def wrapper_relabel_segm(annot_segm):
annot, segm = annot_segm
try:
segm = seg_lbs.relabel_max_overlap_unique(annot, segm)
except Exception:
logging.error(traceback.format_exc())
return segm
def export_visual(df_row, path_out, relabel=True):
""" given visualisation of segmented image and annotation
:param {str: ...} df_row:
:param str path_out: path to the visualisation directory
:param bool relabel: whether relabel segmentation as sequential
"""
annot, _ = tl_data.load_image_2d(df_row['path_1'])
segm, _ = tl_data.load_image_2d(df_row['path_2'])
img = None
if 'path_3' in df_row:
img, _ = tl_data.load_image_2d(df_row['path_3'])
if relabel:
annot = relabel_sequential(annot)[0]
segm = seg_lbs.relabel_max_overlap_unique(annot, segm)
fig = seg_visu.figure_overlap_annot_segm_image(annot, segm, img)
name = os.path.splitext(os.path.basename(df_row['path_1']))[0]
logging.debug('>> exporting -> %s', name)
fig.savefig(os.path.join(path_out, '%s.png' % name))
def compute_metrics(row):
""" load segmentation and compute similarity metrics
:param dict row:
:return {str: float}:
"""
logging.debug('loading annot "%s"\n and segm "%s"', row['path_annot'],
row['path_egg-segm'])
annot, _ = tl_data.load_image_2d(row['path_annot'])
segm, _ = tl_data.load_image_2d(row['path_egg-segm'])
if annot.shape != segm.shape:
raise ImageDimensionError('dimension do mot match %r - %r' %
(annot.shape, segm.shape))
jacobs = []
segm = seg_lbs.relabel_max_overlap_unique(annot, segm, keep_bg=True)
for lb in np.unique(annot)[1:]:
annot_obj = (annot == lb)
segm_obj = (segm == lb)
# label_hist = seg_lb.histogram_regions_labels_counts(segm, annot_obj)
# segm_obj = np.argmax(label_hist, axis=1)[segm]
sum_or = np.sum(np.logical_or(annot_obj, segm_obj))
jaccoby = np.sum(np.logical_and(annot_obj, segm_obj)) / float(sum_or)
jacobs.append(jaccoby)
if not jacobs:
jacobs.append(0)
# avg_weight = 'samples' if len(np.unique(annot)) > 2 else 'binary'
y_true, y_pred = annot.ravel(), segm.ravel()
dict_eval = {
'name': os.path.basename(row['path_annot']),
'ARS': metrics.adjusted_rand_score(y_true, y_pred),
'Jaccard': np.mean(jacobs),
'f1': metrics.f1_score(y_true, y_pred, average='micro'),
'accuracy': metrics.accuracy_score(y_true, y_pred),
'precision': metrics.precision_score(y_true, y_pred, average='micro'),
'recall': metrics.recall_score(y_true, y_pred, average='micro'),
}
return dict_eval