def test_ellipse_fitting(self,
name='insitu7545',
table_prob=TABLE_FB_PROBA):
""" """
img, _ = tl_data.load_image_2d(os.path.join(PATH_IMAGES,
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'))
path_center = os.path.join(PATH_CENTRE, name + '.csv')
centers = pd.read_csv(path_center, index_col=0).values[:, [1, 0]]
slic, points_all, labels = seg_fit.get_slic_points_labels(
seg, slic_size=20, slic_regul=0.3)
weights = np.bincount(slic.ravel())
points_centers = seg_fit.prepare_boundary_points_ray_edge(
seg, centers, close_points=5)
segm = np.zeros(seg.shape)
ellipses, crits = [], []
for i, points in enumerate(points_centers):
model, _ = seg_fit.ransac_segm(points,
seg_fit.EllipseModelSegm,
points_all,
weights,
labels,
table_prob,
min_samples=0.6,
residual_threshold=15,
max_trials=50)
if model is None:
continue
ellipses.append(model.params)
crit = model.criterion(points_all, weights, labels, table_prob)
crits.append(np.round(crit))
logging.info('model params: %s', repr(model.params))
logging.info('-> crit: %f', crit)
c1, c2, h, w, phi = model.params
rr, cc = tl_visu.ellipse(int(c1), int(c2), int(h), int(w), phi,
segm.shape)
segm[rr, cc] = (i + 1)
if img.ndim == 3:
img = img[:, :, 0]
fig = tl_visu.figure_ellipse_fitting(img, seg, ellipses, centers,
crits)
fig_name = 'ellipse-fitting_%s.pdf' % name
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.ravel())
self.assertGreaterEqual(score, 0.5)
def add_overlap_ellipse(segm, ellipse_params, label, thr_overlap=1.):
""" add to existing image ellipse with specific label
if the new ellipse does not ouvelap with already existing object / ellipse
:param ndarray segm: segmentation
:param tuple ellipse_params: parameters
:param int label: selected label
:param float thr_overlap: relative overlap with existing objects
:return ndarray:
>>> seg = np.zeros((15, 20), dtype=int)
>>> ell_params = 7, 10, 5, 8, np.deg2rad(30)
>>> ell = add_overlap_ellipse(seg, ell_params, 1)
>>> ell
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> ell2_params = 4, 5, 2, 3, np.deg2rad(-30)
>>> add_overlap_ellipse(ell, ell2_params, 2)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
"""
if not ellipse_params:
return segm
mask = np.zeros(segm.shape)
c1, c2, h, w, phi = ellipse_params
rr, cc = ellipse(int(c1),
int(c2),
int(h),
int(w),
orientation=phi,
shape=segm.shape)
mask[rr, cc] = 1
# filter overlapping ellipses
for lb in range(1, int(np.max(segm) + 1)):
overlap = np.sum(np.logical_and(segm == lb, mask == 1))
# together = np.sum(np.logical_or(segm == lb, mask == 1))
# ratio = float(overlap) / float(together)
sizes = [s for s in [np.sum(segm == lb), np.sum(mask == 1)] if s > 0]
if not sizes:
return segm
ratio = float(overlap) / float(min(sizes))
# if there is already ellipse with such size, return just the segment
if ratio > thr_overlap:
return segm
segm[mask == 1] = label
return segm