def test_count_transitions_segment(self):
img = self.img[:, :, 0]
annot = self.annot.astype(int)
slic = segment_slic_img2d(img, sp_size=15, relative_compact=0.2)
label_hist = histogram_regions_labels_norm(slic, annot)
labels = np.argmax(label_hist, axis=1)
trans = count_label_transitions_connected_segments({'a': slic}, {'a': labels})
path_csv = os.path.join(PATH_OUTPUT, 'labels_transitions.csv')
pd.DataFrame(trans).to_csv(path_csv)
gc_regul = compute_pairwise_cost_from_transitions(trans, 10.)
np.random.seed(0)
features = np.tile(labels, (5, 1)).T.astype(float)
features += np.random.random(features.shape) - 0.5
gmm = estim_class_model_gmm(features, 4)
proba = gmm.predict_proba(features)
segment_graph_cut_general(slic, proba, gc_regul)
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
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
: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'])
slic = seg_spx.segment_slic_img2d(img, sp_size=params['slic_size'],
rltv_compact=params['slic_regul'])
img = seg_pipe.convert_img_color_space(img, params.get('clr_space', 'rgb'))
features, _ = seg_fts.compute_selected_features_img2d(img, slic,
params['features'])
labels = classif.predict(features)
segm = labels[slic]
path_img = os.path.join(path_out, idx_name + '.png')
logging.debug('export segmentation: %s', path_img)
img_seg = Image.fromarray(segm.astype(np.uint8))
img_seg.convert('L').save(path_img)
# io.imsave(path_img, segm)
# plt.imsave(os.path.join(path_out, idx_name + '_rgb.png'), seg_pipe)
if path_visu is not None and os.path.isdir(path_visu):
export_draw_image_segm_contour(img, segm, path_visu, idx_name)
try: # in case some classiefier do not support predict_proba
proba = classif.predict_proba(features)
segm_soft = proba[slic]
path_npz = os.path.join(path_out, idx_name + '.npz')
np.savez_compressed(path_npz, segm_soft)
except Exception:
logging.warning('classif: %s not support predict_proba(.)',
repr(classif))
proba = None
segm_soft = None
# if probabilities was not estimated of GC regul. is zero
if proba is not None and params['gc_regul'] > 0:
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)
labels_gc = seg_gc.segment_graph_cut_general(slic, proba, img, features,
gc_regul, edge_type=params['gc_edge_type'])
# labels_gc = seg_gc.segment_graph_cut_simple(slic, proba, gc_regul)
segm_gc = labels_gc[slic]
# relabel according classif classes
segm_gc = classif.classes_[segm_gc]
path_img = os.path.join(path_out, idx_name + '_gc.png')
logging.debug('export segmentation: %s', path_img)
img_seg_gc = Image.fromarray(segm_gc.astype(np.uint8))
img_seg_gc.convert('L').save(path_img)
# io.imsave(path_img, segm_gc)
if path_visu is not None and os.path.isdir(path_visu):
export_draw_image_segm_contour(img, segm_gc, path_visu,
idx_name, '_gc')
if show_debug_imgs:
labels_map = np.argmax(proba, axis=1)
plt.imsave(os.path.join(path_visu, idx_name + '_map.png'),
labels_map[slic])
if not segm_soft is None:
for lb in range(segm_soft.shape[2]):
uc_name = idx_name + '_gc_unary-lb%i.png' % lb
plt.imsave(os.path.join(path_visu, uc_name),
segm_soft[:, :, lb], vmin=0., vmax=1.,
cmap=plt.cm.Greens)
else:
segm_gc = np.zeros(segm.shape)
# gc.collect(), time.sleep(1)
return idx_name, segm, segm_gc
