MaskedImage

class menpo.image.MaskedImage(image_data, mask=None, copy=True)[source]

Bases: Image

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 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 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

_view_2d(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, figure_size=(10, 8))[source]

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, 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
_view_landmarks_2d(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=20, 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, figure_size=(10, 8))[source]

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^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.
as_PILImage()

Return a PIL copy of the image. Depending on the image data type, different operations are performed:

dtype Processing
uint8 No processing, directly converted to PIL
bool Scale by 255, convert to uint8
float32 Scale by 255, convert to uint8
float64 Scale by 255, convert to uint8
OTHER Raise ValueError

Image must only have 1 or 3 channels and be 2 dimensional. Non uint8 images must be in the rage [0, 1] to be converted.

Returns:

pil_image (PILImage) – PIL copy of image

Raises:
  • ValueError – If image is not 2D and 1 channel or 3 channels.
  • ValueError – If pixels data type is not float32, float64, bool or uint8
  • ValueError – If pixels data type is float32 or float64 and the pixel range is outside of [0, 1]
as_greyscale(mode='luminosity', channel=None)

Returns a greyscale version of the image. If the image does not represent a 2D RGB image, then the luminosity mode will fail.

Parameters:
  • mode ({average, luminosity, channel}, optional) –
    mode Greyscale Algorithm
    average Equal average of all channels
    luminosity Calculates the luminance using the CCIR 601 formula:

    \[Y' = 0.2989 R' + 0.5870 G' + 0.1140 B'\]
    channel A specific channel is chosen as the intensity value.
  • channel (int, optional) – The channel to be taken. Only used if mode is channel.
Returns:

greyscale_image (MaskedImage) – A copy of this image in greyscale.

as_histogram(keep_channels=True, bins='unique')

Histogram binning of the values of this image.

Parameters:
  • keep_channels (bool, optional) – If set to False, it returns a single histogram for all the channels of the image. If set to True, it returns a list of histograms, one for each channel.
  • bins ({unique}, positive int or sequence of scalars, optional) – If set equal to 'unique', the bins of the histograms are centred on the unique values of each channel. If set equal to a positive int, then this is the number of bins. If set equal to a sequence of scalars, these will be used as bins centres.
Returns:

  • hist (ndarray or list with n_channels ndarrays inside) – The histogram(s). If keep_channels=False, then hist is an ndarray. If keep_channels=True, then hist is a list with len(hist)=n_channels.
  • bin_edges (ndarray or list with n_channels ndarrays inside) – An array or a list of arrays corresponding to the above histograms that store the bins’ edges.

Raises:

ValueError – Bins can be either ‘unique’, positive int or a sequence of scalars.

Examples

Visualizing the histogram when a list of array bin edges is provided:

>>> hist, bin_edges = image.as_histogram()
>>> for k in range(len(hist)):
>>>     plt.subplot(1,len(hist),k)
>>>     width = 0.7 * (bin_edges[k][1] - bin_edges[k][0])
>>>     centre = (bin_edges[k][:-1] + bin_edges[k][1:]) / 2
>>>     plt.bar(centre, hist[k], align='center', width=width)
as_masked(mask=None, copy=True)

Return a copy of this image with an attached mask behavior.

A custom mask may be provided, or None. See the MaskedImage constructor for details of how the kwargs will be handled.

Parameters:
  • mask ((self.shape) ndarray or BooleanImage) – A mask to attach to the newly generated masked image.
  • copy (bool, optional) – If False, the produced MaskedImage will share pixels with self. Only suggested to be used for performance.
Returns:

masked_image (MaskedImage) – An image with the same pixels and landmarks as this one, but with a mask.

as_unmasked(copy=True, fill=None)[source]

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 Image will share pixels with self. Only suggested to be used for performance.
  • fill (float or None, optional) – If None the mask is simply discarded. If a number, the unmasked regions are filled with the given value.
Returns:

image (Image) – An image with the same pixels and landmarks as this one, but with no mask.

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.
build_mask_around_landmarks(patch_size, group=None, label=None)[source]

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.
centre()

The geometric centre of the Image - the subpixel that is in the middle.

Useful for aligning shapes and images.

Type:(n_dims,) ndarray
constrain_landmarks_to_bounds()

Move landmarks that are located outside the image bounds on the bounds.

constrain_mask_to_landmarks(group=None, label=None, batch_size=None, point_in_pointcloud='pwa', trilist=None)[source]

Restricts this mask 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.
  • 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.
  • 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 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 PointCloud.
  • trilist ((t, 3) ndarray, optional) – Deprecated. Please provide a Trimesh instead of relying on this parameter.
constrain_points_to_bounds(points)

Constrains the points provided to be within the bounds of this image.

