from functools import partial
from warnings import warn
import numpy as np
from menpo.transform import Translation
from .base import Image
def pwa_point_in_pointcloud(pcloud, indices, batch_size=None):
"""
Make sure that the decision of whether a point is inside or outside
the PointCloud is exactly the same as how PWA calculates triangle
containment. Then, we use the trick of setting the mask to all the
points that were NOT outside the triangulation. Otherwise, all points
were inside and we just return those as ``True``. In general, points
on the boundary are counted as inside the polygon.
Parameters
----------
pcloud : :map:`PointCloud`
The pointcloud to use for the containment test.
indices : (d, n_dims) `ndarray`
The list of pixel indices to test.
batch_size : `int` or ``None``, optional
See constrain_to_pointcloud for more information about the batch_size
parameter.
Returns
-------
mask : (d,) `bool ndarray`
Whether each pixel index was in inside the convex hull of the
pointcloud or not.
"""
from menpo.transform.piecewiseaffine import PiecewiseAffine
from menpo.transform.piecewiseaffine import TriangleContainmentError
try:
pwa = PiecewiseAffine(pcloud, pcloud)
pwa.apply(indices, batch_size=batch_size)
return np.ones(indices.shape[0], dtype=np.bool)
except TriangleContainmentError as e:
return ~e.points_outside_source_domain
def convex_hull_point_in_pointcloud(pcloud, indices):
"""
Uses the matplotlib ``contains_points`` method, which in turn uses:
"Crossings Multiply algorithm of InsideTest"
By Eric Haines, 3D/Eye Inc, erich@eye.com
http://erich.realtimerendering.com/ptinpoly/
This algorithm uses a per-pixel test and thus tends to produce smoother
edges. We also guarantee that all points inside PointCloud will be
included by calculating the **convex hull** of the pointcloud before
doing the point inside test.
Points on the boundary are counted as **outside** the polygon.
Parameters
----------
pcloud : :map:`PointCloud`
The pointcloud to use for the containment test.
indices : (d, n_dims) `ndarray`
The list of pixel indices to test.
Returns
-------
mask : (d,) `bool ndarray`
Whether each pixel index was in inside the convex hull of the
pointcloud or not.
"""
from scipy.spatial import ConvexHull
from matplotlib.path import Path
c_hull = ConvexHull(pcloud.points)
polygon = pcloud.points[c_hull.vertices, :]
return Path(polygon).contains_points(indices)
[docs]class BooleanImage(Image):
r"""
A mask image made from binary pixels. The region of the image that is
left exposed by the mask is referred to as the 'masked region'. The
set of 'masked' pixels is those pixels corresponding to a ``True`` value in
the mask.
Parameters
----------
mask_data : ``(M, N, ..., L)`` `ndarray`
The binary mask data. Note that there is no channel axis - a 2D Mask
Image is built from just a 2D numpy array of mask_data.
Automatically coerced in to boolean values.
copy: `bool`, optional
If ``False``, the image_data will not be copied on assignment. Note that
if the array you provide is not boolean, there **will still be copy**.
In general this should only be used if you know what you are doing.
"""
def __init__(self, mask_data, copy=True):
# Add a channel dimension. We do this little reshape trick to add
# the axis because this maintains C-contiguous'ness
mask_data = mask_data.reshape((1,) + mask_data.shape)
# If we are trying not to copy, but the data we have isn't boolean,
# then unfortunately, we forced to copy anyway!
if mask_data.dtype != np.bool:
mask_data = np.array(mask_data, dtype=np.bool, copy=True, order="C")
if not copy:
warn(
"The copy flag was NOT honoured. A copy HAS been made. "
"Please ensure the data you pass is C-contiguous."
)
super(BooleanImage, self).__init__(mask_data, copy=copy)
[docs] @classmethod
def init_blank(cls, shape, fill=True, round="ceil", **kwargs):
r"""
Returns a blank :map:`BooleanImage` of the requested shape
Parameters
----------
shape : `tuple` or `list`
The shape of the image. Any floating point values are rounded
according to the ``round`` kwarg.
fill : `bool`, optional
The mask value to be set everywhere.
round: ``{ceil, floor, round}``, optional
Rounding function to be applied to floating point shapes.
