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pegasus.py
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import copy
import os
import sys
sys.path.append("./submodules/gaussian-splatting-pegasus")
sys.path.append("./submodules/colmap-wrapper")
from argparse import ArgumentParser
import tqdm
import numpy as np
import pylab as plt
import torch
import threading
from pathlib import Path
import warnings
# Own
from colmap_wrapper.dataloader import (read_images_binary, read_cameras_binary)
from arguments import ModelParams, PipelineParams, get_combined_args
from gaussian_renderer import render, network_gui
from utils.sh_utils import RGB2SH
from utils.general_utils import safe_state
from src.dataset.ycb_objects import *
from src.dataset.cup_noodle_dataset import *
from src.dataset.dataset_envs import *
from src.engine.physical_simulation import PybulletEngine
from src.utility.graphic_utils import *
from src.gs.pegasus_setup import PegasusSetup
from src.gs.render import render_rgb_and_depth, render_visib_mask, render_silhouette_mask, \
render_semanticsegmentation_mask
class PEGASUS(object):
GUI_NETWORKING_ACTIVATED: bool = False
IP: str = "127.0.0.1"
PORT: int = 6009
LOAD_ITERATION: int = 30_000
SH_DEGREE: int = 3
def __init__(self,
dataset_path: str,
env_dataset_path: str,
urdf_asset_folder: Union[str, list],
gs_env_list: list,
gs_object_list: list,
mode: Literal["dynamic", "static"] = 'static',
camera_trajectory_mode: Literal["random", "sequence", 'random+zoom'] = 'random',
render_height: int = 480,
render_width: int = 640,
num_cameras: int = 1,
simulation_steps: int = 100,
num_camera_interpolation_steps: int = 1,
QUIET: bool = False, # What is this for?
publish2gui: bool = False
):
self.URDF_ASSET_FOLDER = urdf_asset_folder
# Set up command line argument parser
self.parser = ArgumentParser(description="Testing script parameters")
self.model = ModelParams(self.parser, sentinel=True)
self.pipeline = PipelineParams(self.parser)
self.dataset_path = dataset_path
if env_dataset_path:
self.env_dataset_path = env_dataset_path
else:
self.env_dataset_path = dataset_path
self.render_height = render_height
self.render_width = render_width
self.dataset_base_path = './dataset'
self.num_cameras = num_cameras
self.num_camera_interpolation_steps = num_camera_interpolation_steps
self.fps = 50
self.QUIET = QUIET
self.GUI = publish2gui
self.mode = mode
self.simulation_steps = simulation_steps
self.camera_trajectory_mode = camera_trajectory_mode
if publish2gui and not self.GUI_NETWORKING_ACTIVATED:
network_gui.init(self.IP, self.PORT)
self.GUI_NETWORKING_ACTIVATED = True
# Preload gaussian splatting point clouds
with (torch.no_grad()):
self.gaussian_environment_pre_load = {}
for env_idx in range(0, len(gs_env_list)):
gaussian_environment = GaussianModel(self.SH_DEGREE)
gaussian_environment.meta_info = gs_env_list[env_idx]
gaussian_environment.load_ply(gs_env_list[env_idx].gaussian_point_cloud_path(self.LOAD_ITERATION))
# load colmap data
cam_extr = read_images_binary(Path(gs_env_list[env_idx].reconstruction_path) / 'sparse/0/images.bin')
cam_intr = read_cameras_binary(Path(gs_env_list[env_idx].reconstruction_path) / 'sparse/0/cameras.bin')
self.gaussian_environment_pre_load.update(
{
gs_env_list[env_idx].object_name:
{