Parameters:points ((d,) ndarray) – Points to be snapped to the image boundaries.
Returns:bounded_points ((d,) ndarray) – Points snapped to not stray outside the image edges.
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
crop(min_indices, max_indices, constrain_to_boundary=False)

Return a cropped copy of 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 ImageBoundaryError will be raised if an attempt is made to go beyond the edge of the image.
Returns:

cropped_image (type(self)) – A new instance of self, but cropped.

Raises:
  • ValueErrormin_indices and max_indices both have to be of length n_dims. All max_indices must be greater than min_indices.
  • 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_inplace(*args, **kwargs)

Deprecated: please use crop() instead.

crop_to_landmarks(group=None, label=None, boundary=0, constrain_to_boundary=True)

Return a copy of this image cropped so that it is bounded around a set of landmarks with an optional n_pixel boundary

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 None all landmarks in the group are used.
  • boundary (int, optional) – An extra padding to be added all around the landmarks bounds.
  • 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:

image (Image) – A copy of this image cropped to its landmarks.

Raises:

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_to_landmarks_inplace(*args, **kwargs)

Deprecated: please use crop_to_landmarks() instead.

crop_to_landmarks_proportion(boundary_proportion, group=None, label=None, minimum=True, constrain_to_boundary=True)

Crop this image to be bounded around a set of landmarks with a border proportional to the landmark spread or range.

Parameters:
  • boundary_proportion (float) – Additional padding to be added all around the landmarks bounds defined as a proportion of the landmarks range. See the minimum parameter for a definition of how the range is calculated.
  • 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 None all landmarks in the group are used.
  • minimum (bool, optional) – If True the specified proportion is relative to the minimum value of the landmarks’ per-dimension range; if False w.r.t. the maximum value of the landmarks’ per-dimension range.
  • constrain_to_boundary (bool, optional) – If True, the crop will be snapped to not go beyond this images boundary. If False, an ImageBoundaryError will be raised if an attempt is made to go beyond the edge of the image.
Returns:

image (Image) – This image, cropped to its landmarks with a border proportional to the landmark spread or range.

Raises:

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_to_landmarks_proportion_inplace(*args, **kwargs)

Deprecated: please use crop_to_landmarks_proportion() instead.

crop_to_true_mask(boundary=0, constrain_to_boundary=True)[source]

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 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.
Returns:

cropped_image (type(self)) – A copy of this image, cropped to the true mask.

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.

diagonal()

The diagonal size of this image

Type:float
extract_channels(channels)

A copy of this image with only the specified channels.

Parameters:channels (int or [int]) – The channel index or list of channel indices to retain.
Returns:image (type(self)) – A copy of this image with only the channels requested.
extract_patches(patch_centers, patch_size=(16, 16), sample_offsets=None, as_single_array=False)

Extract a set of patches from an image. Given a set of patch centers and a patch size, patches are extracted from within the image, centred on the given coordinates. Sample offsets denote a set of offsets to extract from within a patch. This is very useful if you want to extract a dense set of features around a set of landmarks and simply sample the same grid of patches around the landmarks.

If sample offsets are used, to access the offsets for each patch you need to slice the resulting list. So for 2 offsets, the first centers offset patches would be patches[:2].

Currently only 2D images are supported.

Parameters:
  • patch_centers (PointCloud) – The centers to extract patches around.
  • patch_size (tuple or ndarray, optional) – The size of the patch to extract
  • sample_offsets (PointCloud, optional) – The offsets to sample from within a patch. So (0, 0) is the centre of the patch (no offset) and (1, 0) would be sampling the patch from 1 pixel up the first axis away from the centre.
  • as_single_array (bool, optional) – If True, an (n_center * n_offset, self.shape...) ndarray, thus a single numpy array is returned containing each patch. If False, a list of Image objects is returned representing each patch.
Returns:

patches (list or ndarray) – Returns the extracted patches. Returns a list if as_single_array=True and an ndarray if as_single_array=False.

Raises:

ValueError – If image is not 2D

extract_patches_around_landmarks(group=None, label=None, patch_size=(16, 16), sample_offsets=None, as_single_array=False)

Extract patches around landmarks existing on this image. Provided the group label and optionally the landmark label extract a set of patches.

See extract_patches for more information.

Currently only 2D images are supported.

Parameters:
  • group (str or None optional) – The landmark group to use as patch centres.
  • label (str or None optional) – The landmark label within the group to use as centres.
  • patch_size (tuple or ndarray, optional) – The size of the patch to extract
  • sample_offsets (PointCloud, optional) – The offsets to sample from within a patch. So (0,0) is the centre of the patch (no offset) and (1, 0) would be sampling the patch from 1 pixel up the first axis away from the centre.
  • as_single_array (bool, optional) – If True, an (n_center * n_offset, self.shape...) ndarray, thus a single numpy array is returned containing each patch. If False, a list of Image objects is returned representing each patch.
Returns:

patches (list or ndarray) – Returns the extracted patches. Returns a list if as_single_array=True and an ndarray if as_single_array=False.

