def export_visual(name, annot, segm, img, path_out, drop_labels, segm_alpha=1.):
""" given visualisation of segmented image and annotation
:param dict df_row:
:param str path_out: path to the visualisation directory
:param [int] drop_labels: whether skip some labels
"""
# relabel for simpler visualisations of class differences
if np.sum(annot < 0) > 0:
annot[annot < 0] = -1
_, lut, _ = relabel_sequential(annot + 1)
lut = fill_lut(lut, segm, offset=1)
annot = lut[annot.astype(int) + 1] - 1
segm = lut[segm.astype(int) + 1] - 1
else:
annot, lut, _ = relabel_sequential(annot)
lut = fill_lut(lut, segm, offset=0)
segm = lut[segm.astype(int)]
# normalise alpha in range (0, 1)
segm_alpha = tl_visu.norm_aplha(segm_alpha)
fig = tl_visu.figure_overlap_annot_segm_image(annot, segm, img,
drop_labels=drop_labels,
segm_alpha=segm_alpha)
logging.debug('>> exporting -> %s', name)
fig.savefig(os.path.join(path_out, '%s.png' % name))
plt.close(fig)
def visualise_overlap(
path_img,
path_seg,
path_out,
b_img_scale=BOOL_IMAGE_RESCALE_INTENSITY,
b_img_contour=BOOL_SAVE_IMAGE_CONTOUR,
b_relabel=BOOL_ANNOT_RELABEL,
segm_alpha=MIDDLE_ALPHA_OVERLAP,
):
img, _ = tl_data.load_image_2d(path_img)
seg, _ = tl_data.load_image_2d(path_seg)
# normalise alpha in range (0, 1)
segm_alpha = tl_visu.norm_aplha(segm_alpha)
if b_relabel:
seg, _, _ = segmentation.relabel_sequential(seg.copy())
if img.ndim == 2: # for gray images of ovary
img = np.rollaxis(np.tile(img, (3, 1, 1)), 0, 3)
if b_img_scale:
p_low, p_high = np.percentile(img, q=(3, 98))
# plt.imshow(255 - img, cmap='Greys')
img = exposure.rescale_intensity(img,
in_range=(p_low, p_high),
out_range='uint8')
if b_img_contour:
path_im_visu = os.path.splitext(path_out)[0] + '_contour.png'
img_contour = segmentation.mark_boundaries(img[:, :, :3],
seg,
color=COLOR_CONTOUR,
mode='subpixel')
plt.imsave(path_im_visu, img_contour)
# else: # for colour images of disc
# mask = (np.sum(img, axis=2) == 0)
# img[mask] = [255, 255, 255]
fig = tl_visu.figure_image_segm_results(img,
seg,
SIZE_SUB_FIGURE,
mid_labels_alpha=segm_alpha,
mid_image_gray=MIDDLE_IMAGE_GRAY)
fig.savefig(path_out)
plt.close(fig)