'gs': gaussian_environment,
'cam_extr': cam_extr,
'cam_intr': cam_intr,
}
})
self.gaussian_object_pre_load = {}
for obj_idx in range(0, len(gs_object_list)):
gs_object_list[obj_idx].mode = 'fused'
gaussian_object = GaussianModel(self.SH_DEGREE)
gaussian_object.load_ply(
gs_object_list[obj_idx].gaussian_point_cloud_path(iteration=self.LOAD_ITERATION))
gaussian_object.meta_info = gs_object_list[obj_idx]
self.gaussian_object_pre_load.update({gs_object_list[obj_idx].object_name: gaussian_object})
def init(self, dataset_name, scene_id):
self.dataset_name = dataset_name
self.scene_id = scene_id
# Init all params for gaussian splatting (trajectory, videos)
self.pegasus_setup = PegasusSetup(pybullet_trajectory_path=self.physics_file,
dataset_path=self.dataset_path,
env_dataset_path=self.env_dataset_path,
render_height=self.render_height,
render_width=self.render_width,
mode=self.mode)
self.pegasus_setup.cam_extr = self.gaussian_environment_pre_load[self.selected_env_name]['cam_extr']
self.pegasus_setup.cam_intr = self.gaussian_environment_pre_load[self.selected_env_name]['cam_intr']
# Setup bop dataset writer
self.pegasus_dataset = PegasusBOPDatasetWriter(dataset_name=dataset_name,
dataset_output_path=Path(self.dataset_base_path),
camera_intr=self.pegasus_setup.cam_intr,
render_width=self.pegasus_setup.render_width,
render_height=self.pegasus_setup.render_height,
object_models=self.pegasus_setup.object_data.keys(),
object_dataset_path=self.dataset_path,
scene_id=scene_id)
self.viewport_cam_list = self.pegasus_setup.create_camera_trajectory(num_cameras=self.num_cameras,
num_interpolation_steps=self.num_camera_interpolation_steps,
mode=self.camera_trajectory_mode)
self.pegasus_setup.init_video_streams(
output=self.pegasus_dataset.dataset_path / 'video/{:06d}'.format(scene_id), fps=self.fps)
# hacky way of saving path to model pcd
sys.argv.append('-m')
sys.argv.append(self.pegasus_setup.environment.gs_model_path)
self.args = get_combined_args(self.parser)
print("Rendering Environment" + self.pegasus_setup.environment.reconstruction_path)
# Initialize system state (RNG)
safe_state(self.QUIET)
self.pipe = self.pipeline.extract(self.args)
dataset = self.model.extract(self.args)
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
self.background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
def init_bullet(self,
env_list: list,
obj_list: list,
dataset_name: str,
scene_id: int,
min_num_objects: int = 1,
max_num_objects: int = 1,
random: bool = True):
engine_path = Path('src/tools/') / self.dataset_base_path / dataset_name
self.py_engine = PybulletEngine(asset_folder=URDF_ASSET_FOLDER,
output_path_json=(engine_path / 'engine/{:06d}_simulation_steps.json'.format(
scene_id)).__str__(),
simulation_steps=self.simulation_steps,
gui=self.GUI)
if not random:
np.random.seed(42)
else:
np.random.seed(None)
self.physics_file = self.py_engine.trajectory_path
if min_num_objects > obj_list.__len__():
min_num_objects = obj_list.__len__()
warnings.warn(
"Number of min objects selected is larger than parsed objects. Set number of objects to lowest possible.")
if max_num_objects > obj_list.__len__():
max_num_objects = obj_list.__len__()
warnings.warn(
"Number of objects selected is lower than parsed objects. Set number of objects to lowest possible.")
