PyTorch3D 人体三维姿态捕捉 python 代码

以下是使用PyTorch3D实现人体三维姿态捕捉的Python代码示例：

import torch
import numpy as np
import cv2
import matplotlib.pyplot as plt
from pytorch3d.structures import Meshes
from pytorch3d.renderer import (
    look_at_view_transform,
    OpenGLPerspectiveCameras, 
    FoVPerspectiveCameras, 
    PointLights, 
    RasterizationSettings, 
    MeshRenderer, 
    MeshRasterizer, 
    SoftPhongShader,
    TexturesVertex
)
from pytorch3d.ops import sample_points_from_meshes
from pytorch3d.loss import chamfer_distance, mesh_edge_loss, mesh_laplacian_smoothing

# 加载 SMPL 模型
from smplpytorch.pytorch.smpl_layer import SMPL_Layer
smpl_layer = SMPL_Layer(model_path='data/smpl')

# 加载图像
image_path = 'data/image.jpg'
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)

# 从图像中检测人体关键点
# 这里使用的是 OpenPose 进行关键点检测
# ...

# 根据关键点估计人体姿态
# 这里使用的是 SMPL 进行姿态估计
# ...

# 创建人体模型
verts = smpl_layer(thetas=thetas, betas=betas)
faces = torch.from_numpy(smpl_layer.faces.astype(np.int32))
tex = torch.ones_like(verts)[None]
textures = TexturesVertex(verts_features=tex)
mesh = Meshes(verts=[verts], faces=[faces], textures=textures)

# 设置相机参数
R, T = look_at_view_transform(10.0, 10.0, 10.0)
cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
raster_settings = RasterizationSettings(
    image_size=512, 
    blur_radius=0.0, 
    faces_per_pixel=1, 
    bin_size=0
)

# 创建渲染器
lights = PointLights(device=device, location=((2.0, 2.0, -2.0),))
renderer = MeshRenderer(
    rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings),
    shader=SoftPhongShader(device=device, lights=lights)
)

# 渲染人体模型
images = renderer(mesh)

# 可视化结果
plt.imshow(images[0, ..., :3].cpu().numpy())