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6da47c0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 | """Core inference utilities for SAM 3D Body model (hands + body fusion)."""
from collections.abc import Callable
from typing import Any, Literal, cast
import numpy as np
import torch
from jaxtyping import Float, Int, UInt8
from numpy import ndarray
from serde import from_dict, serde
from torch import Tensor
from torchvision.transforms import ToTensor
from sam3d_body.data.transforms import (
Compose,
GetBBoxCenterScale,
TopdownAffine,
VisionTransformWrapper,
)
from sam3d_body.data.utils.prepare_batch import PreparedBatchDict, prepare_batch
from sam3d_body.models.meta_arch import SAM3DBody
from sam3d_body.models.meta_arch.sam3d_body import BodyPredContainer
from sam3d_body.utils import recursive_to
@serde
class PoseOutputsNP:
"""Batch pose outputs in numpy form straight from the model forward pass."""
pred_pose_raw: Float[ndarray, "n pose_raw=266"]
"""Raw 266D pose vector per item (SMPL-X ordering)."""
pred_pose_rotmat: Float[ndarray, ""] | None
"""Optional rotation matrices derived from ``pred_pose_raw``."""
global_rot: Float[ndarray, "n 3"]
"""Root/global rotation in radians for each item (XYZ Euler)."""
body_pose: Float[ndarray, "n body_pose_params=133"]
"""Body pose parameters (133D continuous) per item."""
shape: Float[ndarray, "n shape_params=45"]
"""Body shape PCA coefficients (45D) per item."""
scale: Float[ndarray, "n scale_params=28"]
"""Body scale PCA coefficients (28D) per item."""
hand: Float[ndarray, "n hand_pose_params=108"]
"""Hand pose parameters in PCA space (108D) per item."""
face: Float[ndarray, "n expr_params=72"]
"""Facial expression PCA coefficients (72D) per item."""
pred_keypoints_3d: Float[ndarray, "n joints3d 3"]
"""3D keypoints in camera coordinates for each item."""
pred_vertices: Float[ndarray, "n verts=18439 3"]
"""Full mesh vertices in camera coordinates per item."""
pred_joint_coords: Float[ndarray, "n joints3d 3"]
"""Internal skeleton joint centers (camera coordinates) per item."""
faces: Int[ndarray, "faces 3"]
"""Mesh face indices shared across items."""
joint_global_rots: Float[ndarray, "n joints_rot 3 3"]
"""Global rotation matrices per joint for each item."""
mhr_model_params: Float[ndarray, "n mhr_params"]
"""Model hyper-regularization parameters per item."""
pred_cam: Float[ndarray, "n 3"]
"""Weak-perspective camera parameters (sx, sy, tx) per item."""
pred_keypoints_2d_verts: Float[ndarray, "n verts 2"]
"""2D projected vertices per item (pixels)."""
pred_keypoints_2d: Float[ndarray, "n joints2d 2"]
"""2D projected keypoints per item (pixels)."""
pred_cam_t: Float[ndarray, "n 3"]
"""Camera-space translation vectors applied to each mesh."""
focal_length: Float[ndarray, "n"]
"""Focal lengths per item (pixels)."""
pred_keypoints_2d_depth: Float[ndarray, "n joints2d"]
"""Depth values for 2D keypoints per item."""
pred_keypoints_2d_cropped: Float[ndarray, "n joints2d 2"]
"""2D keypoints in the cropped input frame per item (pixels)."""
@serde
class FinalPosePrediction:
"""Per-person prediction bundle returned by SAM 3D Body."""
bbox: Float[ndarray, "4"]
"""Axis-aligned XYXY box in the original image (pixels)."""
focal_length: Float[ndarray, ""]
"""Scalar focal length for the frame (pixels)."""
pred_keypoints_3d: Float[ndarray, "joints 3"]
"""3D keypoints in camera coordinates (OpenCV: x right, y down, z forward)."""
pred_keypoints_2d: Float[ndarray, "joints 2"]
"""2D keypoints in image pixel coordinates."""
pred_vertices: Float[ndarray, "verts 3"]
"""Full body mesh vertices in camera coordinates."""
pred_cam_t: Float[ndarray, "3"]
"""Camera-space translation (x, y, z) applied to the mesh."""
