name

2025-07-25 15:05:31 +08:00
parent 24e1e31234
commit 65769e5eb6
18 changed files with 3642 additions and 67 deletions
--- a/.gitignore
+++ b/.gitignore
@ -14,3 +14,6 @@ smplpytorch/native/models/*.pkl
 exp/
 output/
 make_gif.py
+*.pkl
+*.whl
+*.png
--- a/.python-version
+++ b/.python-version
@ -0,0 +1 @@
+3.12
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@ -0,0 +1,15 @@
+{
+    // 使用 IntelliSense 了解相关属性。 
+    // 悬停以查看现有属性的描述。
+    // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python 调试程序: 当前文件",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}
--- a/dataset/single_people_smpl.json
+++ b/dataset/single_people_smpl.json
@ -0,0 +1,122 @@
+[
+    [
+        0.3718571662902832,
+        0.6803165078163147,
+        -0.7502949833869934
+    ],
+    [
+        0.45830726623535156,
+        0.6820494532585144,
+        -0.6965712904930115
+    ],
+    [
+        0.28540709614753723,
+        0.6785836219787598,
+        -0.8040187358856201
+    ],
+    [
+        0.3710346221923828,
+        0.8273340463638306,
+        -0.6851714849472046
+    ],
+    [
+        0.4792303442955017,
+        0.2899134159088135,
+        -0.6879411935806274
+    ],
+    [
+        0.2960263788700104,
+        0.29219183325767517,
+        -0.8322873711585999
+    ],
+    [
+        0.3702120780944824,
+        0.9743515253067017,
+        -0.6200480461120605
+    ],
+    [
+        0.5172274112701416,
+        -0.09073961526155472,
+        -0.7129285335540771
+    ],
+    [
+        0.3149631917476654,
+        -0.08920176327228546,
+        -0.9007936120033264
+    ],
+    [
+        0.3702120780944824,
+        1.0243514776229858,
+        -0.6200480461120605
+    ],
+    [
+        0.5201388001441956,
+        -0.13739190995693207,
+        -0.6414260864257812
+    ],
+    [
+        0.2730123996734619,
+        -0.13770559430122375,
+        -0.8578707575798035
+    ],
+    [
+        0.3782634735107422,
+        1.2141607999801636,
+        -0.7061752080917358
+    ],
+    [
+        0.4474300742149353,
+        1.1862998008728027,
+        -0.6677815914154053
+    ],
+    [
+        0.3090968728065491,
+        1.1820218563079834,
+        -0.7445688247680664
+    ],
+    [
+        0.36856698989868164,
+        1.2683864831924438,
+        -0.4898010492324829
+    ],
+    [
+        0.5165966749191284,
+        1.1984386444091797,
+        -0.6293879151344299
+    ],
+    [
+        0.23993025720119476,
+        1.1898829936981201,
+        -0.7829625010490417
+    ],
+    [
+        0.478150337934494,
+        0.971466600894928,
+        -0.5572866201400757
+    ],
+    [
+        0.20990325510501862,
+        0.936416506767273,
+        -0.8448361158370972
+    ],
+    [
+        0.3065527677536011,
+        1.1182676553726196,
+        -0.5591280460357666
+    ],
+    [
+        0.17920757830142975,
+        0.6927539706230164,
+        -0.7849017381668091
+    ],
+    [
+        0.25156882405281067,
+        1.1860939264297485,
+        -0.5887487530708313
+    ],
+    [
+        0.1682051420211792,
+        0.6014264822006226,
+        -0.7367973327636719
+    ]
+]
--- a/dataset/single_people_smpl_2.json
+++ b/dataset/single_people_smpl_2.json
@ -0,0 +1,122 @@
+[
+    [
+        0.3723624646663666,
+        0.6807540655136108,
+        -0.7536930441856384
+    ],
+    [
+        0.4606369733810425,
+        0.6819767355918884,
+        -0.7026320695877075
+    ],
+    [
+        0.2840879261493683,
+        0.6795313954353333,
+        -0.8047540187835693
+    ],
+    [
+        0.37141507863998413,
+        0.8276264667510986,
+        -0.6875424385070801
+    ],
+    [
+        0.479561448097229,
+        0.2904614806175232,
+        -0.6911981701850891
+    ],
+    [
+        0.29553577303886414,
+        0.2926357686519623,
+        -0.832754373550415
+    ],
+    [
+        0.3704676926136017,
+        0.9744989275932312,
+        -0.621391773223877
+    ],
+    [
+        0.5182272791862488,
+        -0.09084545075893402,
+        -0.7154797911643982
+    ],
+    [
+        0.31344518065452576,
+        -0.08931314945220947,
+        -0.9003074169158936
+    ],
+    [
+        0.3704676926136017,
+        1.0244989395141602,
+        -0.621391773223877
+    ],
+    [
+        0.5207709074020386,
+        -0.1377965807914734,
+        -0.6441547870635986
+    ],
+    [
+        0.2722875475883484,
+        -0.13784976303577423,
+        -0.8576371669769287
+    ],
+    [
+        0.3782576322555542,
+        1.214632511138916,
+        -0.7064842581748962
+    ],
+    [
+        0.4484415650367737,
+        1.1865851879119873,
+        -0.6695226430892944
+    ],
+    [
+        0.3080736994743347,
+        1.1826797723770142,
+        -0.743445873260498
+    ],
+    [
+        0.3685729205608368,
+        1.2682437896728516,
+        -0.48909056186676025
+    ],
+    [
+        0.5186254978179932,
+        1.1985379457473755,
+        -0.6325609683990479
+    ],
+    [
+        0.23788979649543762,
+        1.1907269954681396,
+        -0.7804075479507446
+    ],
+    [
+        0.4849953353404999,
+        0.969235897064209,
+        -0.5583885312080383
+    ],
+    [
+        0.20581978559494019,
+        0.9366675019264221,
+        -0.8437999486923218
+    ],
+    [
+        0.3075758218765259,
+        1.1038239002227783,
+        -0.5611312389373779
+    ],
+    [
+        0.17861124873161316,
+        0.6925873756408691,
+        -0.7852163314819336
+    ],
+    [
+        0.24666009843349457,
+        1.1842905282974243,
+        -0.5826389193534851
+    ],
+    [
+        0.16754546761512756,
+        0.6011085510253906,
+        -0.7378559708595276
+    ]
+]
--- a/demo.py
+++ b/demo.py
@ -14,10 +14,10 @@ if __name__ == '__main__':
    smpl_layer = SMPL_Layer(
        center_idx=0,
        gender='male',
-        model_root='smplpytorch/native/models')
+        model_root='/home/lmd/Code/Pose_to_SMPL_an_230402/smplpytorch/native/models')

