from __future__ import division
from warnings import warn
import numpy as np
binary_erosion = None # expensive, from scipy.ndimage
from menpo.visualize.base import ImageViewer
gradient = None # avoid circular reference, from menpo.feature
from .base import Image
from .boolean import BooleanImage
[docs]class MaskedImage(Image):
r"""
Represents an `n`-dimensional `k`-channel image, which has a mask.
Images can be masked in order to identify a region of interest. All
images implicitly have a mask that is defined as the the entire image.
The mask is an instance of :map:`BooleanImage`.
Parameters
----------
image_data : ``(M, N ..., Q, C)`` `ndarray`
The pixel data for the image, where the last axis represents the
number of channels.
mask : ``(M, N)`` `bool ndarray` or :map:`BooleanImage`, optional
A binary array representing the mask. Must be the same
shape as the image. Only one mask is supported for an image (so the
mask is applied to every channel equally).
copy: `bool`, optional
If ``False``, the ``image_data`` will not be copied on assignment. If a
mask is provided, this also won't be copied. In general this should only
be used if you know what you are doing.
Raises
------
ValueError
Mask is not the same shape as the image
"""
def __init__(self, image_data, mask=None, copy=True):
super(MaskedImage, self).__init__(image_data, copy=copy)
if mask is not None:
# Check if we need to create a BooleanImage or not
if not isinstance(mask, BooleanImage):
# So it's a numpy array.
mask_image = BooleanImage(mask, copy=copy)
else:
# It's a BooleanImage object.
if copy:
mask = mask.copy()
mask_image = mask
if mask_image.shape == self.shape:
self.mask = mask_image
else:
raise ValueError("Trying to construct a Masked Image of "
"shape {} with a Mask of differing "
"shape {}".format(self.shape,
mask.shape))
else:
# no mask provided - make the default.
self.mask = BooleanImage.blank(self.shape, fill=True)
[docs] def as_unmasked(self, copy=True):
r"""
Return a copy of this image without the masking behavior.
By default the mask is simply discarded. In the future more options
may be possible.
Parameters
----------
copy : `bool`, optional
If ``False``, the produced :map:`Image` will share pixels with
``self``. Only suggested to be used for performance.
Returns
-------
image : :map:`Image`
An image with the same pixels and landmarks as this one, but with
no mask.
"""
img = Image(self.pixels, copy=copy)
img.landmarks = self.landmarks
return img
@classmethod
[docs] def blank(cls, shape, n_channels=1, fill=0, dtype=np.float, mask=None):
r"""
Returns a blank image
Parameters
----------
shape : `tuple` or `list`
The shape of the image. Any floating point values are rounded up
to the nearest integer.
n_channels: `int`, optional
The number of channels to create the image with.
fill : `int`, optional
The value to fill all pixels with.
dtype: `numpy datatype`, optional
The datatype of the image.
mask: ``(M, N)`` `bool ndarray` or :map:`BooleanImage`
An optional mask that can be applied to the image. Has to have a
shape equal to that of the image.
Notes
-----
Subclasses of :map:`MaskedImage` need to overwrite this method and
explicitly call this superclass method
::
super(SubClass, cls).blank(shape,**kwargs)
in order to appropriately propagate the subclass type to ``cls``.
Returns
-------
blank_image : :class:`MaskedImage`
A new masked image of the requested size.
"""
# Ensure that the '+' operator means concatenate tuples
shape = tuple(np.ceil(shape).astype(np.int))
if fill == 0:
pixels = np.zeros(shape + (n_channels,), dtype=dtype)
else:
pixels = np.ones(shape + (n_channels,), dtype=dtype) * fill
return cls(pixels, copy=False, mask=mask)
[docs] def n_true_pixels(self):
r"""
The number of ``True`` values in the mask.
:type: `int`
"""
return self.mask.n_true()
[docs] def n_false_pixels(self):
r"""
The number of ``False`` values in the mask.
:type: `int`
"""
return self.mask.n_false()
[docs] def n_true_elements(self):
r"""
The number of ``True`` elements of the image over all the channels.
:type: `int`
"""
return self.n_true_pixels() * self.n_channels
[docs] def n_false_elements(self):
r"""
The number of ``False`` elements of the image over all the channels.
