def filter_depth(xyz, seg, regions):
plane_info = {
'label': [],
'coeff': [],
'support': [],
'rot': [],
'area': []
}
for idx, l in enumerate(regions['label']):
mask = seg == l
pt_sample = TextRegions.sample_grid_neighbours(
mask, TextRegions.ransac_fit_trials, step=3)
if pt_sample is None:
continue # not enough points for RANSAC
# get-depths
pt = xyz[mask]
plane_model = su.isplanar(pt, pt_sample, TextRegions.dist_thresh,
TextRegions.num_inlier,
TextRegions.min_z_projection)
if plane_model is not None:
plane_coeff = plane_model[0]
if np.abs(plane_coeff[2]) > TextRegions.min_z_projection:
plane_info['label'].append(l)
plane_info['coeff'].append(plane_model[0])
plane_info['support'].append(plane_model[1])
plane_info['rot'].append(regions['rot'][idx])
plane_info['area'].append(regions['area'][idx])
return plane_info
def filter_depth(xyz,seg,regions):
plane_info = {'label':[],
'coeff':[],
'support':[],
'rot':[],
'area':[]}
for idx,l in enumerate(regions['label']):
mask = seg==l
pt_sample = TextRegions.sample_grid_neighbours(mask,TextRegions.ransac_fit_trials,step=3)
if pt_sample is None:
continue #not enough points for RANSAC
# get-depths
pt = xyz[mask]
plane_model = su.isplanar(pt, pt_sample,
TextRegions.dist_thresh,
TextRegions.num_inlier,
TextRegions.min_z_projection)
if plane_model is not None:
plane_coeff = plane_model[0]
if np.abs(plane_coeff[2])>TextRegions.min_z_projection:
plane_info['label'].append(l)
plane_info['coeff'].append(plane_model[0])
plane_info['support'].append(plane_model[1])
plane_info['rot'].append(regions['rot'][idx])
plane_info['area'].append(regions['area'][idx])
return plane_info
