Source code for menpo.image.masked

from warnings import warn

import numpy as np

from menpo.base import MenpoDeprecationWarning, copy_landmarks_and_path
from menpo.transform import Translation
from menpo.visualize.base import ImageViewer
from .base import Image
from .boolean import BooleanImage


[docs]class OutOfMaskSampleError(ValueError): r""" Exception that is thrown when an attempt is made to sample an MaskedImage in an area that is masked out (where the mask is ``False``). Parameters ---------- sampled_mask : `bool ndarray` The sampled mask, ``True`` where the image's mask was ``True`` and ``False`` otherwise. Useful for masking out the sampling array. sampled_values : `ndarray` The sampled values, no attempt at masking is made. """ def __init__(self, sampled_mask, sampled_values): super(OutOfMaskSampleError, self).__init__() self.sampled_mask = sampled_mask self.sampled_values = sampled_values
[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 : ``(C, M, N ..., Q)`` `ndarray` The pixel data for the image, where the first 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.init_blank(self.shape, fill=True)
[docs] @classmethod def init_blank(cls, shape, n_channels=1, fill=0, dtype=np.float, mask=None): r"""Generate a blank masked 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).init_blank(shape,**kwargs) in order to appropriately propagate the subclass type to ``cls``. Returns ------- blank_image : :map:`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((n_channels,) + shape, dtype=dtype) else: pixels = np.ones((n_channels,) + shape, dtype=dtype) * fill return cls(pixels, copy=False, mask=mask)
[docs] @classmethod def init_from_channels_at_back(cls, pixels, mask=None): r""" Create an Image from a set of pixels where the channels axis is on the last axis (the back). This is common in other frameworks, and therefore this method provides a convenient means of creating a menpo Image from such data. Note that a copy is always created due to the need to rearrange the data. Parameters ---------- pixels : ``(M, N ..., Q, C)`` `ndarray` Array representing the image pixels, with the last axis being 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). Returns ------- image : :map:`Image` A new image from the given pixels, with the FIRST axis as the channels. """ im = Image.init_from_channels_at_back(pixels) if mask is not None: mask = mask.copy() return MaskedImage(im.pixels, mask=mask, copy=False)
[docs] @classmethod def init_from_pointcloud( cls, pointcloud, group=None, boundary=0, constrain_mask=True, n_channels=1, fill=0, dtype=np.float, ): 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. 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 data type, optional The data type of the image. constrain_mask : `bool`, optional If ``True``, the mask will be constrained to the convex hull of the provided pointcloud. If ``False``, the mask will be all ``True``. 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) if constrain_mask: new_mask = BooleanImage.init_from_pointcloud( origin_pc, group=None, boundary=boundary, constrain=True, fill=False ) else: new_mask = None new_image = cls.init_blank( image_shape, n_channels=n_channels, fill=fill, dtype=dtype, mask=new_mask ) if group is not None: new_image.landmarks[group] = origin_pc return new_image
[docs] def as_unmasked(self, copy=True, fill=None): r""" Return a copy of this image without the masking behavior. By default the mask is simply discarded. However, there is an optional kwarg, ``fill``, that can be set which will fill the **non-masked** areas with the given value. Parameters ---------- copy : `bool`, optional If ``False``, the produced :map:`Image` will share pixels with ``self``. Only suggested to be used for performance. fill : `float` or ``(n_channels,)`` iterable or ``None``, optional If ``None`` the mask is simply discarded. If a scalar or iterable, the *unmasked* regions are filled with the given value. 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) if fill is not None: if not np.isscalar(fill): fill = np.array(fill).reshape(self.n_channels, -1) img.pixels[..., ~self.mask.mask] = fill return copy_landmarks_and_path(self, img)
[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: ``(n_channels, mask.n_true)`` `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""" Deprecated - please use the equivalent ``from_vector`` """ warn( "This method is no longer supported and will be removed in a " "future version of Menpo. " "Use .from_vector() instead.", MenpoDeprecationWarning, ) self._set_masked_pixels(pixels, copy=copy)
# TODO: Replace _from_vector_inplace with this. 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.n_channels,) + self.shape) 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([self.n_channels, -1]) 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 (n_channels x M x N x ...) if self.mask.all_true(): # we can just reshape the array! image_data = vector.reshape(((n_channels,) + self.shape)) else: image_data = np.zeros((n_channels,) + self.shape, dtype=vector.dtype) pixels_per_channel = vector.reshape((n_channels, -1)) image_data[..., self.mask.mask] = pixels_per_channel new_image = MaskedImage(image_data, mask=self.mask) return copy_landmarks_and_path(self, new_image)
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((self.n_channels, -1)), copy=copy)
