def segment_rg2sp_graphcut(slic,
seg,
centers,
labels_fg_prob,
path_model,
coef_shape,
coef_pairwise=5,
allow_obj_swap=True,
prob_label_trans=(0.1, 0.03),
dict_thresholds=RG2SP_THRESHOLDS,
debug_export=''):
""" wrapper for region growing method with some debug exporting """
if os.path.splitext(path_model)[-1] == '.npz':
shape_model = np.load(path_model)
else:
shape_model = pickle.load(open(path_model, 'rb'))
dict_debug = dict() if os.path.isdir(debug_export) else None
labels_gc = seg_rg.region_growing_shape_slic_graphcut(
seg,
slic,
centers, (shape_model['mix_model'], shape_model['cdfs']),
shape_model['name'],
labels_fg_prob,
coef_shape,
coef_pairwise,
prob_label_trans,
optim_global=True,
allow_obj_swap=allow_obj_swap,
dict_thresholds=dict_thresholds,
nb_iter=250,
dict_debug_history=dict_debug)
if dict_debug is not None:
nb_iter = len(dict_debug['energy'])
for i in range(nb_iter):
fig = tl_visu.figure_rg2sp_debug_complete(seg, slic, dict_debug, i)
fig.savefig(os.path.join(debug_export, 'iter_%03d' % i))
plt.close(fig)
segm_obj = labels_gc[slic]
return segm_obj, centers, None
def test_region_growing_graphcut(self, name='insitu7545'):
""" """
if not os.path.exists(PATH_PKL_MODEL):
self.test_shape_modeling()
# file_model = pickle.load(open(PATH_PKL_MODEL, 'r'))
npz_file = np.load(PATH_PKL_MODEL)
file_model = dict(npz_file[npz_file.files[0]].tolist())
logging.info('loaded model: %s', repr(file_model.keys()))
list_mean_cdf = file_model['cdfs']
model = file_model['mix_model']
img, _ = tl_data.load_image_2d(os.path.join(PATH_IMAGE, name + '.jpg'))
seg, _ = tl_data.load_image_2d(os.path.join(PATH_SEGM, name + '.png'))
annot, _ = tl_data.load_image_2d(
os.path.join(PATH_ANNOT, name + '.png'))
centers = pd.read_csv(os.path.join(PATH_CENTRE, name + '.csv'),
index_col=0).values
centers[:, [0, 1]] = centers[:, [1, 0]]
slic = seg_spx.segment_slic_img2d(img, sp_size=25, rltv_compact=0.3)
dict_debug = {}
labels_gc = seg_rg.region_growing_shape_slic_graphcut(
seg,
slic,
centers, (model, list_mean_cdf),
'set_cdfs',
LABELS_FG_PROB,
coef_shape=5.,
coef_pairwise=15.,
prob_label_trans=[0.1, 0.03],
optim_global=False,
nb_iter=65,
allow_obj_swap=False,
dict_thresholds=DEFAULT_RG2SP_THRESHOLDS,
dict_debug_history=dict_debug)
segm_obj = labels_gc[slic]
logging.info('debug: %s', repr(dict_debug.keys()))
for i in np.linspace(0, len(dict_debug['energy']) - 1, 5):
fig = tl_visu.figure_rg2sp_debug_complete(seg,
slic,
dict_debug,
int(i),
max_size=5)
fig_name = 'RG2Sp_graph-cut_%s_debug-%03d.pdf' % (name, i)
fig.savefig(os.path.join(PATH_OUTPUT, fig_name),
bbox_inches='tight',
pad_inches=0)
plt.close(fig)
score = adjusted_rand_score(annot.ravel(), segm_obj.ravel())
self.assertGreaterEqual(score, 0.5)
expert_segm(name,
img,
seg,
segm_obj,
annot,
str_type='RG2Sp_graph-cut')