Returns
-------
blank_image : :map:`BooleanImage`
A blank mask of the requested size
"""
from .base import round_image_shape
shape = round_image_shape(shape, round)
if fill:
mask = np.ones(shape, dtype=np.bool)
else:
mask = np.zeros(shape, dtype=np.bool)
return cls(mask, copy=False)
[docs] @classmethod
def init_from_channels_at_back(cls, pixels):
r"""
This method is not required for ``BooleanImage`` types as boolean images
do not expect a channel axis for construction.
Parameters
----------
pixels : ``(M, N ..., Q)`` `ndarray`
Array representing the image pixels, with NO channel axis.
Returns
-------
image : :map:`BooleanImage`
A new image from the given boolean pixels.
"""
return cls(pixels)
[docs] @classmethod
def init_from_pointcloud(
cls, pointcloud, group=None, boundary=0, constrain=True, fill=True
):
r"""
Create an Image that is big enough to contain the given pointcloud.
The pointcloud will be translated to the origin and then translated
according to its bounds in order to fit inside the new image.
An optional boundary can be provided in order to increase the space
around the boundary of the pointcloud. The boundary will be added
to *all sides of the image* and so a boundary of 5 provides 10 pixels
of boundary total for each dimension.
By default, the mask will be constrained to the convex hull of the
provided pointcloud.
Parameters
----------
pointcloud : :map:`PointCloud`
Pointcloud to place inside the newly created image.
group : `str`, optional
If ``None``, the pointcloud will only be used to create the image.
If a `str` then the pointcloud will be attached as a landmark
group to the image, with the given string as key.
boundary : `float`
A optional padding distance that is added to the pointcloud bounds.
Default is ``0``, meaning the max/min of tightest possible
containing image is returned.
fill : `int`, optional
The value to fill all pixels with.
constrain : `bool`, optional
If ``True``, the ``True`` values will be image will be constrained
to the convex hull of the provided pointcloud. If ``False``,
the mask will be the value of ``fill``.
Returns
-------
image : :map:`MaskedImage`
A new image with the same size as the given pointcloud, optionally
with the pointcloud attached as landmarks and the mask constrained
to the convex hull of the pointcloud.
"""
# Translate pointcloud to the origin
minimum = pointcloud.bounds(boundary=boundary)[0]
origin_pc = Translation(-minimum).apply(pointcloud)
image_shape = origin_pc.range(boundary=boundary)
new_image = cls.init_blank(image_shape, fill=fill)
if constrain:
new_image = new_image.constrain_to_pointcloud(origin_pc)
if group is not None:
new_image.landmarks[group] = origin_pc
return new_image
[docs] def as_masked(self, mask=None, copy=True):
r"""
Impossible for a :map:`BooleanImage` to be transformed to a
:map:`MaskedImage`.
"""
raise NotImplementedError("as_masked cannot be invoked on a " "BooleanImage.")
@property
def mask(self):
r"""
Returns the pixels of the mask with no channel axis. This is what
should be used to mask any k-dimensional image.
:type: ``(M, N, ..., L)``, `bool ndarray`
"""
return self.pixels[0, ...]
[docs] def n_true(self):
r"""
The number of ``True`` values in the mask.
:type: `int`
"""
return np.sum(self.pixels)
[docs] def n_false(self):
r"""
The number of ``False`` values in the mask.
:type: `int`
"""
return self.n_pixels - self.n_true()
[docs] def all_true(self):
r"""
``True`` iff every element of the mask is ``True``.
:type: `bool`
"""
return np.all(self.pixels)
[docs] def proportion_true(self):
r"""
The proportion of the mask which is ``True``.
