semvec/pypi-packages huggingface dataset:
smart-arabidopsis-traits 0.5.4
Extract geometric traits from top-view images of plants.
License: BSD-3-Clause
Requires Python: >=3.6.8

# Speedy Measurement of Arabidopsis Rosette Traits (SMART)

Author: Suxing Liu

![CI](https://github.com/Computational-Plant-Science/arabidopsis-rosette-analysis/workflows/CI/badge.svg)

![Optional Text](../master/media/Smart.png) 

Robust and parameter-free plant image segmentation and trait extraction.

1. Process with plant image top view, including whole tray plant image, this tool will segment it into individual images.
2. Robust segmentation based on parameter-free color clustering method.
3. Extract individual plant gemetrical traits, and write output into excel file.


## Requirements

Either [Docker](https://www.docker.com/) or [Singularity](https://sylabs.io/singularity/) is required to run this project in a Unix environment.

## Usage

### Docker

```bash
docker pull computationalplantscience/smart
docker run -v "$(pwd)":/opt/arabidopsis-rosette-analysis -w /opt/arabidopsis-rosette-analysis computationalplantscience/arabidopsis-rosette-analysis python3 /opt/arabidopsis-rosette-analysis/trait_extract_parallel.py -i input -o output -ft "jpg,png"
```

### Singularity

```bash
singularity exec docker://computationalplantscience/arabidopsis-rosette-analysis python3 trait_extract_parallel.py -i input -o output -ft "jpg,png"
```

![Optional Text](../master/media/image_01.png)

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**Contents**

- [Requirements](#requirements)
- [Usage](#usage)

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## Requirements

The easiest way to run this project is with [Docker](https://www.docker.com/) or [Singularity ](https://sylabs.io/singularity/).

To pull the `computationalplantscience/smart` image, the current working directory, and open a shell with Docker:

`docker run -it -v $(pwd):/opt/dev -w /opt/dev computationalplantscience/smart bash`

Singularity users:

`singularity shell docker://computationalplantscience/smart`

## Usage

### Segmentation

To perform color segmentation:

`python3 /opt/smart/core/color_seg.py -p /path/to/input/file -r /path/to/output/folder`

You can also pass a folder path (`-p /path/to/dir`). By default any `JPG` and `PNG` are included. You can choose filetype explicitly with e.g. `-ft jpg`.

To extract traits:

`python3 /opt/smart/core/trait_extract_parallel_ori.py -p /path/to/input/file -r /path/to/output/folder`

You can also use a folder path as above, likewise for filetype specification.

By default this script will not perform leaf segmentation and analysis. To enable leaf analysis, use the `-l` flag.

To indicate that your input is a multiple-tray or -individual photo, add the `-m` flag.