[docs] def _view_2d( self, figure_id=None, new_figure=False, channels=None, masked=True, interpolation="bilinear", cmap_name=None, alpha=1.0, 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, axes_x_ticks=None, axes_y_ticks=None, figure_size=(7, 7), ): 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} cmap_name: `str`, optional, If ``None``, single channel and three channel images default to greyscale and rgb colormaps respectively. 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` or (`float`, `float`) or ``None``, optional The limits of the x axis. If `float`, then it sets padding on the right and left of the Image as a percentage of the Image's width. If `tuple` or `list`, then it defines the axis limits. If ``None``, then the limits are set automatically. axes_y_limits : (`float`, `float`) `tuple` or ``None``, optional The limits of the y axis. If `float`, then it sets padding on the top and bottom of the Image as a percentage of the Image's height. If `tuple` or `list`, then it defines the axis limits. If ``None``, then the limits are set automatically. axes_x_ticks : `list` or `tuple` or ``None``, optional The ticks of the x axis. axes_y_ticks : `list` or `tuple` or ``None``, optional The ticks 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 return ImageViewer( figure_id, new_figure, self.n_dims, self.pixels, channels=channels, mask=mask, ).render( interpolation=interpolation, cmap_name=cmap_name, alpha=alpha, 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, axes_x_ticks=axes_x_ticks, axes_y_ticks=axes_y_ticks, figure_size=figure_size, )
[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", cmap_name=None, alpha=1.0, render_lines=True, line_colour=None, line_style="-", line_width=1, render_markers=True, marker_style="o", marker_size=5, marker_face_colour=None, marker_edge_colour=None, marker_edge_width=1.0, 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, 1.0), 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, axes_x_ticks=None, axes_y_ticks=None, figure_size=(7, 7), ): """ 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} cmap_name: `str`, optional, If ``None``, single channel and three channel images default to greyscale and rgb colormaps respectively. 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. 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` or (`float`, `float`) or ``None``, optional The limits of the x axis. If `float`, then it sets padding on the right and left of the Image as a percentage of the Image's width. If `tuple` or `list`, then it defines the axis limits. If ``None``, then the limits are set automatically. axes_y_limits : (`float`, `float`) `tuple` or ``None``, optional The limits of the y axis. If `float`, then it sets padding on the top and bottom of the Image as a percentage of the Image's height. If `tuple` or `list`, then it defines the axis limits. If ``None``, then the limits are set automatically. axes_x_ticks : `list` or `tuple` or ``None``, optional The ticks of the x axis. axes_y_ticks : `list` or `tuple` or ``None``, optional The ticks 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, cmap_name, 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, axes_x_ticks, axes_y_ticks, figure_size, )
[docs] def crop_to_true_mask( self, boundary=0, constrain_to_boundary=True, return_transform=False ): 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``. return_transform : `bool`, optional If ``True``, then the :map:`Transform` object that was used to perform the cropping is also returned. Returns ------- cropped_image : ``type(self)`` A copy of this image, cropped to the true mask. transform : :map:`Transform` The transform that was used. It only applies if `return_transform` is ``True``. 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. return self.crop( min_indices, max_indices, constrain_to_boundary=constrain_to_boundary, return_transform=return_transform, )
[docs] def sample( self, points_to_sample, order=1, mode="constant", cval=0.0, verify_mask=False ): 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. If verify_mask is True and the points to sample are *outside* of the mask (fall on a ``False`` value in the mask), an exception is raised. This exception contains the information of which points were outside of the mask (``False``) and *also* returns the sampled points. Note this is more expensive and thus may be disabled by setting verify_mask to False. Parameters ---------- points_to_sample : :map:`PointCloud` Array of points to sample from the image. Should be `(n_points, n_dims)` order : `int`, optional The order of interpolation. The order has to be in the range [0,5]. See warp_to_shape for more information. 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. verify_mask : `bool`, optional If True, also sample the mask at the given points and check the mask is valid at all points. Note that sampling masks with higher order splines may cause interpolated mask values that are rounded to zero and thus cause false positives. Returns ------- sampled_pixels : (`n_points`, `n_channels`) `ndarray` The interpolated values taken across every channel of the image. Raises ------ OutOfMaskSampleError One of the points to sample was outside of the valid area of the mask (``False`` in the mask). This exception contains both the mask of valid sample points, **as well as** the sampled points themselves, in case you want to ignore the error. Only raised if verify_mask is True. """ sampled_values = Image.sample( self, points_to_sample, order=order, mode=mode, cval=cval ) if verify_mask: sampled_mask = self.mask.sample(points_to_sample, mode=mode, cval=cval) if not np.all(sampled_mask): raise OutOfMaskSampleError(sampled_mask, sampled_values) return sampled_values
