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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__msgc_step3_discontinuity_localization | def __msgc_step3_discontinuity_localization(self):
"""
Estimate discontinuity in basis of low resolution image segmentation.
:return: discontinuity in low resolution
"""
import scipy
start = self._start_time
seg = 1 - self.segmentation.astype(np.int8)
sel... | python | def __msgc_step3_discontinuity_localization(self):
"""
Estimate discontinuity in basis of low resolution image segmentation.
:return: discontinuity in low resolution
"""
import scipy
start = self._start_time
seg = 1 - self.segmentation.astype(np.int8)
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__multiscale_gc_lo2hi_run | def __multiscale_gc_lo2hi_run(self): # , pyed):
"""
Run Graph-Cut segmentation with refinement of low resolution multiscale graph.
In first step is performed normal GC on low resolution data
Second step construct finer grid on edges of segmentation from first
step.
There... | python | def __multiscale_gc_lo2hi_run(self): # , pyed):
"""
Run Graph-Cut segmentation with refinement of low resolution multiscale graph.
In first step is performed normal GC on low resolution data
Second step construct finer grid on edges of segmentation from first
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__multiscale_gc_hi2lo_run | def __multiscale_gc_hi2lo_run(self): # , pyed):
"""
Run Graph-Cut segmentation with simplifiyng of high resolution multiscale graph.
In first step is performed normal GC on low resolution data
Second step construct finer grid on edges of segmentation from first
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The... | python | def __multiscale_gc_hi2lo_run(self): # , pyed):
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__ordered_values_by_indexes | def __ordered_values_by_indexes(self, data, inds):
"""
Return values (intensities) by indexes.
Used for multiscale graph cut.
data = [[0 1 1],
[0 2 2],
[0 2 2]]
inds = [[0 1 2],
[3 4 4],
[5 4 4]]
return: [... | python | def __ordered_values_by_indexes(self, data, inds):
"""
Return values (intensities) by indexes.
Used for multiscale graph cut.
data = [[0 1 1],
[0 2 2],
[0 2 2]]
inds = [[0 1 2],
[3 4 4],
[5 4 4]]
return: [... | [
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__hi2lo_multiscale_indexes | def __hi2lo_multiscale_indexes(self, mask, orig_shape): # , zoom):
"""
Function computes multiscale indexes of ndarray.
mask: Says where is original resolution (0) and where is small
resolution (1). Mask is in small resolution.
orig_shape: Original shape of input data.
... | python | def __hi2lo_multiscale_indexes(self, mask, orig_shape): # , zoom):
"""
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mask: Says where is original resolution (0) and where is small
resolution (1). Mask is in small resolution.
orig_shape: Original shape of input data.
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.interactivity | def interactivity(self, min_val=None, max_val=None, qt_app=None):
"""
Interactive seed setting with 3d seed editor
"""
from .seed_editor_qt import QTSeedEditor
from PyQt4.QtGui import QApplication
if min_val is None:
min_val = np.min(self.img)
if max... | python | def interactivity(self, min_val=None, max_val=None, qt_app=None):
"""
Interactive seed setting with 3d seed editor
"""
from .seed_editor_qt import QTSeedEditor
from PyQt4.QtGui import QApplication
if min_val is None:
min_val = np.min(self.img)
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.set_seeds | def set_seeds(self, seeds):
"""
Function for manual seed setting. Sets variable seeds and prepares
voxels for density model.
:param seeds: ndarray (0 - nothing, 1 - object, 2 - background,
3 - object just hard constraints, no model training, 4 - background
just hard cons... | python | def set_seeds(self, seeds):
"""
Function for manual seed setting. Sets variable seeds and prepares
voxels for density model.
:param seeds: ndarray (0 - nothing, 1 - object, 2 - background,
3 - object just hard constraints, no model training, 4 - background
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.run | def run(self, run_fit_model=True):
"""
Run the Graph Cut segmentation according to preset parameters.
:param run_fit_model: Allow to skip model fit when the model is prepared before
:return:
"""
if run_fit_model:
self.fit_model(self.img, self.voxelsize, self... | python | def run(self, run_fit_model=True):
"""
Run the Graph Cut segmentation according to preset parameters.
