BoghdadyJR/codesearch_python · Data Studio

text stringlengths 75 104k
def boundary_maximum_power(graph, xxx_todo_changeme7): """ Boundary term processing adjacent voxels maximum value using a power relationship. An implementation of a boundary term, suitable to be used with the `~medpy.graphcut.generate.graph_from_voxels` function. The same as `boundary_dif...
def boundary_difference_power(graph, xxx_todo_changeme8): r""" Boundary term processing adjacent voxels difference value using a power relationship. An implementation of a boundary term, suitable to be used with the `~medpy.graphcut.generate.graph_from_voxels` function. Finds all edges be...
def __skeleton_maximum(graph, image, boundary_term, spacing): """ A skeleton for the calculation of maximum intensity based boundary terms. This function is equivalent to energy_voxel.__skeleton_difference(), but uses the maximum intensity rather than the intensity difference of neighbouring voxels...
def __skeleton_difference(graph, image, boundary_term, spacing): """ A skeleton for the calculation of intensity difference based boundary terms. Iterates over the images dimensions and generates for each an array of absolute neighbouring voxel :math:`(p, q)` intensity differences :math:`\|I_p, I_q\|...
def __skeleton_base(graph, image, boundary_term, neighbourhood_function, spacing): """ Base of the skeleton for voxel based boundary term calculation. This function holds the low level procedures shared by nearly all boundary terms. @param graph An initialized graph.GCGraph object @type gr...
def __flatten_index(pos, shape): """ Takes a three dimensional index (x,y,z) and computes the index required to access the same element in the flattened version of the array. """ res = 0 acc = 1 for pi, si in zip(reversed(pos), reversed(shape)): res += pi * acc acc *= si ...
def mutual_information(i1, i2, bins=256): r""" Computes the mutual information (MI) (a measure of entropy) between two images. MI is not real metric, but a symmetric and nonnegative similarity measures that takes high values for similar images. Negative values are also possible. Intuitively, m...
def __range(a, bins): '''Compute the histogram range of the values in the array a according to scipy.stats.histogram.''' a = numpy.asarray(a) a_max = a.max() a_min = a.min() s = 0.5 * (a_max - a_min) / float(bins - 1) return (a_min - s, a_max + s)
def __entropy(data): '''Compute entropy of the flattened data set (e.g. a density distribution).''' # normalize and convert to float data = data/float(numpy.sum(data)) # for each grey-value g with a probability p(g) = 0, the entropy is defined as 0, therefore we remove these values and also flatten the ...
def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() if not '{}' in args.output: raise argparse.ArgumentError(args.output, 'The output argument string must contain the sequence "{}".') return args
def centerdistance(image, voxelspacing = None, mask = slice(None)): r""" Takes a simple or multi-spectral image and returns its voxel-wise center distance in mm. A multi-spectral image must be supplied as a list or tuple of its spectra. Optionally a binary mask can be supplied to select the voxels ...
def centerdistance_xdminus1(image, dim, voxelspacing = None, mask = slice(None)): r""" Implementation of `centerdistance` that allows to compute sub-volume wise centerdistances. The same notes as for `centerdistance` apply. Parameters ---------- image : array_like or list/tuple of ...
def indices(image, voxelspacing = None, mask = slice(None)): r""" Takes an image and returns the voxels ndim-indices as voxel-wise feature. The voxel spacing is taken into account, i.e. the indices are not array indices, but millimeter indices. This is a multi-element feature where each element...
def shifted_mean_gauss(image, offset = None, sigma = 5, voxelspacing = None, mask = slice(None)): r""" The approximate mean over a small region at an offset from each voxel. Functions like `local_mean_gauss`, but instead of computing the average over a small patch around the current voxel, the regi...
def mask_distance(image, voxelspacing = None, mask = slice(None)): r""" Computes the distance of each point under the mask to the mask border taking the voxel-spacing into account. Note that this feature is independent of the actual image content, but depends solely the mask image. Therefore al...
def local_mean_gauss(image, sigma = 5, voxelspacing = None, mask = slice(None)): r""" Takes a simple or multi-spectral image and returns the approximate mean over a small region around each voxel. A multi-spectral image must be supplied as a list or tuple of its spectra. Optionally a binary mas...
def gaussian_gradient_magnitude(image, sigma = 5, voxelspacing = None, mask = slice(None)): r""" Computes the gradient magnitude (edge-detection) of the supplied image using gaussian derivates and returns the intensity values. Optionally a binary mask can be supplied to select the voxels for which ...
def median(image, size = 5, voxelspacing = None, mask = slice(None)): """ Computes the multi-dimensional median filter and returns the resulting values per voxel. Optionally a binary mask can be supplied to select the voxels for which the feature should be extracted. Parameters ---...
def local_histogram(image, bins=19, rang="image", cutoffp=(0.0, 100.0), size=None, footprint=None, output=None, mode="ignore", origin=0, mask=slice(None)): r""" Computes multi-dimensional histograms over a region around each voxel. Supply an image and (optionally) a mask and get the local histogram of ...
def hemispheric_difference(image, sigma_active = 7, sigma_reference = 7, cut_plane = 0, voxelspacing = None, mask = slice(None)): r""" Computes the hemispheric intensity difference between the brain hemispheres of an brain image. Cuts the image along the middle of the supplied cut-plane. This results i...
def _extract_hemispheric_difference(image, mask = slice(None), sigma_active = 7, sigma_reference = 7, cut_plane = 0, voxelspacing = None): """ Internal, single-image version of `hemispheric_difference`. """ # constants INTERPOLATION_RANGE = int(10) # how many neighbouring values to take into account...
def _extract_local_histogram(image, mask=slice(None), bins=19, rang="image", cutoffp=(0.0, 100.0), size=None, footprint=None, output=None, mode="ignore", origin=0): """ Internal, single-image version of @see local_histogram Note: Values outside of the histograms range are not considered. Note: Mode...
def _extract_median(image, mask = slice(None), size = 1, voxelspacing = None): """ Internal, single-image version of `median`. """ # set voxel spacing if voxelspacing is None: voxelspacing = [1.] * image.ndim # determine structure element size in voxel units size = _create_s...
