Ejemplo n.º 1
0
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
Ejemplo n.º 2
0
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
Ejemplo n.º 3
0
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