# PointCloud¶

class menpo.shape.PointCloud(points, copy=True)[source]

Bases: Shape

An N-dimensional point cloud. This is internally represented as an ndarray of shape (n_points, n_dims). This class is important for dealing with complex functionality such as viewing and representing metadata such as landmarks.

Currently only 2D and 3D pointclouds are viewable.

Parameters: points ((n_points, n_dims) ndarray) – The array representing the points. copy (bool, optional) – If False, the points will not be copied on assignment. Note that this will miss out on additional checks. Further note that we still demand that the array is C-contiguous - if it isn’t, a copy will be generated anyway. In general this should only be used if you know what you are doing.
_view_2d(figure_id=None, new_figure=False, image_view=True, render_markers=True, marker_style='o', marker_size=5, marker_face_colour='r', marker_edge_colour='k', 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_axes=True, 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), label=None, **kwargs)[source]

Visualization of the PointCloud in 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. image_view (bool, optional) – If True the PointCloud will be viewed as if it is in the image coordinate system. 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_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 PointCloud as a percentage of the PointCloud’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 PointCloud as a percentage of the PointCloud’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. label (str, optional) – The name entry in case of a legend. viewer (PointGraphViewer2d) – The viewer object.
_view_landmarks_2d(group=None, with_labels=None, without_labels=None, figure_id=None, new_figure=False, image_view=True, render_markers=True, marker_style='s', marker_size=7, marker_face_colour='k', marker_edge_colour='k', marker_edge_width=1.0, render_lines_lms=True, line_colour_lms=None, line_style_lms='-', line_width_lms=1, render_markers_lms=True, marker_style_lms='o', marker_size_lms=5, marker_face_colour_lms=None, marker_edge_colour_lms=None, marker_edge_width_lms=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))[source]

Visualize the landmarks. This method will appear on the PointCloud as view_landmarks.

Parameters:
• group (str orNone optional) – 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.
• image_view (bool, optional) – If True the PointCloud will be viewed as if it is in the image coordinate system.
• 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_lines_lms (bool, optional) – If True, the edges of the landmarks will be rendered.
• line_colour_lms (See Below, optional) –

The colour of the lines of the landmarks. Example options:

{r, g, b, c, m, k, w}
or
(3, ) ndarray

• line_style_lms ({-, --, -., :}, optional) – The style of the lines of the landmarks.
• line_width_lms (float, optional) – The width of the lines of the landmarks.
• render_markers – If True, the markers of the landmarks will be rendered.
• marker_style

The style of the markers of the landmarks. Example options

{., ,, o, v, ^, <, >, +, x, D, d, s, p, *, h, H, 1, 2, 3, 4, 8}

• marker_size – The size of the markers of the landmarks in points.
• marker_face_colour

The face (filling) colour of the markers of the landmarks. Example options

{r, g, b, c, m, k, w}
or
(3, ) ndarray

• marker_edge_colour

The edge colour of the markers of the landmarks. Example options

{r, g, b, c, m, k, w}
or
(3, ) ndarray

• marker_edge_width – The width of the markers’ edge of the landmarks.
• 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 PointCloud as a percentage of the PointCloud’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 PointCloud as a percentage of the PointCloud’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.
as_vector(**kwargs)

Returns a flattened representation of the object as a single vector.

Returns: vector ((N,) ndarray) – The core representation of the object, flattened into a single vector. Note that this is always a view back on to the original object, but is not writable.
bounding_box()[source]

Return a bounding box from two corner points as a directed graph. In the case of a 2D pointcloud, first point (0) should be nearest the origin. In the case of an image, this ordering would appear as:

0<--3
|   ^
|   |
v   |
1-->2


In the case of a pointcloud, the ordering will appear as:

3<--2
|   ^
|   |
v   |
0-->1


In the case of a 3D pointcloud, the first point (0) should be the near closest to the origin and the second point is the far opposite corner.

Returns: bounding_box (PointDirectedGraph) – The axis aligned bounding box of the PointCloud.
bounds(boundary=0)[source]

The minimum to maximum extent of the PointCloud. An optional boundary argument can be provided to expand the bounds by a constant margin.

Parameters: boundary (float) – A optional padding distance that is added to the bounds. Default is 0, meaning the max/min of tightest possible containing square/cube/hypercube is returned. min_b ((n_dims,) ndarray) – The minimum extent of the PointCloud and boundary along each dimension max_b ((n_dims,) ndarray) – The maximum extent of the PointCloud and boundary along each dimension
centre()[source]

The mean of all the points in this PointCloud (centre of mass).

Returns: centre ((n_dims) ndarray) – The mean of this PointCloud’s points.
centre_of_bounds()[source]

The centre of the absolute bounds of this PointCloud. Contrast with centre(), which is the mean point position.

Returns: centre (n_dims ndarray) – The centre of the bounds of this PointCloud.
constrain_to_bounds(bounds)[source]

Returns a copy of this PointCloud, constrained to lie exactly within the given bounds. Any points outside the bounds will be ‘snapped’ to lie exactly on the boundary.

Parameters: bounds ((n_dims, n_dims) tuple of scalars) – The bounds to constrain this pointcloud within. constrained (PointCloud) – The constrained pointcloud.
copy()

Generate an efficient copy of this object.