    # Generate random pose and shape parameters
-    pose_params = torch.rand(batch_size, 72) * 0.01
+    pose_params = (torch.rand(batch_size, 72)*2-1) * np.pi
    shape_params = torch.rand(batch_size, 10) * 0.03

    # GPU mode
@ -36,5 +36,5 @@ if __name__ == '__main__':
        model_faces=smpl_layer.th_faces,
        with_joints=True,
        kintree_table=smpl_layer.kintree_table,
-        savepath='image.png',
+        savepath='image1.png',
        show=True)
--- a/display_utils.py
+++ b/display_utils.py
@ -3,11 +3,13 @@ from matplotlib import pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 from mpl_toolkits.mplot3d.art3d import Poly3DCollection
 # plt.switch_backend('agg')
+from torch import Tensor
+from typing import Optional


 def display_model(
        model_info,
-        model_faces=None,
+        model_faces:Optional[Tensor]=None,
        with_joints=False,
        kintree_table=None,
        ax=None,
@ -27,7 +29,7 @@ def display_model(
    if model_faces is None:
        ax.scatter(verts[:, 0], verts[:, 1], verts[:, 2], alpha=0.2)
    elif not only_joint:
-        mesh = Poly3DCollection(verts[model_faces], alpha=0.2)
+        mesh = Poly3DCollection(verts[model_faces.cpu()], alpha=0.2)
        face_color = (141 / 255, 184 / 255, 226 / 255)
        edge_color = (50 / 255, 50 / 255, 50 / 255)
        mesh.set_edgecolor(edge_color)
@ -46,8 +48,8 @@ def display_model(
    if savepath:
        # print('Saving figure at {}.'.format(savepath))
        plt.savefig(savepath, bbox_inches='tight', pad_inches=0)
-    if show:
-        plt.show()
+    # if show:
+    #     plt.show()
    plt.close()
    return ax