# Selected random environment (only 1 env)
select_env = env_list[np.random.randint(0, env_list.__len__())]
self.selected_env_name = select_env.object_name
# Number of objects
random_num_objects = np.random.randint(min_num_objects, max_num_objects + 1)
# Indecies for selected objects
# random_objects_idx = np.unique(np.random.randint(0, obj_list.__len__(), num_objects)).tolist()
random_objects_idx = np.random.choice(range(obj_list.__len__()), random_num_objects, replace=False).tolist()
print('Env: {}. Selected {} objects.'.format(select_env.__class__.__name__, random_objects_idx.__len__()))
random_objects = [obj_list[i] for i in random_objects_idx]
# Add selected envs and objects into pybullet engine
self.py_engine.add_object(object_instance=select_env, start_pos=select_env.START_POSITION_PYBULLET)
for obj in random_objects:
self.py_engine.add_object(object_instance=obj, start_pos=select_env.define_start_pos())
self.py_engine.simulate()
def init_start_position(self):
self.semantic_colors = generate_colors(
self.pegasus_setup.object_data.__len__()) # TOdo: this should be only executed once!
gaussians_object_list = {}
for object_name in self.pegasus_setup.object_data.keys():
for id in self.pegasus_setup.object_data[object_name]['bullet_id']:
gs_object = self.gaussian_object_pre_load[object_name]
gs_object._features_dc_color = copy.deepcopy(gs_object._features_dc)
gs_object._features_rest_color = copy.deepcopy(gs_object._features_rest)
gs_semeantic_color = self.semantic_colors[id - 1]
gs_object._features_dc_semantics = RGB2SH(gs_semeantic_color)
gs_object._features_rest_semantics = torch.asarray([0, 0, 0])
gaussians_object_list.update({id: copy.deepcopy(gs_object)})
# Static
if self.pegasus_setup.mode == 'static':
self.current_gaussians_object_list = self.pegasus_setup.static_object_pose(
gaussians_object_list=gaussians_object_list)
# Dynamic
elif self.pegasus_setup.mode == 'dynamic':
self.current_gaussians_object_list = self.pegasus_setup.dynamic_object_pose(
gaussians_object_list=gaussians_object_list)
else:
raise ValueError('Mode -{}- not available'.format(self.pegasus_setup.mode))
def generate_dataset(self, data_points: list, save_bop: bool = True, save_video: bool = True):
with (torch.no_grad()):
if self.GUI:
if network_gui.conn is None:
network_gui.try_connect()
bar = tqdm.tqdm(total=len(self.viewport_cam_list))
for i in range(len(self.viewport_cam_list)):
gaussian_scene = copy.deepcopy(
list(self.gaussian_environment_pre_load[self.selected_env_name].values())[0])
gaussian_environment = list(self.gaussian_environment_pre_load[self.selected_env_name].values())[0]
# Compose scene for rgb rendering
for gs_object_id in self.current_gaussians_object_list.keys():
curr_object = self.current_gaussians_object_list[gs_object_id]
curr_object._features_dc = copy.deepcopy(curr_object._features_dc_color)
curr_object._features_rest = copy.deepcopy(curr_object._features_rest_color)
gaussian_scene.merge_gaussians(gaussian=curr_object)
if self.GUI:
try:
net_image_bytes = None
custom_cam, do_training, pipe.convert_SHs_python, pipe.compute_cov3D_python, keep_alive, scaling_modifer = network_gui.receive()
if custom_cam is not None:
