Img2pose

🚀 img2pose

img2pose是一个用于图像特征提取的模型，它借助Faster R - CNN预测照片中所有人脸的6自由度姿态（DoF），还能将3D人脸投影到2D平面以识别每个人脸的边界框，且无需其他人脸检测模型。

🚀 快速开始

以下是使用img2pose模型的示例代码：

import numpy as np
import os
import json
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from feat.facepose_detectors.img2pose.deps.models import FasterDoFRCNN, postprocess_img2pose
from feat.utils.io import get_resource_path
from torchvision.models.detection.backbone_utils import resnet_fpn_backbone


# Load Model Configurations
facepose_config_file = hf_hub_download(repo_id= "py-feat/img2pose", filename="config.json", cache_dir=get_resource_path())
with open(facepose_config_file, "r") as f:
    facepose_config = json.load(f)
           
# Initialize img2pose
device = 'cpu'
backbone = resnet_fpn_backbone(backbone_name="resnet18", weights=None)
backbone.eval()
backbone.to(device)
facepose_detector = FasterDoFRCNN(backbone=backbone,
                            num_classes=2,
                            min_size=facepose_config['min_size'],
                            max_size=facepose_config['max_size'],
                            pose_mean=torch.tensor(facepose_config['pose_mean']),
                            pose_stddev=torch.tensor(facepose_config['pose_stddev']),
                            threed_68_points=torch.tensor(facepose_config['threed_points']),
                            rpn_pre_nms_top_n_test=facepose_config['rpn_pre_nms_top_n_test'],
                            rpn_post_nms_top_n_test=facepose_config['rpn_post_nms_top_n_test'],
                            bbox_x_factor=facepose_config['bbox_x_factor'],
                            bbox_y_factor=facepose_config['bbox_y_factor'],
                            expand_forehead=facepose_config['expand_forehead'])
facepose_model_file = hf_hub_download(repo_id= "py-feat/img2pose", filename="model.safetensors", cache_dir=get_resource_path())
facepose_checkpoint = load_file(facepose_model_file)
facepose_detector.load_state_dict(facepose_checkpoint)
facepose_detector.eval()
facepose_detector.to(device)

# Test model
face_image = "path/to/your/test_image.jpg"  # Replace with your image

img2pose_output = facepose_detector(face_image)

# Postprocess
img2pose_output = postprocess_img2pose(img2pose_output[0])
bbox = img2pose_output['boxes']
poses = img2pose_output['dofs']
facescores = img2pose_output['scores']

✨ 主要特性

• 使用Faster R - CNN预测人脸的6自由度姿态（DoF）。
• 可以将3D人脸投影到2D平面，识别每个人脸的边界框。
• 无需额外的人脸检测模型。

📚 详细文档

模型详情

模型来源

• 仓库：GitHub Repository
• 论文：img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

引用

如果您在研究或应用中使用了此模型，请引用以下论文：

Vítor Albiero, Xingyu Chen, Xi Yin, Guan Pang, Tal Hassner, "img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation," CVPR, 2021, arXiv:2012.07791

@inproceedings{albiero2021img2pose,
  title={img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation},
  author={Albiero, Vítor and Chen, Xingyu and Yin, Xi and Pang, Guan and Hassner, Tal},
  booktitle={CVPR},
  year={2021},
  url={https://arxiv.org/abs/2012.07791},
}

致谢

我们感谢Albiero Vítor以宽松的许可协议分享他们的代码和训练权重。

📄 许可证

本模型采用CC - BY - NC - 4.0许可证。