def _extract_gaussian_gradient_magnitude(image, mask = slice(None), sigma = 1, voxelspacing = None): """ Internal, single-image version of `gaussian_gradient_magnitude`. """ # set voxel spacing if voxelspacing is None: voxelspacing = [1.] * image.ndim # determine gaussian kernel...
def _extract_shifted_mean_gauss(image, mask = slice(None), offset = None, sigma = 1, voxelspacing = None): """ Internal, single-image version of `shifted_mean_gauss`. """ # set voxel spacing if voxelspacing is None: voxelspacing = [1.] * image.ndim # set offset if offset is None:...
def _extract_mask_distance(image, mask = slice(None), voxelspacing = None): """ Internal, single-image version of `mask_distance`. """ if isinstance(mask, slice): mask = numpy.ones(image.shape, numpy.bool) distance_map = distance_transform_edt(mask, sampling=voxelspacing) retur...
def _extract_local_mean_gauss(image, mask = slice(None), sigma = 1, voxelspacing = None): """ Internal, single-image version of `local_mean_gauss`. """ # set voxel spacing if voxelspacing is None: voxelspacing = [1.] * image.ndim # determine gaussian kernel size in voxel units ...
def _extract_centerdistance(image, mask = slice(None), voxelspacing = None): """ Internal, single-image version of `centerdistance`. """ image = numpy.array(image, copy=False) if None == voxelspacing: voxelspacing = [1.] * image.ndim # get image center and an array holding ...
def _extract_intensities(image, mask = slice(None)): """ Internal, single-image version of `intensities`. """ return numpy.array(image, copy=True)[mask].ravel()
def _substract_hemispheres(active, reference, active_sigma, reference_sigma, voxel_spacing): """ Helper function for `_extract_hemispheric_difference`. Smoothes both images and then substracts the reference from the active image. """ active_kernel = _create_structure_array(active_sigma, voxel_spacin...
def _create_structure_array(structure_array, voxelspacing): """ Convenient function to take a structure array (single number valid for all dimensions or a sequence with a distinct number for each dimension) assumed to be in mm and returns a structure array (a sequence) adapted to the image space using t...
def _extract_feature(fun, image, mask = slice(None), **kwargs): """ Convenient function to cope with multi-spectral images and feature normalization. Parameters ---------- fun : function The feature extraction function to call image : array_like or list/tuple of array_like ...
def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() # check output image exists if override not forced if not args.force: if os.path.exists(args.output + args.image[-4:]): raise ArgumentError('The supplied ...
def _fill(self, text = ""): "Indent a piece of text, according to the current indentation level" if self._do_indent: self._write("\n"+" "*self._indent + text) else: self._write(text)
def _dispatch(self, tree): "_dispatcher function, _dispatching tree type T to method _T." if isinstance(tree, list): for t in tree: self._dispatch(t) return meth = getattr(self, "_"+tree.__class__.__name__) if tree.__class__.__name__ == 'NoneType' ...
def _AssAttr(self, t): """ Handle assigning an attribute of an object """ self._dispatch(t.expr) self._write('.'+t.attrname)
def _Assign(self, t): """ Expression Assignment such as "a = 1". This only handles assignment in expressions. Keyword assignment is handled separately. """ self._fill() for target in t.nodes: self._dispatch(target) self._write(" = ") ...
def _AssTuple(self, t): """ Tuple on left hand side of an expression. """ # _write each elements, separated by a comma. for element in t.nodes[:-1]: self._dispatch(element) self._write(", ") # Handle the last one without writing comma last_elemen...
def _AugAssign(self, t): """ +=,-=,=,/=,*=, etc. operations """ self._fill() self._dispatch(t.node) self._write(' '+t.op+' ') self._dispatch(t.expr) if not self._do_indent: self._write(';')
def _CallFunc(self, t): """ Function call. """ self._dispatch(t.node) self._write("(") comma = False for e in t.args: if comma: self._write(", ") else: comma = True self._dispatch(e) if t.star_args: if comma: self._w...
def _From(self, t): """ Handle "from xyz import foo, bar as baz". """ # fixme: Are From and ImportFrom handled differently? self._fill("from ") self._write(t.modname) self._write(" import ") for i, (name,asname) in enumerate(t.names): if i != 0: ...
def _Function(self, t): """ Handle function definitions """ if t.decorators is not None: self._fill("@") self._dispatch(t.decorators) self._fill("def "+t.name + "(") defaults = [None] * (len(t.argnames) - len(t.defaults)) + list(t.defaults) for i, ...
def _Getattr(self, t): """ Handle getting an attribute of an object """ if isinstance(t.expr, (Div, Mul, Sub, Add)): self._write('(') self._dispatch(t.expr) self._write(')') else: self._dispatch(t.expr) self._write('.'+...
def _Import(self, t): """ Handle "import xyz.foo". """ self._fill("import ") for i, (name,asname) in enumerate(t.names): if i != 0: self._write(", ") self._write(name) if asname is not None: self._write(" as "+a...
def _Keyword(self, t): """ Keyword value assignment within function calls and definitions. """ self._write(t.name) self._write("=") self._dispatch(t.expr)
def existingDirectory(string): """ A custom type for the argparse commandline parser. Check whether the supplied string points to a valid directory. Examples -------- >>> parser.add_argument('argname', type=existingDirectory, help='help') """ if not os.path.isdir(string): ...
def __sequenceAscendingStrict(l): "Test a sequences values to be in strictly ascending order." it = iter(l) next(it) if not all(b > a for a, b in zip(l, it)): raise argparse.ArgumentTypeError('All values must be given in strictly ascending order.') return l
def train(self, images): r""" Train a standard intensity space and an associated transformation model. Note that the passed images should be masked to contain only the foreground. Parameters ---------- images : sequence of array_likes A numbe...
def transform(self, image, surpress_mapping_check = False): r""" Transform an images intensity values to the learned standard intensity space. Note that the passed image should be masked to contain only the foreground. The transformation is guaranteed to be lossless i.e...
def train_transform(self, images, surpress_mapping_check = False): r""" See also -------- train, transform """ ret = self.train(images) outputs = [self.transform(i, surpress_mapping_check) for i in images] return ret, outputs
