BooleanImage

class menpo.image.BooleanImage(mask_data, copy=True)[source]

Bases: Image

A mask image made from binary pixels. The region of the image that is left exposed by the mask is referred to as the ‘masked region’. The set of ‘masked’ pixels is those pixels corresponding to a True value in the mask.

Parameters:
  • mask_data ((M, N, ..., L) ndarray) – The binary mask data. Note that there is no channel axis - a 2D Mask Image is built from just a 2D numpy array of mask_data. Automatically coerced in to boolean values.
  • copy (bool, optional) – If False, the image_data will not be copied on assignment. Note that if the array you provide is not boolean, there will still be copy. In general this should only be used if you know what you are doing.
all_true()[source]

True iff every element of the mask is True.

Type:bool
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)[source]

Impossible for a BooleanImage to be transformed to a MaskedImage.

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.
bounds_false(boundary=0, constrain_to_bounds=True)[source]

Returns the minimum to maximum indices along all dimensions that the mask includes which fully surround the False mask values. In the case of a 2D Image for instance, the min and max define two corners of a rectangle bounding the False pixel values.

Parameters:
  • boundary (int >= 0, optional) – A number of pixels that should be added to the extent. A negative value can be used to shrink the bounds in.
  • constrain_to_bounds (bool, optional) – If True, the bounding extent is snapped to not go beyond the edge of the image. If False, the bounds are left unchanged.
Returns:

  • min_b ((D,) ndarray) – The minimum extent of the True mask region with the boundary along each dimension. If constrain_to_bounds=True, is clipped to legal image bounds.
  • max_b ((D,) ndarray) – The maximum extent of the True mask region with the boundary along each dimension. If constrain_to_bounds=True, is clipped to legal image bounds.

bounds_true(boundary=0, constrain_to_bounds=True)[source]

Returns the minimum to maximum indices along all dimensions that the mask includes which fully surround the True mask values. In the case of a 2D Image for instance, the min and max define two corners of a rectangle bounding the True pixel values.

Parameters:
  • boundary (int, optional) – A number of pixels that should be added to the extent. A negative value can be used to shrink the bounds in.
  • constrain_to_bounds (bool, optional) – If True, the bounding extent is snapped to not go beyond the edge of the image. If False, the bounds are left unchanged.
  • Returns
  • --------
  • min_b ((D,) ndarray) – The minimum extent of the True mask region with the boundary along each dimension. If constrain_to_bounds=True, is clipped to legal image bounds.
  • max_b ((D,) ndarray) – The maximum extent of the True mask region with the boundary along each dimension. If constrain_to_bounds=True, is clipped to legal image bounds.
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_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.
constrain_to_landmarks(group=None, label=None, trilist=None, batch_size=None)[source]

Restricts this mask to be equal to the convex hull around the landmarks chosen. This is not a per-pixel convex hull, but instead relies on a triangulated approximation.

Parameters:
  • group (str, optional) – The key of the landmark set that should be used. If None, and if there is only one set of landmarks, this set will be used.
  • label (str, optional) – The label of of the landmark manager that you wish to use. If no label is passed, the convex hull of all landmarks is used.
  • trilist ((t, 3) ndarray, optional) – Triangle list to be used on the landmarked points in selecting the mask region. If None, defaults to performing Delaunay triangulation on the points.
  • 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.
constrain_to_pointcloud(pointcloud, batch_size=None, point_in_pointcloud='pwa', trilist=None)[source]

Restricts this mask to be equal to the convex hull around a pointcloud. The choice of whether a pixel is inside or outside of the pointcloud is determined by the point_in_pointcloud parameter. By default a Piecewise Affine transform is used to test for containment, which is useful when 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:
  • pointcloud (PointCloud) – The pointcloud of points that should be constrained to.
  • 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.
Raises:
  • ValueError – If the image is not 2D and a default implementation is chosen.
  • ValueError – If the chosen point_in_pointcloud is unknown.
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.

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

false_indices()[source]

The indices of pixels that are Flase.

Type:(n_dims, n_false) ndarray
from_vector(vector, copy=True)[source]

Takes a flattened vector and returns a new BooleanImage formed by reshaping the vector to the correct dimensions. Note that this is rebuilding a boolean image itself from boolean values. The mask is in no way interpreted in performing the operation, in contrast to MaskedImage, where only the masked region is used in from_vector() and :meth`as_vector`. Any image landmarks are transferred in the process.

Parameters:
  • vector ((n_pixels,) bool ndarray) – A flattened vector of all the pixels of a BooleanImage.
  • copy (bool, optional) – If False, no copy of the vector will be taken.
Returns:

image (BooleanImage) – New BooleanImage of same shape as this image

Raises:

Warning – If copy=False cannot be honored.

from_vector_inplace(vector, copy=True)

Takes a flattened vector and update this image by reshaping the vector to the correct dimensions.