--- a/fit/configs/CMU_Mocap.json
+++ b/fit/configs/CMU_Mocap.json
@ -12,7 +12,7 @@
    "USE_GPU": 1,
    "DATASET": {
        "NAME": "UTD-MHAD",
-        "PATH": "../Action2Motion/CMU Mocap/mocap/mocap_3djoints/",
+        "PATH": "/home/lmd/Code/Pose_to_SMPL_an_230402/database",
        "TARGET_PATH": "",
        "DATA_MAP": [
            [
--- a/fit/configs/UTD_MHAD.json
+++ b/fit/configs/UTD_MHAD.json
@ -12,72 +12,100 @@
    "USE_GPU": 1,
    "DATASET": {
        "NAME": "UTD-MHAD",
-        "PATH": "../UTD-MHAD/Skeleton/Skeleton/",
+        "PATH": "/home/lmd/Code/Pose_to_SMPL_an_230402/database",
        "TARGET_PATH": "",
        "DATA_MAP": [
            [
-                12,
+                1,
                1
+            ],
+             [
+                2,
+                2
            ],
            [
-                0,
+                3,
                3
            ],
            [
-                16,
+                4,
                4
            ],
            [
-                18,
+                5,
                5
            ],
            [
-                20,
+                6,
                6
            ],
            [
-                22,
+                7,
                7
            ],
            [
-                17,
+                8,
                8
            ],
            [
-                19,
+                9,
                9
            ],
            [
-                21,
+                10,
                10
            ],
            [
-                23,
+                11,
                11
            ],
            [
-                1,
+                12,
                12
            ],
            [
-                4,
+                13,
                13
            ],
            [
-                7,
+                14,
                14
+            ],
+             [
+                15,
+                15
            ],
            [
-                2,
+                16,
                16
            ],
            [
-                5,
+                17,
                17
            ],
            [
-                8,
+                18,
                18
+            ],
+             [
+                19,
+                19
+            ],
+             [
+                20,
+                20
+            ],
+             [
+                21,
+                21
+            ],
+             [
+                22,
+                22
+            ],
+             [
+                23,
+                23
            ]
        ]
    },
--- a/fit/tools/label.py
+++ b/fit/tools/label.py
@ -1284,8 +1284,9 @@ def get_label(file_name, dataset_name):
        key = file_name[-5:]
        return HumanAct12[key]
    elif dataset_name == 'UTD_MHAD':
-        key = file_name.split('_')[0][1:]
-        return UTD_MHAD[key]
+        ###key = file_name.split('_')[0][1:]
+        ###return UTD_MHAD[key]
+        return "single_person"
    elif dataset_name == 'CMU_Mocap':
        key = file_name.split(':')[0]
        return CMU_Mocap[key] if key in CMU_Mocap.keys() else ""
--- a/fit/tools/load.py
+++ b/fit/tools/load.py
@ -1,25 +1,46 @@
 import scipy.io
 import numpy as np
-import json
+import json  # 引入json模块