pred_pose_raw: Float[ndarray, "pose_params=266"]
"""Raw 266D pose vector (SMPL-X style ordering)."""
global_rot: Float[ndarray, "3"]
"""Root/global rotation in radians (XYZ Euler)."""
body_pose_params: Float[ndarray, "body_pose_params=133"]
"""Body pose parameters (133D continuous)."""
hand_pose_params: Float[ndarray, "hand_pose_params=108"]
"""Hand pose parameters (108D PCA space)."""
scale_params: Float[ndarray, "scale_params=28"]
"""Body scale PCA coefficients (28D)."""
shape_params: Float[ndarray, "shape_params=45"]
"""Body shape PCA coefficients (45D)."""
expr_params: Float[ndarray, "expr_params=72"]
"""Facial expression PCA coefficients (72D)."""
mask: UInt8[ndarray, "h w 1"] | None = None
"""Optional instance segmentation mask (H×W×1, uint8)."""
pred_joint_coords: Float[ndarray, "joints 3"] | None = None
"""Full internal skeleton joint centers (camera coordinates)."""
pred_global_rots: Float[ndarray, "joints 3 3"] | None = None
"""Global rotation matrices per joint aligned with ``pred_joint_coords``."""
lhand_bbox: Float[ndarray, "4"] | None = None
"""Optional left-hand XYXY box in the original image (pixels)."""
rhand_bbox: Float[ndarray, "4"] | None = None
"""Optional right-hand XYXY box in the original image (pixels)."""
Transform = Callable[[dict], dict | None]
class SAM3DBodyEstimator:
"""Wraps the SAM 3D Body meta-architecture for single-frame inference."""
def __init__(
self,
sam_3d_body_model: SAM3DBody,
) -> None:
"""Initialize preprocessing pipelines and cache reusable assets.
Args:
sam_3d_body_model: Loaded ``SAM3DBody`` instance (checkpoints already restored).
"""
self.model: SAM3DBody = sam_3d_body_model
self.thresh_wrist_angle: float = 1.4
# For mesh visualization
self.faces: Int[ndarray, "n_faces=36874 3"] = self.model.head_pose.faces.cpu().numpy() # type: ignore
# Define transforms
body_transforms: list[Transform] = [
cast(Transform, GetBBoxCenterScale()),
cast(Transform, TopdownAffine(input_size=512, use_udp=False)),
cast(Transform, VisionTransformWrapper(ToTensor())),
]
hand_transforms: list[Transform] = [
cast(Transform, GetBBoxCenterScale(padding=0.9)),
cast(Transform, TopdownAffine(input_size=512, use_udp=False)),
cast(Transform, VisionTransformWrapper(ToTensor())),
]
self.transform: Compose = Compose(body_transforms)
self.transform_hand: Compose = Compose(hand_transforms)
@torch.no_grad()
def process_one_image(
self,
rgb_hw3: UInt8[ndarray, "h w 3"],
xyxy: Float[ndarray, "n 4"] | None = None,
masks: Float[ndarray, "n h w"] | None = None,
masks_score: Float[ndarray, "n"] | None = None,
K_33: Float[ndarray, "3 3"] | None = None,
inference_type: Literal["full", "body", "hand"] = "full",
) -> list[FinalPosePrediction]:
"""Run full SAM 3D Body inference for one RGB frame.
Args:
rgb_hw3: Input image in RGB order with dtype ``uint8`` and shape ``[H, W, 3]``.
xyxy: Optional person boxes (XYXY, pixels) to bypass detector; defaults to the
full-frame box when ``None``.
masks: Optional binary instance masks aligned with ``xyxy`` (shape ``[N, H, W]``);
when provided, segmentation is skipped.
masks_score: Optional confidence scores for ``masks``.
K_33: Optional camera intrinsic matrix ``[3, 3]``; if ``None``, the model will rely on
its default relative-FOV heuristic. Intrinsics follow the project convention
of mapping world points into the camera frame via ``cam_T_world`` style matrices.
inference_type: Controls which decoders run: ``"full"`` (body + hands), ``"body"``
(body-only), or ``"hand"`` (hand-only output paths).
Returns:
A list of ``FinalPosePrediction`` structures, one per detected person.
