def wrapper_compute_color2d_slic_features_labels(img_annot, sp_size, sp_regul,
dict_features, label_purity):
img, annot = img_annot
# in case of binary annotation convert it to integers labels
annot = annot.astype(int)
assert img.shape[:2] == annot.shape[:2], \
'image (%s) and annot (%s) should match' \
% (repr(img.shape), repr(annot.shape))
slic, features = compute_color2d_superpixels_features(img,
dict_features,
sp_size=sp_size,
sp_regul=sp_regul,
fts_norm=False)
neg_label = np.max(annot) + 1 if np.sum(annot < 0) > 0 else None
if neg_label is not None:
annot[annot < 0] = neg_label
label_hist = seg_lbs.histogram_regions_labels_norm(slic, annot)
labels = np.argmax(label_hist, axis=1)
purity = np.max(label_hist, axis=1)
if neg_label is not None:
labels[labels == neg_label] = -1
labels[purity < label_purity] = -1
return slic, features, labels
def load_image_annot_compute_features_labels(idx_row,
params,
show_debug_imgs=SHOW_DEBUG_IMAGES
):
""" load image and annotation, and compute superpixel features and labels
:param (int, {...}) idx_row: row from table with paths
:param {str: ...} params: segmentation parameters
:param bool show_debug_imgs: whether show debug images
:return (...):
"""
def _path_out_img(params, dir_name, name):
return os.path.join(params['path_exp'], dir_name, name + '.png')
idx, row = idx_row
idx_name = get_idx_name(idx, row['path_image'])
img = load_image(row['path_image'], params['img_type'])
annot = load_image(row['path_annot'], '2d_segm')
logging.debug('.. processing: %s', idx_name)
assert img.shape[:2] == annot.shape[:2], \
'individual size of image %s and seg_pipe %s for "%s" - "%s"' % \
(repr(img.shape), repr(annot.shape), row['path_image'], row['path_annot'])
if show_debug_imgs:
plt.imsave(_path_out_img(params, FOLDER_IMAGE, idx_name),
img,
cmap=plt.cm.gray)
plt.imsave(_path_out_img(params, FOLDER_ANNOT, idx_name), annot)
# duplicate gray band to be as rgb
# if img.ndim == 2:
# img = np.rollaxis(np.tile(img, (3, 1, 1)), 0, 3)
slic = seg_spx.segment_slic_img2d(img,
sp_size=params['slic_size'],
rltv_compact=params['slic_regul'])
img = tl_data.convert_img_color_from_rgb(img,
params.get('clr_space', 'rgb'))
logging.debug('computed SLIC with %i labels', slic.max())
if show_debug_imgs:
img_rgb = use_rgb_image(img)
img_slic = segmentation.mark_boundaries(img_rgb,
slic,
color=(1, 0, 0),
mode='subpixel')
plt.imsave(_path_out_img(params, FOLDER_SLIC, idx_name),
np.clip(img_slic, 0, 1))
slic_label_hist = seg_label.histogram_regions_labels_norm(slic, annot)
labels = np.argmax(slic_label_hist, axis=1)
slic_annot = labels[slic]
if show_debug_imgs:
plt.imsave(_path_out_img(params, FOLDER_SLIC_ANNOT, idx_name),
np.clip(slic_annot, 0, slic_annot.max()))
features, feature_names = seg_fts.compute_selected_features_img2d(
img, slic, params['features'])
return idx_name, img, annot, slic, features, labels, \
slic_label_hist, feature_names
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 wrapper_compute_color2d_slic_features_labels(img_annot, sp_size, sp_regul,
dict_features, label_purity):
img, annot = img_annot
# in case of binary annotation convert it to integers labels
annot = annot.astype(int)
if img.shape[:2] != annot.shape[:2]:
raise ImageDimensionError('image %r and annot %r should match' %
(img.shape, annot.shape))
slic, features = compute_color2d_superpixels_features(img,
dict_features,
sp_size=sp_size,
sp_regul=sp_regul)
neg_label = np.max(annot) + 1 if np.sum(annot < 0) > 0 else None
if neg_label is not None:
annot[annot < 0] = neg_label
label_hist = histogram_regions_labels_norm(slic, annot)
labels = np.argmax(label_hist, axis=1)
purity = np.max(label_hist, axis=1)
if neg_label is not None:
labels[labels == neg_label] = -1
labels[purity < label_purity] = -1
return slic, features, labels
