Sure, here is the English version of the README:

Introduction

| English

This project is based on MMPOSE. For more examples, please refer to MMPOSE demos.

CUDA Installation

Skip this step if you have already installed Anaconda3.

```shell wget https://repo.anaconda.com/archive/Anaconda3-2021.05-Linux-x86_64.sh

bash Anaconda3-2021.05-Linux-x86_64.sh

source ~/.bashrc ```

Test if the installation was successful conda list Please note to replace the Anaconda3 installation script link with the latest one from the official Anaconda website.

Installation Steps

The following are the installation steps. Please note that these steps may vary depending on your environment.

Step 0: Install PyTorch

bash pip install torch==1.10.0+cu111 torchvision==0.11.1+cu111 torchaudio==0.10.0+cu111 -f https://download.pytorch.org/whl/cu111/torch_stable.html

Step 1: Create and activate a conda environment

bash conda create --name openmmlab python=3.8 -y conda activate openmmlab

Step 2: Install OpenMIM

bash pip install -U openmim

Step 3: Install MMCV and MMDetection

bash mim install mmengine mim install "mmcv>=2.0.1" mim install "mmdet>=3.1.0"

Step 4: Install project dependencies

bash pip install -r requirements.txt

Step 5: Install the project

bash pip install -v -e .

Step 6: Install MMPOSE

bash mim install "mmpose>=1.1.0"

Testing

The following is a test command, which compares the original image (demo/test.jpg) and the result image (vis_results/test.jpg).

bash python demo/topdown_demo_with_mmdet.py \ demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \ https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \ configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_hrnet-w48_dark-8xb32-210e_coco-wholebody-384x288.py \ https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth \ --input demo/test.jpg \ --output-root vis_results/ --save-predictions --black-background

Result Display

After running the test command, you can find the result image in the vis_results/ directory.

Original image:

Result image:

Contribution

If you encounter any issues during use or have any suggestions, feel free to submit an Issue or a Pull Request.