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import os
import cv2
import numpy as np
import torch
import trimesh
import lib.vis.viewer as viewer_utils
from lib.vis.wham_tools.tools import checkerboard_geometry
def camera_marker_geometry(radius, height):
vertices = np.array(
[
[-radius, -radius, 0],
[radius, -radius, 0],
[radius, radius, 0],
[-radius, radius, 0],
[0, 0, - height],
]
)
faces = np.array(
[[0, 1, 2], [0, 2, 3], [1, 0, 4], [2, 1, 4], [3, 2, 4], [0, 3, 4],]
)
face_colors = np.array(
[
[0.5, 0.5, 0.5, 1.0],
[0.5, 0.5, 0.5, 1.0],
[0.0, 1.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 1.0],
[0.0, 1.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 1.0],
]
)
return vertices, faces, face_colors
def run_vis2_on_video(res_dict, res_dict2, output_pth, focal_length, image_names, R_c2w=None, t_c2w=None):
img0 = cv2.imread(image_names[0])
height, width, _ = img0.shape
world_mano = {}
world_mano['vertices'] = res_dict['vertices']
world_mano['faces'] = res_dict['faces']
world_mano2 = {}
world_mano2['vertices'] = res_dict2['vertices']
world_mano2['faces'] = res_dict2['faces']
vis_dict = {}
color_idx = 0
world_mano['vertices'] = world_mano['vertices']
for _id, _verts in enumerate(world_mano['vertices']):
verts = _verts.cpu().numpy() # T, N, 3
body_faces = world_mano['faces']
body_meshes = {
"v3d": verts,
"f3d": body_faces,
"vc": None,
"name": f"hand_{_id}",
# "color": "pace-green",
"color": "director-purple",
}
vis_dict[f"hand_{_id}"] = body_meshes
color_idx += 1
world_mano2['vertices'] = world_mano2['vertices']
for _id, _verts in enumerate(world_mano2['vertices']):
verts = _verts.cpu().numpy() # T, N, 3
body_faces = world_mano2['faces']
body_meshes = {
"v3d": verts,
"f3d": body_faces,
"vc": None,
"name": f"hand2_{_id}",
# "color": "pace-blue",
"color": "director-blue",
}
vis_dict[f"hand2_{_id}"] = body_meshes
color_idx += 1
v, f, vc, fc = checkerboard_geometry(length=100, c1=0, c2=0, up="z")
v[:, 2] -= 2 # z plane
gound_meshes = {
"v3d": v,
"f3d": f,
"vc": vc,
"name": "ground",
"fc": fc,
"color": -1,
}
vis_dict["ground"] = gound_meshes
num_frames = len(world_mano['vertices'][_id])
Rt = np.zeros((num_frames, 3, 4))
Rt[:, :3, :3] = R_c2w[:num_frames]
Rt[:, :3, 3] = t_c2w[:num_frames]
verts, faces, face_colors = camera_marker_geometry(0.05, 0.1)
verts = np.einsum("tij,nj->tni", Rt[:, :3, :3], verts) + Rt[:, None, :3, 3]
camera_meshes = {
"v3d": verts,
"f3d": faces,
"vc": None,
"name": "camera",
"fc": face_colors,
"color": -1,
}
vis_dict["camera"] = camera_meshes
side_source = torch.tensor([0.463, -0.478, 2.456])
side_target = torch.tensor([0.026, -0.481, -3.184])
up = torch.tensor([1.0, 0.0, 0.0])
view_camera = lookat_matrix(side_source, side_target, up)
viewer_Rt = np.tile(view_camera[:3, :4], (num_frames, 1, 1))
meshes = viewer_utils.construct_viewer_meshes(
vis_dict, draw_edges=False, flat_shading=False
)
vis_h, vis_w = (1000, 1000)
K = np.array(
[
[1000, 0, vis_w / 2],
[0, 1000, vis_h / 2],
[0, 0, 1]
]
)
data = viewer_utils.ViewerData(viewer_Rt, K, vis_w, vis_h)
batch = (meshes, data)