:param run_fit_model: Allow to skip model fit when the model is prepared before
:return:
"""
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__set_hard_hard_constraints | def __set_hard_hard_constraints(self, tdata1, tdata2, seeds):
"""
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2: object 2 - full seeds
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"""
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__similarity_for_tlinks_obj_bgr | def __similarity_for_tlinks_obj_bgr(
self,
data,
voxelsize,
# voxels1, voxels2,
# seeds, otherfeatures=None
):
"""
Compute edge values for graph cut tlinks based on image intensity
and texture.
"""
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):
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Compute edge values for graph cut tlinks based on image intensity
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.__create_nlinks | def __create_nlinks(self, data, inds=None, boundary_penalties_fcn=None):
"""
Compute nlinks grid from data shape information. For boundary penalties
are data (intensities) values are used.
ins: Default is None. Used for multiscale GC. This are indexes of
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.debug_get_reconstructed_similarity | def debug_get_reconstructed_similarity(
self,
data3d=None,
voxelsize=None,
seeds=None,
area_weight=1,
hard_constraints=True,
return_unariesalt=False,
):
"""
Use actual model to calculate similarity. If no input is given the last image is used.
... | python | def debug_get_reconstructed_similarity(
self,
data3d=None,
voxelsize=None,
seeds=None,
area_weight=1,
hard_constraints=True,
return_unariesalt=False,
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Use actual model to calculate similarity. If no input is given the last image is used.
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.debug_show_reconstructed_similarity | def debug_show_reconstructed_similarity(
self,
data3d=None,
voxelsize=None,
seeds=None,
area_weight=1,
hard_constraints=True,
show=True,
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Show tlinks.
:param data3d: ndarray with input d... | python | def debug_show_reconstructed_similarity(
self,
data3d=None,
voxelsize=None,
seeds=None,
area_weight=1,
hard_constraints=True,
show=True,
bins=20,
slice_number=None,
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.debug_inspect_node | def debug_inspect_node(self, node_msindex):
"""
Get info about the node. See pycut.inspect_node() for details.
Processing is done in temporary shape.
:param node_seed:
:return: node_unariesalt, node_neighboor_edges_and_weights, node_neighboor_seeds
"""
return ins... | python | def debug_inspect_node(self, node_msindex):
"""
Get info about the node. See pycut.inspect_node() for details.
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mjirik/imcut | imcut/pycut.py | ImageGraphCut.debug_interactive_inspect_node | def debug_interactive_inspect_node(self):
"""
Call after segmentation to see selected node neighborhood.
User have to select one node by click.
:return:
"""
if (
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np.asarray(self.msinds.shape) - np.asarray(sel... | python | def debug_interactive_inspect_node(self):
"""
Call after segmentation to see selected node neighborhood.
User have to select one node by click.
:return:
"""
if (
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mjirik/imcut | imcut/pycut.py | ImageGraphCut._ssgc_prepare_data_and_run_computation | def _ssgc_prepare_data_and_run_computation(
self,
# voxels1, voxels2,
hard_constraints=True,
area_weight=1,
):
"""
Setting of data.
You need set seeds if you want use hard_constraints.
"""
# from PyQt4.QtCore import pyqtRemoveInputHook
... | python | def _ssgc_prepare_data_and_run_computation(
self,
# voxels1, voxels2,
hard_constraints=True,
area_weight=1,
):
"""
Setting of data.
You need set seeds if you want use hard_constraints.
"""
# from PyQt4.QtCore import pyqtRemoveInputHook
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mjirik/imcut | imcut/image_manipulation.py | resize_to_shape | def resize_to_shape(data, shape, zoom=None, mode="nearest", order=0):
"""
Function resize input data to specific shape.
:param data: input 3d array-like data
:param shape: shape of output data
:param zoom: zoom is used for back compatibility
:mode: default is 'nearest'
"""
# @TODO remove... | python | def resize_to_shape(data, shape, zoom=None, mode="nearest", order=0):
"""
Function resize input data to specific shape.