:type: `float`
"""
return (self.n_true() * 1.0) / self.n_pixels
[docs] def proportion_false(self):
r"""
The proportion of the mask which is ``False``
:type: `float`
"""
return (self.n_false() * 1.0) / self.n_pixels
[docs] def true_indices(self):
r"""
The indices of pixels that are ``True``.
:type: ``(n_dims, n_true)`` `ndarray`
"""
if self.all_true():
return self.indices()
else:
# Ignore the channel axis
return np.vstack(np.nonzero(self.pixels[0])).T
[docs] def false_indices(self):
r"""
The indices of pixels that are ``Flase``.
:type: ``(n_dims, n_false)`` `ndarray`
"""
# Ignore the channel axis
return np.vstack(np.nonzero(~self.pixels[0])).T
def __str__(self):
return "{} {}D mask, {:.1%} " "of which is True".format(
self._str_shape(), self.n_dims, self.proportion_true()
)
[docs] def from_vector(self, vector, copy=True):
r"""
Takes a flattened vector and returns a new :map:`BooleanImage` formed
by reshaping the vector to the correct dimensions. Note that this is
rebuilding a boolean image **itself** from boolean values. The mask
is in no way interpreted in performing the operation, in contrast to
:map:`MaskedImage`, where only the masked region is used in
:meth:`from_vector` and :meth`as_vector`. Any image landmarks are
transferred in the process.
Parameters
----------
vector : ``(n_pixels,)`` `bool ndarray`
A flattened vector of all the pixels of a :map:`BooleanImage`.
copy : `bool`, optional
If ``False``, no copy of the vector will be taken.
Returns
-------
image : :map:`BooleanImage`
New BooleanImage of same shape as this image
Raises
------
Warning
If ``copy=False`` cannot be honored.
"""
mask = BooleanImage(vector.reshape(self.shape), copy=copy)
if self.has_landmarks:
mask.landmarks = self.landmarks
if hasattr(self, "path"):
mask.path = self.path
return mask
[docs] def invert(self):
r"""
Returns a copy of this boolean image, which is inverted.
Returns
-------
inverted : :map:`BooleanImage`
A copy of this boolean mask, where all ``True`` values are ``False``
and all ``False`` values are ``True``.
"""
inverse = self.copy()
inverse.pixels = ~self.pixels
return inverse
[docs] def bounds_true(self, boundary=0, constrain_to_bounds=True):
r"""
Returns the minimum to maximum indices along all dimensions that the
mask includes which fully surround the ``True`` mask values. In the case
of a 2D Image for instance, the min and max define two corners of a
rectangle bounding the True pixel values.
Parameters
----------
boundary : `int`, optional
A number of pixels that should be added to the extent. A
negative value can be used to shrink the bounds in.
constrain_to_bounds: `bool`, optional
If ``True``, the bounding extent is snapped to not go beyond
the edge of the image. If ``False``, the bounds are left unchanged.
Returns
--------
min_b : ``(D,)`` `ndarray`
The minimum extent of the ``True`` mask region with the boundary
along each dimension. If ``constrain_to_bounds=True``,
is clipped to legal image bounds.
max_b : ``(D,)`` `ndarray`
The maximum extent of the ``True`` mask region with the boundary
along each dimension. If ``constrain_to_bounds=True``,
is clipped to legal image bounds.
"""
mpi = self.true_indices()
maxes = np.max(mpi, axis=0) + boundary
mins = np.min(mpi, axis=0) - boundary
if constrain_to_bounds:
maxes = self.constrain_points_to_bounds(maxes)
mins = self.constrain_points_to_bounds(mins)
return mins, maxes
[docs] def bounds_false(self, boundary=0, constrain_to_bounds=True):
r"""
Returns the minimum to maximum indices along all dimensions that the
mask includes which fully surround the False mask values. In the case
of a 2D Image for instance, the min and max define two corners of a
rectangle bounding the False pixel values.
Parameters
----------
boundary : `int` >= 0, optional
A number of pixels that should be added to the extent. A
negative value can be used to shrink the bounds in.
constrain_to_bounds: `bool`, optional
If ``True``, the bounding extent is snapped to not go beyond
the edge of the image. If ``False``, the bounds are left unchanged.