Note that Numpy arrays and other Copyable objects on self will be deeply copied. Dictionaries and sets will be shallow copied, and everything else will be assigned (no copy will be made).

Classes that store state other than numpy arrays and immutable types should overwrite this method to ensure all state is copied.

Returns: type(self) – A copy of this object
distance_to(pointcloud, **kwargs)[source]

Returns a distance matrix between this PointCloud and another. By default the Euclidean distance is calculated - see scipy.spatial.distance.cdist for valid kwargs to change the metric and other properties.

Parameters: pointcloud (PointCloud) – The second pointcloud to compute distances between. This must be of the same dimension as this PointCloud. distance_matrix ((n_points, n_points) ndarray) – The symmetric pairwise distance matrix between the two PointClouds s.t. distance_matrix[i, j] is the distance between the i’th point of this PointCloud and the j’th point of the input PointCloud.
from_mask(mask)[source]

A 1D boolean array with the same number of elements as the number of points in the PointCloud. This is then broadcast across the dimensions of the PointCloud and returns a new PointCloud containing only those points that were True in the mask.

Parameters: mask ((n_points,) ndarray) – 1D array of booleans pointcloud (PointCloud) – A new pointcloud that has been masked. ValueError – Mask must have same number of points as pointcloud.
from_vector(vector)

Build a new instance of the object from it’s vectorized state.

self is used to fill out the missing state required to rebuild a full object from it’s standardized flattened state. This is the default implementation, which is which is a deepcopy of the object followed by a call to from_vector_inplace(). This method can be overridden for a performance benefit if desired.

Parameters: vector ((n_parameters,) ndarray) – Flattened representation of the object. object (type(self)) – An new instance of this class.
from_vector_inplace(vector)

Deprecated. Use the non-mutating API, from_vector.

For internal usage in performance-sensitive spots, see _from_vector_inplace()

Parameters: vector ((n_parameters,) ndarray) – Flattened representation of this object
h_points()[source]

Convert poincloud to a homogeneous array: (n_dims + 1, n_points)

Type: type(self)
has_nan_values()

Tests if the vectorized form of the object contains nan values or not. This is particularly useful for objects with unknown values that have been mapped to nan values.

Returns: has_nan_values (bool) – If the vectorized object contains nan values.
classmethod init_2d_grid(shape, spacing=None)[source]

Create a pointcloud that exists on a regular 2D grid. The first dimension is the number of rows in the grid and the second dimension of the shape is the number of columns. spacing optionally allows the definition of the distance between points (uniform over points). The spacing may be different for rows and columns.

Parameters: shape (tuple of 2 int) – The size of the grid to create, this defines the number of points across each dimension in the grid. The first element is the number of rows and the second is the number of columns. spacing (int or tuple of 2 int, optional) – The spacing between points. If a single int is provided, this is applied uniformly across each dimension. If a tuple is provided, the spacing is applied non-uniformly as defined e.g. (2, 3) gives a spacing of 2 for the rows and 3 for the columns. shape_cls (type(cls)) – A PointCloud or subclass arranged in a grid.
classmethod init_from_depth_image(depth_image)[source]

Return a 3D point cloud from the given depth image. The depth image is assumed to represent height/depth values and the XY coordinates are assumed to unit spaced and represent image coordinates. This is particularly useful for visualising depth values that have been recovered from images.

Parameters: depth_image (Image or subclass) – A single channel image that contains depth values - as commonly returned by RGBD cameras, for example. depth_cloud (type(cls)) – A new 3D PointCloud with unit XY coordinates and the given depth values as Z coordinates.
norm(**kwargs)[source]

Returns the norm of this PointCloud. This is a translation and rotation invariant measure of the point cloud’s intrinsic size - in other words, it is always taken around the point cloud’s centre.

By default, the Frobenius norm is taken, but this can be changed by setting kwargs - see numpy.linalg.norm for valid options.

Returns: norm (float) – The norm of this PointCloud
range(boundary=0)[source]

The range of the extent of the PointCloud.

Parameters: boundary (float) – A optional padding distance that is used to extend the bounds from which the range is computed. Default is 0, no extension is performed. range ((n_dims,) ndarray) – The range of the PointCloud extent in each dimension.
tojson()[source]

Convert this PointCloud to a dictionary representation suitable for inclusion in the LJSON landmark format.

Returns: json (dict) – Dictionary with points keys.
with_dims(dims)[source]

Return a copy of this shape with only particular dimensions retained.

Parameters: dims (valid numpy array slice) – The slice that will be used on the dimensionality axis of the shape under transform. For example, to go from a 3D shape to a 2D one, [0, 1] could be provided or np.array([True, True, False]). copy of self, with only the requested dims
has_landmarks

Whether the object has landmarks.

Type: bool
landmarks

The landmarks object.

lms

Deprecated. Maintained for compatibility, will be removed in a future version. Returns a copy of this object, which previously would have held the ‘underlying’ PointCloud subclass.

Type: self
n_dims

The number of dimensions in the pointcloud.

Type: int
n_landmark_groups

The number of landmark groups on this object.

Type: int
n_parameters

The length of the vector that this object produces.

Type: int
n_points

The number of points in the pointcloud.

Type: int