"""
height: int = rgb_hw3.shape[0]
width: int = rgb_hw3.shape[1]
if xyxy is None:
xyxy = np.array([0, 0, width, height], dtype=np.float32).reshape(1, 4)
# If there are no detected humans, don't run prediction
if len(xyxy) == 0:
return []
# number of people detected
n_dets: int = xyxy.shape[0]
#################### Construct batch data samples ####################
batch: PreparedBatchDict = prepare_batch(rgb_hw3, self.transform, xyxy, masks, masks_score)
#################### Run model inference on an image ####################
batch: PreparedBatchDict = recursive_to(batch, "cuda")
self.model._initialize_batch(batch)
batch_img: Float[Tensor, "B=1 N 3 H W"] = batch["img"]
# Handle camera intrinsics
# - either provided externally or generated via default FOV estimator
if K_33 is None:
print("")
else:
K_b33: Float[Tensor, "b=1 3 3"] = torch.as_tensor(
K_33[np.newaxis, ...], device=batch_img.device, dtype=batch_img.dtype
)
batch["cam_int"] = K_b33.clone()
outputs: BodyPredContainer = self.model.run_inference(
rgb_hw3,
batch,
inference_type=inference_type,
transform_hand=self.transform_hand,
thresh_wrist_angle=self.thresh_wrist_angle,
)
pose_output: dict[str, Any] = outputs.pose_output
batch_lhand: dict[str, Any] | None = outputs.batch_lhand
batch_rhand: dict[str, Any] | None = outputs.batch_rhand
mhr_dict: dict[str, Any] = pose_output["mhr"]
out_np_dict: dict[str, ndarray] = cast(dict[str, ndarray], recursive_to(recursive_to(mhr_dict, "cpu"), "numpy"))
out_np: PoseOutputsNP = from_dict(PoseOutputsNP, out_np_dict)
all_out: list[FinalPosePrediction] = []
bbox_tensor: Float[Tensor, "B=1 N 4"] = batch["bbox"]
for idx in range(n_dets):
mask_arr: UInt8[ndarray, "h w 1"] | None = None
if masks is not None:
mask_arr = masks[idx]
if mask_arr.ndim == 2:
mask_arr = mask_arr[..., np.newaxis]
mask_arr = (mask_arr > 0.5).astype(np.uint8, copy=False)
pred = FinalPosePrediction(
bbox=bbox_tensor[0, idx].cpu().numpy(),
focal_length=np.asarray(out_np.focal_length[idx]),
pred_keypoints_3d=out_np.pred_keypoints_3d[idx],
pred_keypoints_2d=out_np.pred_keypoints_2d[idx],
pred_vertices=out_np.pred_vertices[idx],
pred_cam_t=out_np.pred_cam_t[idx],
pred_pose_raw=out_np.pred_pose_raw[idx],
global_rot=out_np.global_rot[idx],
body_pose_params=out_np.body_pose[idx],
hand_pose_params=out_np.hand[idx],
scale_params=out_np.scale[idx],
shape_params=out_np.shape[idx],
expr_params=out_np.face[idx],
mask=mask_arr,
pred_joint_coords=out_np.pred_joint_coords[idx],
pred_global_rots=out_np.joint_global_rots[idx],
)
if inference_type == "full" and batch_lhand is not None and batch_rhand is not None:
lhand_center = batch_lhand["bbox_center"].flatten(0, 1)[idx]
lhand_scale = batch_lhand["bbox_scale"].flatten(0, 1)[idx]
pred.lhand_bbox = np.array(
[
(lhand_center[0] - lhand_scale[0] / 2).item(),
(lhand_center[1] - lhand_scale[1] / 2).item(),
(lhand_center[0] + lhand_scale[0] / 2).item(),
(lhand_center[1] + lhand_scale[1] / 2).item(),
]
)
rhand_center = batch_rhand["bbox_center"].flatten(0, 1)[idx]
rhand_scale = batch_rhand["bbox_scale"].flatten(0, 1)[idx]
pred.rhand_bbox = np.array(
[
(rhand_center[0] - rhand_scale[0] / 2).item(),
(rhand_center[1] - rhand_scale[1] / 2).item(),
(rhand_center[0] + rhand_scale[0] / 2).item(),
(rhand_center[1] + rhand_scale[1] / 2).item(),
]
)
all_out.append(pred)
return all_out
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