
----------
input : array_like
Nd-image data to process.
labels : array_like, optional
Labels defining sub-regions in `input`.
If not None, must be same shape as `input`.
index : int or sequence of ints, optional
`labels` to include in output. If None (default), all values where
`labels` is non-zero are used.
Returns
-------
variance : float or ndarray
Values of variance, for each sub-region if `labels` and `index` are
specified.
See Also
--------
label, standard_deviation, maximum, minimum, extrema
Examples
--------
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> from scipy import ndimage
>>> ndimage.variance(a)
7.609375
Features to process can be specified using `labels` and `index`:
>>> lbl, nlbl = ndimage.label(a)
>>> ndimage.variance(a, lbl, index=np.arange(1, nlbl+1))
array([ 2.1875, 2.25 , 9. ])
If no index is given, all non-zero `labels` are processed:
>>> ndimage.variance(a, lbl)
6.1875
"""
count, sum, sum_c_sq = _stats(input, labels, index, centered=True)
return sum_c_sq / np.asanyarray(count).astype(float)
def standard_deviation(input, labels=None, index=None):
"""
Calculate the standard deviation of the values of an N-D image array,
optionally at specified sub-regions.
Parameters
----------
input : array_like
N-D image data to process.
labels : array_like, optional
Labels to identify sub-regions in `input`.
If not None, must be same shape as `input`.
index : int or sequence of ints, optional
`labels` to include in output. If None (default), all values where
`labels` is non-zero are used.
Returns
-------
standard_deviation : float or ndarray
Values of standard deviation, for each sub-region if `labels` and
`index` are specified.
See Also
--------
label, variance, maximum, minimum, extrema
Examples
--------
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> from scipy import ndimage
>>> ndimage.standard_deviation(a)
2.7585095613392387
Features to process can be specified using `labels` and `index`:
>>> lbl, nlbl = ndimage.label(a)
>>> ndimage.standard_deviation(a, lbl, index=np.arange(1, nlbl+1))
array([ 1.479, 1.5 , 3. ])
If no index is given, non-zero `labels` are processed:
>>> ndimage.standard_deviation(a, lbl)
2.4874685927665499
"""
return numpy.sqrt(variance(input, labels, index))
def _select(input, labels=None, index=None, find_min=False, find_max=False,
find_min_positions=False, find_max_positions=False,
find_median=False):
"""Returns min, max, or both, plus their positions (if requested), and
median."""
input = numpy.asanyarray(input)
find_positions = find_min_positions or find_max_positions
positions = None
if find_positions:
positions = numpy.arange(input.size).reshape(input.shape)
def single_group(vals, positions):
result = []
if find_min:
result += [vals.min()]
if find_min_positions:
result += [positions[vals == vals.min()][0]]
if find_max:
result += [vals.max()]
if find_max_positions:
result += [positions[vals == vals.max()][0]]
if find_median:
result += [numpy.median(vals)]
return result
if labels is None:
return single_group(input, positions)
# ensure input and labels match sizes
input, labels = numpy.broadcast_arrays(input, labels)
if index is None:
mask = (labels > 0)
masked_positions = None
if find_positions:
masked_positions = positions[mask]
return single_group(input[mask], masked_positions)
if numpy.isscalar(index):
mask = (labels == index)
masked_positions = None
if find_positions:
masked_positions = positions[mask]
return single_group(input[mask], masked_positions)