# noinspection PyMethodOverriding
[docs] def warp_to_mask( self, template_mask, transform, warp_landmarks=False, order=1, mode="constant", cval=0.0, batch_size=None, return_transform=False, ): 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. 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 : ``type(self)`` 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 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, batch_size=batch_size, ) # Set the template mask as our mask warped_image.mask = template_mask # optionally return the transform if return_transform: return warped_image, transform else: return warped_image
# noinspection PyMethodOverriding
[docs] def warp_to_shape( self, template_shape, transform, warp_landmarks=False, order=1, mode="constant", cval=0.0, batch_size=None, return_transform=False, ): """ 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. 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:`MaskedImage` 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 warped_image = Image.warp_to_shape( self, template_shape, transform, warp_landmarks=warp_landmarks, order=order, mode=mode, cval=cval, batch_size=batch_size, ) # 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 = warped_image.as_masked(mask=mask, copy=False) if hasattr(warped_image, "path"): masked_warped_image.path = warped_image.path # optionally return the transform if return_transform: return masked_warped_image, transform else: return masked_warped_image
[docs] def normalize_std(self, mode="all", limit_to_mask=True): r""" Returns a copy of this image normalized 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. Returns ------- image : ``type(self)`` A copy of this image, normalized. """ warn( "This method is no longer supported and will be removed in a " "future version of Menpo. " "Use .normalize_std() instead (features package).", MenpoDeprecationWarning, ) return self._normalize(np.std, mode=mode, limit_to_mask=limit_to_mask)
[docs] def normalize_norm(self, mode="all", limit_to_mask=True, **kwargs): r""" Returns a copy of this image normalized 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. Returns ------- image : ``type(self)`` A copy of this image, normalized. """ warn( "This method is no longer supported and will be removed in a " "future version of Menpo. " "Use .normalize_norm() instead (features package).", MenpoDeprecationWarning, ) def scale_func(pixels, axis=None): return np.linalg.norm(pixels, axis=axis, **kwargs) return self._normalize(scale_func, mode=mode, limit_to_mask=limit_to_mask)
def _normalize(self, scale_func, mode="all", limit_to_mask=True): from menpo.feature import normalize if limit_to_mask: pixels = self else: pixels = self.as_unmasked(copy=False) new_img = normalize(pixels, scale_func=scale_func, mode=mode) if limit_to_mask: return new_img else: return new_img.as_masked(copy=False, mask=self.mask.copy())
[docs] def constrain_mask_to_landmarks( self, group=None, batch_size=None, point_in_pointcloud="pwa" ): r""" Returns a copy of this image whereby the mask is restricted to be equal to the convex hull around the chosen landmarks. 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 building efficiently aligning images. For large images, a faster and pixel-accurate method can be used ('convex_hull'). Alternatively, a callable can be provided to override the test. By default, the provided implementations are only valid for 2D images. 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. 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. 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. Can be accurate to a Piecewise Affine transform, a pixel accurate convex hull or any arbitrary callable. 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:`MaskedImage` A new image where the mask is constrained by the provided landmarks. """ copy = self.copy() copy.mask = copy.mask.constrain_to_pointcloud( copy.landmarks[group], batch_size=batch_size, point_in_pointcloud=point_in_pointcloud, ) return copy
[docs] def build_mask_around_landmarks(self, patch_shape, group=None): r""" Deprecated - please use the equivalent `constrain_mask_to_patches_around_landmarks` method. """ warn( "This method is no longer supported and will be removed in a " "future version of Menpo. " "Use .constrain_mask_to_patches_around_landmarks() instead.", MenpoDeprecationWarning, ) return self.constrain_mask_to_patches_around_landmarks( patch_shape=patch_shape, group=group )
[docs] def constrain_mask_to_patches_around_landmarks(self, patch_shape, group=None): r""" Returns a copy of this image whereby the mask is restricted to be patches around each landmark in the chosen landmark group. The patch will be centred on the nearest pixel for each point in the chosen landmark group. Parameters ---------- patch_shape : `tuple` The size of the patch. 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. Returns ------- constrained : :map:`MaskedImage` A new image where the mask is constrained as patches centred on each point in the provided landmarks. """ copy = self.copy() # get the selected pointcloud pc = copy.landmarks[group] # temporarily set all mask values to False copy.mask.pixels[:] = False # create a patches array of the correct size, full of True values patches = np.ones( (pc.n_points, 1, 1, int(patch_shape[0]), int(patch_shape[1])), dtype=np.bool ) # set True patches around pointcloud centers copy.mask = copy.mask.set_patches(patches, pc) return copy
