frederik
219 lines
9.0 KiB
Python
219 lines
9.0 KiB
Python
import glob
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import gzip
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import json
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import logging
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import os
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import sqlite3
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import subprocess
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import tempfile
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from time import sleep
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import requests
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from PIL import Image, ImageDraw, ImageFont
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from .helpers import get_settings, ParseFileName, Detection, get_font, DB_PATH
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from .notifications import sendAppriseNotifications
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log = logging.getLogger(__name__)
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def extract(in_file, out_file, start, stop):
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result = subprocess.run(['sox', '-V1', f'{in_file}', f'{out_file}', 'trim', f'={start}', f'={stop}'],
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check=True, capture_output=True)
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ret = result.stdout.decode('utf-8')
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err = result.stderr.decode('utf-8')
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if err:
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raise RuntimeError(f'{ret}:\n {err}')
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return ret
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def extract_safe(in_file, out_file, start, stop):
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conf = get_settings()
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# This section sets the SPACER that will be used to pad the audio clip with
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# context. If EXTRACTION_LENGTH is 10, for instance, 3 seconds are removed
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# from that value and divided by 2, so that the 3 seconds of the call are
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# within 3.5 seconds of audio context before and after.
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try:
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ex_len = conf.getint('EXTRACTION_LENGTH')
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except ValueError:
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ex_len = 6
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spacer = (ex_len - 3) / 2
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safe_start = max(0, start - spacer)
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safe_stop = min(conf.getint('RECORDING_LENGTH'), stop + spacer)
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extract(in_file, out_file, safe_start, safe_stop)
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def spectrogram(in_file, title, comment, raw=False):
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fd, tmp_file = tempfile.mkstemp(suffix='.png')
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os.close(fd)
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args = ['sox', '-V1', f'{in_file}', '-n', 'remix', '1', 'rate', '24k', 'spectrogram',
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'-t', '', '-c', '', '-o', tmp_file]
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args += ['-r'] if raw else []
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result = subprocess.run(args, check=True, capture_output=True)
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ret = result.stdout.decode('utf-8')
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err = result.stderr.decode('utf-8')
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if err:
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raise RuntimeError(f'{ret}:\n {err}')
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img = Image.open(tmp_file)
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height = img.size[1]
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width = img.size[0]
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draw = ImageDraw.Draw(img)
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title_font = ImageFont.truetype(get_font()['path'], 13)
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_, _, w, _ = draw.textbbox((0, 0), title, font=title_font)
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draw.text(((width-w)/2, 6), title, fill="white", font=title_font)
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comment_font = ImageFont.truetype(get_font()['path'], 11)
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_, _, _, h = draw.textbbox((0, 0), comment, font=comment_font)
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draw.text((1, height - (h + 1)), comment, fill="white", font=comment_font)
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img.save(f'{in_file}.png')
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os.remove(tmp_file)
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def extract_detection(file: ParseFileName, detection: Detection):
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conf = get_settings()
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new_file_name = f'{detection.common_name_safe}-{detection.confidence_pct}-{detection.date}-birdnet-{file.RTSP_id}{detection.time}.{conf["AUDIOFMT"]}'
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new_dir = os.path.join(conf['EXTRACTED'], 'By_Date', f'{detection.date}', f'{detection.common_name_safe}')
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new_file = os.path.join(new_dir, new_file_name)
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if os.path.isfile(new_file):
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log.warning('Extraction exists. Moving on: %s', new_file)
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else:
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os.makedirs(new_dir, exist_ok=True)
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extract_safe(file.file_name, new_file, detection.start, detection.stop)
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spectrogram(new_file, detection.common_name, new_file.replace(os.path.expanduser('~/'), ''))
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return new_file
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def write_to_db(file: ParseFileName, detection: Detection):
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conf = get_settings()
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# Connect to SQLite Database
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for attempt_number in range(3):
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try:
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con = sqlite3.connect(DB_PATH)
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cur = con.cursor()
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cur.execute("INSERT INTO detections VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
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(detection.date, detection.time, detection.scientific_name, detection.common_name, detection.confidence,
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conf['LATITUDE'], conf['LONGITUDE'], conf['CONFIDENCE'], str(detection.week), conf['SENSITIVITY'],
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conf['OVERLAP'], os.path.basename(detection.file_name_extr)))
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# (Date, Time, Sci_Name, Com_Name, str(score),
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# Lat, Lon, Cutoff, Week, Sens,
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# Overlap, File_Name))
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con.commit()
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con.close()
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break
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except BaseException as e:
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log.warning("Database busy: %s", e)
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sleep(2)
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def summary(file: ParseFileName, detection: Detection):
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# Date;Time;Sci_Name;Com_Name;Confidence;Lat;Lon;Cutoff;Week;Sens;Overlap