:type: `int`
"""
return self.n_false_pixels() * self.n_channels
[docs] def indices(self):
r"""
Return the indices of all true pixels in this image.
:type: ``(n_dims, n_true_pixels)`` `ndarray`
"""
return self.mask.true_indices()
[docs] def masked_pixels(self):
r"""
Get the pixels covered by the `True` values in the mask.
:type: ``(mask.n_true, n_channels)`` `ndarray`
"""
if self.mask.all_true():
return self.pixels
return self.pixels[self.mask.mask]
[docs] def set_masked_pixels(self, pixels, copy=True):
r"""
Update the masked pixels only to new values.
Parameters
----------
pixels: `ndarray`
The new pixels to set.
copy: `bool`, optional
If ``False`` a copy will be avoided in assignment. This can only
happen if the mask is all ``True`` - in all other cases it will
raise a warning.
Raises
------
Warning
If the ``copy=False`` flag cannot be honored.
"""
if self.mask.all_true():
# reshape the vector into the image again
pixels = pixels.reshape(self.shape + (self.n_channels,))
if not copy:
if not pixels.flags.c_contiguous:
warn('The copy flag was NOT honoured. A copy HAS been '
'made. Copy can only be avoided if MaskedImage has '
'an all_true mask and the pixels provided are '
'C-contiguous.')
pixels = pixels.copy()
else:
pixels = pixels.copy()
self.pixels = pixels
else:
self.pixels[self.mask.mask] = pixels
# oh dear, couldn't avoid a copy. Did the user try to?
if not copy:
warn('The copy flag was NOT honoured. A copy HAS been made. '
'copy can only be avoided if MaskedImage has an all_true '
'mask.')
def __str__(self):
return ('{} {}D MaskedImage with {} channels. '
'Attached mask {:.1%} true'.format(
self._str_shape, self.n_dims, self.n_channels,
self.mask.proportion_true()))
def _as_vector(self, keep_channels=False):
r"""
Convert image to a vectorized form. Note that the only pixels
returned here are from the masked region on the image.
Parameters
----------
keep_channels : `bool`, optional
========== =================================
Value Return shape
========== =================================
``True`` ``(mask.n_true, n_channels)``
``False`` ``(mask.n_true * n_channels,)``
========== =================================
Returns
-------
vectorized_image : (shape given by ``keep_channels``) `ndarray`
Vectorized image
"""
if keep_channels:
return self.masked_pixels().reshape([-1, self.n_channels])
else:
return self.masked_pixels().ravel()
[docs] def from_vector(self, vector, n_channels=None):
r"""
Takes a flattened vector and returns a new image formed by reshaping
the vector to the correct pixels and channels. Note that the only
region of the image that will be filled is the masked region.
On masked images, the vector is always copied.
The ``n_channels`` argument is useful for when we want to add an extra
channel to an image but maintain the shape. For example, when
calculating the gradient.
Note that landmarks are transferred in the process.
Parameters
----------
vector : ``(n_pixels,)``
A flattened vector of all pixels and channels of an image.
n_channels : `int`, optional
If given, will assume that vector is the same shape as this image,
but with a possibly different number of channels.
Returns
-------
image : :class:`MaskedImage`
New image of same shape as this image and the number of
specified channels.
"""
# This is useful for when we want to add an extra channel to an image
# but maintain the shape. For example, when calculating the gradient
n_channels = self.n_channels if n_channels is None else n_channels
# Creates zeros of size (M x N x ... x n_channels)
if self.mask.all_true():
# we can just reshape the array!
image_data = vector.reshape((self.shape + (n_channels,)))
else:
image_data = np.zeros(self.shape + (n_channels,))
pixels_per_channel = vector.reshape((-1, n_channels))
image_data[self.mask.mask] = pixels_per_channel
new_image = MaskedImage(image_data, mask=self.mask)
new_image.landmarks = self.landmarks
return new_image
[docs] def from_vector_inplace(self, vector, copy=True):
r"""
Takes a flattened vector and updates this image by reshaping
the vector to the correct pixels and channels. Note that the only
region of the image that will be filled is the masked region.
Parameters
----------
vector : ``(n_parameters,)``
A flattened vector of all pixels and channels of an image.
copy : `bool`, optional
If ``False``, the vector will be set as the pixels with no copy
made.
