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7f36f09b9ad167241e0c4bcef22990c9ece19ccb
AvianVisitors/scripts/server.py
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540 lines
22 KiB
Python
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import apprise
from pathlib import Path
from tzlocal import get_localzone
import datetime
import sqlite3
import requests
import json
import time
import math
import numpy as np
import librosa
import operator
import socket
import threading
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = ''
try:
import tflite_runtime.interpreter as tflite
except BaseException:
from tensorflow import lite as tflite
HEADER = 64
PORT = 5050
SERVER = socket.gethostbyname(socket.gethostname())
ADDR = (SERVER, PORT)
FORMAT = 'utf-8'
DISCONNECT_MESSAGE = "!DISCONNECT"
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
try:
server.bind(ADDR)
except BaseException:
print("Waiting on socket")
time.sleep(5)
# Open most recent Configuration and grab DB_PWD as a python variable
userDir = os.path.expanduser('~')
with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
this_run = f.readlines()
audiofmt = "." + str(str(str([i for i in this_run if i.startswith('AUDIOFMT')]).split('=')[1]).split('\\')[0])
priv_thresh = float("." + str(str(str([i for i in this_run if i.startswith('PRIVACY_THRESHOLD')]).split('=')[1]).split('\\')[0])) / 10
def loadModel():
global INPUT_LAYER_INDEX
global OUTPUT_LAYER_INDEX
global MDATA_INPUT_INDEX
global CLASSES
print('LOADING TF LITE MODEL...', end=' ')
# Load TFLite model and allocate tensors.
modelpath = userDir + '/BirdNET-Pi/model/BirdNET_6K_GLOBAL_MODEL.tflite'
myinterpreter = tflite.Interpreter(model_path=modelpath, num_threads=2)
myinterpreter.allocate_tensors()
# Get input and output tensors.
input_details = myinterpreter.get_input_details()
output_details = myinterpreter.get_output_details()
# Get input tensor index
INPUT_LAYER_INDEX = input_details[0]['index']
MDATA_INPUT_INDEX = input_details[1]['index']
OUTPUT_LAYER_INDEX = output_details[0]['index']
# Load labels
CLASSES = []
labelspath = userDir + '/BirdNET-Pi/model/labels.txt'
with open(labelspath, 'r') as lfile:
for line in lfile.readlines():
CLASSES.append(line.replace('\n', ''))
print('DONE!')
return myinterpreter
def loadCustomSpeciesList(path):
slist = []
if os.path.isfile(path):
with open(path, 'r') as csfile:
for line in csfile.readlines():
slist.append(line.replace('\r', '').replace('\n', ''))
return slist
def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
# Split signal with overlap
sig_splits = []
for i in range(0, len(sig), int((seconds - overlap) * rate)):
split = sig[i:i + int(seconds * rate)]
# End of signal?
if len(split) < int(minlen * rate):
break
# Signal chunk too short? Fill with zeros.
if len(split) < int(rate * seconds):
temp = np.zeros((int(rate * seconds)))
temp[:len(split)] = split
split = temp
sig_splits.append(split)
return sig_splits
def readAudioData(path, overlap, sample_rate=48000):
print('READING AUDIO DATA...', end=' ', flush=True)
# Open file with librosa (uses ffmpeg or libav)
sig, rate = librosa.load(path, sr=sample_rate, mono=True, res_type='kaiser_fast')
# Split audio into 3-second chunks
chunks = splitSignal(sig, rate, overlap)
print('DONE! READ', str(len(chunks)), 'CHUNKS.')
