def load_center_evaluate(idx_row,
df_annot,
path_annot,
path_visu=None,
col_prefix=''):
""" complete pipeline fon input image and seg_pipe, such that load them,
generate points, compute features and using given classifier predict labels
:param (int, DF:row) idx_row:
:param df_annot:
:param str path_annot:
:param str path_visu:
:param str col_prefix:
:return {str: float}:
"""
idx, row = idx_row
dict_row = dict(row)
dict_row['image'] = os.path.splitext(
os.path.basename(dict_row['path_image']))[0]
if idx not in df_annot.index:
logging.debug(
'particular image/slice "%s" does not contain eggs '
'of selected stage %s', idx, col_prefix)
return dict_row
name, img, segm, centres = run_train.load_image_segm_center((None, row))
if centres is None:
logging.debug('center missing "%s"', idx)
return dict_row
assert all(c in df_annot.columns for c in tl_visu.COLUMNS_POSITION_EGG_ANNOT), \
'some required columns %s are missing for %s' % \
(tl_visu.COLUMNS_POSITION_EGG_ANNOT, df_annot.columns)
mask_eggs = estimate_eggs_from_info(df_annot.loc[idx], img.shape[:2])
try:
if EXPORT_ANNOT_EGGS:
path_img = os.path.join(path_annot, idx + '.png')
tl_data.io_imsave(path_img, mask_eggs.astype(np.uint8))
if VISUAL_ANNOT_EGGS:
fig = tl_visu.figure_image_segm_results(img, mask_eggs)
fig.savefig(os.path.join(path_visu, idx + '_eggs.png'))
plt.close(fig)
if VISUAL_SEGM_CENTRES:
run_clust.export_draw_image_centers_clusters(path_visu,
name,
img,
centres,
segm=segm)
labels = np.array([1] * len(centres))
dict_stat = compute_statistic_eggs_centres(dict_row, centres, labels,
mask_eggs, img, segm,
path_visu, col_prefix)
except Exception:
logging.exception('load_center_evaluate')
dict_stat = dict_row
return dict_stat
def export_visual(idx_name, img, segm, debug_visual=None, path_out=None, path_visu=None):
""" export visualisations
:param str idx_name:
:param ndarray img: input image
:param ndarray segm: resulting segmentation
:param debug_visual: dictionary with debug images
:param str path_out: path to dir with segmentation
:param str path_visu: path to dir with debug images
"""
logging.info('export results and visualization...')
if set(np.unique(segm)) <= {0, 1}:
segm *= 255
path_img = os.path.join(path_out, str(idx_name) + '.png')
logging.debug('exporting segmentation: %s', path_img)
im_seg = Image.fromarray(segm.astype(np.uint8))
im_seg.convert('L').save(path_img)
# io.imsave(path_img, segm)
if path_visu is not None and os.path.isdir(path_visu):
path_fig = os.path.join(path_visu, str(idx_name) + '.png')
logging.debug('exporting segmentation results: %s', path_fig)
fig = tl_visu.figure_image_segm_results(img, segm)
fig.savefig(path_fig)
plt.close(fig)
if path_visu is not None and os.path.isdir(path_visu) and debug_visual is not None:
path_fig = os.path.join(path_visu, str(idx_name) + '_debug.png')
logging.debug('exporting (debug) visualization: %s', path_fig)
fig = tl_visu.figure_segm_graphcut_debug(debug_visual)
fig.savefig(path_fig, bbox_inches='tight', pad_inches=0.1)
plt.close(fig)
def show_segm_results_2d(img, seg, path_dir, fig_name='temp-segm_.png'):
""" show and expert segmentation results
:param ndarray img: input image
:param ndarray seg: resulting segmentation
:param str path_dir: path to the visualisations
:param str fig_name: figure name
"""
fig = figure_image_segm_results(img, seg)
path_fig = os.path.join(path_dir, fig_name)
fig.savefig(path_fig, bbox_inches='tight', pad_inches=0.1)
if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
plt.show()
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)
def export_draw_image_segm_contour(img, segm, path_out, name, suffix=''):
logging.debug('export draw image segmentation countours: %s', name)
fig = tl_visu.figure_image_segm_results(img, segm)
fig.savefig(os.path.join(path_out, name + suffix + '.png'))
plt.close(fig)
