Human-body-key-point-detection

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Human-body-key-point-detection

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Implemented human keypoint detection and created a simple motion counting app using keypoint detection

Human-body-key-point-detection 최신버젼 다운로드

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# Human-body-key-point-detection
**Project**: Fitness counter using human key point detection

**Usage:**

```bash
git clone https://github.com/1zeryu/Human-body-key-point-detection.git
```

### Human pose estimation

This repository is the implementation of the paper [BlazePose: On-device Real-time Body Pose tracking](https://arxiv.org/pdf/2006.10204v1.pdf) [1], which is a lightweight, on-device single person-specific human pose estimation model. The inference pipeline is shown in Figure 1.

![1659860335399](https://raw.githubusercontent.com/1zeryu/Human-body-key-point-detection/master/img/1659860335399.png)

![1659860311613](https://raw.githubusercontent.com/1zeryu/Human-body-key-point-detection/master/img/1659860311613.png)

The code is available at: [Human-body-key-point-detection](https://github.com/1zeryu/Human-body-key-point-detection/tree/master/models).

### Mediapipe and Installation

In this project, I am calling the already trained BlazePose model from mediapipe for **human keypoint detection**.

You can configure the required environment by:

```python
pip install mediapipe==0.8.7.4 opencv-python numpy
```

You can click on the following links for more information:  [Pose-mediapipe](https://google.github.io/mediapipe/solutions/pose)

You can also quickly learn how to detect key points in the human body by looking at my [tutorial](https://github.com/1zeryu/Human-body-key-point-detection/tree/master/tutorial).

### Movement classification
For common exercise movements such as squats and push-ups, one up and one down is the completion of one movement, and we need to train a classifier to identify whether the stance is up or down at this point.

A common approach is to use the kNN algorithm. You can click on this URL to read the [relevant code](https://github.com/1zeryu/Human-body-key-point-detection/blob/master/models/SquatCounter.ipynb).

The code here needs to be run using **Google's colab**, and you can add the required toolkit with the following code.

```python
pip install pillow==8.1.0
pip install matplotlib==3.3.4
pip install numpy==1.19.3
pip install opencv-python==4.5.1.48
pip install tqdm==4.56.0
pip install requests==2.25.1
pip install mediapipe==0.8.3
```

### Deploy

I chose to deploy the fitness counter to **Android**,Use Google's Android application template (ML kit):  [Pose Detection  | ML Kit  | Google Developers](https://developers.google.com/ml-kit/vision/pose-detection)

You need to modify, debug and generate apk file in professional Android development tool - Android Studio:  [Download Android Studio & App Tools - Android Developers](https://developer.android.com/studio)

### Reference

\[1\]:Bazarevsky, Valentin, et al. "Blazepose: On-device real-time body pose tracking." *arXiv preprint arXiv:2006.10204* (2020).

#### Some Useful Technical Links

[基于BlazePose算法的机器人人体姿势识别与模仿)](https://github.com/TYZQ/graduation_project_2022)

[Videos on Bilibili](https://www.bilibili.com/video/BV1dL4y1h7Q6?spm_id_from=333.1007.top_right_bar_window_custom_collection.content.click)

[人体姿态识别](https://blog.csdn.net/jieqiang3/article/details/122195209)

[人体关键点的动作分类](https://blog.csdn.net/chenpy/article/details/121466383)

[MediaPipe](https://mediapipe.dev/)

[Pose Detection  | ML Kit  | Google Developers](https://developers.google.com/ml-kit/vision/pose-detection)

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