Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,373 Bytes
19da45c |
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 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Adapted from https://github.com/facebookresearch/vggt/blob/main/visual_util.py
import matplotlib
import numpy as np
import trimesh
from scipy.spatial.transform import Rotation
from aether.utils.postprocess_utils import depth_edge
def predictions_to_glb(
predictions,
filter_by_frames="all",
show_cam=True,
max_depth=100.0,
rtol=0.03,
frame_rel_idx: float = 0.0,
) -> trimesh.Scene:
"""
Converts predictions to a 3D scene represented as a GLB file.
Args:
predictions (dict): Dictionary containing model predictions with keys:
- world_points: 3D point coordinates (S, H, W, 3)
- images: Input images (S, H, W, 3)
- depths: Depths (S, H, W)
- camera poses: Camera poses (S, 4, 4)
filter_by_frames (str): Frame filter specification (default: "all")
show_cam (bool): Include camera visualization (default: True)
max_depth (float): Maximum depth value (default: 100.0)
rtol (float): Relative tolerance for depth edge detection (default: 0.2)
frame_rel_idx (float): Relative index of the frame to visualize (default: 0.0)
Returns:
trimesh.Scene: Processed 3D scene containing point cloud and cameras
Raises:
ValueError: If input predictions structure is invalid
"""
if not isinstance(predictions, dict):
raise ValueError("predictions must be a dictionary")
selected_frame_idx = None
if filter_by_frames != "all" and filter_by_frames != "All":
try:
# Extract the index part before the colon
selected_frame_idx = int(filter_by_frames.split(":")[0])
except (ValueError, IndexError):
pass
pred_world_points = predictions["world_points"]
# Get images from predictions
images = predictions["images"]
# Use extrinsic matrices instead of pred_extrinsic_list
camera_poses = predictions["camera_poses"]
if selected_frame_idx is not None:
pred_world_points = pred_world_points[selected_frame_idx][None]
images = images[selected_frame_idx][None]
camera_poses = camera_poses[selected_frame_idx][None]
vertices_3d = pred_world_points.reshape(-1, 3)
# Handle different image formats - check if images need transposing
if images.ndim == 4 and images.shape[1] == 3: # NCHW format
colors_rgb = np.transpose(images, (0, 2, 3, 1))
else: # Assume already in NHWC format
colors_rgb = images
colors_rgb = (colors_rgb.reshape(-1, 3) * 255).astype(np.uint8)
depths = predictions["depths"]
masks = depths < max_depth
edge = ~depth_edge(depths, rtol=rtol, mask=masks)
masks = (masks & edge).reshape(-1)
vertices_3d = vertices_3d[masks]
colors_rgb = colors_rgb[masks]
if vertices_3d is None or np.asarray(vertices_3d).size == 0:
vertices_3d = np.array([[1, 0, 0]])
colors_rgb = np.array([[255, 255, 255]])
scene_scale = 1
else:
# Calculate the 5th and 95th percentiles along each axis
lower_percentile = np.percentile(vertices_3d, 5, axis=0)
upper_percentile = np.percentile(vertices_3d, 95, axis=0)
# Calculate the diagonal length of the percentile bounding box
scene_scale = np.linalg.norm(upper_percentile - lower_percentile)
colormap = matplotlib.colormaps.get_cmap("gist_rainbow")
# Initialize a 3D scene
scene_3d = trimesh.Scene()
# Add point cloud data to the scene
point_cloud_data = trimesh.PointCloud(vertices=vertices_3d, colors=colors_rgb)
scene_3d.add_geometry(point_cloud_data)
# Prepare 4x4 matrices for camera extrinsics
num_cameras = len(camera_poses)
extrinsics_matrices = np.zeros((num_cameras, 4, 4))
extrinsics_matrices[:, :3, :4] = camera_poses[:, :3, :4]
extrinsics_matrices[:, 3, 3] = 1
if show_cam:
# Add camera models to the scene
for i in range(num_cameras):
camera_to_world = camera_poses[i]
rgba_color = colormap(frame_rel_idx)
current_color = tuple(int(255 * x) for x in rgba_color[:3])
integrate_camera_into_scene(
scene_3d, camera_to_world, current_color, scene_scale
)
return scene_3d
def integrate_camera_into_scene(
scene: trimesh.Scene,
transform: np.ndarray,
face_colors: tuple,
scene_scale: float,
):
"""
Integrates a fake camera mesh into the 3D scene.