If ``True`` a copy of the vector is taken.
Raises
------
Warning
If ``copy=False`` cannot be honored.
"""
self.set_masked_pixels(vector.reshape((-1, self.n_channels)),
copy=copy)
[docs] def _view_2d(self, figure_id=None, new_figure=False, channels=None,
masked=True, interpolation="bilinear", alpha=1.,
render_axes=False, axes_font_name='sans-serif',
axes_font_size=10, axes_font_style='normal',
axes_font_weight='normal', axes_x_limits=None,
axes_y_limits=None, figure_size=(10, 8)):
r"""
View the image using the default image viewer. This method will appear
on the Image as ``view`` if the Image is 2D.
Returns
-------
figure_id : `object`, optional
The id of the figure to be used.
new_figure : `bool`, optional
If ``True``, a new figure is created.
channels : `int` or `list` of `int` or ``all`` or ``None``
If `int` or `list` of `int`, the specified channel(s) will be
rendered. If ``all``, all the channels will be rendered in subplots.
If ``None`` and the image is RGB, it will be rendered in RGB mode.
If ``None`` and the image is not RGB, it is equivalent to ``all``.
masked : `bool`, optional
If ``True``, only the masked pixels will be rendered.
interpolation : See Below, optional
The interpolation used to render the image. For example, if
``bilinear``, the image will be smooth and if ``nearest``, the
image will be pixelated.
Example options ::
{none, nearest, bilinear, bicubic, spline16, spline36,
hanning, hamming, hermite, kaiser, quadric, catrom, gaussian,
bessel, mitchell, sinc, lanczos}
alpha : `float`, optional
The alpha blending value, between 0 (transparent) and 1 (opaque).
render_axes : `bool`, optional
If ``True``, the axes will be rendered.
axes_font_name : See Below, optional
The font of the axes.
Example options ::
{serif, sans-serif, cursive, fantasy, monospace}
axes_font_size : `int`, optional
The font size of the axes.
axes_font_style : {``normal``, ``italic``, ``oblique``}, optional
The font style of the axes.
axes_font_weight : See Below, optional
The font weight of the axes.
Example options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
axes_x_limits : (`float`, `float`) `tuple` or ``None``, optional
The limits of the x axis.
axes_y_limits : (`float`, `float`) `tuple` or ``None``, optional
The limits of the y axis.
figure_size : (`float`, `float`) `tuple` or ``None``, optional
The size of the figure in inches.
Raises
------
ValueError
If Image is not 2D
"""
mask = self.mask.mask if masked else None
pixels_to_view = self.pixels
return ImageViewer(figure_id, new_figure, self.n_dims,
pixels_to_view, channels=channels,
mask=mask).render(render_axes=render_axes,
axes_font_name=axes_font_name,
axes_font_size=axes_font_size,
axes_font_style=axes_font_style,
axes_font_weight=axes_font_weight,
axes_x_limits=axes_x_limits,
axes_y_limits=axes_y_limits,
figure_size=figure_size,
interpolation=interpolation,
alpha=alpha)
[docs] def _view_landmarks_2d(self, channels=None, masked=True, group=None,
with_labels=None, without_labels=None,
figure_id=None, new_figure=False,
interpolation='bilinear', alpha=1.,
render_lines=True, line_colour=None, line_style='-',
line_width=1, render_markers=True, marker_style='o',
marker_size=20, marker_face_colour=None,
marker_edge_colour=None, marker_edge_width=1.,
render_numbering=False,
numbers_horizontal_align='center',
numbers_vertical_align='bottom',
numbers_font_name='sans-serif', numbers_font_size=10,
numbers_font_style='normal',
numbers_font_weight='normal',
numbers_font_colour='k', render_legend=False,
legend_title='', legend_font_name='sans-serif',
legend_font_style='normal', legend_font_size=10,
legend_font_weight='normal',
legend_marker_scale=None,
legend_location=2, legend_bbox_to_anchor=(1.05, 1.),
legend_border_axes_pad=None, legend_n_columns=1,
legend_horizontal_spacing=None,
legend_vertical_spacing=None, legend_border=True,
legend_border_padding=None, legend_shadow=False,
legend_rounded_corners=False, render_axes=False,
axes_font_name='sans-serif', axes_font_size=10,
axes_font_style='normal', axes_font_weight='normal',
axes_x_limits=None, axes_y_limits=None,
figure_size=(10, 8)):
"""
Visualize the landmarks. This method will appear on the Image as
``view_landmarks`` if the Image is 2D.