net_image = render(custom_cam, gaussian_scene, pipe, background, scaling_modifer)["render"]
net_image_bytes = memoryview(
(torch.clamp(net_image, min=0, max=1.0) * 255).byte().permute(1, 2,
0).contiguous().cpu().numpy())
network_gui.send(net_image_bytes, dataset.source_path)
except Exception as e:
network_gui.conn = None
# print("Termination error: ", e)
if i < len(self.viewport_cam_list):
# Set camera from camera trajectory
viewpoint_cam = self.viewport_cam_list[i]
rgb_image = None
depth_image = None
mask_silhouette = None
individual_seg_masks = None
seg_image = None
semantic_segmentation_mask = None
object_center_image = None
if 'rgb' in data_points:
# Render rgb
rgb_image, depth_image = render_rgb_and_depth(cam=viewpoint_cam,
gs_scene=gaussian_scene,
pipe_settings=self.pipe,
bg=self.background,
debug=False)
if 'seg_sil' in data_points:
# Render silhouette mask
mask_silhouette = render_silhouette_mask(cam=viewpoint_cam,
gs_object_list=self.current_gaussians_object_list,
gs_env=gaussian_environment,
width=self.pegasus_setup.render_width,
height=self.pegasus_setup.render_height,
color_set=self.semantic_colors,
pipe_settings=self.pipe,
bg=self.background)
if 'seg_vis' in data_points:
# Render visible mask
individual_seg_masks, seg_image = render_visib_mask(cam=viewpoint_cam,
gs_environment=gaussian_environment,
gs_object_list=self.current_gaussians_object_list,
color_set=self.semantic_colors,
width=self.pegasus_setup.render_width,
height=self.pegasus_setup.render_height,
pipe_settings=self.pipe,
bg=self.background)
if 'sem_seg' in data_points:
semantic_segmentation_mask = render_semanticsegmentation_mask(cam=viewpoint_cam,
gs_environment=gaussian_environment,
gs_object_list=self.current_gaussians_object_list,
color_set=self.semantic_colors,
width=self.pegasus_setup.render_width,
height=self.pegasus_setup.render_height,
pipe_settings=self.pipe,
bg=self.background,
debug=False)
self.pegasus_dataset.add_scene_camera_json(frame_id=i)
if save_bop:
if False:
self.pegasus_dataset.write_training_data(
rgb_image=(np.ascontiguousarray(rgb_image) * 255).astype('uint8'),
seg_image=individual_seg_masks,
semantic_masks=semantic_segmentation_mask,
mask_silhouette=mask_silhouette,
depth_image=(depth_image.numpy() * 1000).astype(np.uint16),
frame_id=i) # meters in millimeter
if True:
threading.Thread(target=write_training_data,
args=((np.ascontiguousarray(rgb_image) * 255).astype('uint8'),
self.pegasus_dataset.rgb_path,
individual_seg_masks,
self.pegasus_dataset.mask_visib_path,
mask_silhouette,
self.pegasus_dataset.mask_path,
semantic_segmentation_mask,
self.pegasus_dataset.sem_mask_path,
(depth_image.numpy() * 1000).astype(np.uint16),
self.pegasus_dataset.depth_path,
i),
).start()
self.pegasus_dataset.add_scene_gt_json(time_step=i,
gs_object_list=self.current_gaussians_object_list,
cam=viewpoint_cam,
rgb_image=(np.ascontiguousarray(rgb_image) * 255).astype(
'uint8'),
debug=False)
object_center_image = self.pegasus_setup.draw_object_center(
image=(np.ascontiguousarray(rgb_image) * 255).astype('uint8'),
gaussians_object_list=self.current_gaussians_object_list,
camera=viewpoint_cam,
semantic_colors=self.semantic_colors,
K=self.pegasus_dataset.K)
if False:
# imageio.imwrite('image_object_pose.png', object_center_image)
plt.imshow(object_center_image)
plt.show()
if save_video:
# Save images to video stream
self.pegasus_setup.write_image2video(
rgb=(np.ascontiguousarray(rgb_image) * 255).astype('uint8'),
depth=depth_image,
seg=seg_image,
center_image=object_center_image)
bar.update(1)
if self.pegasus_setup.mode == 'dynamic':
self.current_gaussians_object_list = self.pegasus_setup.update_object_pose(
gaussians_object_list=self.current_gaussians_object_list,
timestep=i + 1)
def save2bop(self):
self.pegasus_setup.close_video_streams()
self.pegasus_dataset.write_scene_camera_json()
self.pegasus_dataset.write_scene_gt_json()
print('Saved BOP data')
if __name__ == '__main__':
DATASET_PATH = '/home/se86kimy/Documents/data/RamenDataset'
PEGASET_PATH = '/home/se86kimy/Documents/data/PEGASET'
ENV_DATASET_PATH = '/home/se86kimy/Documents/data/RamenDataset'
URDF_ASSET_FOLDER = ['/home/se86kimy/Documents/data/RamenDataset/urdf',
'/home/se86kimy/Documents/data/PEGASET/urdf']
os.environ['PEGASUS_PATH'] = os.path.join(os.path.abspath(os.path.curdir), "dataset")
from src.dataset.data_writer import PegasusBOPDatasetWriter, write_training_data, write_models, \
convert_scenewise_to_imagewise_ndds, calculate_gt_info
env1 = MannholeCover(dataset_path=ENV_DATASET_PATH)
env2 = Cobblestone(dataset_path=ENV_DATASET_PATH)
env3 = Asphalt(dataset_path=ENV_DATASET_PATH)
env4 = Tiles(dataset_path=ENV_DATASET_PATH)
env5 = Grass(dataset_path=ENV_DATASET_PATH)
env6 = Asphalt2(dataset_path=DATASET_PATH)
env7 = Tiles2(dataset_path=DATASET_PATH)
env8 = Asphalt2(dataset_path=DATASET_PATH)
env9 = Wood(dataset_path=DATASET_PATH)
obj1 = CrackerBox(dataset_path=PEGASET_PATH)
obj2 = ChocoJello(dataset_path=PEGASET_PATH)
obj3 = RedBowl(dataset_path=PEGASET_PATH)
obj4 = WoodenBlock(dataset_path=PEGASET_PATH)
obj5 = DominoSugar(dataset_path=PEGASET_PATH)
obj6 = YellowMustard(dataset_path=PEGASET_PATH)
obj7 = Banana(dataset_path=PEGASET_PATH)
obj8 = MaxwellCoffee(dataset_path=PEGASET_PATH)
obj9 = RedCup(dataset_path=PEGASET_PATH)
obj10 = Pitcher(dataset_path=PEGASET_PATH)
obj11 = SoftScrub(dataset_path=PEGASET_PATH)
obj12 = TomatoSoup(dataset_path=PEGASET_PATH)
obj13 = Spam(dataset_path=PEGASET_PATH)
obj14 = StrawberryJello(dataset_path=PEGASET_PATH)
obj15 = Tuna(dataset_path=PEGASET_PATH)
# obj16 = Drill(dataset_path=DATASET_PATH)
obj17 = Pen(dataset_path=PEGASET_PATH) # Black (y)
obj18 = Scissors(dataset_path=PEGASET_PATH) # Black (y)
obj19 = SmallClamp(dataset_path=PEGASET_PATH) # Black
obj20 = LargeClamp(dataset_path=PEGASET_PATH) # Black
obj21 = FoamBrick(dataset_path=PEGASET_PATH)
# Ramen Dataset
obj101 = CupNoodle01(dataset_path=DATASET_PATH)
obj102 = CupNoodle02(dataset_path=DATASET_PATH)
obj103 = CupNoodle03(dataset_path=DATASET_PATH)
obj104 = CupNoodle04(dataset_path=DATASET_PATH)
obj105 = CupNoodle05(dataset_path=DATASET_PATH)
obj106 = CupNoodle06(dataset_path=DATASET_PATH)
obj107 = CupNoodle07(dataset_path=DATASET_PATH)
obj108 = CupNoodle08(dataset_path=DATASET_PATH)
obj109 = CupNoodle09(dataset_path=DATASET_PATH)
obj110 = CupNoodle10(dataset_path=DATASET_PATH)
obj111 = CupNoodle11(dataset_path=DATASET_PATH)
obj112 = CupNoodle12(dataset_path=DATASET_PATH)