[docs] def set_boundary_pixels(self, value=0.0, n_pixels=1): r""" Returns a copy of this :map:`MaskedImage` for which n pixels along the its mask boundary have been set to a particular value. This is useful in situations where there is absent data in the image which can cause, for example, erroneous computations of gradient or features. Parameters ---------- value : `float` or (n_channels, 1) ndarray n_pixels : `int`, optional The number of pixels along the mask boundary that will be set to 0. Returns ------- new_image : :map:`MaskedImage` The copy of the image for which the ``n`` pixels along its mask boundary have been set to a particular value. """ from scipy.ndimage import binary_erosion # expensive copy = self.copy() # Erode the edge of the mask in by one pixel eroded_mask = binary_erosion(copy.mask.mask, iterations=n_pixels) # 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, copy.mask.mask, out=eroded_mask) # set all the boundary pixels to a particular value copy.pixels[..., eroded_mask] = value return copy
[docs] def erode(self, n_pixels=1): r""" Returns a copy of this :map:`MaskedImage` in which the mask has been shrunk by n pixels along its boundary. Parameters ---------- n_pixels : `int`, optional The number of pixels by which we want to shrink the mask along its own boundary. Returns ------- eroded_image : :map:`MaskedImage` The copy of the masked image in which the mask has been shrunk by n pixels along its boundary. """ 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=n_pixels) image = self.copy() image.mask = BooleanImage(eroded_mask) return image
[docs] def dilate(self, n_pixels=1): r""" Returns a copy of this :map:`MaskedImage` in which its mask has been expanded by n pixels along its boundary. Parameters ---------- n_pixels : `int`, optional The number of pixels by which we want to expand the mask along its own boundary. Returns ------- dilated_image : :map:`MaskedImage` The copy of the masked image in which the mask has been expanded by n pixels along its boundary. """ from scipy.ndimage import binary_dilation # expensive # Erode the edge of the mask in by one pixel dilated_mask = binary_dilation(self.mask.mask, iterations=n_pixels) image = self.copy() image.mask = BooleanImage(dilated_mask) return image
[docs] def rasterize_landmarks( self, group=None, render_lines=True, line_style="-", line_colour="b", line_width=1, render_markers=True, marker_style="o", marker_size=1, marker_face_colour="b", marker_edge_colour="b", marker_edge_width=1, backend="matplotlib", ): r""" This method provides the ability to rasterize 2D landmarks onto the image. The returned image has the specified landmark groups rasterized onto the image - which is useful for things like creating result examples or rendering videos with annotations. Since multiple landmark groups can be specified, all arguments can take lists of parameters that map to the provided groups list. Therefore, the parameters must be lists of the correct length or a single parameter to apply to every landmark group. Multiple backends are provided, all with different strengths. The 'pillow' backend is very fast, but not very flexible. The `matplotlib` backend should be feature compatible with other Menpo rendering methods, but is much slower due to the overhead of creating a figure to render into. Images will always be rendered masked with a black background. If an unmasked image is required, please use :meth:`as_unmasked`. Parameters ---------- group : `str` or `list` of `str`, optional The landmark group key, or a list of keys. render_lines : `bool`, optional If ``True``, and the provided landmark group is a :map:`PointDirectedGraph`, the edges are rendered. line_style : `str`, optional The style of the edge line. Not all backends support this argument. line_colour : `str` or `tuple`, optional A Matplotlib style colour or a backend dependant colour. line_width : `int`, optional The width of the line to rasterize. render_markers : `bool`, optional If ``True``, render markers at the coordinates of each landmark. marker_style : `str`, optional A Matplotlib marker style. Not all backends support all marker styles. marker_size : `int`, optional The size of the marker - different backends use different scale spaces so consistent output may by difficult. marker_face_colour : `str`, optional A Matplotlib style colour or a backend dependant colour. marker_edge_colour : `str`, optional A Matplotlib style colour or a backend dependant colour. marker_edge_width : `int`, optional The width of the marker edge. Not all backends support this. backend : {'matplotlib', 'pillow'}, optional The backend to use. Returns ------- rasterized_image : :map:`Image` The image with the landmarks rasterized directly into the pixels. Raises ------ ValueError Only 2D images are supported. ValueError Only RGB (3-channel) or Greyscale (1-channel) images are supported. """ from .rasterize import rasterize_landmarks_2d # Ensure that the image is ALWAYS masked - to make it consistent # between backends - the background will be black. as_unmasked should be # used to fiddle with the background colour. im = self.as_unmasked(copy=True, fill=0) return rasterize_landmarks_2d( im, group=group, render_lines=render_lines, line_style=line_style, line_colour=line_colour, line_width=line_width, render_markers=render_markers, marker_style=marker_style, marker_size=marker_size, marker_face_colour=marker_face_colour, marker_edge_colour=marker_edge_colour, marker_edge_width=marker_edge_width, backend=backend, )