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Running on Zero
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36ae195 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # Project EmbodiedGen
#
# Copyright (c) 2025 Horizon Robotics. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied. See the License for the specific language governing
# permissions and limitations under the License.
import numpy as np
import torch
def monkey_patch_maniskill():
"""Monkey patches ManiSkillScene to support sensor image retrieval and RGBA rendering."""
from mani_skill.envs.scene import ManiSkillScene
def get_sensor_images(
self, obs: dict[str, any]
) -> dict[str, dict[str, torch.Tensor]]:
"""Retrieve images from all sensors based on observations."""
sensor_data = dict()
for name, sensor in self.sensors.items():
sensor_data[name] = sensor.get_images(obs[name])
return sensor_data
def get_human_render_camera_images(
self, camera_name: str = None, return_alpha: bool = False
) -> dict[str, torch.Tensor]:
"""Render images from human-view cameras, optionally generating alpha channel from segmentation."""
def get_rgba_tensor(camera, return_alpha):
color = camera.get_obs(
rgb=True, depth=False, segmentation=False, position=False
)["rgb"]
if return_alpha:
seg_labels = camera.get_obs(
rgb=False, depth=False, segmentation=True, position=False
)["segmentation"]
masks = np.where((seg_labels.cpu() > 1), 255, 0).astype(
np.uint8
)
masks = torch.tensor(masks).to(color.device)
color = torch.concat([color, masks], dim=-1)
return color
image_data = dict()
if self.gpu_sim_enabled:
if self.parallel_in_single_scene:
for name, camera in self.human_render_cameras.items():
camera.camera._render_cameras[0].take_picture()
rgba = get_rgba_tensor(camera, return_alpha)
image_data[name] = rgba
else:
for name, camera in self.human_render_cameras.items():
if camera_name is not None and name != camera_name:
continue
assert camera.config.shader_config.shader_pack not in [
"rt",
"rt-fast",
"rt-med",
], "ray tracing shaders do not work with parallel rendering"
camera.capture()
rgba = get_rgba_tensor(camera, return_alpha)
image_data[name] = rgba
else:
for name, camera in self.human_render_cameras.items():
if camera_name is not None and name != camera_name:
continue
camera.capture()
rgba = get_rgba_tensor(camera, return_alpha)
image_data[name] = rgba
return image_data
ManiSkillScene.get_sensor_images = get_sensor_images
ManiSkillScene.get_human_render_camera_images = (
get_human_render_camera_images
)
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