Parameters
----------
channels : `int` or `list` of `int` or ``all`` or ``None``
If `int` or `list` of `int`, the specified channel(s) will be
rendered. If ``all``, all the channels will be rendered in subplots.
If ``None`` and the image is RGB, it will be rendered in RGB mode.
If ``None`` and the image is not RGB, it is equivalent to ``all``.
masked : `bool`, optional
If ``True``, only the masked pixels will be rendered.
group : `str` or``None`` optionals
The landmark group to be visualized. If ``None`` and there are more
than one landmark groups, an error is raised.
with_labels : ``None`` or `str` or `list` of `str`, optional
If not ``None``, only show the given label(s). Should **not** be
used with the ``without_labels`` kwarg.
without_labels : ``None`` or `str` or `list` of `str`, optional
If not ``None``, show all except the given label(s). Should **not**
be used with the ``with_labels`` kwarg.
figure_id : `object`, optional
The id of the figure to be used.
new_figure : `bool`, optional
If ``True``, a new figure is created.
interpolation : See Below, optional
The interpolation used to render the image. For example, if
``bilinear``, the image will be smooth and if ``nearest``, the
image will be pixelated. Example options ::
{none, nearest, bilinear, bicubic, spline16, spline36, hanning,
hamming, hermite, kaiser, quadric, catrom, gaussian, bessel,
mitchell, sinc, lanczos}
alpha : `float`, optional
The alpha blending value, between 0 (transparent) and 1 (opaque).
render_lines : `bool`, optional
If ``True``, the edges will be rendered.
line_colour : See Below, optional
The colour of the lines.
Example options::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
line_style : ``{-, --, -., :}``, optional
The style of the lines.
line_width : `float`, optional
The width of the lines.
render_markers : `bool`, optional
If ``True``, the markers will be rendered.
marker_style : See Below, optional
The style of the markers. Example options ::
{., ,, o, v, ^, <, >, +, x, D, d, s, p, *, h, H, 1, 2, 3, 4, 8}
marker_size : `int`, optional
The size of the markers in points^2.
marker_face_colour : See Below, optional
The face (filling) colour of the markers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
marker_edge_colour : See Below, optional
The edge colour of the markers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
marker_edge_width : `float`, optional
The width of the markers' edge.
render_numbering : `bool`, optional
If ``True``, the landmarks will be numbered.
numbers_horizontal_align : ``{center, right, left}``, optional
The horizontal alignment of the numbers' texts.
numbers_vertical_align : ``{center, top, bottom, baseline}``, optional
The vertical alignment of the numbers' texts.
numbers_font_name : See Below, optional
The font of the numbers. Example options ::
{serif, sans-serif, cursive, fantasy, monospace}
numbers_font_size : `int`, optional
The font size of the numbers.
numbers_font_style : ``{normal, italic, oblique}``, optional
The font style of the numbers.
numbers_font_weight : See Below, optional
The font weight of the numbers.
Example options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
numbers_font_colour : See Below, optional
The font colour of the numbers.
Example options ::
{r, g, b, c, m, k, w}
or
(3, ) ndarray
render_legend : `bool`, optional
If ``True``, the legend will be rendered.
legend_title : `str`, optional
The title of the legend.
legend_font_name : See below, optional
The font of the legend. Example options ::
{serif, sans-serif, cursive, fantasy, monospace}
legend_font_style : ``{normal, italic, oblique}``, optional
The font style of the legend.
legend_font_size : `int`, optional
The font size of the legend.
legend_font_weight : See Below, optional
The font weight of the legend.