Returns
-------
min_b : ``(D,)`` `ndarray`
The minimum extent of the ``True`` mask region with the boundary
along each dimension. If ``constrain_to_bounds=True``,
is clipped to legal image bounds.
max_b : ``(D,)`` `ndarray`
The maximum extent of the ``True`` mask region with the boundary
along each dimension. If ``constrain_to_bounds=True``,
is clipped to legal image bounds.
"""
return self.invert().bounds_true(
boundary=boundary, constrain_to_bounds=constrain_to_bounds
)
# noinspection PyMethodOverriding
[docs] def sample(self, points_to_sample, mode="constant", cval=False, **kwargs):
r"""
Sample this image at the given sub-pixel accurate points. The input
PointCloud should have the same number of dimensions as the image e.g.
a 2D PointCloud for a 2D multi-channel image. A numpy array will be
returned the has the values for every given point across each channel
of the image.
Parameters
----------
points_to_sample : :map:`PointCloud`
Array of points to sample from the image. Should be
`(n_points, n_dims)`
mode : ``{constant, nearest, reflect, wrap}``, optional
Points outside the boundaries of the input are filled according
to the given mode.
cval : `float`, optional
Used in conjunction with mode ``constant``, the value outside
the image boundaries.
Returns
-------
sampled_pixels : (`n_points`, `n_channels`) `bool ndarray`
The interpolated values taken across every channel of the image.
"""
# enforce the order as 0, as this is boolean data, then call super
return Image.sample(self, points_to_sample, order=0, mode=mode, cval=cval)
# noinspection PyMethodOverriding
[docs] def warp_to_mask(
self,
template_mask,
transform,
warp_landmarks=True,
mode="constant",
cval=False,
batch_size=None,
return_transform=False,
):
r"""
Return a copy of this :map:`BooleanImage` warped into a different
reference space.
Note that warping into a mask is slower than warping into a full image.
If you don't need a non-linear mask, consider warp_to_shape instead.
Parameters
----------
template_mask : :map:`BooleanImage`
Defines the shape of the result, and what pixels should be
sampled.
transform : :map:`Transform`
Transform **from the template space back to this image**.
Defines, for each pixel location on the template, which pixel
location should be sampled from on this image.
warp_landmarks : `bool`, optional
If ``True``, result will have the same landmark dictionary
as self, but with each landmark updated to the warped position.
mode : ``{constant, nearest, reflect or wrap}``, optional
Points outside the boundaries of the input are filled according
to the given mode.
cval : `float`, optional
Used in conjunction with mode ``constant``, the value outside
the image boundaries.
batch_size : `int` or ``None``, optional
This should only be considered for large images. Setting this
value can cause warping to become much slower, particular for
cached warps such as Piecewise Affine. This size indicates
how many points in the image should be warped at a time, which
keeps memory usage low. If ``None``, no batching is used and all
points are warped at once.
return_transform : `bool`, optional
This argument is for internal use only. If ``True``, then the
:map:`Transform` object is also returned.
Returns
-------
warped_image : :map:`BooleanImage`
A copy of this image, warped.
transform : :map:`Transform`
The transform that was used. It only applies if
`return_transform` is ``True``.
"""
# enforce the order as 0, as this is boolean data, then call super
return Image.warp_to_mask(
self,
template_mask,
transform,
warp_landmarks=warp_landmarks,
order=0,
mode=mode,
cval=cval,
batch_size=batch_size,
return_transform=return_transform,
)
# noinspection PyMethodOverriding
[docs] def warp_to_shape(
self,
template_shape,
transform,
warp_landmarks=True,
mode="constant",
cval=False,
order=None,
batch_size=None,
return_transform=False,
):
"""
Return a copy of this :map:`BooleanImage` warped into a different
reference space.
Note that the order keyword argument is in fact ignored, as any order
other than 0 makes no sense on a binary image. The keyword argument is
present only for compatibility with the :map:`Image` warp_to_shape API.
