logging.info(
"Vehicle voxel data loaded from preexisting file {}".format(
vox_veh_file))
valid_litter_found = "litter" in assembly.voxel_data # Does cached geom incl litter?
else:
logging.info('Commencing voxelization of vehicle model')
assembly.voxelate_geometry()
# Voxelate Litter_Open model on a grid of same size
x_pts, y_pts, z_pts = [
assembly.voxel_data[k] for k in ("x_grid", "y_grid", "z_grid")
]
valid_litter_found = assembly.voxelate_litter(x_pts, y_pts, z_pts)
data_io.save_multi_array(vox_veh_folder, vox_veh_file,
assembly.voxel_data)
logging.info("Vehicle successfully voxelated.")
# All later operations act on pre-voxelated models. Create voxel objects with redundant info.
vehicle = Vehicle(assembly.voxel_data,
vehicle_csys=assembly.get_vehicle_csys())
# Find exit points (hatches + where rear doorway and floor intersect)
# The door and floor may never intersect at all; instead, use the intersection between the
# voxelated arrays, vehicle_interior w/ door.
vox_veh_cabin = vehicle.get_vox_from_coord(
assembly.get_manikin_positions(datum_name="SWRI_H_POINT",
veh_role="troop")[0])
vehicle_interior = vehicle.get_labels(mask_from_voxel=vox_veh_cabin)
# Run floor and ceil finding routines to prepare object for later scripts. Finding floor
def main(settings):
"""
Determine the best pose of the manikin for each position and orientation.
"""
basename = "{}_{}"
# Get one matrix per pose, representing the best possible fit of that pose at a given point
# in the vehicle
walkability_by_pose_name = {manikin_name: load_fit_of_pose_in_vehicle(
basename.format(settings["run_id"], manikin_name),
settings)
for manikin_name in settings["manikin_poses"]}
# TODO: number of best pose may be superfluous. Can probably disable saving this info.
best_pose_number_by_point = score_from_list(walkability_by_pose_name,
settings["manikin_poses"],
settings,
score_key='num',
orientation="overall")
best_score_by_point = score_from_list(walkability_by_pose_name,
settings["manikin_poses"],
settings,
score_key='score',
orientation="overall")
best_pose_for_orientation = {}
best_score_for_orientation = {}
for ang in settings["manikin_orientations"]:
best_pose_for_orientation[str(ang)] = score_from_list(walkability_by_pose_name,
settings["manikin_poses"],
settings,
score_key='num',
orientation=ang)
best_score_for_orientation[str(ang)] = score_from_list(walkability_by_pose_name,
settings["manikin_poses"],
settings,
score_key='score',
orientation=ang)
# Save results
result_folder = "results/{}/{}".format(settings["run_id"], settings["voxel_size"])
saved_file_basename = "{}_vox{}_best_scores".format(
settings["run_id"], settings["voxel_size"])
save_results(best_score_by_point,
result_folder,
saved_file_basename,
plot=settings["show_2d"],
plot_options={"label": "Walkability scores",
"tick_labels": sorted([v['score'] for k, v
in settings["manikin_poses"].items()])})
saved_file_basename = "{}_vox{}_best_poses".format(
settings["run_id"], settings["voxel_size"])
save_results(best_pose_number_by_point,
result_folder,
saved_file_basename,
plot=settings["show_2d"],
plot_options={"label": "Best pose at point",
"tick_labels": sorted([v['num'] for k, v in
settings["manikin_poses"].items()])})
# Also save the best scores per orientation
data_io.save_multi_array(result_folder,
"{}_vox{}_best_poses_by_orientation".format(settings["run_id"],
settings["voxel_size"]),
best_pose_for_orientation)
data_io.save_multi_array(result_folder,
"{}_vox{}_best_scores_by_orientation".format(settings["run_id"],
settings["voxel_size"]),
best_score_for_orientation)
if settings["show_2d"]:
# After saving score data to one big npz file, make the individual plots to match
for ang in settings["manikin_orientations"]:
saved_file_basename = "{}_vox{}_best_scores_ang{}".format(settings["run_id"],
settings["voxel_size"], ang)
render_results.save_heatmap(result_folder,
saved_file_basename,
best_score_for_orientation[str(ang)],
label="Best score for orientation {}".format(ang),
tick_labels=sorted([v['score']
for k, v in settings["manikin_poses"].items()]))
if SETTINGS["debug"] and os.path.exists(os.path.join(vox_veh_folder, vox_veh_file + '.npz')):
# Load from pre-existing file if possible (if debugging option is on)
assembly.voxel_data = data_io.load_array(vox_veh_folder, vox_veh_file, multi_array=True)
logging.info("Vehicle voxel data loaded from preexisting file {}".format(vox_veh_file))
valid_litter_found = "litter" in assembly.voxel_data # Does cached geom incl litter?
else:
logging.info('Commencing voxelization of vehicle model')
assembly.voxelate_geometry()
# Voxelate Litter_Open model on a grid of same size
x_pts, y_pts, z_pts = [assembly.voxel_data[k] for k in ("x_grid", "y_grid", "z_grid")]
valid_litter_found = assembly.voxelate_litter(x_pts, y_pts, z_pts)
data_io.save_multi_array(vox_veh_folder, vox_veh_file, assembly.voxel_data)
logging.info("Vehicle successfully voxelated.")
# All later operations act on pre-voxelated models. Create voxel objects with redundant info.
vehicle = Vehicle(assembly.voxel_data, vehicle_csys=assembly.get_vehicle_csys())
# Find exit points (hatches + where rear doorway and floor intersect)
# The door and floor may never intersect at all; instead, use the intersection between the
# voxelated arrays, vehicle_interior w/ door.
vox_veh_cabin = vehicle.get_vox_from_coord(
assembly.get_manikin_positions(datum_name="SWRI_H_POINT", veh_role="troop")[0])
vehicle_interior = vehicle.get_labels(mask_from_voxel=vox_veh_cabin)
# Run floor and ceil finding routines to prepare object for later scripts. Finding floor
# automatically also gets ceiling.
vehicle.get_floor(vox_veh_cabin)
