def init_layout(self,
view_set=cfg.defacing_view_set,
num_rows_per_view=cfg.defacing_num_rows_per_view,
num_slices_per_view=cfg.defacing_num_slices_per_view,
padding=cfg.default_padding):
"""initializes the layout"""
plt.style.use('dark_background')
# vmin/vmax are controlled, because we rescale all to [0, 1]
self.display_params = dict(interpolation='none',
aspect='equal',
origin='lower',
cmap='gray',
vmin=0.0,
vmax=1.0)
self.figsize = cfg.default_review_figsize
self.collage = Collage(view_set=view_set,
num_slices=num_slices_per_view,
num_rows=num_rows_per_view,
display_params=self.display_params,
bounding_rect=cfg.bbox_defacing_MRI_review,
figsize=self.figsize)
self.fig = self.collage.fig
self.fig.canvas.set_window_title('VisualQC defacing : {} {} '
''.format(self.in_dir,
self.defaced_name))
self.padding = padding
def test_collage_class():
img_path = pjoin(base_dir, '3569_bl_PPMI.nii')
img = read_image(img_path, None)
scaled = scale_0to1(img)
c = Collage(num_slices=15, view_set=(0, 1), num_rows=3)
try:
c.attach(scaled)
except:
raise ValueError('Attach does not work')
try:
c.transform_and_attach(scaled, np.square)
except:
raise ValueError('transform_and_attach does not work')
try:
print(c)
except:
raise ValueError('repr implementation failed')
def checkerboard(
img_spec1=None,
img_spec2=None,
patch_size=10,
view_set=(0, 1, 2),
num_slices=(10, ),
num_rows=2,
rescale_method='global',
background_threshold=0.05,
annot=None,
padding=5,
output_path=None,
figsize=None,
):
"""
Checkerboard mixer.
Parameters
----------
img_spec1 : str or nibabel image-like object
MR image (or path to one) to be visualized
img_spec2 : str or nibabel image-like object
MR image (or path to one) to be visualized
patch_size : int or list or (int, int) or None
size of checker patch (either square or rectangular)
If None, number of voxels/patch are chosen such that,
there will be 7 patches through the width/height.
view_set : iterable
Integers specifying the dimensions to be visualized.
Choices: one or more of (0, 1, 2) for a 3D image
num_slices : int or iterable of size as view_set
number of slices to be selected for each view
Must be of the same length as view_set,
each element specifying the number of slices for each dimension.
If only one number is given, same number will be chosen for all dimensions.
num_rows : int
number of rows (top to bottom) per each of 3 dimensions
rescale_method : bool or str or list or None
Range to rescale the intensity values to
Default: 'global', min and max values computed based on ranges from both images.
If false or None, no rescaling is done (does not work yet).
background_threshold : float or str
A threshold value below which all the background voxels will be set to zero.
Default : 0.05. Other option is a string specifying a percentile: '5%', '10%'.
Specify None if you don't want any thresholding.
annot : str
Text to display to annotate the visualization
padding : int
number of voxels to pad around each panel.
output_path : str
path to save the generate collage to.
figsize : list
Size of figure in inches to be passed on to plt.figure() e.g. [12, 12] or [20, 20]
Returns
-------
fig : figure handle
handle to the collage figure generated.
"""
img_one, img_two = _preprocess_images(img_spec1,
img_spec2,
rescale_method=rescale_method,
bkground_thresh=background_threshold,
padding=padding)
display_params = dict(interpolation='none',
aspect='auto',
origin='lower',
cmap='gray',
vmin=0.0,
vmax=1.0)
mixer = partial(_checker_mixer, checker_size=patch_size)
collage = Collage(view_set=view_set,
num_slices=num_slices,
num_rows=num_rows,
figsize=figsize,
display_params=display_params)
collage.transform_and_attach((img_one, img_two), func=mixer)
collage.save(output_path=output_path, annot=annot)
return collage