Raises:

ValueError – If image is not 2D

from_vector(vector, n_channels=None)[source]

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 (MaskedImage) – New image of same shape as this image and the number of specified channels.

from_vector_inplace(vector, copy=True)[source]

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.

gaussian_pyramid(n_levels=3, downscale=2, sigma=None)

Return the gaussian pyramid of this image. The first image of the pyramid will be the original, unmodified, image, and counts as level 1.

Parameters:
  • n_levels (int, optional) – Total number of levels in the pyramid, including the original unmodified image
  • downscale (float, optional) – Downscale factor.
  • sigma (float, optional) – Sigma for gaussian filter. Default is downscale / 3. which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the gaussian distribution.
Yields:

image_pyramid (generator) – Generator yielding pyramid layers as Image objects.

gradient(**kwargs)

Returns an Image 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. Take care to note the ordering of the returned gradient (the gradient over each spatial dimension is taken over each channel).

The first axis of the gradient of a 2D, 3-channel image, will have length 6, the ordering being I[:, 0, 0] = [R0_y, G0_y, B0_y, R0_x, G0_x, B0_x]. To be clear, all the y-gradients are returned over each channel, then all the x-gradients.

Returns:gradient (Image) – 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.
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.
indices()[source]

Return the indices of all true pixels in this image.

Type:(n_dims, n_true_pixels) ndarray
classmethod init_blank(shape, n_channels=1, fill=0, dtype=<Mock object>, mask=None)[source]

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 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 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 (MaskedImage) – A new masked image of the requested size.
masked_pixels()[source]

Get the pixels covered by the True values in the mask.

Type:(n_channels, mask.n_true) ndarray
n_false_elements()[source]

The number of False elements of the image over all the channels.

Type:int
n_false_pixels()[source]

The number of False values in the mask.

Type:int
n_true_elements()[source]

The number of True elements of the image over all the channels.

Type:int
n_true_pixels()[source]

The number of True values in the mask.

Type:int
normalize_norm_inplace(mode='all', limit_to_mask=True, **kwargs)[source]

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.
normalize_std_inplace(mode='all', limit_to_mask=True)[source]

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.
pyramid(n_levels=3, downscale=2)

Return a rescaled pyramid of this image. The first image of the pyramid will be the original, unmodified, image, and counts as level 1.

Parameters:
  • n_levels (int, optional) – Total number of levels in the pyramid, including the original unmodified image
  • downscale (float, optional) – Downscale factor.
Yields:

image_pyramid (generator) – Generator yielding pyramid layers as Image objects.

rescale(scale, round='ceil', order=1)

Return a copy of this image, rescaled by a given factor. Landmarks are rescaled appropriately.

Parameters:
  • scale (float or tuple of floats) – The scale factor. If a tuple, the scale to apply to each dimension. If a single float, the scale will be applied uniformly across each dimension.
  • round ({ceil, floor, round}, optional) – Rounding function to be applied to floating point shapes.
  • 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
Returns:

rescaled_image (type(self)) – A copy of this image, rescaled.

Raises:

ValueError – If less scales than dimensions are provided. If any scale is less than or equal to 0.

rescale_landmarks_to_diagonal_range(diagonal_range, group=None, label=None, round='ceil', order=1)

Return a copy of this image, rescaled so that the diagonal_range of the bounding box containing its landmarks matches the specified diagonal_range range.

Parameters:
  • diagonal_range ((n_dims,) ndarray) – The diagonal_range range that we want the landmarks of the returned image to have.
  • 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 None all landmarks in the group are used.
  • round ({ceil, floor, round}, optional) – Rounding function to be applied to floating point shapes.
  • 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
Returns:

rescaled_image (type(self)) – A copy of this image, rescaled.

rescale_pixels(minimum, maximum, per_channel=True)

A copy of this image with pixels linearly rescaled to fit a range.

Note that the only pixels that will considered and rescaled are those that feature in the vectorized form of this image. If you want to use this routine on all the pixels in a MaskedImage, consider using as_unmasked() prior to this call.

Parameters:
  • minimum (float) – The minimal value of the rescaled pixels
  • maximum (float) – The maximal value of the rescaled pixels
  • per_channel (boolean, optional) – If True, each channel will be rescaled independently. If False, the scaling will be over all channels.
Returns:

rescaled_image (type(self)) – A copy of this image with pixels linearly rescaled to fit in the range provided.

rescale_to_diagonal(diagonal, round='ceil')

Return a copy of this image, rescaled so that the it’s diagonal is a new size.

