def create_image(data, sizes, clim, colors_ratio, prespective, modality,
colormap, horz_cross, vert_corss):
image_name = '{}_{}.png'.format(modality, prespective)
if image_name not in bpy.data.images:
image = bpy.data.images.new(image_name,
width=sizes[0],
height=sizes[1])
else:
image = bpy.data.images[image_name]
try:
colors = calc_colors(data, clim[0], colors_ratio, colormap)
# add alpha value
pixels = np.ones((colors.shape[0], colors.shape[1], 4))
pixels[:, :, :3] = colors
# print(prespective, horz_cross, vert_corss)
if 0 <= vert_corss < data.shape[1]:
for x in range(data.shape[0]):
pixels[x, vert_corss] = [0, 1, 0, 1]
if 0 <= horz_cross < data.shape[0]:
for y in range(data.shape[1]):
pixels[horz_cross, y] = [0, 1, 0, 1]
image.pixels = pixels.ravel()
return image
except:
print(traceback.format_exc())
return None
def calc_slice_pixels(mmvt,
data,
sizes,
max_sizes,
clim,
colors_ratio,
colormap,
zoom_around_voxel,
pixels_zoom,
mark_voxel=True,
pial_vol_mask_data=None,
dural_vol_mask_data=None,
fmri_vol_data=None):
colors = calc_colors(data, clim[0], colors_ratio, colormap)
extra = [
int((max_sizes[0] - sizes[0]) / 2),
int((max_sizes[1] - sizes[1]) / 2)
]
if max_sizes[0] > sizes[0] and max_sizes[1] == sizes[1]:
dark = np.zeros((colors.shape[0], extra[0], 3))
colors = np.concatenate((dark, colors, dark), axis=1)
if max_sizes[1] > sizes[1] and max_sizes[0] == sizes[0]:
dark = np.zeros((extra[1], colors.shape[1], 3))
colors = np.concatenate((dark, colors, dark), axis=0)
if zoom_around_voxel and mark_voxel:
# todo: in very close zoom the red doesn't cover the whole pixel
zoom_factor = np.rint(256 / (pixels_zoom * 2)).astype(int)
colors[128:128 + zoom_factor, 128:128 + zoom_factor] = [1, 0, 0]
if pial_vol_mask_data is not None:
colors[np.where(pial_vol_mask_data)] = tuple(
bpy.context.scene.slices_show_pial_color)
if dural_vol_mask_data is not None:
colors[np.where(dural_vol_mask_data)] = tuple(
bpy.context.scene.slices_show_dural_color)
if fmri_vol_data is not None:
if mmvt.coloring.get_use_abs_threshold():
fmri_inds = np.where(
np.abs(fmri_vol_data) >
bpy.context.scene.coloring_lower_threshold)
else:
fmri_inds = np.where(
fmri_vol_data) > bpy.context.scene.coloring_lower_threshold
if len(fmri_inds[0]) > 0:
colors[fmri_inds] = mmvt.coloring.calc_colors(
fmri_vol_data[fmri_inds])
pixels = np.ones((colors.shape[0], colors.shape[1], 4))
pixels[:, :, :3] = colors
return pixels
def calc_slice_pixels(data, sizes, max_sizes, clim, colors_ratio, colormap, zoom_around_voxel, pixels_zoom,
mark_voxel=True, pial_vol_mask_data=None):
colors = calc_colors(data, clim[0], colors_ratio, colormap)
extra = [int((max_sizes[0] - sizes[0]) / 2), int((max_sizes[1] - sizes[1]) / 2)]
if max_sizes[0] > sizes[0] and max_sizes[1] == sizes[1]:
dark = np.zeros((colors.shape[0], extra[0], 3))
colors = np.concatenate((dark, colors, dark), axis=1)
if max_sizes[1] > sizes[1] and max_sizes[0] == sizes[0]:
dark = np.zeros((extra[1], colors.shape[1], 3))
colors = np.concatenate((dark, colors, dark), axis=0)
if zoom_around_voxel and mark_voxel:
# todo: in very close zoom the red doesn't cover the whole pixel
zoom_factor = np.rint(256 / (pixels_zoom * 2)).astype(int)
colors[128:128 + zoom_factor, 128:128 + zoom_factor] = [1, 0, 0]
if pial_vol_mask_data is not None:
colors[np.where(pial_vol_mask_data)] = tuple(bpy.context.scene.slices_show_pial_color)
pixels = np.ones((colors.shape[0], colors.shape[1], 4))
pixels[:, :, :3] = colors
return pixels
def calc_slice_pixels(
mmvt,
data,
sizes,
max_sizes,
clim,
colors_ratio,
colormap,
zoom_around_voxel,
pixels_zoom,
mark_voxel=True,
vol_mask_data_dict={},
fmri_vol_data=None,
t1_ct_mask=None): # pial_vol_mask_data=None, dural_vol_mask_data=None
colors = calc_colors(data, clim[0], colors_ratio, colormap)
max_sizes = [256, 256, 256]
extra = [
int((max_sizes[0] - sizes[0]) / 2),
int((max_sizes[1] - sizes[1]) / 2)
]
if max_sizes[0] > sizes[0]: # and max_sizes[1] == sizes[1]:
dark = np.zeros((colors.shape[0], extra[0], 3))
colors = np.concatenate((dark, colors, dark), axis=1)
if max_sizes[1] > sizes[1]: # and max_sizes[0] == sizes[0]:
dark = np.zeros((extra[1], colors.shape[1], 3))
colors = np.concatenate((dark, colors, dark), axis=0)
extra = [max_sizes[0] - colors.shape[0], max_sizes[1] - colors.shape[1]]
if extra[0] > 0:
colors = np.concatenate((np.zeros(
(extra[0], colors.shape[1], 3)), colors),
axis=0)
if extra[1] > 0:
colors = np.concatenate((np.zeros(
(colors.shape[0], extra[1], 3)), colors),
axis=1)
if zoom_around_voxel and mark_voxel:
# todo: in very close zoom the red doesn't cover the whole pixel
zoom_factor = np.rint(256 / (pixels_zoom * 2)).astype(int)
colors[128:128 + zoom_factor, 128:128 + zoom_factor] = [1, 0, 0]
for surf_name, surf_data in vol_mask_data_dict.items():
colors[np.where(surf_data)] = tuple(
bpy.context.scene.get('slices_show_{}_color'.format(surf_name),
(1, 0, 0)))
# if pial_vol_mask_data is not None:
# colors[np.where(pial_vol_mask_data)] = tuple(bpy.context.scene.slices_show_pial_color)
# if dural_vol_mask_data is not None:
# colors[np.where(dural_vol_mask_data)] = tuple(bpy.context.scene.slices_show_dural_color)
if fmri_vol_data is not None:
if mmvt.coloring.get_use_abs_threshold():
fmri_inds = np.where(
np.abs(fmri_vol_data) >
bpy.context.scene.coloring_lower_threshold)
else:
fmri_inds = np.where(
fmri_vol_data) > bpy.context.scene.coloring_lower_threshold
if len(fmri_inds[0]) > 0:
colors[fmri_inds] = mmvt.coloring.calc_colors(
fmri_vol_data[fmri_inds])
if t1_ct_mask is not None:
colors[np.where(t1_ct_mask)] = (0, 0, 256)
pixels = np.ones((colors.shape[0], colors.shape[1], 4))
pixels[:, :, :3] = colors
return pixels