Example options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold, demibold, demi, bold, heavy, extra bold, black}
legend_marker_scale : `float`, optional
The relative size of the legend markers with respect to the original
legend_location : `int`, optional
The location of the legend. The predefined values are:
=============== ==
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== ==
legend_bbox_to_anchor : (`float`, `float`) `tuple`, optional
The bbox that the legend will be anchored.
legend_border_axes_pad : `float`, optional
The pad between the axes and legend border.
legend_n_columns : `int`, optional
The number of the legend's columns.
legend_horizontal_spacing : `float`, optional
The spacing between the columns.
legend_vertical_spacing : `float`, optional
The vertical space between the legend entries.
legend_border : `bool`, optional
If ``True``, a frame will be drawn around the legend.
legend_border_padding : `float`, optional
The fractional whitespace inside the legend border.
legend_shadow : `bool`, optional
If ``True``, a shadow will be drawn behind legend.
legend_rounded_corners : `bool`, optional
If ``True``, the frame's corners will be rounded (fancybox).
render_axes : `bool`, optional
If ``True``, the axes will be rendered.
axes_font_name : See Below, optional
The font of the axes. Example options ::
{serif, sans-serif, cursive, fantasy, monospace}
axes_font_size : `int`, optional
The font size of the axes.
axes_font_style : ``{normal, italic, oblique}``, optional
The font style of the axes.
axes_font_weight : See Below, optional
The font weight of the axes.
Example options ::
{ultralight, light, normal, regular, book, medium, roman,
semibold,demibold, demi, bold, heavy, extra bold, black}
axes_x_limits : (`float`, `float`) `tuple` or ``None`` optional
The limits of the x axis.
axes_y_limits : (`float`, `float`) `tuple` or ``None`` optional
The limits of the y axis.
figure_size : (`float`, `float`) `tuple` or ``None`` optional
The size of the figure in inches.
Raises
------
ValueError
If both ``with_labels`` and ``without_labels`` are passed.
ValueError
If the landmark manager doesn't contain the provided group label.
"""
from menpo.visualize import view_image_landmarks
return view_image_landmarks(
self, channels, masked, group, with_labels, without_labels,
figure_id, new_figure, interpolation, alpha, render_lines,
line_colour, line_style, line_width, render_markers, marker_style,
marker_size, marker_face_colour, marker_edge_colour,
marker_edge_width, render_numbering, numbers_horizontal_align,
numbers_vertical_align, numbers_font_name, numbers_font_size,
numbers_font_style, numbers_font_weight, numbers_font_colour,
render_legend, legend_title, legend_font_name, legend_font_style,
legend_font_size, legend_font_weight, legend_marker_scale,
legend_location, legend_bbox_to_anchor, legend_border_axes_pad,
legend_n_columns, legend_horizontal_spacing,
legend_vertical_spacing, legend_border, legend_border_padding,
legend_shadow, legend_rounded_corners, render_axes, axes_font_name,
axes_font_size, axes_font_style, axes_font_weight, axes_x_limits,
axes_y_limits, figure_size)
[docs] def crop_inplace(self, min_indices, max_indices,
constrain_to_boundary=True):
r"""
Crops this image using the given minimum and maximum indices.
Landmarks are correctly adjusted so they maintain their position
relative to the newly cropped image.
Parameters
----------
min_indices: ``(n_dims, )`` `ndarray`
The minimum index over each dimension.
max_indices: ``(n_dims, )`` `ndarray`
The maximum index over each dimension.
constrain_to_boundary : `bool`, optional
If ``True`` the crop will be snapped to not go beyond this images
boundary. If ``False``, an :map:`ImageBoundaryError` will be raised
if an attempt is made to go beyond the edge of the image.
Returns
-------
cropped_image : `type(self)`
This image, but cropped.
Raises
------
ValueError
``min_indices`` and ``max_indices`` both have to be of length
``n_dims``. All ``max_indices`` must be greater than
``min_indices``.
:map`ImageBoundaryError`
Raised if ``constrain_to_boundary=False``, and an attempt is made
to crop the image in a way that violates the image bounds.
"""
# crop our image
super(MaskedImage, self).crop_inplace(
min_indices, max_indices,
constrain_to_boundary=constrain_to_boundary)
# crop our mask
self.mask.crop_inplace(min_indices, max_indices,
constrain_to_boundary=constrain_to_boundary)
return self
[docs] def crop_to_true_mask(self, boundary=0, constrain_to_boundary=True):
r"""
Crop this image to be bounded just the `True` values of it's mask.