:param data: input 3d array-like data
:param shape: shape of output data
:param zoom: zoom is used for back compatibility
:mode: default is 'nearest'
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mjirik/imcut | imcut/image_manipulation.py | seed_zoom | def seed_zoom(seeds, zoom):
"""
Smart zoom for sparse matrix. If there is resize to bigger resolution
thin line of label could be lost. This function prefers labels larger
then zero. If there is only one small voxel in larger volume with zeros
it is selected.
"""
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# loseeds... | python | def seed_zoom(seeds, zoom):
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Smart zoom for sparse matrix. If there is resize to bigger resolution
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mjirik/imcut | imcut/image_manipulation.py | zoom_to_shape | def zoom_to_shape(data, shape, dtype=None):
"""
Zoom data to specific shape.
"""
import scipy
import scipy.ndimage
zoomd = np.array(shape) / np.array(data.shape, dtype=np.double)
import warnings
datares = scipy.ndimage.interpolation.zoom(data, zoomd, order=0, mode="reflect")
if da... | python | def zoom_to_shape(data, shape, dtype=None):
"""
Zoom data to specific shape.
"""
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import scipy.ndimage
zoomd = np.array(shape) / np.array(data.shape, dtype=np.double)
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datares = scipy.ndimage.interpolation.zoom(data, zoomd, order=0, mode="reflect")
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mjirik/imcut | imcut/image_manipulation.py | crop | def crop(data, crinfo):
"""
Crop the data.
crop(data, crinfo)
:param crinfo: min and max for each axis - [[minX, maxX], [minY, maxY], [minZ, maxZ]]
"""
crinfo = fix_crinfo(crinfo)
return data[
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__int_or_none(crinfo[... | python | def crop(data, crinfo):
"""
Crop the data.
crop(data, crinfo)
:param crinfo: min and max for each axis - [[minX, maxX], [minY, maxY], [minZ, maxZ]]
"""
crinfo = fix_crinfo(crinfo)
return data[
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mjirik/imcut | imcut/image_manipulation.py | combinecrinfo | def combinecrinfo(crinfo1, crinfo2):
"""
Combine two crinfos. First used is crinfo1, second used is crinfo2.
"""
crinfo1 = fix_crinfo(crinfo1)
crinfo2 = fix_crinfo(crinfo2)
crinfo = [
[crinfo1[0][0] + crinfo2[0][0], crinfo1[0][0] + crinfo2[0][1]],
[crinfo1[1][0] + crinfo2[1][0],... | python | def combinecrinfo(crinfo1, crinfo2):
"""
Combine two crinfos. First used is crinfo1, second used is crinfo2.
"""
crinfo1 = fix_crinfo(crinfo1)
crinfo2 = fix_crinfo(crinfo2)
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mjirik/imcut | imcut/image_manipulation.py | crinfo_from_specific_data | def crinfo_from_specific_data(data, margin=0):
"""
Create crinfo of minimum orthogonal nonzero block in input data.
:param data: input data
:param margin: add margin to minimum block
:return:
"""
# hledáme automatický ořez, nonzero dá indexy
logger.debug("crinfo")
logger.debug(str(m... | python | def crinfo_from_specific_data(data, margin=0):
"""
Create crinfo of minimum orthogonal nonzero block in input data.
:param data: input data
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:return:
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# hledáme automatický ořez, nonzero dá indexy
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mjirik/imcut | imcut/image_manipulation.py | uncrop | def uncrop(data, crinfo, orig_shape, resize=False, outside_mode="constant", cval=0):
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Put some boundary to input image.
:param data: input data
:param crinfo: array with minimum and maximum index along each axis
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mjirik/imcut | imcut/image_manipulation.py | fix_crinfo | def fix_crinfo(crinfo, to="axis"):
"""
Function recognize order of crinfo and convert it to proper format.
"""
crinfo = np.asarray(crinfo)
if crinfo.shape[0] == 2:
crinfo = crinfo.T
return crinfo | python | def fix_crinfo(crinfo, to="axis"):
"""
Function recognize order of crinfo and convert it to proper format.
