def compute_color2d_superpixels_features(image,
dict_features,
sp_size=30,
sp_regul=0.2,
fts_norm=True):
""" segment image into superpixels and estimate features per superpixel
:param ndarray image: input RGB image
:param int sp_size: initial size of a superpixel(meaning edge length)
:param float sp_regul: regularisation in range(0;1) where "0" gives elastic
and "1" nearly square segments
:param {str: [str]} dict_features: list of features to be extracted
:param bool fts_norm: weather normalise features
:return [[int]], [[floats]]: superpixels and related of features
"""
assert sp_regul > 0., 'slic. regularisation must be positive'
logging.debug('run Superpixel clustering.')
slic = seg_sp.segment_slic_img2d(image,
sp_size=sp_size,
rltv_compact=sp_regul)
# plt.figure(), plt.imshow(slic)
logging.debug('extract slic/superpixels features.')
features, _ = seg_fts.compute_selected_features_img2d(
image, slic, dict_features)
logging.debug('list of features RAW: %s', repr(features.shape))
features[np.isnan(features)] = 0
if fts_norm:
logging.debug('norm all features.')
features, _ = seg_fts.norm_features(features)
logging.debug('list of features NORM: %s', repr(features.shape))
return slic, features
def pipe_gray3d_slic_features_model_graphcut(
image,
nb_classes,
dict_features,
spacing=(12, 1, 1),
sp_size=15,
sp_regul=0.2,
gc_regul=0.1,
):
""" complete pipe-line for segmentation using superpixels, extracting features
and graphCut segmentation
:param ndarray image: 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 tuple(int,int,int) spacing:
:param float gc_regul: regularisation for GC
:return list(list(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_model_graphcut(image, 2, {'color': ['mean']})
>>> segm.shape
(5, 125, 150)
"""
logging.info('PIPELINE Superpixels-Features-GraphCut')
slic = segment_slic_img3d_gray(image,
sp_size=sp_size,
relative_compact=sp_regul,
space=spacing)
# plt.imshow(segments)
logging.info('extract segments/superpixels features.')
# f = features.computeColourMean(image, segments)
features, _ = compute_selected_features_gray3d(image, slic, dict_features)
# merge features together
logging.debug('list of features RAW: %r', features.shape)
features[np.isnan(features)] = 0
logging.info('norm all features.')
features, _ = norm_features(features)
logging.debug('list of features NORM: %r', features.shape)
model = estim_class_model(features, nb_classes)
proba = model.predict_proba(features)
logging.debug('list of probabilities: %r', proba.shape)
# resultGraph = graphCut.segment_graph_cut_int_vals(segments, prob, gcReg)
graph_labels = segment_graph_cut_general(slic, proba, image, features,
gc_regul)
return graph_labels[slic]