# remap labels to unique integers if necessary, or if the largest
# label is larger than the number of values.
if (not _safely_castable_to_int(labels.dtype) or
labels.min() < 0 or labels.max() > labels.size):
# remap labels, and indexes
unique_labels, labels = numpy.unique(labels, return_inverse=True)
idxs = numpy.searchsorted(unique_labels, index)
# make all of idxs valid
idxs[idxs >= unique_labels.size] = 0
found = (unique_labels[idxs] == index)
else:
# labels are an integer type, and there aren't too many
idxs = numpy.asanyarray(index, numpy.int_).copy()
found = (idxs >= 0) & (idxs <= labels.max())
idxs[~ found] = labels.max() + 1
if find_median:
order = numpy.lexsort((input.ravel(), labels.ravel()))
else:
order = input.ravel().argsort()
input = input.ravel()[order]
labels = labels.ravel()[order]
if find_positions:
positions = positions.ravel()[order]
result = []
if find_min:
mins = numpy.zeros(labels.max() + 2, input.dtype)
mins[labels[::-1]] = input[::-1]
result += [mins[idxs]]
if find_min_positions:
minpos = numpy.zeros(labels.max() + 2, int)
minpos[labels[::-1]] = positions[::-1]
result += [minpos[idxs]]
if find_max:
maxs = numpy.zeros(labels.max() + 2, input.dtype)
maxs[labels] = input
result += [maxs[idxs]]
if find_max_positions:
maxpos = numpy.zeros(labels.max() + 2, int)
maxpos[labels] = positions
result += [maxpos[idxs]]
if find_median:
locs = numpy.arange(len(labels))
lo = numpy.zeros(labels.max() + 2, numpy.int_)
lo[labels[::-1]] = locs[::-1]
hi = numpy.zeros(labels.max() + 2, numpy.int_)
hi[labels] = locs
lo = lo[idxs]
hi = hi[idxs]
# lo is an index to the lowest value in input for each label,
# hi is an index to the largest value.
# move them to be either the same ((hi - lo) % 2 == 0) or next
# to each other ((hi - lo) % 2 == 1), then average.
step = (hi - lo) // 2
lo += step
hi -= step
result += [(input[lo] + input[hi]) / 2.0]
return result
def minimum(input, labels=None, index=None):
"""
Calculate the minimum of the values of an array over labeled regions.
Parameters
----------
input : array_like
Array_like of values. For each region specified by `labels`, the
minimal values of `input` over the region is computed.
labels : array_like, optional
An array_like of integers marking different regions over which the
minimum value of `input` is to be computed. `labels` must have the
same shape as `input`. If `labels` is not specified, the minimum
over the whole array is returned.
index : array_like, optional
A list of region labels that are taken into account for computing the
minima. If index is None, the minimum over all elements where `labels`
is non-zero is returned.
Returns
-------
minimum : float or list of floats
List of minima of `input` over the regions determined by `labels` and
whose index is in `index`. If `index` or `labels` are not specified, a
float is returned: the minimal value of `input` if `labels` is None,
and the minimal value of elements where `labels` is greater than zero
if `index` is None.
See also
--------
label, maximum, median, minimum_position, extrema, sum, mean, variance,
standard_deviation
Notes
-----
The function returns a Python list and not a NumPy array, use
`np.array` to convert the list to an array.
Examples
--------
>>> from scipy import ndimage
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> labels, labels_nb = ndimage.label(a)
>>> labels
array([[1, 1, 0, 0],
[1, 1, 0, 2],
[0, 0, 0, 2],
[3, 3, 0, 0]])
>>> ndimage.minimum(a, labels=labels, index=np.arange(1, labels_nb + 1))
[1.0, 4.0, 3.0]
>>> ndimage.minimum(a)
0.0
>>> ndimage.minimum(a, labels=labels)
1.0
"""
return _select(input, labels, index, find_min=True)[0]
def maximum(input, labels=None, index=None):
"""
Calculate the maximum of the values of an array over labeled regions.
Parameters
----------
input : array_like
Array_like of values. For each region specified by `labels`, the
maximal values of `input` over the region is computed.
labels : array_like, optional
An array of integers marking different regions over which the
maximum value of `input` is to be computed. `labels` must have the
same shape as `input`. If `labels` is not specified, the maximum
over the whole array is returned.
index : array_like, optional
A list of region labels that are taken into account for computing the
maxima. If index is None, the maximum over all elements where `labels`
is non-zero is returned.