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# 2023-03-03;12:48:01;Phleocryptes melanops;Wren-like Rushbird;0.76950216;-1;-1;0.7;9;1.25;0.0
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conf = get_settings()
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s = (f'{detection.date};{detection.time};{detection.scientific_name};{detection.common_name};'
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f'{detection.confidence};'
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f'{conf["LATITUDE"]};{conf["LONGITUDE"]};{conf["CONFIDENCE"]};{detection.week};{conf["SENSITIVITY"]};'
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f'{conf["OVERLAP"]}')
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return s
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def write_to_file(file: ParseFileName, detection: Detection):
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with open(os.path.expanduser('~/BirdNET-Pi/BirdDB.txt'), 'a') as rfile:
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rfile.write(f'{summary(file, detection)}\n')
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def update_json_file(file: ParseFileName, detections: [Detection]):
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if file.RTSP_id is None:
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mask = f'{os.path.dirname(file.file_name)}/*.json'
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else:
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mask = f'{os.path.dirname(file.file_name)}/*{file.RTSP_id}*.json'
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for f in glob.glob(mask):
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log.debug(f'deleting {f}')
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os.remove(f)
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write_to_json_file(file, detections)
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def write_to_json_file(file: ParseFileName, detections: [Detection]):
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conf = get_settings()
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json_file = f'{file.file_name}.json'
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log.debug(f'WRITING RESULTS TO {json_file}')
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dets = {'file_name': os.path.basename(json_file), 'timestamp': file.iso8601, 'delay': conf['RECORDING_LENGTH'],
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'detections': [{"start": det.start, "common_name": det.common_name, "confidence": det.confidence} for det in
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detections]}
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with open(json_file, 'w') as rfile:
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rfile.write(json.dumps(dets))
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log.debug(f'DONE! WROTE {len(detections)} RESULTS.')
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def apprise(file: ParseFileName, detections: [Detection]):
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species_apprised_this_run = []
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conf = get_settings()
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for detection in detections:
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# Apprise of detection if not already alerted this run.
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if detection.species not in species_apprised_this_run:
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try:
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sendAppriseNotifications(detection.species, str(detection.confidence), str(detection.confidence_pct),
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os.path.basename(detection.file_name_extr), detection.date, detection.time, str(detection.week),
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conf['LATITUDE'], conf['LONGITUDE'], conf['CONFIDENCE'], conf['SENSITIVITY'],
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conf['OVERLAP'], dict(conf), DB_PATH)
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except BaseException as e:
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log.exception('Error during Apprise:', exc_info=e)
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species_apprised_this_run.append(detection.species)
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def bird_weather(file: ParseFileName, detections: [Detection]):
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conf = get_settings()
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if conf['BIRDWEATHER_ID'] == "":
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return
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if detections:
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# POST soundscape to server
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soundscape_url = (f'https://app.birdweather.com/api/v1/stations/'
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f'{conf["BIRDWEATHER_ID"]}/soundscapes?timestamp={file.iso8601}')
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with open(file.file_name, 'rb') as f:
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wav_data = f.read()
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gzip_wav_data = gzip.compress(wav_data)
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try:
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response = requests.post(url=soundscape_url, data=gzip_wav_data, timeout=30,
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headers={'Content-Type': 'application/octet-stream', 'Content-Encoding': 'gzip'})
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log.info("Soundscape POST Response Status - %d", response.status_code)
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sdata = response.json()
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except BaseException as e:
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log.error("Cannot POST soundscape: %s", e)
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return
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if not sdata.get('success'):
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log.error(sdata.get('message'))
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return
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soundscape_id = sdata['soundscape']['id']
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for detection in detections:
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# POST detection to server
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detection_url = f'https://app.birdweather.com/api/v1/stations/{conf["BIRDWEATHER_ID"]}/detections'
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data = {'timestamp': detection.iso8601, 'lat': conf['LATITUDE'], 'lon': conf['LONGITUDE'],
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'soundscapeId': soundscape_id,
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'soundscapeStartTime': detection.start, 'soundscapeEndTime': detection.stop,
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'commonName': detection.common_name, 'scientificName': detection.scientific_name,
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'algorithm': '2p4' if conf['MODEL'] == 'BirdNET_GLOBAL_6K_V2.4_Model_FP16' else 'alpha',
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'confidence': detection.confidence}
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log.debug(data)
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try:
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response = requests.post(detection_url, json=data, timeout=20)
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log.info("Detection POST Response Status - %d", response.status_code)
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except BaseException as e:
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log.error("Cannot POST detection: %s", e)
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def heartbeat():
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conf = get_settings()
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if conf['HEARTBEAT_URL']:
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try:
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result = requests.get(url=conf['HEARTBEAT_URL'], timeout=10)
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log.info('Heartbeat: %s', result.text)
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except BaseException as e:
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log.error('Error during heartbeat: %s', e)
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