Metadata-Version: 2.1
Name: aicsmlsegment
Version: 0.0.2
Summary: Scripts for ML structure segmentation.
Home-page: UNKNOWN
Author: AICS
Author-email: jianxuc@alleninstitute.org
License: Allen Institute Software License
Platform: UNKNOWN
Requires-Dist: numpy (>=1.15.1)
Requires-Dist: scipy (>=1.1.0)
Requires-Dist: scikit-image (>=0.14.0)
Requires-Dist: pandas (>=0.23.4)
Requires-Dist: aicsimageio (>=0.4.3)
Requires-Dist: aicsimageprocessing
Requires-Dist: tqdm
Provides-Extra: all
Requires-Dist: pytest ; extra == 'all'
Requires-Dist: pytest-cov ; extra == 'all'
Requires-Dist: flake8 ; extra == 'all'
Provides-Extra: lint_group
Requires-Dist: flake8 ; extra == 'lint_group'
Provides-Extra: test_group
Requires-Dist: pytest ; extra == 'test_group'
Requires-Dist: pytest-cov ; extra == 'test_group'

### Installation 

1. create a conda environment: 

```bash
conda create --name mlsegmenter python=3.6
```

2. activate your environment and do the installation within the environment:

```bash 
source activate mlsegmenter 
```

3. Install Pytorch

Go to [PyTorch website](https://pytorch.org/get-started/locally/), and find the right installation command for you. 

* we use version 1.0 (which is the stable version at the time of our development)
* we use Linux (OS), Conda (package), python 3.6 (Language), CUDA=9.0 (Question about CUDA? see [setup CUDA](./docs/check_cuda.md)). So, the installation command for us is

```bash
conda install pytorch torchvision -c pytorch
```

4. Install Allen Cell Segmenter (deep learning part)

```bash
git clone https://aicsbitbucket.corp.alleninstitute.org/scm/assay/aics-ml-segmentation.git
cd ./aics-ml-segmentation
pip install -e .
```



