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_debugs_2d(images, path_dir, fig_name='temp-debug_.png'):
""" show and expert partial segmentation results
:param dict images:
:param str path_dir: path to the visualisations
:param str fig_name: figure name
"""
if not images:
return
fig = figure_segm_graphcut_debug(images)
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 segment_image(imgs_idx_path,
params,
classif,
path_out,
path_visu=None,
show_debug_imgs=SHOW_DEBUG_IMAGES):
""" perform image segmentation on input image with given paramters
and trained classifier, and save results
:param (int, str) imgs_idx_path:
:param {str: ...} params: segmentation parameters
:param obj classif: trained classifier
:param str path_out: path for output
:param str path_visu: the existing patch means export also visualisation
:param bool show_debug_imgs: whether show debug images
:return (str, ndarray, ndarray):
"""
idx, path_img = parse_imgs_idx_path(imgs_idx_path)
logging.debug('segmenting image: "%s"', path_img)
idx_name = get_idx_name(idx, path_img)
img = load_image(path_img, params['img_type'])
debug_visual = dict() if show_debug_imgs else None
gc_regul = params['gc_regul']
if params['gc_use_trans']:
label_penalty = seg_gc.compute_pairwise_cost_from_transitions(
params['label_transitions'])
gc_regul = (gc_regul * label_penalty)
segm_gc, segm_soft = seg_pipe.segment_color2d_slic_features_model_graphcut(
img,
classif,
sp_size=params['slic_size'],
sp_regul=params['slic_regul'],
dict_features=params['features'],
gc_regul=gc_regul,
gc_edge_type=params['gc_edge_type'],
debug_visual=debug_visual)
segm_map = np.argmax(segm_soft, axis=-1)
for segm, suffix in [(segm_gc, ''), (segm_map, '_MAP')]:
path_img = os.path.join(path_out, idx_name + suffix + '.png')
logging.debug('export segmentation: %s', path_img)
if np.max(segm) <= 1:
img_seg = Image.fromarray((segm * 255).astype(np.uint8))
else:
img_seg = Image.fromarray(segm.astype(np.uint8))
img_seg.convert('L').save(path_img)
# io.imsave(path_img, segm_gc)
path_npz = os.path.join(path_out, idx_name + '.npz')
np.savez_compressed(path_npz, segm_soft)
# plt.imsave(os.path.join(path_out, idx_name + '_rgb.png'), seg_pipe)
if params.get('visual', False) and path_visu is not None \
and os.path.isdir(path_visu):
export_draw_image_segm_contour(img, segm_gc, path_visu, idx_name,
'_GC')
export_draw_image_segm_contour(img, segm_map, path_visu, idx_name,
'_MAP')
if show_debug_imgs 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)
gc.collect()
time.sleep(1)
return idx_name, segm_map, segm_gc
