table_height = np.mean(data, axis=1)[2] pc.scan_dataset.table_plane_translation = np.matrix([0., 0., table_height]).T #---------------------------------------------------- ''' # Save results in a nice format. ''' if False: filename = pc.config.path + 'data/' + unique_name + '_segmentation_results.py' roslib.load_manifest('display_stuff') import save_labeled_cloud save_labeled_cloud.save_results(pc.pts3d_bound, pc.intensities, labels, pc.idx_bound, pc.pts3d) save_labeled_cloud.save_feature_data(feature_data['features'], feature_data['point_indices']) save_labeled_cloud.save_map2D(pc.map[0], table_height) #for placement. ''' Comments * pc.pts3d_bound #3 by Nbound * pc.idx_bound #1 by Nbound, indexes to re-project information into full pointcloud. * labels # Nbound, use if loading classifer on featuer_data * pc.map_polys # --> Nbound, labels created from polygons. # Overrided by leaning-based labels, so they are identical # at the end of this document. * Nbound = len(intensities_bound) #--> example range(19598) * feature_data['features'] # Nbound x 35, actually SLIGHTLY less, only 19541. * feature_data['point_indices'] * Nfeature = feature_data['set_size'] #--> slightly less than maximum. #I assume this is at most NUMBER_OF_POINTS -Others: * pc.image_labels * pc.img_mapped
table_height = np.mean(data, axis=1)[2] pc.scan_dataset.table_plane_translation = np.matrix([0.,0.,table_height]).T #---------------------------------------------------- ''' # Save results in a nice format. ''' if False: filename = pc.config.path + 'data/' + unique_name + '_segmentation_results.py' roslib.load_manifest('display_stuff'); import save_labeled_cloud; save_labeled_cloud.save_results(pc.pts3d_bound, pc.intensities, labels, pc.idx_bound, pc.pts3d) save_labeled_cloud.save_feature_data(feature_data['features'], feature_data['point_indices']) save_labeled_cloud.save_map2D(pc.camPts_bound, table_height) #for placement. ''' Comments * pc.pts3d_bound #3 by Nbound * pc.idx_bound #1 by Nbound, indexes to re-project information into full pointcloud. * labels # Nbound, use if loading classifer on featuer_data * pc.map_polys # --> Nbound, labels created from polygons. # Overrided by leaning-based labels, so they are identical # at the end of this document. * Nbound = len(intensities_bound) #--> example range(19598) * feature_data['features'] # Nbound x 35, actually SLIGHTLY less, only 19541. * feature_data['point_indices'] * Nfeature = feature_data['set_size'] #--> slightly less than maximum. #I assume this is at most NUMBER_OF_POINTS -Others: * pc.image_labels * pc.img_mapped
table_height = np.mean(data, axis=1)[2] pc.scan_dataset.table_plane_translation = np.matrix([0.,0.,table_height]).T #---------------------------------------------------- ''' # Save results in a nice format. ''' if False: filename = pc.config.path + 'data/' + unique_name + '_segmentation_results.py' roslib.load_manifest('display_stuff'); import save_labeled_cloud; save_labeled_cloud.save_results(pc.pts3d_bound, pc.intensities, labels, pc.idx_bound, pc.pts3d) save_labeled_cloud.save_feature_data(feature_data['features'], feature_data['point_indices']) save_labeled_cloud.save_map2D(pc.map[0], table_height) #for placement. ''' Comments * pc.pts3d_bound #3 by Nbound * pc.idx_bound #1 by Nbound, indexes to re-project information into full pointcloud. * labels # Nbound, use if loading classifer on featuer_data * pc.map_polys # --> Nbound, labels created from polygons. # Overrided by leaning-based labels, so they are identical # at the end of this document. * Nbound = len(intensities_bound) #--> example range(19598) * feature_data['features'] # Nbound x 35, actually SLIGHTLY less, only 19541. * feature_data['point_indices'] * Nfeature = feature_data['set_size'] #--> slightly less than maximum. #I assume this is at most NUMBER_OF_POINTS -Others: * pc.image_labels * pc.img_mapped