def load_unit(self, unit_id):
"""Loads the image data for display."""
# starting fresh
for attr in ('current_img_raw', 'current_img', 'saturated_img',
'tails_trimmed_img', 'background_img'):
if hasattr(self, attr):
delattr(self, attr)
t1_mri_path = self.path_getter_inputs(unit_id)
self.current_img_raw = read_image(t1_mri_path, error_msg='T1 mri')
# crop and rescale
self.current_img = scale_0to1(
crop_image(self.current_img_raw, self.padding))
self.currently_showing = None
skip_subject = False
if np.count_nonzero(self.current_img) == 0:
skip_subject = True
print('MR image is empty!')
# # where to save the visualization to
# out_vis_path = pjoin(self.out_dir, 'visual_qc_{}_{}'.format(self.vis_type, unit_id))
return skip_subject
def rescale_without_outliers(img, trim_percentile=1, padding=5):
"""This utility crops the image first, then trims the outliers, and then
rescales it [0, 1]"""
from mrivis.utils import crop_image
return scale_0to1(crop_image(img, padding),
exclude_outliers_below=trim_percentile,
exclude_outliers_above=trim_percentile)
def attach_image_to_foreground_axes(self, image3d, cmap='gray'):
"""Attaches a given image to the foreground axes and bring it forth"""
image3d = crop_image(image3d, self.padding)
image3d = scale_0to1(image3d)
slices = pick_slices(image3d, self.views, self.num_slices_per_view)
for ax_index, (dim_index, slice_index) in enumerate(slices):
slice_data = get_axis(image3d, dim_index, slice_index)
self.images_fg[ax_index].set(data=slice_data, cmap=cmap)
for ax in self.fg_axes:
ax.set(visible=True, zorder=self.layer_order_zoomedin)
def display_unit(self):
"""Adds slice collage to the given axes"""
# crop and rescale
img = crop_image(self.current_img, self.padding)
img = scale_0to1(img)
# adding slices
slices = pick_slices(img, self.views, self.num_slices_per_view)
for ax_index, (dim_index, slice_index) in enumerate(slices):
slice_data = get_axis(img, dim_index, slice_index)
self.images[ax_index].set_data(slice_data)
# updating histogram
self.update_histogram(img)
def collage(img_spec,
num_rows=2,
num_cols=6,
rescale_method='global',
cmap='gray',
annot=None,
padding=5,
bkground_thresh=None,
output_path=None,
figsize=None,
**kwargs):
"Produces a collage of various slices from different orientations in the given 3D image"
num_rows, num_cols, padding = check_params(num_rows, num_cols, padding)
img = read_image(img_spec, bkground_thresh=bkground_thresh)
img = crop_image(img, padding)
img, (min_value, max_value) = check_rescaling_collage(img,
rescale_method,
return_extrema=True)
num_slices_per_view = num_rows * num_cols
slices = pick_slices(img, num_slices_per_view)
plt.style.use('dark_background')
num_axes = 3
if figsize is None:
figsize = [3 * num_axes * num_rows, 3 * num_cols]
fig, ax = plt.subplots(num_axes * num_rows, num_cols, figsize=figsize)
# displaying some annotation text if provided
if annot is not None:
fig.suptitle(annot, backgroundcolor='black', color='g')
display_params = dict(interpolation='none',
cmap=cmap,
aspect='equal',
origin='lower',
vmin=min_value,
vmax=max_value)
ax = ax.flatten()
ax_counter = 0
for dim_index in range(3):
for slice_num in slices[dim_index]:
plt.sca(ax[ax_counter])
ax_counter = ax_counter + 1
slice1 = get_axis(img, dim_index, slice_num)
# slice1 = crop_image(slice1, padding)
plt.imshow(slice1, **display_params)
plt.axis('off')
fig.tight_layout()
if output_path is not None:
output_path = output_path.replace(' ', '_')
fig.savefig(output_path + '.png', bbox_inches='tight')
# plt.close()
return fig