#!/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) # Open most recent Configuration and grab DB_PWD as a python variable with open('/home/pi/BirdNET-Pi/thisrun.txt', 'r') as f: this_run = f.readlines() db_pwd = str(str(str([i for i in this_run if i.startswith('DB_PWD')]).split('=')[1]).split('\\')[0]) 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=db_pwd) 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()