Parameters
----------
template_shape : ``(n_dims, )`` `tuple` or `ndarray`
Defines the shape of the result, and what pixel indices should be
sampled (all of them).
transform : :map:`Transform`
Transform **from the template_shape space back to this image**.
Defines, for each index on template_shape, which pixel location
should be sampled from on this image.
warp_landmarks : `bool`, optional
If ``True``, result will have the same landmark dictionary
as self, but with each landmark updated to the warped position.
mode : ``{constant, nearest, reflect or wrap}``, optional
Points outside the boundaries of the input are filled according
to the given mode.
cval : `float`, optional
Used in conjunction with mode ``constant``, the value outside
the image boundaries.
batch_size : `int` or ``None``, optional
This should only be considered for large images. Setting this
value can cause warping to become much slower, particular for
cached warps such as Piecewise Affine. This size indicates
how many points in the image should be warped at a time, which
keeps memory usage low. If ``None``, no batching is used and all
points are warped at once.
return_transform : `bool`, optional
This argument is for internal use only. If ``True``, then the
:map:`Transform` object is also returned.
Returns
-------
warped_image : :map:`BooleanImage`
A copy of this image, warped.
transform : :map:`Transform`
The transform that was used. It only applies if
`return_transform` is ``True``.
"""
# call the super variant and get ourselves an Image back
# note that we force the use of order=0 for BooleanImages.
warped = Image.warp_to_shape(
self,
template_shape,
transform,
warp_landmarks=warp_landmarks,
order=0,
mode=mode,
cval=cval,
batch_size=batch_size,
)
# unfortunately we can't escape copying here, let BooleanImage
# convert us to np.bool
boolean_image = BooleanImage(warped.pixels.reshape(template_shape))
if warped.has_landmarks:
boolean_image.landmarks = warped.landmarks
if hasattr(warped, "path"):
boolean_image.path = warped.path
# optionally return the transform
if return_transform:
return boolean_image, transform
else:
return boolean_image
def _build_warp_to_mask(self, template_mask, sampled_pixel_values, **kwargs):
r"""
Builds the warped image from the template mask and sampled pixel values.
"""
# start from a copy of the template_mask
warped_img = template_mask.copy()
if warped_img.all_true():
# great, just reshape the sampled_pixel_values
warped_img.pixels = sampled_pixel_values.reshape((1,) + warped_img.shape)
else:
# we have to fill out mask with the sampled mask..
warped_img.pixels[:, warped_img.mask] = sampled_pixel_values
return warped_img
[docs] def constrain_to_landmarks(self, group=None, batch_size=None):
r"""
Returns a copy of this image whereby the ``True`` values in the image
are restricted to be equal to the convex hull around the landmarks
chosen. This is not a per-pixel convex hull, but instead relies on a
triangulated approximation. If the landmarks in question are an instance
of :map:`TriMesh`, the triangulation of the landmarks will be used in
the convex hull calculation. If the landmarks are an instance of
:map:`PointCloud`, Delaunay triangulation will be used to create a
triangulation.
Parameters
----------
group : `str`, optional
The key of the landmark set that should be used. If ``None``,
and if there is only one set of landmarks, this set will be used.
batch_size : `int` or ``None``, optional
This should only be considered for large images. Setting this value
will cause constraining to become much slower. This size indicates
how many points in the image should be checked at a time, which
keeps memory usage low. If ``None``, no batching is used and all
points are checked at once.
Returns
-------
constrained : :map:`BooleanImage`
The new boolean image, constrained by the given landmark group.
"""
return self.constrain_to_pointcloud(
self.landmarks[group], batch_size=batch_size
)
[docs] def constrain_to_pointcloud(
self, pointcloud, batch_size=None, point_in_pointcloud="pwa"
):
r"""
Returns a copy of this image whereby the ``True`` values in the image
are restricted to be equal to the convex hull around a pointcloud. The
choice of whether a pixel is inside or outside of the pointcloud is
determined by the ``point_in_pointcloud`` parameter. By default a
Piecewise Affine transform is used to test for containment, which is
useful when aligning images by their landmarks. Triangluation will be
decided by Delauny - if you wish to customise it, a :map:`TriMesh`
instance can be passed for the ``pointcloud`` argument. In this case,
the triangulation of the Trimesh will be used to define the retained
region.