def __compute_stdrange(self, images): r""" Computes a common standard intensity range over a number of images. Depending on the settings of the internal self.__stdrange variable, either (1) the already fixed values are returned, (2) a complete standard intensity range is...
def __check_mapping(self, landmarks): """ Checks whether the image, from which the supplied landmarks were extracted, can be transformed to the learned standard intensity space without loss of information. """ sc_udiff = numpy.asarray(self.__sc_umaxs)[1:] - numpy.asarray(...
def is_in_interval(n, l, r, border = 'included'): """ Checks whether a number is inside the interval l, r. """ if 'included' == border: return (n >= l) and (n <= r) elif 'excluded' == border: return (n > l) and (n < r) else: raise Value...
def are_in_interval(s, l, r, border = 'included'): """ Checks whether all number in the sequence s lie inside the interval formed by l and r. """ return numpy.all([IntensityRangeStandardization.is_in_interval(x, l, r, border) for x in s])
def linear_model(x, y): """ Returns a linear model transformation function fitted on the two supplied points. y = m*x + b Note: Assumes that slope > 0, otherwise division through zero might occur. """ x1, x2 = x y1, y2 = y m = (y2 - y1) / (x2 - x1) ...
def ght_alternative (img, template, indices): """ Alternative implementation of the general hough transform, which uses iteration over indices rather than broadcasting rules like `ght`. It is therefore considerably slower, especially for large, multi-dimensional arrays. The only application are...
def ght(img, template): r""" Implementation of the general hough transform for all dimensions. Providing a template, this method searches in the image for structures similar to the one depicted by the template. The returned hough image denotes how well the structure fit in each index. ...
def template_sphere (radius, dimensions): r""" Returns a spherical binary structure of a of the supplied radius that can be used as template input to the generalized hough transform. Parameters ---------- radius : integer The circles radius in voxels. dimensions : integer Th...
def template_ellipsoid(shape): r""" Returns an ellipsoid binary structure of a of the supplied radius that can be used as template input to the generalized hough transform. Parameters ---------- shape : tuple of integers The main axes of the ellipsoid in voxel units. Return...
def graph_to_dimacs(g, f): """ Persists the supplied graph in valid dimacs format into the file. Parameters ---------- g : `~medpy.graphcut.graph.Graph` A graph object to persist. f : file A file-like object. """ # write comments f.write('c Created by medpy\n') ...
def looks_like_issubclass(obj, classname): """ Return True if the object has a class or superclass with the given class name. Ignores old-style classes. """ t = obj if t.__name__ == classname: return True for klass in t.__mro__: if klass.__name__ == classname: re...
def mkneighbors_graph(observations, n_neighbours, metric, mode='connectivity', metric_params = None): """ Computes the (weighted) graph of mutual k-Neighbors for observations. Notes ----- The distance between an observation and itself is never computed and instead set to ``numpy.inf``. I.e....
def pdist(objects, dmeasure, diagval = numpy.inf): """ Compute the pair-wise distances between arbitrary objects. Notes ----- ``dmeasure`` is assumed to be symmetry i.e. between object a and object b the function will be called only ones. Parameters ---------- objects...
def setHandler(self, hdlr): r"""Replace the current handler with a new one. Parameters ---------- hdlr : logging.Handler A subclass of Handler that should used to handle the logging output. Notes ----- If none should be replaces, but...
def setLevel(self, level): r"""Overrides the parent method to adapt the formatting string to the level. Parameters ---------- level : int The new log level to set. See the logging levels in the logging module for details. Examples -------...
def xminus1d(img, fun, dim, args, *kwargs): r""" Applies the function fun along all X-1D dimensional volumes of the images img dimension dim. E.g. you want to apply a gauss filter to each slice of a 3D MRI brain image, simply supply the function as fun, the image as img and the dimension alon...
def pad(input, size=None, footprint=None, output=None, mode="reflect", cval=0.0): r""" Returns a copy of the input, padded by the supplied structuring element. In the case of odd dimensionality, the structure element will be centered as following on the currently processed position:: [...
def intersection(i1, h1, i2, h2): r""" Returns the intersecting parts of two images in real world coordinates. Takes both, voxelspacing and image offset into account. Note that the returned new offset might be inaccurate up to 1/2 voxel size for each dimension due to ave...
def __make_footprint(input, size, footprint): "Creates a standard footprint element ala scipy.ndimage." if footprint is None: if size is None: raise RuntimeError("no footprint or filter size provided") sizes = _ni_support._normalize_sequence(size, input.ndim) footprint = nump...
def boundary_difference_of_means(graph, label_image, original_image): # label image is not required to hold continuous ids or to start from 1 r""" Boundary term based on the difference of means between adjacent image regions. An implementation of the boundary term, suitable to be used with the `~medpy....
def boundary_stawiaski(graph, label_image, gradient_image): # label image is not required to hold continuous ids or to start from 1 r""" Boundary term based on the sum of border voxel pairs differences. An implementation of the boundary term in [1]_, suitable to be used with the `~medpy.graphcut.gener...
def boundary_stawiaski_directed(graph, label_image, xxx_todo_changeme): # label image is not required to hold continuous ids or to start from 1 r""" Boundary term based on the sum of border voxel pairs differences, directed version. An implementation of the boundary term in [1]_, suitable to be used wi...
def regional_atlas(graph, label_image, xxx_todo_changeme1): # label image is required to hold continuous ids starting from 1 r""" Regional term based on a probability atlas. An implementation of a regional term, suitable to be used with the `~medpy.graphcut.generate.graph_from_labels` function. ...
def __compute_edges_nd(label_image): """ Computes the region neighbourhood defined by a star shaped n-dimensional structuring element (as returned by scipy.ndimage.generate_binary_structure(ndim, 1)) for the supplied region/label image. Note The returned set contains neither duplicates, nor self-ref...
