ItemToRoom = np.array(wanted['ItemToRoom'])
RoomList = wanted['RoomList']
SurfaceList = wanted['SurfaceList']
wantedClass = wanted['wantedClass']
for target_classname in wantedClass:
for env_id in range(7): # env_id ranges from 0 to 6
# start simulation (only once for each apartment)
comm = UnityCommunication()
comm.reset(env_id)
#comm.setBathroomTexture(env_id)
#============================ START PYTHON SCRIPT ===========================
response, origraph = comm.environment_graph()
origraph = deleteGraphByClassname(origraph, target_classname)
success, message = comm.expand_scene(origraph)
#print(origraph)
#exit()
#loop through all nodes and check whether we have wanted
DestIds, DestPrefabs, destRooms, destSurface = findAllPossibleDestNodes(target_classname, wantedClass, ItemToRoom, SurfaceToRoom, RoomList, SurfaceList, origraph)
#loop through missed classes
#randomly sample prefab from it and add to the graph
for i, destid in enumerate(DestIds):
newIDs = 1000 #fixed; dont change; always starts from 1000 and onwards
comm.reset(env_id)
response, origraph = comm.environment_graph()
#print(origraph)
else:
os.mkdir(writedir)
writedirjason = jasondirname + '/apartment_' + str(env_id) + '/'
if os.path.exists(writedirjason):
print('folder already exists')
else:
os.mkdir(writedirjason)
# start simulation (only once for each apartment)
comm = UnityCommunication()
comm.reset(env_id)
#comm.setBathroomTexture(env_id)
#wantedgraph = pickle.load( open( graphdirname + '/apartment_' + str(env_id) + ".pkl", "rb" ) )
#print(wantedgraph)
res, origraph = comm.environment_graph()
for node in origraph['nodes']:
target_id = node['id'] #target object to recognize
targetprefabname = node['prefab_name']
#remove those objects not belong to these general cateogires
if node['class_name'] not in wantedClass:
continue
print('Processing prefab id: ' + str(target_id))
targetroomname = find_rooms(origraph, node)
targetroombbox = find_nodes_byclassname(
origraph, targetroomname)[0]['bounding_box']
avgtargetsize = np.mean(node['bounding_box']['size'])
_ = comm.camera_image(cameras_select, mode='normal', image_height=480, image_width=640)
ok, imgs = comm.camera_image(cameras_select, mode='normal', image_height=480, image_width=640)
images.append(imgs)
return messages_expand, images
comm = UnityCommunication()
print('Inferring preconditions...')
preconds = add_preconds.get_preconds_script(script).printCondsJSON()
print(preconds)
print('Loading graph')
comm.reset(0)
_, graph_input = comm.environment_graph()
print('Executing script')
print(script)
info = check_programs.check_script(
script, preconds, graph_path=None, inp_graph_dict=graph_input)
message, final_state, graph_state_list, graph_dict, id_mapping, info, helper, modif_script = info
success = (message == 'Script is executable')
if success:
print('Generating snapshots')
messages, images = obtain_snapshots(graph_state_list, graph_input, comm)
grid_img = build_grid_images(images)
cv2.imwrite('snapshot_test.png', grid_img)
print('Snapshot saved in demo/snapshot_test.png')