LookingForSeagrass with Semantic Segmentation

This is the code which corresponds to the Seagrass Semantic Segmentation paper which was puplished at the IEEE/OCENs conference 2019 in Marseille

@INPROCEEDINGS{8867064,
author={F. {Weidmann} and J. {Jäger} and G. {Reus} and S. T. {Schultz} and C. {Kruschel} and V. {Wolff} and K. {Fricke-Neuderth}},
booktitle={OCEANS 2019 - Marseille},
title={A Closer Look at Seagrass Meadows: Semantic Segmentation for Visual Coverage Estimation},
year={2019},
volume={},
number={},
pages={1-6},
keywords={Convolution;Semantics;Image segmentation;Training;Decoding;Kernel;Encoding},
doi={10.1109/OCEANSE.2019.8867064},
ISSN={},
month={June},}

Documentation for usage

Tensorflow is structured in it`s components:

• graph.py - Builds the static graph of tensorflow
• main.py - Main file which calls Train, Eval or Predict and loads config files.
• data.py - Data class which loads the dataset and provides data for the machine learning algorithm
• train.py - Contains the train loop for each epoch
• nnUtils.py - Provides builders for the different layers e.g. pooling layer
• evaluate.py - Takes test data and evaluates the trained model for metrics in metricsSemseg.py
• predict.py - Takes one image and uses the trained model to create the segmentated image
• sanityCheck - Predicts 60 images from the test set and displays them. The corresponding ground-truth is also displayed.

Recreated network architectures from papers descriptions:

• dilNet.py
• uNet.py

Usage:

import main
main.deepSS(<MODE>, <Neural Network Name>)

Whereas MODE(first parameter) can be either train, predict, eval or sanityCheck. The names of the neural networks are the filename without the .py extension. Information about the modes:

• train - trains the current neural network with the current dataset
• predict - creates an segmented image with the name “predict.png” and outputs it in the root folder
• eval - evaluates the test split of the dataset with the metrics in metricsSemSeg.py

Example call:

import main
main.deepSS("eval","deeplabV3plusSS")

The model is saved into a folder named models which is two directories above relative to the main file. The same for the tensorflow log files which are located in ../../logs and the dataset folder in ../../data.

Main JSON config file:

• batchSize: Batchsize for every train round
• steps: How many steps each epoch should at least be done. Is only effective when smaller then dataset,
• dataset: A name for the dataset,
• classes: How many classes are to be differentiated with the model,
• neuralNetwork: Name of the neural network,
• learningRate: Float for the learning rate,
• threads: Int, how many CPU threads should be used for data provider.
• epochs: How many epochs the model should be trained with,
• gpu: GPU number

Dataset JSON config file:

• name: Name of the dataset,
• size: Int, size of the dataset,
• trainSize: float, percentage for how big the train size should be e.g. 95% of the dataset -> 0.95,
• testSize: float, percentage for the test size
• x: int, x value of the to be used image,
• y: int, y value of the to be used image,
• imageChannels: int, how many channels does the input image have,
• downsize: String, “True” when the images should be downsized, “False” when not,
• classes : How many classes are to be differentiated with the model,
• path: Relative path to the dataset foler,
• fileName: Filename of the serialized object,
• images: Subfolder of the train input images,
• labels: Subfolder of the label input images(Ground-truth),
• ClassToRGB: Int Array, where the index is the class and the value and RGB array that is associated with this class