return chunks
def convertMetadata(m):
# Convert week to cosine
if m[2] >= 1 and m[2] <= 48:
m[2] = math.cos(math.radians(m[2] * 7.5)) + 1
else:
m[2] = -1
# Add binary mask
mask = np.ones((3,))
if m[0] == -1 or m[1] == -1:
mask = np.zeros((3,))
if m[2] == -1:
mask[2] = 0.0
return np.concatenate([m, mask])
def custom_sigmoid(x, sensitivity=1.0):
return 1 / (1.0 + np.exp(-sensitivity * x))
def predict(sample, sensitivity):
global INTERPRETER
# Make a prediction
INTERPRETER.set_tensor(INPUT_LAYER_INDEX, np.array(sample[0], dtype='float32'))
INTERPRETER.set_tensor(MDATA_INPUT_INDEX, np.array(sample[1], dtype='float32'))
INTERPRETER.invoke()
prediction = INTERPRETER.get_tensor(OUTPUT_LAYER_INDEX)[0]
# Apply custom sigmoid
p_sigmoid = custom_sigmoid(prediction, sensitivity)
# Get label and scores for pooled predictions
p_labels = dict(zip(CLASSES, p_sigmoid))
# Sort by score
p_sorted = sorted(p_labels.items(), key=operator.itemgetter(1), reverse=True)
# # print("DATABASE SIZE:", len(p_sorted))
# # print("HUMAN-CUTOFF AT:", int(len(p_sorted)*priv_thresh)/10)
#
# # Remove species that are on blacklist
human_cutoff = max(10, int(len(p_sorted) * priv_thresh))
for i in range(min(10, len(p_sorted))):
if p_sorted[i][0] == 'Human_Human':
with open(userDir + '/BirdNET-Pi/HUMAN.txt', 'a') as rfile:
rfile.write(str(datetime.datetime.now()) + str(p_sorted[i]) + ' ' + str(human_cutoff) + '\n')
return p_sorted[:human_cutoff]
def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
global INTERPRETER
detections = {}
start = time.time()
print('ANALYZING AUDIO...', end=' ', flush=True)
# Convert and prepare metadata
mdata = convertMetadata(np.array([lat, lon, week]))
mdata = np.expand_dims(mdata, 0)
# Parse every chunk
pred_start = 0.0
for c in chunks:
# Prepare as input signal
sig = np.expand_dims(c, 0)
# Make prediction
p = predict([sig, mdata], sensitivity)
# print("PPPPP",p)
HUMAN_DETECTED = False
# Catch if Human is recognized
for x in range(len(p)):
if "Human" in p[x][0]:
HUMAN_DETECTED = True
# Save result and timestamp
pred_end = pred_start + 3.0
# If human detected set all detections to human to make sure voices are not saved
if HUMAN_DETECTED is True:
p = [('Human_Human', 0.0)] * 10
detections[str(pred_start) + ';' + str(pred_end)] = p
pred_start = pred_end - overlap
print('DONE! Time', int((time.time() - start) * 10) / 10.0, 'SECONDS')
# print('DETECTIONS:::::',detections)
return detections
def sendAppriseNotifications(species, confidence):
if os.path.exists(userDir + '/BirdNET-Pi/apprise.txt') and os.path.getsize(userDir + '/BirdNET-Pi/apprise.txt') > 0:
with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
this_run = f.readlines()
title = str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFICATION_TITLE')]).split('=')[1]).split('\\')[0]).replace('"', '')
body = str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFICATION_BODY')]).split('=')[1]).split('\\')[0]).replace('"', '')
if str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFY_EACH_DETECTION')]).split('=')[1]).split('\\')[0]) == "1":
apobj = apprise.Apprise()
config = apprise.AppriseConfig()
config.add(userDir + '/BirdNET-Pi/apprise.txt')
apobj.add(config)
apobj.notify(
body=body.replace("$sciname", species.split("_")[0]).replace("$comname", species.split("_")[1]).replace("$confidence", confidence),
title=title,
)
if str(str(str([i for i in this_run if i.startswith('APPRISE_NOTIFY_NEW_SPECIES')]).split('=')[1]).split('\\')[0]) == "1":
try:
con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
con.row_factory = lambda cursor, row: row[0]
cur = con.cursor()
cur.execute("SELECT DISTINCT(Com_Name) FROM detections")
known_species = cur.fetchall()
sciName, comName = species.split("_")
print("\ncomName: ", comName)
print("\nknown_species: ", known_species)
if comName not in known_species:
apobj = apprise.Apprise()
config = apprise.AppriseConfig()
config.add(userDir + '/BirdNET-Pi/apprise.txt')
apobj.add(config)
apobj.notify(
body=body.replace("$sciname", species.split("_")[0]).replace("$comname", species.split("_")[1]).replace("$confidence", confidence),
title=title,
)
con.close()
except BaseException:
print("Database busy")
time.sleep(2)
def writeResultsToFile(detections, min_conf, path):
print('WRITING RESULTS TO', path, '...', end=' ')
rcnt = 0
with open(path, 'w') as rfile:
rfile.write('Start (s);End (s);Scientific name;Common name;Confidence\n')
for d in detections:
for entry in detections[d]:
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0) and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0)):
sendAppriseNotifications(str(entry[0]), str(entry[1]))
rfile.write(d + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) + '\n')
rcnt += 1
print('DONE! WROTE', rcnt, 'RESULTS.')