 def load(name, path):
+    # 处理UTD-MHAD的JSON文件（你的单帧数据）
    if name == 'UTD_MHAD':
-        arr = scipy.io.loadmat(path)['d_skel']
-        new_arr = np.zeros([arr.shape[2], arr.shape[0], arr.shape[1]])
-        for i in range(arr.shape[2]):
-            for j in range(arr.shape[0]):
-                for k in range(arr.shape[1]):
-                    new_arr[i][j][k] = arr[j][k][i]
-        return new_arr
+        # 判断文件是否为JSON格式（通过后缀）
+        if path.endswith('.json'):
+            with open(path, 'r') as f:
+                data = json.load(f)  # 加载JSON列表
+            # 转换为NumPy数组，确保形状为[关节数, 3]
+            data_np = np.array(data)
+            # 校验数据格式（防止错误）
+            assert data_np.ndim == 2 and data_np.shape[1] == 3, \
+                f"UTD-MHAD JSON格式错误，应为[关节数, 3]，实际为{data_np.shape}"
+            # 若需要单帧维度（[1, 关节数, 3]），可扩展维度
+            return data_np[np.newaxis, ...]  # 输出形状：[1, N, 3]（1表示单帧）
+        
+        # 保留原UTD_MHAD的.mat文件支持（如果还需要处理.mat数据）
+        elif path.endswith('.mat'):
+            arr = scipy.io.loadmat(path)['d_skel']
+            new_arr = np.zeros([arr.shape[2], arr.shape[0], arr.shape[1]])
+            for i in range(arr.shape[2]):
+                for j in range(arr.shape[0]):
+                    for k in range(arr.shape[1]):
+                        new_arr[i][j][k] = arr[j][k][i]
+            return new_arr
+        
+        else:
+            raise ValueError(f"UTD-MHAD不支持的文件格式：{path}")
+
+    # 其他数据集的原有逻辑保持不变
    elif name == 'HumanAct12':
        return np.load(path, allow_pickle=True)
    elif name == "CMU_Mocap":
        return np.load(path, allow_pickle=True)
    elif name == "Human3.6M":
-        return np.load(path, allow_pickle=True)[0::5] # down_sample
+        return np.load(path, allow_pickle=True)[0::5]  # 下采样
    elif name == "NTU":
        return np.load(path, allow_pickle=True)[0::2]
    elif name == "HAA4D":
        return np.load(path, allow_pickle=True)
-        
+    else:
+        raise ValueError(f"不支持的数据集名称：{name}")
--- a/fit/tools/main.py
+++ b/fit/tools/main.py
@ -1,53 +1,104 @@
+"""
+SMPL模型拟合主程序
+该脚本用于将人体姿态数据拟合到SMPL（Skinned Multi-Person Linear）模型中
+主要功能：
+1. 加载人体姿态数据
+2. 使用SMPL模型进行拟合优化
+3. 保存拟合结果和可视化图像
+"""
+
 import os
 import sys
 sys.path.append(os.getcwd())
-from meters import Meters
-from smplpytorch.pytorch.smpl_layer import SMPL_Layer
-from train import train
-from transform import transform
-from save import save_pic, save_params
-from load import load
-import torch
-import numpy as np
-from tensorboardX import SummaryWriter
-from easydict import EasyDict as edict
-import time
-import logging

-import argparse
-import json
+# 导入自定义模块
+from meters import Meters  # 用于跟踪训练指标的工具类
+from smplpytorch.pytorch.smpl_layer import SMPL_Layer  # SMPL模型层
+from train import train  # 训练函数
+from transform import transform  # 数据变换函数
+from save import save_pic, save_params  # 保存结果的函数
+from load import load  # 数据加载函数

+# 导入标准库
+import torch  # PyTorch深度学习框架
+import numpy as np  # 数值计算库
+from easydict import EasyDict as edict  # 用于创建字典对象的便捷工具
+import time  # 时间处理
+import logging  # 日志记录

+import argparse  # 命令行参数解析
+import json  # JSON文件处理
+
+# 启用CUDNN加速，提高训练效率
 torch.backends.cudnn.enabled = True
 torch.backends.cudnn.benchmark = True


 def parse_args():
+    """
+    解析命令行参数
+    
+    Returns:
+        args: 包含解析后参数的命名空间对象
+    """
    parser = argparse.ArgumentParser(description='Fit SMPL')
+    
+    # 实验名称，默认使用当前时间戳
    parser.add_argument('--exp', dest='exp',
                        help='Define exp name',
                        default=time.strftime('%Y-%m-%d %H-%M-%S', time.localtime(time.time())), type=str)
+    
+    # 数据集名称，用于选择对应的配置文件
    parser.add_argument('--dataset_name', '-n', dest='dataset_name',
                        help='select dataset',
                        default='', type=str)
+    
+    # 数据集路径，可以覆盖配置文件中的默认路径
    parser.add_argument('--dataset_path', dest='dataset_path',
                        help='path of dataset',
                        default=None, type=str)
+    
    args = parser.parse_args()
    return args


