def pipe_gray3d_slic_features_gmm_graphcut(image,
nb_classes=4,
spacing=(12, 1, 1),
sp_size=15,
sp_regul=0.2,
gc_regul=0.1,
dict_features=FTS_SET_SIMPLE):
""" complete pipe-line for segmentation using superpixels, extracting features
and graphCut segmentation
:param ndarray img: input RGB image
:param int sp_size: initial size of a superpixel(meaning edge lenght)
:param float sp_regul: regularisation in range(0;1) where "0" gives elastic
and "1" nearly square segments
:param int nb_classes: number of classes to be segmented(indexing from 0)
:param (int, int, int) spacing:
:param float gc_regul: regularisation for GC
:return [[int]]: segmentation matrix maping each pixel into a class
>>> np.random.seed(0)
>>> image = np.random.random((5, 125, 150)) / 2.
>>> image[:, :, :75] += 0.5
>>> segm = pipe_gray3d_slic_features_gmm_graphcut(image)
>>> segm.shape
(5, 125, 150)
"""
logging.info('PIPELINE Superpixels-Features-GraphCut')
slic = seg_sp.segment_slic_img3d_gray(image,
sp_size=sp_size,
rltv_compact=sp_regul,
space=spacing)
# plt.imshow(segments)
logging.info('extract segments/superpixels features.')
# f = features.computeColourMean(image, segments)
features, _ = seg_fts.compute_selected_features_gray3d(
image, slic, dict_features)
# merge features together
logging.debug('list of features RAW: %s', repr(features.shape))
features[np.isnan(features)] = 0
logging.info('norm all features.')
features, _ = seg_fts.norm_features(features)
logging.debug('list of features NORM: %s', repr(features.shape))
model = seg_gc.estim_class_model_gmm(features, nb_classes)
proba = model.predict_proba(features)
logging.debug('list of probabilities: %s', repr(proba.shape))
# resultGraph = graphCut.segment_graph_cut_int_vals(segments, prob, gcReg)
graph_labels = seg_gc.segment_graph_cut_general(slic, proba, image,
features, gc_regul)
return graph_labels[slic]
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)