"""
crinfo = np.asarray(crinfo)
if crinfo.shape[0] == 2:
crinfo = crinfo.T
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mjirik/imcut | imcut/graph.py | grid_edges | def grid_edges(shape, inds=None, return_directions=True):
"""
Get list of grid edges
:param shape:
:param inds:
:param return_directions:
:return:
"""
if inds is None:
inds = np.arange(np.prod(shape)).reshape(shape)
# if not self.segparams['use_boundary_penalties'] and \
... | python | def grid_edges(shape, inds=None, return_directions=True):
"""
Get list of grid edges
:param shape:
:param inds:
:param return_directions:
:return:
"""
if inds is None:
inds = np.arange(np.prod(shape)).reshape(shape)
# if not self.segparams['use_boundary_penalties'] and \
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mjirik/imcut | imcut/graph.py | gen_grid_2d | def gen_grid_2d(shape, voxelsize):
"""
Generate list of edges for a base grid.
"""
nr, nc = shape
nrm1, ncm1 = nr - 1, nc - 1
# sh = nm.asarray(shape)
# calculate number of edges, in 2D: (nrows * (ncols - 1)) + ((nrows - 1) * ncols)
nedges = 0
for direction in range(len(shape)):
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"""
Generate list of edges for a base grid.
"""
nr, nc = shape
nrm1, ncm1 = nr - 1, nc - 1
# sh = nm.asarray(shape)
# calculate number of edges, in 2D: (nrows * (ncols - 1)) + ((nrows - 1) * ncols)
nedges = 0
for direction in range(len(shape)):
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mjirik/imcut | imcut/graph.py | write_grid_to_vtk | def write_grid_to_vtk(fname, nodes, edges, node_flag=None, edge_flag=None):
"""
Write nodes and edges to VTK file
:param fname: VTK filename
:param nodes:
:param edges:
:param node_flag: set if this node is really used in output
:param edge_flag: set if this flag is used in output
:retur... | python | def write_grid_to_vtk(fname, nodes, edges, node_flag=None, edge_flag=None):
"""
Write nodes and edges to VTK file
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:param edges:
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mjirik/imcut | imcut/graph.py | Graph.add_nodes | def add_nodes(self, coors, node_low_or_high=None):
"""
Add new nodes at the end of the list.
"""
last = self.lastnode
if type(coors) is nm.ndarray:
if len(coors.shape) == 1:
coors = coors.reshape((1, coors.size))
nadd = coors.shape[0]
... | python | def add_nodes(self, coors, node_low_or_high=None):
"""
Add new nodes at the end of the list.
"""
last = self.lastnode
if type(coors) is nm.ndarray:
if len(coors.shape) == 1:
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mjirik/imcut | imcut/graph.py | Graph._edge_group_substitution | def _edge_group_substitution(
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Reconnect edges.
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mjirik/imcut | imcut/graph.py | Graph.generate_base_grid | def generate_base_grid(self, vtk_filename=None):
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mjirik/imcut | imcut/graph.py | Graph.split_voxels | def split_voxels(self, vtk_filename=None):
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mjirik/imcut | imcut/features.py | select_from_fv_by_seeds | def select_from_fv_by_seeds(fv, seeds, unique_cls):
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chitamoor/Rester | rester/manifest.py | Variables.expand | def expand(self, expression):
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disqus/gutter | gutter/client/__init__.py | get_gutter_client | def get_gutter_client(
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Creates gutter clients and memoizes them in a registry for future quick access.
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disqus/gutter | gutter/client/operators/misc.py | PercentRange._modulo | def _modulo(self, decimal_argument):
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disqus/gutter | gutter/client/models.py | Switch.enabled_for | def enabled_for(self, inpt):
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disqus/gutter | gutter/client/models.py | Manager.switches | def switches(self):
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List of all switches currently registered.
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List of all switches currently registered.
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disqus/gutter | gutter/client/models.py | Manager.switch | def switch(self, name):
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disqus/gutter | gutter/client/models.py | Manager.register | def register(self, switch, signal=signals.switch_registered):
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kaste/mockito-python | mockito/mockito.py | verify | def verify(obj, times=1, atleast=None, atmost=None, between=None,
inorder=False):
"""Central interface to verify interactions.
`verify` uses a fluent interface::
verify(<obj>, times=2).<method_name>(<args>)
`args` can be as concrete as necessary. Often a catch-all is enough,
especi... | python | def verify(obj, times=1, atleast=None, atmost=None, between=None,
inorder=False):
"""Central interface to verify interactions.
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