Returns
-------
output : float or list of floats
List of maxima of `input` over the regions determined by `labels` and
whose index is in `index`. If `index` or `labels` are not specified, a
float is returned: the maximal value of `input` if `labels` is None,
and the maximal value of elements where `labels` is greater than zero
if `index` is None.
See also
--------
label, minimum, median, maximum_position, extrema, sum, mean, variance,
standard_deviation
Notes
-----
The function returns a Python list and not a NumPy array, use
`np.array` to convert the list to an array.
Examples
--------
>>> a = np.arange(16).reshape((4,4))
>>> a
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> labels = np.zeros_like(a)
>>> labels[:2,:2] = 1
>>> labels[2:, 1:3] = 2
>>> labels
array([[1, 1, 0, 0],
[1, 1, 0, 0],
[0, 2, 2, 0],
[0, 2, 2, 0]])
>>> from scipy import ndimage
>>> ndimage.maximum(a)
15.0
>>> ndimage.maximum(a, labels=labels, index=[1,2])
[5.0, 14.0]
>>> ndimage.maximum(a, labels=labels)
14.0
>>> b = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> labels, labels_nb = ndimage.label(b)
>>> labels
array([[1, 1, 0, 0],
[1, 1, 0, 2],
[0, 0, 0, 2],
[3, 3, 0, 0]])
>>> ndimage.maximum(b, labels=labels, index=np.arange(1, labels_nb + 1))
[5.0, 7.0, 9.0]
"""
return _select(input, labels, index, find_max=True)[0]
def median(input, labels=None, index=None):
"""
Calculate the median of the values of an array over labeled regions.
Parameters
----------
input : array_like
Array_like of values. For each region specified by `labels`, the
median value of `input` over the region is computed.
labels : array_like, optional
An array_like of integers marking different regions over which the
median value of `input` is to be computed. `labels` must have the
same shape as `input`. If `labels` is not specified, the median
over the whole array is returned.
index : array_like, optional
A list of region labels that are taken into account for computing the
medians. If index is None, the median over all elements where `labels`
is non-zero is returned.
Returns
-------
median : float or list of floats
List of medians of `input` over the regions determined by `labels` and
whose index is in `index`. If `index` or `labels` are not specified, a
float is returned: the median value of `input` if `labels` is None,
and the median value of elements where `labels` is greater than zero
if `index` is None.
See also
--------
label, minimum, maximum, extrema, sum, mean, variance, standard_deviation
Notes
-----
The function returns a Python list and not a NumPy array, use
`np.array` to convert the list to an array.
Examples
--------
>>> from scipy import ndimage
>>> a = np.array([[1, 2, 0, 1],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> labels, labels_nb = ndimage.label(a)
>>> labels
array([[1, 1, 0, 2],
[1, 1, 0, 2],
[0, 0, 0, 2],
[3, 3, 0, 0]])
>>> ndimage.median(a, labels=labels, index=np.arange(1, labels_nb + 1))
[2.5, 4.0, 6.0]
>>> ndimage.median(a)
1.0
>>> ndimage.median(a, labels=labels)
3.0
"""
return _select(input, labels, index, find_median=True)[0]
def minimum_position(input, labels=None, index=None):
"""
Find the positions of the minimums of the values of an array at labels.
Parameters
----------
input : array_like
Array_like of values.
labels : array_like, optional
An array of integers marking different regions over which the
position of the minimum value of `input` is to be computed.
`labels` must have the same shape as `input`. If `labels` is not
specified, the location of the first minimum over the whole
array is returned.
The `labels` argument only works when `index` is specified.
index : array_like, optional
A list of region labels that are taken into account for finding the
location of the minima. If `index` is None, the ``first`` minimum
over all elements where `labels` is non-zero is returned.
The `index` argument only works when `labels` is specified.
Returns
-------
output : list of tuples of ints
Tuple of ints or list of tuples of ints that specify the location
of minima of `input` over the regions determined by `labels` and
whose index is in `index`.