def __check_label_image(label_image): """Check the label image for consistent labelling starting from 1.""" encountered_indices = scipy.unique(label_image) expected_indices = scipy.arange(1, label_image.max() + 1) if not encountered_indices.size == expected_indices.size or \ not (encountered_indi...
def getParser(): "Creates and returns the argparse parser object." parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, description=__description__) parser.add_argument('output', help='Target volume.') parser.add_argument('inputs', n...
def _str_member_list(self, name): """ Generate a member listing, autosummary:: table where possible, and a table where not. """ out = [] if self[name]: out += ['.. rubric:: %s' % name, ''] prefix = getattr(self, '_name', '') if prefix...
def relabel_map(label_image, mapping, key=lambda x, y: x[y]): r""" Relabel an image using the supplied mapping. The ``mapping`` can be any kind of subscriptable object. The respective region id is used to access the new value from the ``mapping``. The ``key`` keyword parameter can be used to su...
def relabel(label_image, start = 1): r""" Relabel the regions of a label image. Re-processes the labels to make them consecutively and starting from start. Parameters ---------- label_image : array_like A nD label map. start : integer The id of the first label to assign ...
def relabel_non_zero(label_image, start = 1): r""" Relabel the regions of a label image. Re-processes the labels to make them consecutively and starting from start. Keeps all zero (0) labels, as they are considered background. Parameters ---------- label_image : array_like A nD...
def fit_labels_to_mask(label_image, mask): r""" Reduces a label images by overlaying it with a binary mask and assign the labels either to the mask or to the background. The resulting binary mask is the nearest expression the label image can form of the supplied binary mask. Parameters ----...
def __xd_iterator_pass_on(arr, view, fun): """ Like xd_iterator, but the fun return values are always passed on to the next and only the last returned. """ # create list of iterations iterations = [[None] if dim in view else list(range(arr.shape[dim])) for dim in range(arr.ndim)] # iterate...
def __extract_bbox(arr, bb_old): "Extracts the bounding box of an binary objects hole (assuming only one in existence)." hole = ndimage.binary_fill_holes(arr)- arr bb_list = ndimage.find_objects(ndimage.binary_dilation(hole, iterations = 1)) if 0 == len(bb_list): return bb_old else: bb = bb_list[0] ...
def load(image): r""" Loads the ``image`` and returns a ndarray with the image's pixel content as well as a header object. The header can, with restrictions, be used to extract additional meta-information about the image (e.g. using the methods in `~medpy.io.Header`). Additionally it serves...
def set_source_nodes(self, source_nodes): r""" Set the source nodes and compute their t-weights. Parameters ---------- source_nodes : sequence of integers Declare the source nodes via their ids. Notes ----- It does not get che...
def set_sink_nodes(self, sink_nodes): r""" Set the sink nodes and compute their t-weights. Parameters ---------- sink_nodes : sequence of integers Declare the sink nodes via their ids. Notes ----- It does not get checked if on...
def inconsistent(self): r""" Perform some consistency tests on the graph represented by this object Returns ------- consistent : bool or list False if consistent, else a list of inconsistency messages. Notes ----- This check i...
def set_source_nodes(self, source_nodes): r""" Set multiple source nodes and compute their t-weights. Parameters ---------- source_nodes : sequence of integers Declare the source nodes via their ids. Raises ------ ValueError ...
def set_sink_nodes(self, sink_nodes): r""" Set multiple sink nodes and compute their t-weights. Parameters ---------- sink_nodes : sequence of integers Declare the sink nodes via their ids. Raises ------ ValueError ...
def set_nweight(self, node_from, node_to, weight_there, weight_back): r""" Set a single n-weight / edge-weight. Parameters ---------- node_from : int Node-id from the first node of the edge. node_to : int Node-id from the second node of th...
def set_nweights(self, nweights): r""" Set multiple n-weights / edge-weights. Parameters ---------- nweights : dict A dictionary with (node-id, node-id) tuples as keys and (weight-a-to-b, weight-b-to-a) as values. Notes ----- ...
def set_tweight(self, node, weight_source, weight_sink): r""" Set a single t-weight / terminal-weight. Parameters ---------- node : int Node-id for which to set the terminal weights. weight_source : float Weight to source terminal. ...
def set_tweights(self, tweights): r""" Set multiple t-weights to the current collection of t-weights, overwriting already existing ones. Parameters ---------- tweights : dict A dictionary with node_ids as keys and (weight-to-source, weight-to-sink) tu...
def set_tweights_all(self, tweights): r""" Set all t-weights at once. Parameters ---------- tweights : iterable Containing a pair of numeric values for each of the graphs nodes. Notes ----- Since this method overrides already ...
def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() if args.width <= 0: raise argparse.ArgumentError(args.width, 'The contour width must be a positive number.') return args
def set_pixel_spacing(hdr, spacing): r"""Depreciated synonym of `~medpy.io.header.set_voxel_spacing`.""" warnings.warn('get_pixel_spacing() is depreciated, use set_voxel_spacing() instead', category=DeprecationWarning) set_voxel_spacing(hdr, spacing)
def copy_meta_data(hdr_to, hdr_from): r""" Copy image meta data (voxel spacing and offset) from one header to another. Parameters ---------- hdr_to : object An image header as returned by `load`. hdr_from : object An image header as returned by `load`. """ warnings.w...
def copy_to(self, sitkimage): """ Copy all stored meta information info to an sitk Image. Note that only the spacing and the offset/origin information are guaranteed to be preserved, although the method also tries to copy other meta information such as DICOM tags. Param...
def get_info_consistent(self, ndim): """ Returns the main meta-data information adapted to the supplied image dimensionality. It will try to resolve inconsistencies and other conflicts, altering the information avilable int he most plausible way. Parameters ----...
def dc(result, reference): r""" Dice coefficient Computes the Dice coefficient (also known as Sorensen index) between the binary objects in two images. The metric is defined as .. math:: DC=\frac{2\|A\cap B\|}{\|A\|+\|B\|} , where :math:`A` is the first and...

