name

smpl.py

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+import torch
+import random
+import numpy as np
+import pickle  # 新增：用于加载pkl文件
+
+from smplpytorch.pytorch.smpl_layer import SMPL_Layer
+from display_utils import display_model
+
+
+if __name__ == '__main__':
+    cuda = True
+    batch_size = 1
+
+    # Create the SMPL layer
+    smpl_layer = SMPL_Layer(
+        center_idx=0,
+        gender='male',  # 确保与pkl文件中的性别一致
+        model_root='/home/lmd/Code/Pose_to_SMPL_an_230402/smplpytorch/native/models')
+
+    # 从pkl文件加载参数
+    pkl_path = '/home/lmd/Code/Pose_to_SMPL_an_230402/fit/output/UTD_MHAD/sigle_people_smpl_params.pkl'  # 替换为实际的pkl文件路径
+    with open(pkl_path, 'rb') as f:
+        data = pickle.load(f)
+    
+    # 提取pose和shape参数
+    pose_params = torch.tensor(data['pose_params']).float()  # 确保数据类型为float
+    shape_params = torch.tensor(data['shape_params']).float()
+    
+    # 调整维度（如果需要）
+    if pose_params.dim() == 1:
+        pose_params = pose_params.unsqueeze(0)  # 添加batch维度
+    if shape_params.dim() == 1:
+        shape_params = shape_params.unsqueeze(0)
+    
+    # 验证batch size
+    if pose_params.shape[0] != batch_size:
+        batch_size = pose_params.shape[0]
+        print(f"Warning: Batch size adjusted to {batch_size} based on loaded data.")
+
+    # GPU mode
+    if cuda:
+        pose_params = pose_params.cuda()
+        shape_params = shape_params.cuda()
+        smpl_layer.cuda()
+
+    # Forward from the SMPL layer
+    verts, Jtr = smpl_layer(pose_params, th_betas=shape_params)
+
+    # Draw output vertices and joints
+    display_model(
+        {'verts': verts.cpu().detach(),
+         'joints': Jtr.cpu().detach()},
+        model_faces=smpl_layer.th_faces,
+        with_joints=True,
+        kintree_table=smpl_layer.kintree_table,
+        savepath='image2.png',
+        show=True)