obj113 = CupNoodle13(dataset_path=DATASET_PATH)
obj114 = CupNoodle14(dataset_path=DATASET_PATH)
obj115 = CupNoodle15(dataset_path=DATASET_PATH)
obj116 = CupNoodle16(dataset_path=DATASET_PATH)
obj117 = CupNoodle17(dataset_path=DATASET_PATH)
obj118 = CupNoodle18(dataset_path=DATASET_PATH)
obj119 = CupNoodle19(dataset_path=DATASET_PATH)
obj120 = CupNoodle20(dataset_path=DATASET_PATH)
obj121 = CupNoodle21(dataset_path=DATASET_PATH)
obj122 = CupNoodle22(dataset_path=DATASET_PATH)
obj123 = CupNoodle23(dataset_path=DATASET_PATH)
obj124 = CupNoodle24(dataset_path=DATASET_PATH)
obj125 = CupNoodle25(dataset_path=DATASET_PATH)
obj126 = CupNoodle26(dataset_path=DATASET_PATH)
obj127 = CupNoodle27(dataset_path=DATASET_PATH)
obj128 = CupNoodle28(dataset_path=DATASET_PATH)
obj129 = CupNoodle29(dataset_path=DATASET_PATH)
obj130 = CupNoodle30(dataset_path=DATASET_PATH)
obj_list = [obj17, obj18, obj19, obj20]
env_list = [
env1, env2, env3, env4, env5, env6, env7, env8, env9
]
dataset_path_folder = './dataset/'
dataset_name = 'pegasus_ycb_test'
mode = "dynamic" # 'static'
GUI = False
num_scenes = 10
min_num_objects = 3
max_num_objects = 6
image_height = 480
image_width = 640
render_data_points = ['rgb', 'depth', 'seg_vis', 'seg_sil', 'sem_seg'] # ['rgb', 'depth'],
convert_from_scenewise2imagewise = True
pegasus = PEGASUS(dataset_path=PEGASET_PATH,
env_dataset_path=ENV_DATASET_PATH,
urdf_asset_folder=URDF_ASSET_FOLDER,
gs_env_list=env_list,
gs_object_list=obj_list,
render_height=image_height,
render_width=image_width,
simulation_steps=310,
num_cameras=10,
num_camera_interpolation_steps=30,
publish2gui=GUI,
QUIET=False,
mode="dynamic", # static
camera_trajectory_mode="random",
)
write_models(dataset_path=PEGASET_PATH,
object_list=obj_list,
model_path=(Path(dataset_path_folder) / dataset_name / 'models').__str__())
for scene_id in range(1, num_scenes + 1):
pegasus.init_bullet(env_list=env_list,
obj_list=obj_list,
min_num_objects=min_num_objects,
max_num_objects=max_num_objects,
dataset_name=dataset_name,
scene_id=scene_id)
pegasus.init(dataset_name=dataset_name,
scene_id=scene_id)
pegasus.init_start_position()
pegasus.generate_dataset(data_points=render_data_points,
save_video=True,
save_bop=True)
pegasus.save2bop()
del pegasus.py_engine
if convert_from_scenewise2imagewise:
calculate_gt_info(dataset_name=dataset_name, num_scenes=num_scenes, object_list=[obj.ID for obj in obj_list])
all_scene_ids = list(range(1, num_scenes))
train_ids = list(range(1, int(np.round(0.8 * all_scene_ids.__len__()))))
test_ids = list(range(int(np.round(0.8 * all_scene_ids.__len__())), num_scenes))
train_string = ""
for num in train_ids:
train_string = train_string + str(num) + ","
convert_scenewise_to_imagewise_ndds(input_path=pegasus.pegasus_dataset.train_data_path.__str__(),
output_path=(
pegasus.pegasus_dataset.train_data_path.parent / 'train_ndds').__str__(),
scene_ids_process=train_string[:-1])
test_string = ""
for num in test_ids:
test_string = test_string + str(num) + ","
convert_scenewise_to_imagewise_ndds(input_path=pegasus.pegasus_dataset.train_data_path.__str__(),
output_path=(
pegasus.pegasus_dataset.train_data_path.parent / 'test_ndds').__str__(),
scene_ids_process=test_string[:-1])