def boundary_maximum_power(graph, xxx_todo_changeme7): """ Boundary term processing adjacent voxels maximum value using a power relationship. An implementation of a boundary term, suitable to be used with the `~medpy.graphcut.generate.graph_from_voxels` function. The same as `boundary_dif...

def boundary_difference_power(graph, xxx_todo_changeme8): r""" Boundary term processing adjacent voxels difference value using a power relationship. An implementation of a boundary term, suitable to be used with the `~medpy.graphcut.generate.graph_from_voxels` function. Finds all edges be...

def __skeleton_maximum(graph, image, boundary_term, spacing): """ A skeleton for the calculation of maximum intensity based boundary terms. This function is equivalent to energy_voxel.__skeleton_difference(), but uses the maximum intensity rather than the intensity difference of neighbouring voxels...

def __skeleton_difference(graph, image, boundary_term, spacing): """ A skeleton for the calculation of intensity difference based boundary terms. Iterates over the images dimensions and generates for each an array of absolute neighbouring voxel :math:`(p, q)` intensity differences :math:`|I_p, I_q|...

def __skeleton_base(graph, image, boundary_term, neighbourhood_function, spacing): """ Base of the skeleton for voxel based boundary term calculation. This function holds the low level procedures shared by nearly all boundary terms. @param graph An initialized graph.GCGraph object @type gr...

def __flatten_index(pos, shape): """ Takes a three dimensional index (x,y,z) and computes the index required to access the same element in the flattened version of the array. """ res = 0 acc = 1 for pi, si in zip(reversed(pos), reversed(shape)): res += pi * acc acc *= si ...

def mutual_information(i1, i2, bins=256): r""" Computes the mutual information (MI) (a measure of entropy) between two images. MI is not real metric, but a symmetric and nonnegative similarity measures that takes high values for similar images. Negative values are also possible. Intuitively, m...

def __range(a, bins): '''Compute the histogram range of the values in the array a according to scipy.stats.histogram.''' a = numpy.asarray(a) a_max = a.max() a_min = a.min() s = 0.5 * (a_max - a_min) / float(bins - 1) return (a_min - s, a_max + s)

def __entropy(data): '''Compute entropy of the flattened data set (e.g. a density distribution).''' # normalize and convert to float data = data/float(numpy.sum(data)) # for each grey-value g with a probability p(g) = 0, the entropy is defined as 0, therefore we remove these values and also flatten the ...

def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() if not '{}' in args.output: raise argparse.ArgumentError(args.output, 'The output argument string must contain the sequence "{}".') return args

def centerdistance(image, voxelspacing = None, mask = slice(None)): r""" Takes a simple or multi-spectral image and returns its voxel-wise center distance in mm. A multi-spectral image must be supplied as a list or tuple of its spectra. Optionally a binary mask can be supplied to select the voxels ...

def centerdistance_xdminus1(image, dim, voxelspacing = None, mask = slice(None)): r""" Implementation of `centerdistance` that allows to compute sub-volume wise centerdistances. The same notes as for `centerdistance` apply. Parameters ---------- image : array_like or list/tuple of ...

def indices(image, voxelspacing = None, mask = slice(None)): r""" Takes an image and returns the voxels ndim-indices as voxel-wise feature. The voxel spacing is taken into account, i.e. the indices are not array indices, but millimeter indices. This is a multi-element feature where each element...

def shifted_mean_gauss(image, offset = None, sigma = 5, voxelspacing = None, mask = slice(None)): r""" The approximate mean over a small region at an offset from each voxel. Functions like `local_mean_gauss`, but instead of computing the average over a small patch around the current voxel, the regi...

def mask_distance(image, voxelspacing = None, mask = slice(None)): r""" Computes the distance of each point under the mask to the mask border taking the voxel-spacing into account. Note that this feature is independent of the actual image content, but depends solely the mask image. Therefore al...

def local_mean_gauss(image, sigma = 5, voxelspacing = None, mask = slice(None)): r""" Takes a simple or multi-spectral image and returns the approximate mean over a small region around each voxel. A multi-spectral image must be supplied as a list or tuple of its spectra. Optionally a binary mas...

def gaussian_gradient_magnitude(image, sigma = 5, voxelspacing = None, mask = slice(None)): r""" Computes the gradient magnitude (edge-detection) of the supplied image using gaussian derivates and returns the intensity values. Optionally a binary mask can be supplied to select the voxels for which ...

def median(image, size = 5, voxelspacing = None, mask = slice(None)): """ Computes the multi-dimensional median filter and returns the resulting values per voxel. Optionally a binary mask can be supplied to select the voxels for which the feature should be extracted. Parameters ---...

def local_histogram(image, bins=19, rang="image", cutoffp=(0.0, 100.0), size=None, footprint=None, output=None, mode="ignore", origin=0, mask=slice(None)): r""" Computes multi-dimensional histograms over a region around each voxel. Supply an image and (optionally) a mask and get the local histogram of ...

def hemispheric_difference(image, sigma_active = 7, sigma_reference = 7, cut_plane = 0, voxelspacing = None, mask = slice(None)): r""" Computes the hemispheric intensity difference between the brain hemispheres of an brain image. Cuts the image along the middle of the supplied cut-plane. This results i...

def _extract_hemispheric_difference(image, mask = slice(None), sigma_active = 7, sigma_reference = 7, cut_plane = 0, voxelspacing = None): """ Internal, single-image version of `hemispheric_difference`. """ # constants INTERPOLATION_RANGE = int(10) # how many neighbouring values to take into account...

def _extract_local_histogram(image, mask=slice(None), bins=19, rang="image", cutoffp=(0.0, 100.0), size=None, footprint=None, output=None, mode="ignore", origin=0): """ Internal, single-image version of @see local_histogram Note: Values outside of the histograms range are not considered. Note: Mode...

def _extract_median(image, mask = slice(None), size = 1, voxelspacing = None): """ Internal, single-image version of `median`. """ # set voxel spacing if voxelspacing is None: voxelspacing = [1.] * image.ndim # determine structure element size in voxel units size = _create_s...

