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()