If `index` or `labels` are not specified, a tuple of ints is
returned specifying the location of the first minimal value of `input`.
See also
--------
label, minimum, median, maximum_position, extrema, sum, mean, variance,
standard_deviation
Examples
--------
>>> a = np.array([[10, 20, 30],
... [40, 80, 100],
... [1, 100, 200]])
>>> b = np.array([[1, 2, 0, 1],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> from scipy import ndimage
>>> ndimage.minimum_position(a)
(2, 0)
>>> ndimage.minimum_position(b)
(0, 2)
Features to process can be specified using `labels` and `index`:
>>> label, pos = ndimage.label(a)
>>> ndimage.minimum_position(a, label, index=np.arange(1, pos+1))
[(2, 0)]
>>> label, pos = ndimage.label(b)
>>> ndimage.minimum_position(b, label, index=np.arange(1, pos+1))
[(0, 0), (0, 3), (3, 1)]
"""
dims = numpy.array(numpy.asarray(input).shape)
# see numpy.unravel_index to understand this line.
dim_prod = numpy.cumprod([1] + list(dims[:0:-1]))[::-1]
result = _select(input, labels, index, find_min_positions=True)[0]
if numpy.isscalar(result):
return tuple((result // dim_prod) % dims)
return [tuple(v) for v in (result.reshape(-1, 1) // dim_prod) % dims]
def maximum_position(input, labels=None, index=None):
"""
Find the positions of the maximums of the values of an array at labels.
For each region specified by `labels`, the position of the maximum
value of `input` within the region is returned.
Parameters
----------
input : array_like
Array_like of values.
labels : array_like, optional
An array of integers marking different regions over which the
position of the maximum value of `input` is to be computed.
`labels` must have the same shape as `input`. If `labels` is not
specified, the location of the first maximum over the whole
array is returned.
The `labels` argument only works when `index` is specified.
index : array_like, optional
A list of region labels that are taken into account for finding the
location of the maxima. If `index` is None, the first maximum
over all elements where `labels` is non-zero is returned.
The `index` argument only works when `labels` is specified.
Returns
-------
output : list of tuples of ints
List of tuples of ints that specify the location of maxima of
`input` over the regions determined by `labels` and whose index
is in `index`.
If `index` or `labels` are not specified, a tuple of ints is
returned specifying the location of the ``first`` maximal value
of `input`.
See also
--------
label, minimum, median, maximum_position, extrema, sum, mean, variance,
standard_deviation
Examples
--------
>>> from scipy import ndimage
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> ndimage.maximum_position(a)
(3, 0)
Features to process can be specified using `labels` and `index`:
>>> lbl = np.array([[0, 1, 2, 3],
... [0, 1, 2, 3],
... [0, 1, 2, 3],
... [0, 1, 2, 3]])
>>> ndimage.maximum_position(a, lbl, 1)
(1, 1)
If no index is given, non-zero `labels` are processed:
>>> ndimage.maximum_position(a, lbl)
(2, 3)
If there are no maxima, the position of the first element is returned:
>>> ndimage.maximum_position(a, lbl, 2)
(0, 2)
"""
dims = numpy.array(numpy.asarray(input).shape)
# see numpy.unravel_index to understand this line.
dim_prod = numpy.cumprod([1] + list(dims[:0:-1]))[::-1]
result = _select(input, labels, index, find_max_positions=True)[0]
if numpy.isscalar(result):
return tuple((result // dim_prod) % dims)
return [tuple(v) for v in (result.reshape(-1, 1) // dim_prod) % dims]
def extrema(input, labels=None, index=None):
"""
Calculate the minimums and maximums of the values of an array
at labels, along with their positions.
Parameters
----------
input : ndarray
N-D image data to process.
labels : ndarray, optional
Labels of features in input.
If not None, must be same shape as `input`.
index : int or sequence of ints, optional
Labels to include in output. If None (default), all values where
non-zero `labels` are used.
