awesome-mmpose: A Python repository from zhengdechang

Sure, here is the English version of the README:

Introduction

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.

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

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

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

Step 2: Install OpenMIM

pip install -U openmim

Step 3: Install MMCV and MMDetection

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

Step 4: Install project dependencies

pip install -r requirements.txt

Step 5: Install the project

pip install -v -e .

Step 6: Install MMPOSE

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).

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.

zhengdechang/awesome-mmpose