For large images, a faster and pixel-accurate method can be used (
'convex_hull'). Here, there is no specialization for
:map:`TriMesh` instances. Alternatively, a callable can be provided to
override the test. By default, the provided implementations are only
valid for 2D images.
Parameters
----------
pointcloud : :map:`PointCloud` or :map:`TriMesh`
The pointcloud of points that should be constrained to. See
`point_in_pointcloud` for how in some cases a :map:`TriMesh` may be
used to control triangulation.
batch_size : `int` or ``None``, optional
This should only be considered for large images. Setting this value
will cause constraining to become much slower. This size indicates
how many points in the image should be checked at a time, which
keeps memory usage low. If ``None``, no batching is used and all
points are checked at once. By default, this is only used for
the 'pwa' point_in_pointcloud choice.
point_in_pointcloud : {'pwa', 'convex_hull'} or `callable`
The method used to check if pixels in the image fall inside the
``pointcloud`` or not. If 'pwa', Menpo's :map:`PiecewiseAffine`
transform will be used to test for containment. In this case
``pointcloud`` should be a :map:`TriMesh`. If it isn't, Delauny
triangulation will be used to first triangulate ``pointcloud`` into
a :map:`TriMesh` before testing for containment.
If a callable is passed, it should take two parameters,
the :map:`PointCloud` to constrain with and the pixel locations
((d, n_dims) ndarray) to test and should return a (d, 1) boolean
ndarray of whether the pixels were inside (True) or outside (False)
of the :map:`PointCloud`.
Returns
-------
constrained : :map:`BooleanImage`
The new boolean image, constrained by the given pointcloud.
Raises
------
ValueError
If the image is not 2D and a default implementation is chosen.
ValueError
If the chosen ``point_in_pointcloud`` is unknown.
"""
copy = self.copy()
if point_in_pointcloud in {"pwa", "convex_hull"} and self.n_dims != 2:
raise ValueError(
"Can only constrain mask on 2D images with the "
"default point_in_pointcloud implementations."
"Please provide a custom callable for calculating "
"the new mask in this "
"{}D image".format(self.n_dims)
)
if point_in_pointcloud == "pwa":
point_in_pointcloud = partial(
pwa_point_in_pointcloud, batch_size=batch_size
)
elif point_in_pointcloud == "convex_hull":
point_in_pointcloud = convex_hull_point_in_pointcloud
elif not callable(point_in_pointcloud):
# Not a function, or a string, so we have an error!
raise ValueError(
"point_in_pointcloud must be a callable that "
"take two arguments: the Menpo PointCloud as a "
"boundary and the ndarray of pixel indices "
"to test. {} is an unknown option.".format(point_in_pointcloud)
)
# Only consider indices inside the bounding box of the PointCloud
bounds = pointcloud.bounds()
# Convert to integer to try and reduce boundary fp rounding errors.
bounds = [b.astype(np.int) for b in bounds]
indices = copy.indices()
# This loop is to ensure the code is multi-dimensional
for k in range(self.n_dims):
indices = indices[indices[:, k] >= bounds[0][k], :]
indices = indices[indices[:, k] <= bounds[1][k], :]
# Due to only testing bounding box indices, make sure the mask starts
# off as all False
copy.pixels[:] = False
# slice(0, 1) because we know we only have 1 channel
# Slice all the channels, only inside the bounding box (for setting
# the new mask values).
all_channels = [slice(0, 1)]
slices = tuple(
all_channels
+ [slice(bounds[0][k], bounds[1][k] + 1) for k in range(self.n_dims)]
)
copy.pixels[slices].flat = point_in_pointcloud(pointcloud, indices)
return copy