mrcnn

Radioastronomical object detector tool based on Mask R-CNN instance segmentation framework.

Status

This software is under development and supported only on python 3 + tensorflow 1x.

This software is distributed with GPLv3 license. If you use it for your research, please add repository link or acknowledge authors in your papers.

To build and install the package:

Create a local install directory, e.g. $INSTALL_DIR
Add installation path to your PYTHONPATH environment variable:
export PYTHONPATH=$PYTHONPATH:$INSTALL_DIR/lib/python3.6/site-packages
Build and install package:
python3.6 setup.py sdist bdist_wheel
python3.6 setup build
python3.6 setup install --prefix=$INSTALL_DIR

All dependencies will be automatically downloaded and installed in $INSTALL_DIR.

To use package scripts:

Add binary directory to your PATH environment variable:
export PATH=$PATH:$INSTALL_DIR/bin

mrcnn can be run in different modes:

To train a model: python3.6 $INSTALL_DIR/bin/run.py [OPTIONS] train
To test a model: python3.6 $INSTALL_DIR/bin/run.py [OPTIONS] test
To detect objects on new data: python3.6 $INSTALL_DIR/bin/run.py [OPTIONS] detect
To classify pre-detected objects: python3.6 $INSTALL_DIR/bin/run.py [OPTIONS] classify (TO BE IMPLEMENTED)

Supported options are:

IMAGE PRE-PROCESSING

--grayimg: To pass 1-channel gray-level images to input layer. Default: pass 3-channels RGB images
--no_uint8: To disable conversion from image float values to uint8. Default: convert to uint8
--no_zscale: To disable astropy zscale transform applied to input images. Default: apply
--zscale_contrasts=[CONTRASTS]: zscale contrast values in range [0,1] for the three RGB channels, in string format separated by commas. Default: 0.25,0.25,0.25
--biascontrast: To apply astropy BiasContrast transform to input images. Default: not applied
--bias=[BIAS]: Bias parameter value of BiasContrast transform. Default: 0.5
--contrast=[CONTRAST]: Contrast parameter value of BiasContrast transform. Default: 1

DATA LOADER

--dataloader=[LOADER]: Train/cross-val data loader type. Valid values: {datalist,datalist_json,datadir_json}. Default: datalist
--datalist=[FILENAME]: Data filelist with format: filename_img(.fits),filename_mask(.fits),label (datalist loader) or filename(.json) (datalist_json loader)
--datalist_train=[FILENAME]: Train data filelist with format: filename_img(.fits),filename_mask(.fits),label (datalist loader) or filename(.json) (datalist_json loader)
--datalist_val=[FILENAME]: Cross-validation data filelist with format: filename_img(.fits),filename_mask(.fits),label (datalist loader) or filename(.json) (datalist_json loader)
--datadir=[PATH]: Data top directory traversed recursively to search for json dataset files
--validation_data_fract=[FRACTION]: Fraction of input data used for cross-validation. Default: 0.1
--maxnimgs=[VALUE]: Max number of images to consider in dataset (-1=all). Default: -1
--classdict=[DICT]: Class id dictionary in string format used when loading dataset. Default: {"sidelobe":1,"source":2,"galaxy":3}
--classdict_model=[DICT]: Class id dictionary used for the model (if empty, it is set equal to classdict). Default: empty
--remap_classids: Remap detected object class ids to ground truth object class ids. Default: False
--classid_remap_dict=[DICT]: Dictionary in string format used to remap detected object class ids to ground truth object class ids. Default: empty

MODEL OPTIONS

--weights=[FILENAME]: Path to weights .h5 file. If empty, initialize model with random weights. Default: empty

RUN OPTIONS

TRAIN OPTIONS

TEST OPTIONS

--scoreThr=[SCORE_THRESHOLD]: Score Threshold to use during evaluation. Default: 0.7
--iouThr=[IOU_THRESHOLD]: IoU Threshold to use during evaluation. Default: 0.6
--consider_sources_near_mixed_sidelobes or --no_consider_sources_near_mixed_sidelobes: Whether to consider sources that are flagged to be near or mixed with sidelobes during evaluation. Default: Considered