 def get_config(args):
+    """
+    根据数据集名称加载对应的配置文件
+    
+    Args:
+        args: 命令行参数对象
+        
+    Returns:
+        cfg: 配置对象，包含所有训练和模型参数
+    """
+    # 根据数据集名称构建配置文件路径
    config_path = 'fit/configs/{}.json'.format(args.dataset_name)
+    
+    # 读取JSON配置文件
    with open(config_path, 'r') as f:
        data = json.load(f)
+    
+    # 将字典转换为edict对象，支持点号访问属性
    cfg = edict(data.copy())
+    
+    # 如果命令行指定了数据集路径，则覆盖配置文件中的设置
    if not args.dataset_path == None:
        cfg.DATASET.PATH = args.dataset_path
+    
    return cfg


 def set_device(USE_GPU):
+    """
+    根据配置和硬件可用性设置计算设备
+    
+    Args:
+        USE_GPU: 是否使用GPU的布尔值
+        
+    Returns:
+        device: PyTorch设备对象（'cuda' 或 'cpu'）
+    """
    if USE_GPU and torch.cuda.is_available():
        device = torch.device('cuda')
    else:
@ -56,9 +107,21 @@ def set_device(USE_GPU):


 def get_logger(cur_path):
+    """
+    设置日志记录器，同时输出到文件和控制台
+    
+    Args:
+        cur_path: 当前实验路径，用于保存日志文件
+        
+    Returns:
+        logger: 日志记录器对象
+        writer: TensorBoard写入器（当前设置为None）
+    """
+    # 创建日志记录器
    logger = logging.getLogger(__name__)
    logger.setLevel(level=logging.INFO)

+    # 设置文件输出处理器，将日志保存到log.txt文件
    handler = logging.FileHandler(os.path.join(cur_path, "log.txt"))
    handler.setLevel(logging.INFO)
    formatter = logging.Formatter(
@ -66,6 +129,7 @@ def get_logger(cur_path):
    handler.setFormatter(formatter)
    logger.addHandler(handler)

+    # 设置控制台输出处理器，将日志同时输出到终端
    handler = logging.StreamHandler()
    handler.setLevel(logging.INFO)
    formatter = logging.Formatter(
@ -73,54 +137,106 @@ def get_logger(cur_path):
    handler.setFormatter(formatter)
    logger.addHandler(handler)

-    writer = SummaryWriter(os.path.join(cur_path, 'tb'))
+    # TensorBoard写入器（目前被注释掉，设置为None）
+    # from tensorboardX import SummaryWriter
+    # writer = SummaryWriter(os.path.join(cur_path, 'tb'))
+    writer = None

    return logger, writer


 if __name__ == "__main__":
+    """
+    主函数：执行SMPL模型拟合流程
+    
+    主要步骤：
+    1. 解析命令行参数
+    2. 创建实验目录
+    3. 加载配置文件
+    4. 设置日志记录
+    5. 初始化SMPL模型
+    6. 遍历数据集进行拟合
+    7. 保存结果
+    """
+    # 解析命令行参数
    args = parse_args()

+    # 创建实验目录，使用时间戳或用户指定的实验名称
    cur_path = os.path.join(os.getcwd(), 'exp', args.exp)
-    assert not os.path.exists(cur_path), 'Duplicate exp name'
+    assert not os.path.exists(cur_path), 'Duplicate exp name'  # 确保实验名称不重复
    os.mkdir(cur_path)

+    # 加载配置文件
    cfg = get_config(args)
+    # 将配置保存到实验目录中，便于后续追踪实验设置
    json.dump(dict(cfg), open(os.path.join(cur_path, 'config.json'), 'w'))

+    # 设置日志记录器
    logger, writer = get_logger(cur_path)
    logger.info("Start print log")

+    # 设置计算设备（GPU或CPU）
    device = set_device(USE_GPU=cfg.USE_GPU)
    logger.info('using device: {}'.format(device))

