Metadata-Version: 2.1
Name: greenotyper
Version: 0.6.0rc1
Summary: Plant image-based phenotyping pipeline
Home-page: https://github.com/MarniTausen/Greenotyper
Author: Marni Tausen
Author-email: Marni16ox@gmail.com
License: UNKNOWN
Description: Greenotyper (v0.6.0.rc1)
        ================
        [![Build Status](https://api.travis-ci.com/MarniTausen/Greenotyper.svg?branch=master)](https://travis-ci.com/MarniTausen/Greenotyper)[![codecov](https://codecov.io/gh/MarniTausen/Greenotyper/branch/master/graph/badge.svg)](https://codecov.io/gh/MarniTausen/Greenotyper)
        
        -   [Installation](#installation)
        -   [General workflow guide](#general-workflow-guide)
        -   [GUI interface guide](#gui-interface-guide)
        -   [Command line interface guide](#command-line-interface-guide)
        -   [Pipeline setup guide](#pipeline-setup-guide)
        -   [Neural net training](#neural-net-training)
        
        Greenotyper is a image analysis tool for large scale plant phenotyping experiments.
        
        It uses google's object detection api ([github link](https://github.com/tensorflow/models/tree/master/research/object_detection)) to find the plants and thresholding to measure the size of the plants.
        
        Requirements
        ------------
        
        - python version 3.6 or 3.7
        - tensorflow v2.0.0 or higher
        - PyQt5 v5.9.2 or higher
        - numpy v1.15.2 or higher
        - pillow v5.2.0 or higher
        - scikit-image v0.14.0 or higher
        
        Installation
        ------------
        
        There are precompiled graphical versions in the releases folder.
        
        **Currently there is only a Mac OS X version.** **There is no guarantee that the version works on versions less than 10.14.6**
        
        It is recommended to install the tool in a virtualenv or in an environment in conda. Example:
        ```
        conda create -n greenotyper_env python=3.7
        
        conda activate greenotyper_env
        
        # Install greenotyper either with pip or conda
        ```
        Install greenotyper through pip:
        ```
        pip install greenotyper
        ```
        Install greenotyper through conda:
        ```
        conda install -c *channel\_name* greenotyper
        ```
        
        
        General workflow guide
        ----------------------
        
        Starting a new workflow requires setting up and testing the pipeline. It starts by opening the pipeline planner. Either you open the Greenotyper app, or opening the GUI through the commandline interface:
        ```
        greenotyper --GUI
        ```
        
        To open the pipeline planner, click the Pipeline planner button.
        
        Testing the plant area detection, the network and pipeline settings are all done through the pipeline planner. For information on how use the interface go to the next section, and for general information on Pipeline setups click [here](#pipeline-setup-guide).
        
        Running the pipeline is done either through the commandline or through the GUI. The commandline is more efficient and can more easily be deployed on computing clusters.
        
        The pipeline can be run on individual images or directories of images. The results are a single "database" file, which uses filelocking. (If your file system has blocked filelocking, then there is no guarantee the results will be correctly written when run using multi processing.)
        
        To organize the results into a table you can use the commandline option:
        "greenotyper -p mypipeline.pipeline -o input_file.csv output_file.csv"
        
        GUI interface guide
        -------------------
        
        Command line interface guide
        ----------------------------
        
        Command usage help message:
        ```
        =========== GREENOTYPER (v0.6.0.rc1) ===========
        greenotyper -i image/directory -p settings.pipeline [options]
        
        Options:
          -h, --help            show this help message and exit
          -i IMAGE, --in=IMAGE  Input image or directory of images for inference
                                (required)
          -n NETWORK, --network=NETWORK
                                Input neural network directory (required, if not
                                provided with pipeline file).
          -p PIPELINE, --pipeline=PIPELINE
                                Pipeline file containing all settings
          -t THREADS, --threads=THREADS
                                Number of threads available. Only used to run on
                                multiple images at a time. Default: 1. Settings less
                                than 0 use all available cores.
          -s SIZEDIRECTORY, --size_output=SIZEDIRECTORY
                                Output directory for the size measurements. Default is
                                no output.
          -g GREENNESSDIRECTORY, --greenness_output=GREENNESSDIRECTORY
                                Output directory for the greenness measurements.
                                Default is no output.
          -m MASKDIRECTORY, --mask_output=MASKDIRECTORY
                                Output directory for the produced masks. Default is no
                                output.
          -c CROPDIRECTORY, --crop_output=CROPDIRECTORY
                                Output directory for the cropped images. Default is no
                                output.
          --by_day              Subdividing the outputs based on per day. Recommended
                                to avoid file system overflow.
          --by_individual       Subdividing the outputs based on per day. Recommended
                                to avoid file system overflow.
          --GUI                 Open up the GREENOTYPER GUI.
          -o ORGANIZEOUTPUT, --organize=ORGANIZEOUTPUT
                                Organize and clean the output. Usage:
                                --organize=input_file output_file.   If included only
                                this action will be performed.
        ```
        
        Pipeline setup guide
        --------------------
        
        Neural net training
        -------------------
        
Keywords: phenotyping detection
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Requires-Python: ~=3.6
Description-Content-Type: text/markdown