return
def handle_client(conn, addr):
global INCLUDE_LIST
global EXCLUDE_LIST
# print(f"[NEW CONNECTION] {addr} connected.")
connected = True
while connected:
msg_length = conn.recv(HEADER).decode(FORMAT)
if msg_length:
msg_length = int(msg_length)
msg = conn.recv(msg_length).decode(FORMAT)
if msg == DISCONNECT_MESSAGE:
connected = False
else:
# print(f"[{addr}] {msg}")
args = type('', (), {})()
args.i = ''
args.o = ''
args.birdweather_id = '99999'
args.include_list = 'null'
args.exclude_list = 'null'
args.overlap = 0.0
args.week = -1
args.sensitivity = 1.25
args.min_conf = 0.70
args.lat = -1
args.lon = -1
for line in msg.split('||'):
inputvars = line.split('=')
if inputvars[0] == 'i':
args.i = inputvars[1]
elif inputvars[0] == 'o':
args.o = inputvars[1]
elif inputvars[0] == 'birdweather_id':
args.birdweather_id = inputvars[1]
elif inputvars[0] == 'include_list':
args.include_list = inputvars[1]
elif inputvars[0] == 'exclude_list':
args.exclude_list = inputvars[1]
elif inputvars[0] == 'overlap':
args.overlap = float(inputvars[1])
elif inputvars[0] == 'week':
args.week = int(inputvars[1])
elif inputvars[0] == 'sensitivity':
args.sensitivity = float(inputvars[1])
elif inputvars[0] == 'min_conf':
args.min_conf = float(inputvars[1])
elif inputvars[0] == 'lat':
args.lat = float(inputvars[1])
elif inputvars[0] == 'lon':
args.lon = float(inputvars[1])
# Load custom species lists - INCLUDED and EXCLUDED
if not args.include_list == 'null':
INCLUDE_LIST = loadCustomSpeciesList(args.include_list)
else:
INCLUDE_LIST = []
if not args.exclude_list == 'null':
EXCLUDE_LIST = loadCustomSpeciesList(args.exclude_list)
else:
EXCLUDE_LIST = []
birdweather_id = args.birdweather_id
# Read audio data
audioData = readAudioData(args.i, args.overlap)
# Get Date/Time from filename in case Pi gets behind
# now = datetime.now()
full_file_name = args.i
# print('FULL FILENAME: -' + full_file_name + '-')
file_name = Path(full_file_name).stem
file_date = file_name.split('-birdnet-')[0]
file_time = file_name.split('-birdnet-')[1]
date_time_str = file_date + ' ' + file_time
date_time_obj = datetime.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S')
# print('Date:', date_time_obj.date())
# print('Time:', date_time_obj.time())
print('Date-time:', date_time_obj)
now = date_time_obj
current_date = now.strftime("%Y-%m-%d")
current_time = now.strftime("%H:%M:%S")
current_iso8601 = now.astimezone(get_localzone()).isoformat()
week_number = int(now.strftime("%V"))
week = max(1, min(week_number, 48))
sensitivity = max(0.5, min(1.0 - (args.sensitivity - 1.0), 1.5))
# Process audio data and get detections
detections = analyzeAudioData(audioData, args.lat, args.lon, week, sensitivity, args.overlap)
# Write detections to output file
min_conf = max(0.01, min(args.min_conf, 0.99))
writeResultsToFile(detections, min_conf, args.o)
###############################################################################
###############################################################################
soundscape_uploaded = False
# Write detections to Database
myReturn = ''
for i in detections:
myReturn += str(i) + '-' + str(detections[i][0]) + '\n'
with open(userDir + '/BirdNET-Pi/BirdDB.txt', 'a') as rfile:
for d in detections:
for entry in detections[d]:
if entry[1] >= min_conf and ((entry[0] in INCLUDE_LIST or len(INCLUDE_LIST) == 0)
and (entry[0] not in EXCLUDE_LIST or len(EXCLUDE_LIST) == 0)):
rfile.write(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';'
+ str(entry[1]) + ";" + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';'
+ str(args.sensitivity) + ';' + str(args.overlap) + '\n')
Date = str(current_date)
Time = str(current_time)
species = entry[0]