+    # 初始化SMPL模型层
+    # center_idx=0: 设置中心关节点索引
+    # gender='male': 设置性别为男性（注释掉的cfg.MODEL.GENDER可能用于从配置文件读取）
+    # model_root: SMPL模型文件的路径
    smpl_layer = SMPL_Layer(
        center_idx=0,
-        gender=cfg.MODEL.GENDER,
+        gender='male', #cfg.MODEL.GENDER,
        model_root='smplpytorch/native/models')

+    # 初始化指标记录器，用于跟踪训练损失等指标
    meters = Meters()
    file_num = 0
+    # 遍历数据集目录中的所有文件
    for root, dirs, files in os.walk(cfg.DATASET.PATH):
-        for file in sorted(files):
-            if not 'baseball_swing' in file:
-                continue
-            file_num += 1
+        for file in sorted(files):  # 按文件名排序处理
+            # 可选的文件过滤器（当前被注释掉）
+            # 可以用于只处理特定的文件，如包含'baseball_swing'的文件
+            ###if not 'baseball_swing' in file:
+                ###continue
+            
+            file_num += 1  # 文件计数器
            logger.info(
                'Processing file: {}    [{} / {}]'.format(file, file_num, len(files)))
+            
+            # 加载并变换目标数据
+            # 1. load(): 根据数据集类型加载原始数据
+            # 2. transform(): 将数据转换为模型所需的格式
+            # 3. torch.from_numpy(): 将numpy数组转换为PyTorch张量
+            # 4. .float(): 确保数据类型为float32
            target = torch.from_numpy(transform(args.dataset_name, load(args.dataset_name,
                                                                        os.path.join(root, file)))).float()
            logger.info("target shape:{}".format(target.shape))
+            
+            # 执行SMPL模型拟合训练
+            # 传入SMPL层、目标数据、日志记录器、设备信息等
            res = train(smpl_layer, target,
                        logger, writer, device,
                        args, cfg, meters)
+            
+            # 更新平均损失指标
+            # k=target.shape[0] 表示批次大小，用于加权平均
            meters.update_avg(meters.min_loss, k=target.shape[0])
+            
+            # 重置早停计数器，为下一个文件的训练做准备
            meters.reset_early_stop()
+            
+            # 记录当前的平均损失
            logger.info("avg_loss:{:.4f}".format(meters.avg))

+            # 保存拟合结果
+            # 1. save_params(): 保存拟合得到的SMPL参数
+            # 2. save_pic(): 保存可视化图像，包括拟合结果和原始目标的对比
            save_params(res, file, logger, args.dataset_name)
            save_pic(res, smpl_layer, file, logger, args.dataset_name, target)

+            # 清空GPU缓存，防止内存溢出
            torch.cuda.empty_cache()
+    
+    # 所有文件处理完成，记录最终的平均损失
    logger.info(
        "Fitting finished! Average loss:     {:.9f}".format(meters.avg))
--- a/fit/tools/train.py
+++ b/fit/tools/train.py
@ -78,9 +78,9 @@ def train(smpl_layer, target,
            #         epoch, float(loss),float(scale)))
            print("Epoch {}, lossPerBatch={:.6f}, scale={:.4f}".format(
                     epoch, float(loss),float(scale)))
-            writer.add_scalar('loss', float(loss), epoch)
-            writer.add_scalar('learning_rate', float(
-                optimizer.state_dict()['param_groups'][0]['lr']), epoch)
+            ###writer.add_scalar('loss', float(loss), epoch)
+            ###writer.add_scalar('learning_rate', float(
+                ###optimizer.state_dict()['param_groups'][0]['lr']), epoch)
            # save_single_pic(res,smpl_layer,epoch,logger,args.dataset_name,target)

