Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,916 Bytes
ddc47cd 58164f8 ddc47cd 58164f8 ddc47cd e3ebdbb ddc47cd 5638c1f ddc47cd 5638c1f ddc47cd |
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 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
# 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.
from embodied_gen.utils.monkey_patches import monkey_patch_sam3d
monkey_patch_sam3d()
import os
import sys
from typing import Optional, Union
import numpy as np
from hydra.utils import instantiate
# from modelscope import snapshot_download
from huggingface_hub import snapshot_download
from omegaconf import OmegaConf
from PIL import Image
current_file_path = os.path.abspath(__file__)
current_dir = os.path.dirname(current_file_path)
sys.path.append(os.path.join(current_dir, "../.."))
from thirdparty.sam3d.sam3d_objects.pipeline.inference_pipeline_pointmap import (
InferencePipelinePointMap,
)
__all__ = ["Sam3dInference"]
def load_image(path: str) -> np.ndarray:
image = Image.open(path)
image = np.array(image)
image = image.astype(np.uint8)
return image
def load_mask(path: str) -> np.ndarray:
mask = load_image(path)
mask = mask > 0
if mask.ndim == 3:
mask = mask[..., -1]
return mask
class Sam3dInference:
def __init__(
self, local_dir: str = "weights/sam-3d-objects", compile: bool = False
) -> None:
if not os.path.exists(local_dir):
# snapshot_download("facebook/sam-3d-objects", local_dir=local_dir)
snapshot_download("jetjodh/sam-3d-objects", local_dir=local_dir)
config_file = os.path.join(local_dir, "checkpoints/pipeline.yaml")
config = OmegaConf.load(config_file)
config.rendering_engine = "nvdiffrast"
config.compile_model = compile
config.workspace_dir = os.path.dirname(config_file)
# Generate 4 gs in each pixel.
config["slat_decoder_gs_config_path"] = config.pop(
"slat_decoder_gs_4_config_path", "slat_decoder_gs_4.yaml"
)
config["slat_decoder_gs_ckpt_path"] = config.pop(
"slat_decoder_gs_4_ckpt_path", "slat_decoder_gs_4.ckpt"
)
self.pipeline: InferencePipelinePointMap = instantiate(config)
def merge_mask_to_rgba(
self, image: np.ndarray, mask: np.ndarray
) -> np.ndarray:
mask = mask.astype(np.uint8) * 255
mask = mask[..., None]
rgba_image = np.concatenate([image[..., :3], mask], axis=-1)
return rgba_image
def run(
self,
image: np.ndarray | Image.Image,
mask: np.ndarray = None,
seed: int = None,
pointmap: np.ndarray = None,
use_stage1_distillation: bool = False,
use_stage2_distillation: bool = False,
stage1_inference_steps: int = 25,
stage2_inference_steps: int = 25,
) -> dict:
if isinstance(image, Image.Image):
image = np.array(image)
if mask is not None:
image = self.merge_mask_to_rgba(image, mask)
return self.pipeline.run(
image,
None,
seed,
stage1_only=False,
with_mesh_postprocess=False,
with_texture_baking=False,
with_layout_postprocess=False,
use_vertex_color=True,
use_stage1_distillation=use_stage1_distillation,
use_stage2_distillation=use_stage2_distillation,
stage1_inference_steps=stage1_inference_steps,
stage2_inference_steps=stage2_inference_steps,
pointmap=pointmap,
)
if __name__ == "__main__":
pipeline = Sam3dInference()
# load image
image = load_image(
"/home/users/xinjie.wang/xinjie/sam-3d-objects/notebook/images/shutterstock_stylish_kidsroom_1640806567/image.png"
)
mask = load_mask(
"/home/users/xinjie.wang/xinjie/sam-3d-objects/notebook/images/shutterstock_stylish_kidsroom_1640806567/13.png"
)
import torch
if torch.cuda.is_available():
torch.cuda.reset_peak_memory_stats()
torch.cuda.empty_cache()
from time import time
start = time()
output = pipeline.run(image, mask, seed=42)
print(f"Running cost: {round(time()-start, 1)}")
if torch.cuda.is_available():
max_memory = torch.cuda.max_memory_allocated() / (1024**3)
print(f"(Max VRAM): {max_memory:.2f} GB")
print(f"End: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
output["gs"].save_ply(f"outputs/splat.ply")
print("Your reconstruction has been saved to outputs/splat.ply")
|