def _extract_gaussian_gradient_magnitude(image, mask = slice(None), sigma = 1, voxelspacing = None): """ Internal, single-image version of `gaussian_gradient_magnitude`. """ # set voxel spacing if voxelspacing is None: voxelspacing = [1.] * image.ndim # determine gaussian kernel...

def _extract_shifted_mean_gauss(image, mask = slice(None), offset = None, sigma = 1, voxelspacing = None): """ Internal, single-image version of `shifted_mean_gauss`. """ # set voxel spacing if voxelspacing is None: voxelspacing = [1.] * image.ndim # set offset if offset is None:...

def _extract_mask_distance(image, mask = slice(None), voxelspacing = None): """ Internal, single-image version of `mask_distance`. """ if isinstance(mask, slice): mask = numpy.ones(image.shape, numpy.bool) distance_map = distance_transform_edt(mask, sampling=voxelspacing) retur...

def _extract_local_mean_gauss(image, mask = slice(None), sigma = 1, voxelspacing = None): """ Internal, single-image version of `local_mean_gauss`. """ # set voxel spacing if voxelspacing is None: voxelspacing = [1.] * image.ndim # determine gaussian kernel size in voxel units ...

def _extract_centerdistance(image, mask = slice(None), voxelspacing = None): """ Internal, single-image version of `centerdistance`. """ image = numpy.array(image, copy=False) if None == voxelspacing: voxelspacing = [1.] * image.ndim # get image center and an array holding ...

def _extract_intensities(image, mask = slice(None)): """ Internal, single-image version of `intensities`. """ return numpy.array(image, copy=True)[mask].ravel()

def _substract_hemispheres(active, reference, active_sigma, reference_sigma, voxel_spacing): """ Helper function for `_extract_hemispheric_difference`. Smoothes both images and then substracts the reference from the active image. """ active_kernel = _create_structure_array(active_sigma, voxel_spacin...

def _create_structure_array(structure_array, voxelspacing): """ Convenient function to take a structure array (single number valid for all dimensions or a sequence with a distinct number for each dimension) assumed to be in mm and returns a structure array (a sequence) adapted to the image space using t...

def _extract_feature(fun, image, mask = slice(None), **kwargs): """ Convenient function to cope with multi-spectral images and feature normalization. Parameters ---------- fun : function The feature extraction function to call image : array_like or list/tuple of array_like ...

def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() # check output image exists if override not forced if not args.force: if os.path.exists(args.output + args.image[-4:]): raise ArgumentError('The supplied ...

def _fill(self, text = ""): "Indent a piece of text, according to the current indentation level" if self._do_indent: self._write("\n"+" "*self._indent + text) else: self._write(text)

def _dispatch(self, tree): "_dispatcher function, _dispatching tree type T to method _T." if isinstance(tree, list): for t in tree: self._dispatch(t) return meth = getattr(self, "_"+tree.__class__.__name__) if tree.__class__.__name__ == 'NoneType' ...

def _AssAttr(self, t): """ Handle assigning an attribute of an object """ self._dispatch(t.expr) self._write('.'+t.attrname)

def _Assign(self, t): """ Expression Assignment such as "a = 1". This only handles assignment in expressions. Keyword assignment is handled separately. """ self._fill() for target in t.nodes: self._dispatch(target) self._write(" = ") ...

def _AssTuple(self, t): """ Tuple on left hand side of an expression. """ # _write each elements, separated by a comma. for element in t.nodes[:-1]: self._dispatch(element) self._write(", ") # Handle the last one without writing comma last_elemen...

def _AugAssign(self, t): """ +=,-=,*=,/=,**=, etc. operations """ self._fill() self._dispatch(t.node) self._write(' '+t.op+' ') self._dispatch(t.expr) if not self._do_indent: self._write(';')

def _CallFunc(self, t): """ Function call. """ self._dispatch(t.node) self._write("(") comma = False for e in t.args: if comma: self._write(", ") else: comma = True self._dispatch(e) if t.star_args: if comma: self._w...

def _From(self, t): """ Handle "from xyz import foo, bar as baz". """ # fixme: Are From and ImportFrom handled differently? self._fill("from ") self._write(t.modname) self._write(" import ") for i, (name,asname) in enumerate(t.names): if i != 0: ...

def _Function(self, t): """ Handle function definitions """ if t.decorators is not None: self._fill("@") self._dispatch(t.decorators) self._fill("def "+t.name + "(") defaults = [None] * (len(t.argnames) - len(t.defaults)) + list(t.defaults) for i, ...

def _Getattr(self, t): """ Handle getting an attribute of an object """ if isinstance(t.expr, (Div, Mul, Sub, Add)): self._write('(') self._dispatch(t.expr) self._write(')') else: self._dispatch(t.expr) self._write('.'+...

def _Import(self, t): """ Handle "import xyz.foo". """ self._fill("import ") for i, (name,asname) in enumerate(t.names): if i != 0: self._write(", ") self._write(name) if asname is not None: self._write(" as "+a...

def _Keyword(self, t): """ Keyword value assignment within function calls and definitions. """ self._write(t.name) self._write("=") self._dispatch(t.expr)

def existingDirectory(string): """ A custom type for the argparse commandline parser. Check whether the supplied string points to a valid directory. Examples -------- >>> parser.add_argument('argname', type=existingDirectory, help='help') """ if not os.path.isdir(string): ...

def __sequenceAscendingStrict(l): "Test a sequences values to be in strictly ascending order." it = iter(l) next(it) if not all(b > a for a, b in zip(l, it)): raise argparse.ArgumentTypeError('All values must be given in strictly ascending order.') return l

def train(self, images): r""" Train a standard intensity space and an associated transformation model. Note that the passed images should be masked to contain only the foreground. Parameters ---------- images : sequence of array_likes A numbe...

def transform(self, image, surpress_mapping_check = False): r""" Transform an images intensity values to the learned standard intensity space. Note that the passed image should be masked to contain only the foreground. The transformation is guaranteed to be lossless i.e...

