DeepPoseKit

There are some little fixes for the Original Repo, reference the Original Document for more details.

Here are my install steps which have been tested on the following platforms:

Ubuntu 20.04.5 LTS ARM on VMWare Fusion hosted on Apple Macbook with M Series CPU
Ubuntu 20.04.6 LTS on Microsoft WSL2 hosted on Generic x86_64 PC

You can start from any stage, but start from any small steps are not recommended, unless you are aware what you are doing.

Install Guide

Configure your system

Update system and packages

sudo apt update && sudo apt update && sudo apt upgrade -y && sudo apt autoremove -y && sudo apt update && sudo apt upgrade

Install tools

sudo apt install nano git zsh tree htop curl wget screen tmux openssh-server net-tools gcc make cmake

Install conda Environment

Download Miniconda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-`uname -m`.sh

Install Miniconda

chmod +x Miniconda3-latest-Linux-`uname -m`.sh && ./Miniconda3-latest-Linux-`uname -m`.sh

Create Work Environment
1. Create Virtual Environment
```
conda create -n tf tensorflow
```
2. Enter Virtual Environment
```
conda activate tf
```
3. Install dependencies by installing and removing pre-built version of deepposekit and scikit-learn with pip
```
pip install deepposekit scikit-learn keras-core

pip uninstall deepposekit scikit-learn
```
Build deepposekit and scikit-learn from source to avoid some errors mostly caused by Architecture diffirences of your CPU
1. Fetch source of scikit-learn from GitHub
```
git clone https://github.com/scikit-learn/scikit-learn
```
2. Install dependencies needed by compile progress
```
pip install cython wheel numpy scipy
```
3. Compile and Install scikit-learn
```
cd scikit-learn

pip install -v --no-use-pep517 --no-build-isolation -e .
```
4. Test Install and following command should execute without error
```
python -c "import sklearn; sklearn.show_versions()"
```
Install DeepPoseKit
1. Fetch source of DeepPoseKit from GitHub
```
cd ~ 

git clone https://github.com/hmxf/DeepPoseKit
```
2. Install DeepPoseKit
```
cd DeepPoseKit

python setup.py develop
```
3. Setup architecture-related Environment Variable
```
./scripts/setup.sh
```
Test your Installation with pre-downloaded data within 5 miniutes ;)

Pre-downloaded data is located in data/ directory and has been used by scripts/train.py and scripts/predict.py for fast test purpose only.
```
python scripts/train.py
```
After a model train process, you can verify if model data has been generated successfully under data/ directory. If your model data has been stored as a single file data/saved_model.h5, then you can view its structure and weight parameters by using scripts/hdf5_file_reader.py script.
```
python scripts/hdf5_file_reader.py
```
One more step, you can use the trained model to do some predictions.
```
python scripts/predict.py
```

hmxf/DeepPoseKit

DeepPoseKit

Install Guide