Parameters
----------
boundary: `int`, optional
An extra padding to be added all around the true mask region.
constrain_to_boundary : `bool`, optional
If ``True`` the crop will be snapped to not go beyond this images
boundary. If ``False``, an :map:`ImageBoundaryError` will be raised
if an attempt is made to go beyond the edge of the image. Note that
is only possible if ``boundary != 0``.
Raises
------
ImageBoundaryError
Raised if 11constrain_to_boundary=False`1, and an attempt is
made to crop the image in a way that violates the image bounds.
"""
min_indices, max_indices = self.mask.bounds_true(
boundary=boundary, constrain_to_bounds=False)
# no point doing the bounds check twice - let the crop do it only.
self.crop_inplace(min_indices, max_indices,
constrain_to_boundary=constrain_to_boundary)
[docs] def warp_to_mask(self, template_mask, transform, warp_landmarks=False,
order=1, mode='constant', cval=0.):
r"""
Warps this image into a different reference space.
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.
order : `int`, optional
The order of interpolation. The order has to be in the range [0,5]
========= =====================
Order Interpolation
========= =====================
0 Nearest-neighbor
1 Bi-linear *(default)*
2 Bi-quadratic
3 Bi-cubic
4 Bi-quartic
5 Bi-quintic
========= =====================
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
-------
warped_image : ``type(self)``
A copy of this image, warped.
"""
# call the super variant and get ourselves a MaskedImage back
# with a blank mask
warped_image = Image.warp_to_mask(self, template_mask, transform,
warp_landmarks=warp_landmarks,
order=order, mode=mode, cval=cval)
warped_mask = self.mask.warp_to_mask(template_mask, transform,
warp_landmarks=warp_landmarks,
mode=mode, cval=cval)
warped_image.mask = warped_mask
return warped_image
[docs] def warp_to_shape(self, template_shape, transform, warp_landmarks=False,
order=1, mode='constant', cval=0.):
"""
Return a copy of this :map:`MaskedImage` warped into a different
reference space.
Parameters
----------
template_shape : `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.
order : `int`, optional
The order of interpolation. The order has to be in the range [0,5]
========= =====================
Order Interpolation
========= =====================
0 Nearest-neighbor
1 Bi-linear *(default)*
2 Bi-quadratic
3 Bi-cubic
4 Bi-quartic
5 Bi-quintic
========= =====================
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
-------
warped_image : :map:`MaskedImage`
A copy of this image, warped.
"""
# call the super variant and get ourselves an Image back
warped_image = Image.warp_to_shape(self, template_shape, transform,
warp_landmarks=warp_landmarks,
order=order, mode=mode, cval=cval)
# warp the mask separately and reattach.
mask = self.mask.warp_to_shape(template_shape, transform,
warp_landmarks=warp_landmarks,
mode=mode, cval=cval)
# efficiently turn the Image into a MaskedImage, attaching the
# landmarks
masked_warped_image = MaskedImage(warped_image.pixels, mask=mask,
copy=False)
masked_warped_image.landmarks = warped_image.landmarks
return masked_warped_image
[docs] def normalize_std_inplace(self, mode='all', limit_to_mask=True):
r"""
Normalizes this image such that it's pixel values have zero mean and
unit variance.
Parameters
----------
mode : ``{all, per_channel}``, optional
If ``all``, the normalization is over all channels. If
``per_channel``, each channel individually is mean centred and
normalized in variance.
limit_to_mask : `bool`, optional
If ``True``, the normalization is only performed wrt the masked
pixels.
If ``False``, the normalization is wrt all pixels, regardless of
their masking value.
"""
self._normalize_inplace(np.std, mode=mode,
limit_to_mask=limit_to_mask)
[docs] def normalize_norm_inplace(self, mode='all', limit_to_mask=True,
**kwargs):
r"""
Normalizes this image such that it's pixel values have zero mean and
its norm equals 1.
Parameters
----------
mode : ``{all, per_channel}``, optional
If ``all``, the normalization is over all channels. If
``per_channel``, each channel individually is mean centred and
normalized in variance.
limit_to_mask : `bool`, optional
If ``True``, the normalization is only performed wrt the masked
pixels.
If ``False``, the normalization is wrt all pixels, regardless of
their masking value.