def train_transform(self, images, surpress_mapping_check = False): r""" See also -------- train, transform """ ret = self.train(images) outputs = [self.transform(i, surpress_mapping_check) for i in images] return ret, outputs

def __compute_stdrange(self, images): r""" Computes a common standard intensity range over a number of images. Depending on the settings of the internal self.__stdrange variable, either (1) the already fixed values are returned, (2) a complete standard intensity range is...

def __check_mapping(self, landmarks): """ Checks whether the image, from which the supplied landmarks were extracted, can be transformed to the learned standard intensity space without loss of information. """ sc_udiff = numpy.asarray(self.__sc_umaxs)[1:] - numpy.asarray(...

def is_in_interval(n, l, r, border = 'included'): """ Checks whether a number is inside the interval l, r. """ if 'included' == border: return (n >= l) and (n <= r) elif 'excluded' == border: return (n > l) and (n < r) else: raise Value...

def are_in_interval(s, l, r, border = 'included'): """ Checks whether all number in the sequence s lie inside the interval formed by l and r. """ return numpy.all([IntensityRangeStandardization.is_in_interval(x, l, r, border) for x in s])

def linear_model(x, y): """ Returns a linear model transformation function fitted on the two supplied points. y = m*x + b Note: Assumes that slope > 0, otherwise division through zero might occur. """ x1, x2 = x y1, y2 = y m = (y2 - y1) / (x2 - x1) ...

def ght_alternative (img, template, indices): """ Alternative implementation of the general hough transform, which uses iteration over indices rather than broadcasting rules like `ght`. It is therefore considerably slower, especially for large, multi-dimensional arrays. The only application are...

def ght(img, template): r""" Implementation of the general hough transform for all dimensions. Providing a template, this method searches in the image for structures similar to the one depicted by the template. The returned hough image denotes how well the structure fit in each index. ...

def template_sphere (radius, dimensions): r""" Returns a spherical binary structure of a of the supplied radius that can be used as template input to the generalized hough transform. Parameters ---------- radius : integer The circles radius in voxels. dimensions : integer Th...

def template_ellipsoid(shape): r""" Returns an ellipsoid binary structure of a of the supplied radius that can be used as template input to the generalized hough transform. Parameters ---------- shape : tuple of integers The main axes of the ellipsoid in voxel units. Return...

def graph_to_dimacs(g, f): """ Persists the supplied graph in valid dimacs format into the file. Parameters ---------- g : `~medpy.graphcut.graph.Graph` A graph object to persist. f : file A file-like object. """ # write comments f.write('c Created by medpy\n') ...

def looks_like_issubclass(obj, classname): """ Return True if the object has a class or superclass with the given class name. Ignores old-style classes. """ t = obj if t.__name__ == classname: return True for klass in t.__mro__: if klass.__name__ == classname: re...

def mkneighbors_graph(observations, n_neighbours, metric, mode='connectivity', metric_params = None): """ Computes the (weighted) graph of mutual k-Neighbors for observations. Notes ----- The distance between an observation and itself is never computed and instead set to ``numpy.inf``. I.e....

def pdist(objects, dmeasure, diagval = numpy.inf): """ Compute the pair-wise distances between arbitrary objects. Notes ----- ``dmeasure`` is assumed to be *symmetry* i.e. between object *a* and object *b* the function will be called only ones. Parameters ---------- objects...

def setHandler(self, hdlr): r"""Replace the current handler with a new one. Parameters ---------- hdlr : logging.Handler A subclass of Handler that should used to handle the logging output. Notes ----- If none should be replaces, but...

def setLevel(self, level): r"""Overrides the parent method to adapt the formatting string to the level. Parameters ---------- level : int The new log level to set. See the logging levels in the logging module for details. Examples -------...

def xminus1d(img, fun, dim, *args, **kwargs): r""" Applies the function fun along all X-1D dimensional volumes of the images img dimension dim. E.g. you want to apply a gauss filter to each slice of a 3D MRI brain image, simply supply the function as fun, the image as img and the dimension alon...

def pad(input, size=None, footprint=None, output=None, mode="reflect", cval=0.0): r""" Returns a copy of the input, padded by the supplied structuring element. In the case of odd dimensionality, the structure element will be centered as following on the currently processed position:: [...

def intersection(i1, h1, i2, h2): r""" Returns the intersecting parts of two images in real world coordinates. Takes both, voxelspacing and image offset into account. Note that the returned new offset might be inaccurate up to 1/2 voxel size for each dimension due to ave...

def __make_footprint(input, size, footprint): "Creates a standard footprint element ala scipy.ndimage." if footprint is None: if size is None: raise RuntimeError("no footprint or filter size provided") sizes = _ni_support._normalize_sequence(size, input.ndim) footprint = nump...

def boundary_difference_of_means(graph, label_image, original_image): # label image is not required to hold continuous ids or to start from 1 r""" Boundary term based on the difference of means between adjacent image regions. An implementation of the boundary term, suitable to be used with the `~medpy....

def boundary_stawiaski(graph, label_image, gradient_image): # label image is not required to hold continuous ids or to start from 1 r""" Boundary term based on the sum of border voxel pairs differences. An implementation of the boundary term in [1]_, suitable to be used with the `~medpy.graphcut.gener...

def boundary_stawiaski_directed(graph, label_image, xxx_todo_changeme): # label image is not required to hold continuous ids or to start from 1 r""" Boundary term based on the sum of border voxel pairs differences, directed version. An implementation of the boundary term in [1]_, suitable to be used wi...

def regional_atlas(graph, label_image, xxx_todo_changeme1): # label image is required to hold continuous ids starting from 1 r""" Regional term based on a probability atlas. An implementation of a regional term, suitable to be used with the `~medpy.graphcut.generate.graph_from_labels` function. ...

def __compute_edges_nd(label_image): """ Computes the region neighbourhood defined by a star shaped n-dimensional structuring element (as returned by scipy.ndimage.generate_binary_structure(ndim, 1)) for the supplied region/label image. Note The returned set contains neither duplicates, nor self-ref...