Returns
-------
minimums, maximums : int or ndarray
Values of minimums and maximums in each feature.
min_positions, max_positions : tuple or list of tuples
Each tuple gives the N-D coordinates of the corresponding minimum
or maximum.
See Also
--------
maximum, minimum, maximum_position, minimum_position, center_of_mass
Examples
--------
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> from scipy import ndimage
>>> ndimage.extrema(a)
(0, 9, (0, 2), (3, 0))
Features to process can be specified using `labels` and `index`:
>>> lbl, nlbl = ndimage.label(a)
>>> ndimage.extrema(a, lbl, index=np.arange(1, nlbl+1))
(array([1, 4, 3]),
array([5, 7, 9]),
[(0, 0), (1, 3), (3, 1)],
[(1, 0), (2, 3), (3, 0)])
If no index is given, non-zero `labels` are processed:
>>> ndimage.extrema(a, lbl)
(1, 9, (0, 0), (3, 0))
"""
dims = numpy.array(numpy.asarray(input).shape)
# see numpy.unravel_index to understand this line.
dim_prod = numpy.cumprod([1] + list(dims[:0:-1]))[::-1]
minimums, min_positions, maximums, max_positions = _select(input, labels,
index,
find_min=True,
find_max=True,
find_min_positions=True,
find_max_positions=True)
if numpy.isscalar(minimums):
return (minimums, maximums, tuple((min_positions // dim_prod) % dims),
tuple((max_positions // dim_prod) % dims))
min_positions = [tuple(v) for v in (min_positions.reshape(-1, 1) // dim_prod) % dims]
max_positions = [tuple(v) for v in (max_positions.reshape(-1, 1) // dim_prod) % dims]
return minimums, maximums, min_positions, max_positions
def center_of_mass(input, labels=None, index=None):
"""
Calculate the center of mass of the values of an array at labels.
Parameters
----------
input : ndarray
Data from which to calculate center-of-mass. The masses can either
be positive or negative.
labels : ndarray, optional
Labels for objects in `input`, as generated by `ndimage.label`.
Only used with `index`. Dimensions must be the same as `input`.
index : int or sequence of ints, optional
Labels for which to calculate centers-of-mass. If not specified,
all labels greater than zero are used. Only used with `labels`.
Returns
-------
center_of_mass : tuple, or list of tuples
Coordinates of centers-of-mass.
Examples
--------
>>> a = np.array(([0,0,0,0],
... [0,1,1,0],
... [0,1,1,0],
... [0,1,1,0]))
>>> from scipy import ndimage
>>> ndimage.measurements.center_of_mass(a)
(2.0, 1.5)
Calculation of multiple objects in an image
>>> b = np.array(([0,1,1,0],
... [0,1,0,0],
... [0,0,0,0],
... [0,0,1,1],
... [0,0,1,1]))
>>> lbl = ndimage.label(b)[0]
>>> ndimage.measurements.center_of_mass(b, lbl, [1,2])
[(0.33333333333333331, 1.3333333333333333), (3.5, 2.5)]
Negative masses are also accepted, which can occur for example when
bias is removed from measured data due to random noise.
>>> c = np.array(([-1,0,0,0],
... [0,-1,-1,0],
... [0,1,-1,0],
... [0,1,1,0]))
>>> ndimage.measurements.center_of_mass(c)
(-4.0, 1.0)
If there are division by zero issues, the function does not raise an
error but rather issues a RuntimeWarning before returning inf and/or NaN.
>>> d = np.array([-1, 1])
>>> ndimage.measurements.center_of_mass(d)
(inf,)
"""
normalizer = sum(input, labels, index)
grids = numpy.ogrid[[slice(0, i) for i in input.shape]]
results = [sum(input * grids[dir].astype(float), labels, index) / normalizer
for dir in range(input.ndim)]
if numpy.isscalar(results[0]):
return tuple(results)
return [tuple(v) for v in numpy.array(results).T]
def histogram(input, min, max, bins, labels=None, index=None):
"""
Calculate the histogram of the values of an array, optionally at labels.