viewer = viewer_utils.ARCTICViewer(interactive=True, size=(vis_w, vis_h))
viewer.render_seq(batch, out_folder=os.path.join(output_pth, 'aitviewer'))
def run_vis2_on_video_cam(res_dict, res_dict2, output_pth, focal_length, image_names, R_w2c=None, t_w2c=None):
img0 = cv2.imread(image_names[0])
height, width, _ = img0.shape
world_mano = {}
world_mano['vertices'] = res_dict['vertices']
world_mano['faces'] = res_dict['faces']
world_mano2 = {}
world_mano2['vertices'] = res_dict2['vertices']
world_mano2['faces'] = res_dict2['faces']
vis_dict = {}
color_idx = 0
world_mano['vertices'] = world_mano['vertices']
for _id, _verts in enumerate(world_mano['vertices']):
verts = _verts.cpu().numpy() # T, N, 3
body_faces = world_mano['faces']
body_meshes = {
"v3d": verts,
"f3d": body_faces,
"vc": None,
"name": f"hand_{_id}",
# "color": "pace-green",
"color": "director-purple",
}
vis_dict[f"hand_{_id}"] = body_meshes
color_idx += 1
world_mano2['vertices'] = world_mano2['vertices']
for _id, _verts in enumerate(world_mano2['vertices']):
verts = _verts.cpu().numpy() # T, N, 3
body_faces = world_mano2['faces']
body_meshes = {
"v3d": verts,
"f3d": body_faces,
"vc": None,
"name": f"hand2_{_id}",
# "color": "pace-blue",
"color": "director-blue",
}
vis_dict[f"hand2_{_id}"] = body_meshes
color_idx += 1
meshes = viewer_utils.construct_viewer_meshes(
vis_dict, draw_edges=False, flat_shading=False
)
num_frames = len(world_mano['vertices'][_id])
Rt = np.zeros((num_frames, 3, 4))
Rt[:, :3, :3] = R_w2c[:num_frames]
Rt[:, :3, 3] = t_w2c[:num_frames]
cols, rows = (width, height)
K = np.array(
[
[focal_length, 0, width / 2],
[0, focal_length, height / 2],
[0, 0, 1]
]
)
vis_h = height
vis_w = width
data = viewer_utils.ViewerData(Rt, K, cols, rows, imgnames=image_names)
batch = (meshes, data)
viewer = viewer_utils.ARCTICViewer(interactive=True, size=(vis_w, vis_h))
viewer.render_seq(batch, out_folder=os.path.join(output_pth, 'aitviewer'))
def lookat_matrix(source_pos, target_pos, up):
"""
IMPORTANT: USES RIGHT UP BACK XYZ CONVENTION
:param source_pos (*, 3)
:param target_pos (*, 3)
:param up (3,)
"""
*dims, _ = source_pos.shape
up = up.reshape(*(1,) * len(dims), 3)
up = up / torch.linalg.norm(up, dim=-1, keepdim=True)
back = normalize(target_pos - source_pos)
right = normalize(torch.linalg.cross(up, back))
up = normalize(torch.linalg.cross(back, right))
R = torch.stack([right, up, back], dim=-1)
return make_4x4_pose(R, source_pos)
def make_4x4_pose(R, t):
"""
:param R (*, 3, 3)
:param t (*, 3)
return (*, 4, 4)
"""
dims = R.shape[:-2]
pose_3x4 = torch.cat([R, t.view(*dims, 3, 1)], dim=-1)
bottom = (
torch.tensor([0, 0, 0, 1], device=R.device)
.reshape(*(1,) * len(dims), 1, 4)
.expand(*dims, 1, 4)
)
return torch.cat([pose_3x4, bottom], dim=-2)
def normalize(x):
return x / torch.linalg.norm(x, dim=-1, keepdim=True)
def save_mesh_to_obj(vertices, faces, file_path):
# 创建一个 Trimesh 对象
mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
# 导出为 .obj 文件
mesh.export(file_path)
print(f"Mesh saved to {file_path}")
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