An a-contrario detection sub-algorithm for extracting narrow lines within dense optical data cubes.
Benjamin Feuge-Miller: [email protected] The University of Texas at Austin, Oden Institute Computational Astronautical Sciences and Technologies (CAST) group Date of Modification: December 23, 2021
NOTICE: For copyright and licensing, see 'notices' at bottom of readme
This algorithm takes in a dense data cube of optical time-series data and looks for narrow lines corresponding to streaks or actively tracked trajectories.
For a more detailed discussion of a-contrario analysis see the main PyCIS pipeline which calls this algorithm.
This software is designed to be used as a subsegment of the main PyCIS processing pipeline. The PyCIS main pipeline will control setup, if used. Otherwise, see the main pipleine for instruction on operating PyCIS-LSD
Setup
If scripts cannot be read, run:
<dos2unix *.sh>
Finally, install prerequisites and build the software by running:
<. setup.sh>.
Demo: See the main PyCIS pipeline for demo scripts and data download instructions.
Output Files:
Input and output locations and names are specified within the main file, see <demo.py> for example.
- Input: data/...
- yyymmdd_norad_satname/*.fit - a folder with raw fits frames [see citations in DATASET section below]
- Output:
- results_work/...
- data1_x_y_name.npy - edge line detections, x and y labels for parallelization are absent when merged.
- data2_x_y_name.npy - center line detections, x and y labels for parallelization are absent when merged.
- results_work/...
Runtime estimates for the two-dataset demo are around 30 minutes on one TACC none with full memory, 48-thread opm capability.
See the main PyCIS pipeline for details.
-
pycis_lsd_wrapper.py - interface for launching pycis.c through python
-
pycis.c - main python extension module
-
setup.py - link python-c extension module
-
setup.sh - install gsl/pyenv/Makefile, link
-
LIB:
- constants - set up some constants
- gaussians - gaussian down-sampling for antialiasing (unused)
- gradient - compute gradient magnitude/orientation, and alignment checks
- interface - pipeline helpers for pycis.c
- lsd(2/3/3b) - main lsd pipelines (spatial, temporal, and unified)
- markov - estimate markov kernels and build transition matrices
- misc - lists, angle functions
- NFA - estimate markov or negative binomial approximation tail probability
- rectanges(2/3)- build rectangle objects and iterator to call nfa
- regions(2/3) - build and improve pixel regions for estimating rectangles
- tuples - construction of line tuples and image structures
NOTICE: PyCIS-LSD: An a-contrario detection sub-algorithm for extracting narrow lines within dense optical data cubes. Copyright (C) 2022, Benjamin G. Feuge-Miller, [email protected]
PyCIS-LSD is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
PyCIS-LSD is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see http://www.gnu.org/licenses/.
NOTICE: PyCIS-LSD is modified from the source code of LSDSAR: "LSDSAR, a Markovian a contrario framework for line segment detection in SAR images" by Chenguang Liu, Rémy Abergel, Yann Gousseau and Florence Tupin. (Pattern Recognition, 2019). https://doi.org/10.1016/j.patcog.2019.107034 Date of Modification: April 30, 2021
NOTICE: LSDSAR is modified from the source code of LSD: "LSD: a Line Segment Detector" by Rafael Grompone von Gioi, Jeremie Jakubowicz, Jean-Michel Morel, and Gregory Randall, Image Processing On Line, 2012. DOI:10.5201/ipol.2012.gjmr-lsd http://dx.doi.org/10.5201/ipol.2012.gjmr-lsd Date of Modification: 27/06/2018