Parameters:
  • vector ((n_pixels,) bool ndarray) – A vector vector of all the pixels of a BooleanImage.
  • copy (bool, optional) – If False, the vector will be set as the pixels. If True, a copy of the vector is taken.
Raises:

Warning – If copy=False flag cannot be honored

Note

For BooleanImage this is rebuilding a boolean image itself from boolean values. The mask is in no way interpreted in performing the operation, in contrast to MaskedImage, where only the masked region is used in from_vector_inplace() and as_vector().

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()

Return the indices of all pixels in this image.

Type:(n_dims, n_pixels) ndarray
classmethod init_blank(shape, fill=True, round='ceil', **kwargs)[source]

Returns a blank BooleanImage of the requested shape

Parameters:
  • shape (tuple or list) – The shape of the image. Any floating point values are rounded according to the round kwarg.
  • fill (bool, optional) – The mask value to be set everywhere.
  • round ({ceil, floor, round}, optional) – Rounding function to be applied to floating point shapes.
Returns:

blank_image (BooleanImage) – A blank mask of the requested size

invert()[source]

Returns a copy of this boolean image, which is inverted.

Returns:inverted (BooleanImage) – A copy of this boolean mask, where all True values are False and all False values are True.
invert_inplace()[source]

Inverts this Boolean Image inplace.

n_false()[source]

The number of False values in the mask.

Type:int
n_true()[source]

The number of True values in the mask.

Type:int
normalize_norm_inplace(mode='all', **kwargs)

Normalizes this image such that its 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.
normalize_std_inplace(mode='all', **kwargs)

Normalizes this image such that its 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.
proportion_false()[source]

The proportion of the mask which is False

Type:float
proportion_true()[source]

The proportion of the mask which is True.

Type:float
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, mode='constant', cval=False, **kwargs)[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.

Parameters:
  • points_to_sample (PointCloud) – Array of points to sample from the image. Should be (n_points, n_dims)
  • mode ({constant, nearest, reflect, wrap}, optional) – Points outside the boundaries of the input are filled according to the given mode.
  • cval (float, optional) – Used in conjunction with mode constant, the value outside the image boundaries.
Returns:

sampled_pixels ((n_points, n_channels) bool ndarray) – The interpolated values taken across every channel of the image.

true_indices()[source]

The indices of pixels that are True.

Type:(n_dims, n_true) ndarray
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=True, mode='constant', cval=False, batch_size=None)[source]

Return a copy of this BooleanImage warped into a different reference space.

Note that warping into a mask is slower than warping into a full image. If you don’t need a non-linear mask, consider warp_to_shape instead.

Parameters:
  • template_mask (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.
  • mode ({constant, nearest, reflect or wrap}, optional) – Points outside the boundaries of the input are filled according to the given mode.
  • cval (float, optional) – Used in conjunction with mode constant, the value outside the image boundaries.
  • batch_size (int or None, optional) – This should only be considered for large images. Setting this value can cause warping to become much slower, particular for cached warps such as Piecewise Affine. This size indicates how many points in the image should be warped at a time, which keeps memory usage low. If None, no batching is used and all points are warped at once.
Returns:

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

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

Return a copy of this BooleanImage warped into a different reference space.

Note that the order keyword argument is in fact ignored, as any order other than 0 makes no sense on a binary image. The keyword argument is present only for compatibility with the Image warp_to_shape API.

Parameters:
  • template_shape ((n_dims, ) tuple or ndarray) – Defines the shape of the result, and what pixel indices should be sampled (all of them).
  • transform (Transform) – Transform from the template_shape space back to this image. Defines, for each index on template_shape, which pixel location should be sampled from on this image.
  • warp_landmarks (bool, optional) – If True, result will have the same landmark dictionary as self, but with each landmark updated to the warped position.
  • mode ({constant, nearest, reflect or wrap}, optional) – Points outside the boundaries of the input are filled according to the given mode.
  • cval (float, optional) – Used in conjunction with mode constant, the value outside the image boundaries.
  • batch_size (int or None, optional) – This should only be considered for large images. Setting this value can cause warping to become much slower, particular for cached warps such as Piecewise Affine. This size indicates how many points in the image should be warped at a time, which keeps memory usage low. If None, no batching is used and all points are warped at once.
Returns:

warped_image (BooleanImage) – 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
mask

Returns the pixels of the mask with no channel axis. This is what should be used to mask any k-dimensional image.

Type:(M, N, ..., L), bool ndarray
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