Parameters:
  • diagonal (int) – The diagonal size of the new image.
  • round ({ceil, floor, round}, optional) – Rounding function to be applied to floating point shapes.
Returns:

rescaled_image (type(self)) – A copy of this image, rescaled.

rescale_to_reference_shape(reference_shape, group=None, label=None, round='ceil', order=1)

Return a copy of this image, rescaled so that the scale of a particular group of landmarks matches the scale of the passed reference landmarks.

Parameters:
  • reference_shape (PointCloud) – The reference shape to which the landmarks scale will be matched against.
  • 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 None all landmarks in the group are used.
  • round ({ceil, floor, round}, optional) – Rounding function to be applied to floating point shapes.
  • 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
Returns:

rescaled_image (type(self)) – A copy of this image, rescaled.

resize(shape, order=1)

Return a copy of this image, resized to a particular shape. All image information (landmarks, and mask in the case of MaskedImage) is resized appropriately.

Parameters:
  • shape (tuple) – The new shape to resize to.
  • 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
Returns:

resized_image (type(self)) – A copy of this image, resized.

Raises:

ValueError – If the number of dimensions of the new shape does not match the number of dimensions of the image.

rolled_channels()

Returns the pixels matrix, with the channels rolled to the back axis. This may be required for interacting with external code bases that require images to have channels as the last axis, rather than the menpo convention of channels as the first axis.

Returns:rolled_channels (ndarray) – Pixels with channels as the back (last) axis.
rotate_ccw_about_centre(theta, degrees=True, cval=0.0)

Return a rotation of this image clockwise about its centre.

Parameters:
  • theta (float) – The angle of rotation about the origin.
  • degrees (bool, optional) – If True, theta is interpreted as a degree. If False, theta is interpreted as radians.
  • cval (float, optional) – The value to be set outside the rotated image boundaries.
Returns:

rotated_image (type(self)) – The rotated image.

sample(points_to_sample, order=1, mode='constant', cval=0.0)[source]

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 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.

Parameters:
  • points_to_sample (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.
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.

set_boundary_pixels(value=0.0, n_pixels=1)[source]

Returns a copy of this 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:

MaskedImage – The copy of the image for which the n pixels along its mask boundary have been set to a particular value.

set_masked_pixels(pixels, copy=True)[source]

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.

view_widget(browser_style='buttons', figure_size=(10, 8), style='coloured')

Visualizes the image object using the visualize_images widget. Currently only supports the rendering of 2D images.

Parameters:
  • browser_style ({'buttons', 'slider'}, optional) – It defines whether the selector of the images will have the form of plus/minus buttons or a slider.
  • figure_size ((int, int), optional) – The initial size of the rendered figure.
  • style ({'coloured', 'minimal'}, optional) – If 'coloured', then the style of the widget will be coloured. If minimal, then the style is simple using black and white colours.
warp_to_mask(template_mask, transform, warp_landmarks=False, order=1, mode='constant', cval=0.0, batch_size=None)[source]

Warps this image into a different reference space.

Parameters:
  • template_mask (BooleanImage) – Defines the shape of the result, and what pixels should be sampled.
  • transform (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.
Returns:

warped_image (type(self)) – A copy of this image, warped.

warp_to_shape(template_shape, transform, warp_landmarks=False, order=1, mode='constant', cval=0.0, batch_size=None)[source]

Return a copy of this 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 (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.
Returns:

warped_image (MaskedImage) – A copy of this image, warped.

zoom(scale, cval=0.0)

Zoom this image about the centre point. scale values greater than 1.0 denote zooming in to the image and values less than 1.0 denote zooming out of the image. The size of the image will not change, if you wish to scale an image, please see rescale().

Parameters:
  • scale (float) – scale > 1.0 denotes zooming in. Thus the image will appear larger and areas at the edge of the zoom will be ‘cropped’ out. scale < 1.0 denotes zooming out. The image will be padded by the value of cval.
  • cval (float, optional) – The value to be set outside the rotated image boundaries.
has_landmarks

Whether the object has landmarks.

Type:bool
has_landmarks_outside_bounds

Indicates whether there are landmarks located outside the image bounds.

Type:bool
height

The height of the image.

This is the height according to image semantics, and is thus the size of the second to last dimension.

Type:int
landmarks

The landmarks object.

Type:LandmarkManager
n_channels

The number of channels on each pixel in the image.

Type:int
n_dims

The number of dimensions in the image. The minimum possible n_dims is 2.

Type:int
n_elements

Total number of data points in the image (prod(shape), n_channels)

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_pixels

Total number of pixels in the image (prod(shape),)

Type:int
shape

The shape of the image (with n_channel values at each point).

Type:tuple
width

The width of the image.

This is the width according to image semantics, and is thus the size of the last dimension.

Type:int