"""
def scale_func(pixels, axis=None):
return np.linalg.norm(pixels, axis=axis, **kwargs)
self._normalize_inplace(scale_func, mode=mode,
limit_to_mask=limit_to_mask)
def _normalize_inplace(self, scale_func, mode='all', limit_to_mask=True):
if limit_to_mask:
pixels = self.as_vector(keep_channels=True)
else:
pixels = Image.as_vector(self, keep_channels=True)
if mode == 'all':
centered_pixels = pixels - np.mean(pixels)
scale_factor = scale_func(centered_pixels)
elif mode == 'per_channel':
centered_pixels = pixels - np.mean(pixels, axis=0)
scale_factor = scale_func(centered_pixels, axis=0)
else:
raise ValueError("mode has to be 'all' or 'per_channel' - '{}' "
"was provided instead".format(mode))
if np.any(scale_factor == 0):
raise ValueError("Image has 0 variance - can't be "
"normalized")
else:
normalized_pixels = centered_pixels / scale_factor
if limit_to_mask:
self.from_vector_inplace(normalized_pixels.flatten())
else:
Image.from_vector_inplace(self,
normalized_pixels.flatten())
[docs] def gradient(self, nullify_values_at_mask_boundaries=False):
r"""
Returns a :map:`MaskedImage` which is the gradient of this one. In the
case of multiple channels, it returns the gradient over each axis over
each channel as a flat list.
Parameters
----------
nullify_values_at_mask_boundaries : `bool`, optional
If ``True`` a one pixel boundary is set to 0 around the edge of
the ``True`` mask region. This is useful in situations where
there is absent data in the image which will cause erroneous
gradient settings.
Returns
-------
gradient : :map:`MaskedImage`
The gradient over each axis over each channel. Therefore, the
gradient of a 2D, single channel image, will have length `2`.
The length of a 2D, 3-channel image, will have length `6`.
"""
global binary_erosion, gradient
if gradient is None:
from menpo.feature import gradient # avoid circular reference
# use the feature to take the gradient as normal
grad_image = gradient(self)
if nullify_values_at_mask_boundaries:
if binary_erosion is None:
from scipy.ndimage import binary_erosion # expensive
# Erode the edge of the mask in by one pixel
eroded_mask = binary_erosion(self.mask.mask, iterations=1)
# replace the eroded mask with the diff between the two
# masks. This is only true in the region we want to nullify.
np.logical_and(~eroded_mask, self.mask.mask, out=eroded_mask)
# nullify all the boundary values in the grad image
grad_image.pixels[eroded_mask] = 0.0
return grad_image
[docs] def constrain_mask_to_landmarks(self, group=None, label=None,
trilist=None):
r"""
Restricts this image's mask to be equal to the convex hull around the
landmarks chosen. This is not a per-pixel convex hull, but is instead
estimated by a triangulation of the points that contain the convex
hull.
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.
label: `str`, optional
The label of of the landmark manager that you wish to use. If no
label is passed, the convex hull of all landmarks is used.
trilist: ``(t, 3)`` `ndarray`, optional
Triangle list to be used on the landmarked points in selecting
the mask region. If None defaults to performing Delaunay
triangulation on the points.
"""
self.mask.constrain_to_pointcloud(self.landmarks[group][label],
trilist=trilist)
[docs] def build_mask_around_landmarks(self, patch_size, group=None, label=None):
r"""
Restricts this images mask to be patches around each landmark in
the chosen landmark group. This is useful for visualizing patch
based methods.
Parameters
----------
patch_shape : `tuple`
The size of the patch. Any floating point values are rounded up
to the nearest integer.
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.
label: `str`, optional
The label of of the landmark manager that you wish to use. If no
label is passed, the convex hull of all landmarks is used.
"""
pc = self.landmarks[group][label]
patch_size = np.ceil(patch_size)
patch_half_size = patch_size / 2
mask = np.zeros(self.shape)
max_x = self.shape[0] - 1
max_y = self.shape[1] - 1
for i, point in enumerate(pc.points):
start = np.floor(point - patch_half_size).astype(int)
finish = np.floor(point + patch_half_size).astype(int)
x, y = np.mgrid[start[0]:finish[0], start[1]:finish[1]]
# deal with boundary cases
x[x > max_x] = max_x
y[y > max_y] = max_y
x[x < 0] = 0
y[y < 0] = 0
mask[x.flatten(), y.flatten()] = True
self.mask = BooleanImage(mask)