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adding new server socket and analysis -- preparing

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for test installation
This commit is contained in:
mcguirepr89
2022-02-05 11:26:40 -05:00
parent e8c4e81406
commit 080b1daf6a
6 changed files with 516 additions and 326 deletions
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scripts/server.py
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#!/home/pi/BirdNET-Pi/birdnet/bin/python3
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:
from tensorflow import lite as tflite
import argparse
import operator
import librosa
import numpy as np
import math
import time
from decimal import Decimal
import json
###############################################################################
import requests
import mysql.connector
###############################################################################
import datetime
import pytz
from tzlocal import get_localzone
from pathlib import Path
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.bind(ADDR)
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.
myinterpreter = tflite.Interpreter(model_path='/home/pi/BirdNET-Pi/model/BirdNET_6K_GLOBAL_MODEL.tflite',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 = []
with open('/home/pi/BirdNET-Pi/model/labels.txt', '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)
# Remove species that are on blacklist
for i in range(min(10, len(p_sorted))):
if p_sorted[i][0] in ['Human_Human', 'Non-bird_Non-bird', 'Noise_Noise']:
p_sorted[i] = (p_sorted[i][0], 0.0)
# Only return first the top ten results
return p_sorted[:10]
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)
# Save result and timestamp
pred_end = pred_start + 3.0
detections[str(pred_start) + ';' + str(pred_end)] = p
pred_start = pred_end - overlap
print('DONE! Time', int((time.time() - start) * 10) / 10.0, 'SECONDS')
return detections
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) ):
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 = '/home/pi/test.wav'
args.o = '/home/pi/test.wav.csv'
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:
print("\n", detections[i][0],"\n")
myReturn += str(detections[i][0]) + '||'
with open('/home/pi/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(sensitivity) +';' + str(args.overlap) + '\n')
def insert_variables_into_table(Date, Time, Sci_Name, Com_Name, Confidence, Lat, Lon, Cutoff, Week, Sens, Overlap):
try:
connection = mysql.connector.connect(host='localhost',
database='birds',
user='birder',
password='databasepassword')
cursor = connection.cursor()
mySql_insert_query = """INSERT INTO detections (Date, Time, Sci_Name, Com_Name, Confidence, Lat, Lon, Cutoff, Week, Sens, Overlap)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) """
record = (Date, Time, Sci_Name, Com_Name, Confidence, Lat, Lon, Cutoff, Week, Sens, Overlap)
cursor.execute(mySql_insert_query, record)
connection.commit()
print("Record inserted successfully into detections table")
except mysql.connector.Error as error:
print("Failed to insert record into detections table {}".format(error))
finally:
if connection.is_connected():
connection.close()
print("MySQL connection is closed")
species = entry[0]
sci_name,com_name = species.split('_')
insert_variables_into_table(str(current_date), str(current_time), sci_name, com_name, \
str(entry[1]), str(args.lat), str(args.lon), str(min_conf), str(week), \
str(args.sensitivity), str(args.overlap))
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) + '\n')
if birdweather_id != "99999":
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)
conn.send("Msg received".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()