基于区块链的毕业设计ETH-XGaze baseline – ETH-XGaze基线

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ETH-XGaze baseline

Official implementation of ETH-XGaze dataset baseline.

ETH-XGaze dataset

ETH-XGaze dataset is a gaze estimation dataset consisting of over one million high-resolution images of varying gaze under extreme head poses. We established a simple baseline test on our ETH-XGaze dataset and other datasets. This repository includes the code and pre-trained model. Please find more details about the dataset on our project page.

License

The code is under the license of CC BY-NC-SA 4.0 license

Requirement

  • Python 3.5
  • Pytorch 1.1.0, torchvision
  • opencv-python

For model training

  • h5py to load the training data
  • configparser

For testing

  • dlib for face and facial landmark detection.

Training

  • You need to download the ETH-XGaze dataset for training. After downloading the data, make sure it is the version of pre-processed 224*224 pixels face patch. Put the data under ‘dataxgaze’
  • Run the python main.py to train the model
  • The model will be saved under ‘ckpt’ folder.

Test

The demo.py files show how to perform the gaze estimation from input image. The example image is already in ‘example/input’ folder.

  • First, you need to download the pre-trained model, and put it under “ckpt” folder.
  • And then, run the ‘python demo.py’ for test.

Data normalization

The ‘normalization_example.py’ gives the example of data normalization from the raw dataset to the normalized data.

Citation

If using this code-base and/or the ETH-XGaze dataset in your research, please cite the following publication:

@inproceedings{Zhang2020ETHXGaze,   author    = {Xucong Zhang and Seonwook Park and Thabo Beeler and Derek Bradley and Siyu Tang and Otmar Hilliges},   title     = {ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation},   year      = {2020},   booktitle = {European Conference on Computer Vision (ECCV)} } 

FAQ

Q: Where are the test set labels?
We plan to make evaluations on the test set possible as soon as possible via a web service with a public leaderboard. In this way, we strive to make evaluations on our test set consistent and reliable, and encourage competition in the field of video-based gaze estimation.

Q: What is the data normalization?
As we wrote in our paper, data normalization is a method to crop the face/eye image without head rotation around the roll axis. Please refer to the following paper for details: Revisiting Data Normalization for Appearance-Based Gaze Estimation

Q: Why convert 3D gaze direction (vector) to 2D gaze direction (pitch and yaw)? How to convert between 3D and 2D gaze directions?
Essentially to say, 2D pitch and yaw is enough to describe the gaze direction in the head coordinate system, and using 2D instead of 3D could make the model training easier. There are code examples on how to convert between them in the “utils.py” file as pitchyaw_to_vector and vector_to_pitchyaw.


ETH-xgazen基线

ETH数据集基线的正式实施。

ETH-xgazen数据集

ETH-XGaze数据集是一个凝视估计数据集,由超过一百万张在极端头部姿势下不同凝视的高分辨率图像组成。我们在ETH-XGaze数据集和其他数据集上建立了一个简单的基线测试。这个存储库包括代码和预先训练的模型。请在我们的项目页面上找到有关数据集的更多详细信息。

许可证

代码受CC BY-NC-SA 4.0许可证的许可演示.py文件显示如何从输入图像执行注视估计。示例图像已在“example/input”文件夹中。

模型训练要求

测试要求

训练

测试

数据规范化

引用

常见问题,torchvision用opencv python加载用于人脸和面部地标检测的训练数据。
  • 您需要下载ETH XGaze数据集进行培训。下载数据后,请确认是预处理的224*224像素面片版本。将数据放在“dataxgaze”下运行python主.py要训练模型,模型将保存在“ckpt”文件夹下。
  • 首先,您需要下载预先训练好的模型,并将其放在“ckpt”文件夹下。
  • 然后,运行“python演示.py“为了测试。你知道吗
    • 您需要下载ETH XGaze数据集进行培训。下载数据后,请确认是预处理的224*224像素面片版本。将数据放在“dataxgaze”下运行python主.py要训练模型,模型将保存在“ckpt”文件夹下。
    • 首先,您需要下载预先训练好的模型,并将其放在“ckpt”文件夹下。
    • 然后,运行“python演示.py“为了测试。你知道吗

    测试要求

    • h5py to load the training data
    • configparser

    训练

    • dlib for face and facial landmark detection.

    测试

    • You need to download the ETH-XGaze dataset for training. After downloading the data, make sure it is the version of pre-processed 224*224 pixels face patch. Put the data under ‘dataxgaze’
    • Run the python main.py to train the model
    • The model will be saved under ‘ckpt’ folder.

    数据规范化

    标准化_示例.py’给出了从原始数据集到规范化数据的数据规范化示例。

    • First, you need to download the pre-trained model, and put it under “ckpt” folder.
    • And then, run the ‘python demo.py’ for test.

    引用

    如果在您的研究中使用此代码库和/或ETH-XGaze数据集,请引用以下出版物:

    常见问题,torchvision用opencv python加载用于人脸和面部地标检测的训练数据。
  • 您需要下载ETH XGaze数据集进行培训。下载数据后,请确认是预处理的224*224像素面片版本。将数据放在“dataxgaze”下运行python主.py要训练模型,模型将保存在“ckpt”文件夹下。
  • 首先,您需要下载预先训练好的模型,并将其放在“ckpt”文件夹下。
  • 然后,运行“python演示.py“为了测试。你知道吗
  • Q:测试集标签在哪里?
    我们计划尽快通过一个带有公共排行榜的web服务对测试集进行评估。通过这种方式,我们努力使我们的测试集的评估一致和可靠,并鼓励在基于视频的注视估计领域的竞争。

    @inproceedings{Zhang2020ETHXGaze,   author    = {Xucong Zhang and Seonwook Park and Thabo Beeler and Derek Bradley and Siyu Tang and Otmar Hilliges},   title     = {ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation},   year      = {2020},   booktitle = {European Conference on Computer Vision (ECCV)} } 

    FAQ

    Q:什么是数据标准化?
    正如我们在论文中所写的那样,数据归一化是一种在头部不绕滚动轴旋转的情况下裁剪脸部/眼睛图像的方法。请参阅以下文章了解详细信息:重新访问基于外观的注视估计的数据规范化

    Q:为什么要将三维注视方向(矢量)转换为二维注视方向(俯仰和偏航)?如何在3D和2D注视方向之间转换?
    从本质上说,2D俯仰和偏航足以描述头部坐标系中的注视方向,使用2D代替3D可以使模型训练更容易。“中有关于如何在它们之间转换的代码示例”实用程序.py“文件为pitchyawu to u vector和vectoru to u pitchyaw。

    Q: Why convert 3D gaze direction (vector) to 2D gaze direction (pitch and yaw)? How to convert between 3D and 2D gaze directions?
    Essentially to say, 2D pitch and yaw is enough to describe the gaze direction in the head coordinate system, and using 2D instead of 3D could make the model training easier. There are code examples on how to convert between them in the “utils.py” file as pitchyaw_to_vector and vector_to_pitchyaw.

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