Histogram calculates the frequency of values in an array within bins
determined by `min`, `max`, and `bins`. The `labels` and `index`
keywords can limit the scope of the histogram to specified sub-regions
within the array.
Parameters
----------
input : array_like
Data for which to calculate histogram.
min, max : int
Minimum and maximum values of range of histogram bins.
bins : int
Number of bins.
labels : array_like, optional
Labels for objects in `input`.
If not None, must be same shape as `input`.
index : int or sequence of ints, optional
Label or labels for which to calculate histogram. If None, all values
where label is greater than zero are used
Returns
-------
hist : ndarray
Histogram counts.
Examples
--------
>>> a = np.array([[ 0. , 0.2146, 0.5962, 0. ],
... [ 0. , 0.7778, 0. , 0. ],
... [ 0. , 0. , 0. , 0. ],
... [ 0. , 0. , 0.7181, 0.2787],
... [ 0. , 0. , 0.6573, 0.3094]])
>>> from scipy import ndimage
>>> ndimage.measurements.histogram(a, 0, 1, 10)
array([13, 0, 2, 1, 0, 1, 1, 2, 0, 0])
With labels and no indices, non-zero elements are counted:
>>> lbl, nlbl = ndimage.label(a)
>>> ndimage.measurements.histogram(a, 0, 1, 10, lbl)
array([0, 0, 2, 1, 0, 1, 1, 2, 0, 0])
Indices can be used to count only certain objects:
>>> ndimage.measurements.histogram(a, 0, 1, 10, lbl, 2)
array([0, 0, 1, 1, 0, 0, 1, 1, 0, 0])
"""
_bins = numpy.linspace(min, max, bins + 1)
def _hist(vals):
return numpy.histogram(vals, _bins)[0]
return labeled_comprehension(input, labels, index, _hist, object, None,
pass_positions=False)
def watershed_ift(input, markers, structure=None, output=None):
"""
Apply watershed from markers using image foresting transform algorithm.
Parameters
----------
input : array_like
Input.
markers : array_like
Markers are points within each watershed that form the beginning
of the process. Negative markers are considered background markers
which are processed after the other markers.
structure : structure element, optional
A structuring element defining the connectivity of the object can be
provided. If None, an element is generated with a squared
connectivity equal to one.
output : ndarray, optional
An output array can optionally be provided. The same shape as input.
Returns
-------
watershed_ift : ndarray
Output. Same shape as `input`.
References
----------
.. [1] A.X. Falcao, J. Stolfi and R. de Alencar Lotufo, "The image
foresting transform: theory, algorithms, and applications",
Pattern Analysis and Machine Intelligence, vol. 26, pp. 19-29, 2004.
"""
input = numpy.asarray(input)
if input.dtype.type not in [numpy.uint8, numpy.uint16]:
raise TypeError('only 8 and 16 unsigned inputs are supported')
if structure is None:
structure = morphology.generate_binary_structure(input.ndim, 1)
structure = numpy.asarray(structure, dtype=bool)
if structure.ndim != input.ndim:
raise RuntimeError('structure and input must have equal rank')
for ii in structure.shape:
if ii != 3:
raise RuntimeError('structure dimensions must be equal to 3')
if not structure.flags.contiguous:
structure = structure.copy()
markers = numpy.asarray(markers)
if input.shape != markers.shape:
raise RuntimeError('input and markers must have equal shape')
integral_types = [numpy.int0,
numpy.int8,
numpy.int16,
numpy.int32,
numpy.int_,
numpy.int64,
numpy.intc,
numpy.intp]
if markers.dtype.type not in integral_types:
raise RuntimeError('marker should be of integer type')
if isinstance(output, numpy.ndarray):
if output.dtype.type not in integral_types:
raise RuntimeError('output should be of integer type')
else:
output = markers.dtype
output = _ni_support._get_output(output, input)
_nd_image.watershed_ift(input, markers, structure, output)
return output
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