Args:
scene (trimesh.Scene): The 3D scene to add the camera model.
transform (np.ndarray): Transformation matrix for camera positioning.
face_colors (tuple): Color of the camera face.
scene_scale (float): Scale of the scene.
"""
cam_width = scene_scale * 0.025
cam_height = scene_scale * 0.05
# Create cone shape for camera
rot_45_degree = np.eye(4)
rot_45_degree[:3, :3] = Rotation.from_euler("z", 45, degrees=True).as_matrix()
rot_45_degree[2, 3] = -cam_height
opengl_transform = get_opengl_conversion_matrix()
# Combine transformations
complete_transform = transform @ opengl_transform @ rot_45_degree
camera_cone_shape = trimesh.creation.cone(cam_width, cam_height, sections=4)
# Generate mesh for the camera
slight_rotation = np.eye(4)
slight_rotation[:3, :3] = Rotation.from_euler("z", 2, degrees=True).as_matrix()
vertices_combined = np.concatenate(
[
camera_cone_shape.vertices,
0.95 * camera_cone_shape.vertices,
transform_points(slight_rotation, camera_cone_shape.vertices),
]
)
vertices_transformed = transform_points(complete_transform, vertices_combined)
mesh_faces = compute_camera_faces(camera_cone_shape)
# Add the camera mesh to the scene
camera_mesh = trimesh.Trimesh(vertices=vertices_transformed, faces=mesh_faces)
camera_mesh.visual.face_colors[:, :3] = face_colors
scene.add_geometry(camera_mesh)
def get_opengl_conversion_matrix() -> np.ndarray:
"""
Constructs and returns the OpenGL conversion matrix.
Returns:
numpy.ndarray: A 4x4 OpenGL conversion matrix.
"""
# Create an identity matrix
matrix = np.identity(4)
# Flip the y and z axes
matrix[1, 1] = -1
matrix[2, 2] = -1
return matrix
def transform_points(
transformation: np.ndarray, points: np.ndarray, dim: int = None
) -> np.ndarray:
"""
Applies a 4x4 transformation to a set of points.
Args:
transformation (np.ndarray): Transformation matrix.
points (np.ndarray): Points to be transformed.
dim (int, optional): Dimension for reshaping the result.
Returns:
np.ndarray: Transformed points.
"""
points = np.asarray(points)
initial_shape = points.shape[:-1]
dim = dim or points.shape[-1]
# Apply transformation
transformation = transformation.swapaxes(
-1, -2
) # Transpose the transformation matrix
points = points @ transformation[..., :-1, :] + transformation[..., -1:, :]
# Reshape the result
result = points[..., :dim].reshape(*initial_shape, dim)
return result
def compute_camera_faces(cone_shape: trimesh.Trimesh) -> np.ndarray:
"""
Computes the faces for the camera mesh.
Args:
cone_shape (trimesh.Trimesh): The shape of the camera cone.
Returns:
np.ndarray: Array of faces for the camera mesh.
"""
# Create pseudo cameras
faces_list = []
num_vertices_cone = len(cone_shape.vertices)
for face in cone_shape.faces:
if 0 in face:
continue
v1, v2, v3 = face
v1_offset, v2_offset, v3_offset = face + num_vertices_cone
v1_offset_2, v2_offset_2, v3_offset_2 = face + 2 * num_vertices_cone
faces_list.extend(
[
(v1, v2, v2_offset),
(v1, v1_offset, v3),
(v3_offset, v2, v3),
(v1, v2, v2_offset_2),
(v1, v1_offset_2, v3),
(v3_offset_2, v2, v3),
]
)
faces_list += [(v3, v2, v1) for v1, v2, v3 in faces_list]
return np.array(faces_list)
|