Sci_Name, Com_Name = species.split('_')
score = entry[1]
Confidence = str(round(score * 100))
Lat = str(args.lat)
Lon = str(args.lon)
Cutoff = str(args.min_conf)
Week = str(args.week)
Sens = str(args.sensitivity)
Overlap = str(args.overlap)
Com_Name = Com_Name.replace("'", "")
File_Name = Com_Name.replace(" ", "_") + '-' + Confidence + '-' + \
Date.replace("/", "-") + '-birdnet-' + Time + audiofmt
# Connect to SQLite Database
for attempt_number in range(3):
try:
con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db')
cur = con.cursor()
cur.execute("INSERT INTO detections VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)", (Date, Time,
Sci_Name, Com_Name, str(score), Lat, Lon, Cutoff, Week, Sens, Overlap, File_Name))
con.commit()
con.close()
break
except BaseException:
print("Database busy")
time.sleep(2)
print(str(current_date) +
';' +
str(current_time) +
';' +
entry[0].replace('_', ';') +
';' +
str(entry[1]) +
';' +
str(args.lat) +
';' +
str(args.lon) +
';' +
str(min_conf) +
';' +
str(week) +
';' +
str(args.sensitivity) +
';' +
str(args.overlap) +
Com_Name.replace(" ", "_") +
'-' +
str(score) +
'-' +
str(current_date) +
'-birdnet-' +
str(current_time) +
audiofmt +
'\n')
if birdweather_id != "99999":
try:
if soundscape_uploaded is False:
# POST soundscape to server
soundscape_url = 'https://app.birdweather.com/api/v1/stations/' + \
birdweather_id + \
'/soundscapes' + \
'?timestamp=' + \
current_iso8601
with open(args.i, 'rb') as f:
wav_data = f.read()
response = requests.post(url=soundscape_url, data=wav_data, headers={'Content-Type': 'application/octet-stream'})
print("Soundscape POST Response Status - ", response.status_code)
sdata = response.json()
soundscape_id = sdata['soundscape']['id']
soundscape_uploaded = True
# POST detection to server
detection_url = "https://app.birdweather.com/api/v1/stations/" + birdweather_id + "/detections"
start_time = d.split(';')[0]
end_time = d.split(';')[1]
post_begin = "{ "
now_p_start = now + datetime.timedelta(seconds=float(start_time))
current_iso8601 = now_p_start.astimezone(get_localzone()).isoformat()
post_timestamp = "\"timestamp\": \"" + current_iso8601 + "\","
post_lat = "\"lat\": " + str(args.lat) + ","
post_lon = "\"lon\": " + str(args.lon) + ","
post_soundscape_id = "\"soundscapeId\": " + str(soundscape_id) + ","
post_soundscape_start_time = "\"soundscapeStartTime\": " + start_time + ","
post_soundscape_end_time = "\"soundscapeEndTime\": " + end_time + ","
post_commonName = "\"commonName\": \"" + entry[0].split('_')[1] + "\","
post_scientificName = "\"scientificName\": \"" + entry[0].split('_')[0] + "\","
post_algorithm = "\"algorithm\": " + "\"alpha\"" + ","
post_confidence = "\"confidence\": " + str(entry[1])
post_end = " }"
post_json = post_begin + post_timestamp + post_lat + post_lon + post_soundscape_id + post_soundscape_start_time + \
post_soundscape_end_time + post_commonName + post_scientificName + post_algorithm + post_confidence + post_end
print(post_json)
response = requests.post(detection_url, json=json.loads(post_json))
print("Detection POST Response Status - ", response.status_code)
except BaseException:
print("Cannot POST right now")
conn.send(myReturn.encode(FORMAT))
# time.sleep(3)
conn.close()
def start():
# Load model
global INTERPRETER, INCLUDE_LIST, EXCLUDE_LIST
INTERPRETER = loadModel()
server.listen()
# print(f"[LISTENING] Server is listening on {SERVER}")
while True:
conn, addr = server.accept()
thread = threading.Thread(target=handle_client, args=(conn, addr))
thread.start()
# print(f"[ACTIVE CONNECTIONS] {threading.activeCount() - 1}")
# print("[STARTING] server is starting...")
start()
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