    logger.info('Train ended, min_loss = {:.4f}'.format(
--- a/fit/tools/transform.py
+++ b/fit/tools/transform.py
@ -3,7 +3,7 @@ import numpy as np
 rotate = {
    'HumanAct12': [1., -1., -1.],
    'CMU_Mocap': [0.05, 0.05, 0.05],
-    'UTD_MHAD': [-1., 1., -1.],
+    'UTD_MHAD': [1., 1., 1.],
    'Human3.6M': [-0.001, -0.001, 0.001],
    'NTU': [1., 1., -1.],
    'HAA4D': [1., -1., -1.],
--- a/pyproject.toml
+++ b/pyproject.toml
@ -0,0 +1,64 @@
+[project]
+name = "template_project_alpha"
+version = "0.1.0"
+description = "a basic template project that has the ability to complete most of ML task with PyTorch and MMCV"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+  "anyio>=4.9.0",
+  "awkward>=2.8.5",
+  "beartype>=0.21.0",
+  "click>=8.2.1",
+  "easydict>=1.13",
+  "jaxtyping>=0.3.2",
+  "loguru>=0.7.3",
+  "mmcv",
+  "mmdet>=3.3.0",
+  "mmpose>=1.3.2",
+  "nats-py>=2.10.0",
+  "numba>=0.61.2",
+  "nvidia-nvimgcodec-cu12>=0.5.0.13",
+  "opencv-python>=4.12.0.88",
+  "orjson>=3.10.18",
+  "pyarrow>=20.0.0",
+  "pydantic>=2.11.7",
+  "pytorch3d",
+  "redis>=6.2.0",
+  "result>=0.17.0",
+  "tomli>=2.2.1",
+  "tomli-w>=1.2.0",
+  "torch>=2.7.0",
+  "torchvision>=0.22.0",
+  "ultralytics>=8.3.166",
+  "xtcocotools",
+]
+
+[dependency-groups]
+dev = ["jupyterlab>=4.4.4", "pip", "psutil>=7.0.0", "setuptools"]
+
+[tool.uv]
+no-build-isolation-package = ["chumpy", "xtcocotools"]
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch-cpu", marker = "sys_platform != 'linux'" },
+  { index = "pytorch-cu128", marker = "sys_platform == 'linux'" },
+]
+torchvision = [
+  { index = "pytorch-cpu", marker = "sys_platform != 'linux'" },
+  { index = "pytorch-cu128", marker = "sys_platform == 'linux'" },
+]
+# built with cu128
+mmcv = { path = "misc/mmcv-2.2.0-cp312-cp312-linux_x86_64.whl" }
+xtcocotools = { path = "misc/xtcocotools-1.14.3-cp312-cp312-linux_x86_64.whl" }
+pytorch3d = { path = "misc/pytorch3d-0.7.8-cp312-cp312-linux_x86_64.whl" }
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
--- a/smpl.py
+++ b/smpl.py
@ -0,0 +1,57 @@
+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)
--- a/smplpytorch/pytorch/smpl_layer.py
+++ b/smplpytorch/pytorch/smpl_layer.py
@ -27,12 +27,19 @@ class SMPL_Layer(Module):
        self.center_idx = center_idx
        self.gender = gender

-        if gender == 'neutral':
-            self.model_path = os.path.join(model_root, 'basicModel_neutral_lbs_10_207_0_v1.0.0.pkl')
-        elif gender == 'female':
+        # if gender == 'neutral':
+        #     self.model_path = os.path.join(model_root, 'basicModel_neutral_lbs_10_207_0_v1.0.0.pkl')
+        # elif gender == 'female':
+        #     self.model_path = os.path.join(model_root, 'basicModel_f_lbs_10_207_0_v1.0.0.pkl')
+        # elif gender == 'male':
+        #     self.model_path = os.path.join(model_root, 'basicModel_m_lbs_10_207_0_v1.0.0.pkl')
+
+        if gender == 'female':
            self.model_path = os.path.join(model_root, 'basicModel_f_lbs_10_207_0_v1.0.0.pkl')
        elif gender == 'male':
-            self.model_path = os.path.join(model_root, 'basicModel_m_lbs_10_207_0_v1.0.0.pkl')
+            self.model_path = os.path.join(model_root, 'basicmodel_m_lbs_10_207_0_v1.0.0.pkl')
+        else:
+            raise ValueError("no valid gender")

        smpl_data = ready_arguments(self.model_path)
        self.smpl_data = smpl_data
--- a/uv.lock
+++ b/uv.lock