def __check_label_image(label_image): """Check the label image for consistent labelling starting from 1.""" encountered_indices = scipy.unique(label_image) expected_indices = scipy.arange(1, label_image.max() + 1) if not encountered_indices.size == expected_indices.size or \ not (encountered_indi...

def getParser(): "Creates and returns the argparse parser object." parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, description=__description__) parser.add_argument('output', help='Target volume.') parser.add_argument('inputs', n...

def _str_member_list(self, name): """ Generate a member listing, autosummary:: table where possible, and a table where not. """ out = [] if self[name]: out += ['.. rubric:: %s' % name, ''] prefix = getattr(self, '_name', '') if prefix...

def relabel_map(label_image, mapping, key=lambda x, y: x[y]): r""" Relabel an image using the supplied mapping. The ``mapping`` can be any kind of subscriptable object. The respective region id is used to access the new value from the ``mapping``. The ``key`` keyword parameter can be used to su...

def relabel(label_image, start = 1): r""" Relabel the regions of a label image. Re-processes the labels to make them consecutively and starting from start. Parameters ---------- label_image : array_like A nD label map. start : integer The id of the first label to assign ...

def relabel_non_zero(label_image, start = 1): r""" Relabel the regions of a label image. Re-processes the labels to make them consecutively and starting from start. Keeps all zero (0) labels, as they are considered background. Parameters ---------- label_image : array_like A nD...

def fit_labels_to_mask(label_image, mask): r""" Reduces a label images by overlaying it with a binary mask and assign the labels either to the mask or to the background. The resulting binary mask is the nearest expression the label image can form of the supplied binary mask. Parameters ----...

def __xd_iterator_pass_on(arr, view, fun): """ Like xd_iterator, but the fun return values are always passed on to the next and only the last returned. """ # create list of iterations iterations = [[None] if dim in view else list(range(arr.shape[dim])) for dim in range(arr.ndim)] # iterate...

def __extract_bbox(arr, bb_old): "Extracts the bounding box of an binary objects hole (assuming only one in existence)." hole = ndimage.binary_fill_holes(arr)- arr bb_list = ndimage.find_objects(ndimage.binary_dilation(hole, iterations = 1)) if 0 == len(bb_list): return bb_old else: bb = bb_list[0] ...

def load(image): r""" Loads the ``image`` and returns a ndarray with the image's pixel content as well as a header object. The header can, with restrictions, be used to extract additional meta-information about the image (e.g. using the methods in `~medpy.io.Header`). Additionally it serves...

def set_source_nodes(self, source_nodes): r""" Set the source nodes and compute their t-weights. Parameters ---------- source_nodes : sequence of integers Declare the source nodes via their ids. Notes ----- It does not get che...

def set_sink_nodes(self, sink_nodes): r""" Set the sink nodes and compute their t-weights. Parameters ---------- sink_nodes : sequence of integers Declare the sink nodes via their ids. Notes ----- It does not get checked if on...

def inconsistent(self): r""" Perform some consistency tests on the graph represented by this object Returns ------- consistent : bool or list False if consistent, else a list of inconsistency messages. Notes ----- This check i...

def set_source_nodes(self, source_nodes): r""" Set multiple source nodes and compute their t-weights. Parameters ---------- source_nodes : sequence of integers Declare the source nodes via their ids. Raises ------ ValueError ...

def set_sink_nodes(self, sink_nodes): r""" Set multiple sink nodes and compute their t-weights. Parameters ---------- sink_nodes : sequence of integers Declare the sink nodes via their ids. Raises ------ ValueError ...

def set_nweight(self, node_from, node_to, weight_there, weight_back): r""" Set a single n-weight / edge-weight. Parameters ---------- node_from : int Node-id from the first node of the edge. node_to : int Node-id from the second node of th...

def set_nweights(self, nweights): r""" Set multiple n-weights / edge-weights. Parameters ---------- nweights : dict A dictionary with (node-id, node-id) tuples as keys and (weight-a-to-b, weight-b-to-a) as values. Notes ----- ...

def set_tweight(self, node, weight_source, weight_sink): r""" Set a single t-weight / terminal-weight. Parameters ---------- node : int Node-id for which to set the terminal weights. weight_source : float Weight to source terminal. ...

def set_tweights(self, tweights): r""" Set multiple t-weights to the current collection of t-weights, overwriting already existing ones. Parameters ---------- tweights : dict A dictionary with node_ids as keys and (weight-to-source, weight-to-sink) tu...

def set_tweights_all(self, tweights): r""" Set all t-weights at once. Parameters ---------- tweights : iterable Containing a pair of numeric values for each of the graphs nodes. Notes ----- Since this method overrides already ...

def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() if args.width <= 0: raise argparse.ArgumentError(args.width, 'The contour width must be a positive number.') return args

def set_pixel_spacing(hdr, spacing): r"""Depreciated synonym of `~medpy.io.header.set_voxel_spacing`.""" warnings.warn('get_pixel_spacing() is depreciated, use set_voxel_spacing() instead', category=DeprecationWarning) set_voxel_spacing(hdr, spacing)

def copy_meta_data(hdr_to, hdr_from): r""" Copy image meta data (voxel spacing and offset) from one header to another. Parameters ---------- hdr_to : object An image header as returned by `load`. hdr_from : object An image header as returned by `load`. """ warnings.w...

def copy_to(self, sitkimage): """ Copy all stored meta information info to an sitk Image. Note that only the spacing and the offset/origin information are guaranteed to be preserved, although the method also tries to copy other meta information such as DICOM tags. Param...

def get_info_consistent(self, ndim): """ Returns the main meta-data information adapted to the supplied image dimensionality. It will try to resolve inconsistencies and other conflicts, altering the information avilable int he most plausible way. Parameters ----...

def dc(result, reference): r""" Dice coefficient Computes the Dice coefficient (also known as Sorensen index) between the binary objects in two images. The metric is defined as .. math:: DC=\frac{2|A\cap B|}{|A|+|B|} , where :math:`A` is the first and...