brams_zoo_meteor_identification.py

# -*- coding: utf-8 -*-
"""
BRAMS Zoo meteor identification algorithm
Created on 9 June 2016

@author: stijnc

Copyright (C) 2016 Stijn Calders

This program 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.

This program 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 General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.

Contact details:
________________________________________________
Stijn Calders
Space Physics - Space Weather

Royal Belgian Institute for Space Aeronomy (BIRA-IASB)
Ringlaan 3
B-1180 Brussels
BELGIUM

phone  : +32 (0)2 373.04.19
e-mail : stijn.calders@aeronomie.be
web    : www.aeronomie.be
________________________________________________
"""

import glob
import utils
from datetime import datetime, timedelta
import pickle

#PNG_DIRECTORY = "input/png/"
CSV_DIRECTORY = "input/csv/"
OUTPUT_DIRECTORY = "output/"
MASKSIZE = (595, 864)
DATE = "20190318"
minimum_width = 1
start = datetime(2018, 12, 2)
end = datetime(2018, 12, 19) #end day+1!
STATION = "BEHUMA"

spectrograms = []
for result in utils.perdelta(start, end, timedelta(minutes=5)):
     spectrograms.append("RAD_BEDOUR_"+datetime.strftime(result,"%Y%m%d_%H%M")+"_"+STATION+"_SYS001.png")


csv_files = glob.glob(CSV_DIRECTORY+"*.csv")
date_time, identifications, volunteers = [], [], []
for spectrogram in spectrograms:
    dt = datetime.strptime(spectrogram[11:24], "%Y%m%d_%H%M")
    #Step 1: read detection file
    detection_files = {}
    for csv_file in csv_files:
        tmp = utils.read_detection_file_per_spectrogram(csv_file,spectrogram)
        if tmp is not None:        
            detection_files[csv_file] = tmp
    #Step 2: run meteor identification algorithm
    threshold_image = utils.calculate_threshold_image(detection_files)
    #Step 3: select regions that are above identification threshold
    nbr_volunteers = len(detection_files)
    if nbr_volunteers > 0:
        alpha = utils.optimal_nbr_of_counters[len(detection_files)]
        binary_image = threshold_image[list(threshold_image.keys())[0]].copy() 
        binary_image[binary_image < alpha] = 0
        binary_image[binary_image >= alpha] = 1
        border_threshold = utils.detect_border(binary_image,minimum_width=minimum_width)
        nbr_identifications = len(border_threshold)
        date_time.append(dt) #datetime
        identifications.append(nbr_identifications) #nbr of identifications
        volunteers.append(nbr_volunteers) #nbr of volunteers checking the file
    else:
        print("[warning] spectrogram {} has {} volunteers".format(spectrogram,nbr_volunteers))

pickle.dump( (date_time,identifications,volunteers), open( "output/pickles/brams_zoo_meteor_identification-"+DATE+"-"+STATION+".p", "wb" ) )