From f9132f9934a74814f542f67ca993f9d3d683d258 Mon Sep 17 00:00:00 2001 From: mcguirepr89 Date: Thu, 17 Feb 2022 15:29:03 -0500 Subject: [PATCH] adding new sqlite testing branch --- scripts/server-lite.py | 423 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 423 insertions(+) create mode 100755 scripts/server-lite.py diff --git a/scripts/server-lite.py b/scripts/server-lite.py new file mode 100755 index 0000000..e1787d5 --- /dev/null +++ b/scripts/server-lite.py @@ -0,0 +1,423 @@ +#!/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 sqlite3 +import datetime +from time import sleep +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) +try: + server.bind(ADDR) +except: + print("Waiting on socket") + time.sleep(5) + + + +# 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: + myReturn += str(i) + '-' + str(detections[i][0]) + '\n' + + + 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(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 = "{:.0%}".format(score) + 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) + File_Name = Com_Name.replace(" ", "_") + '-' + Confidence + '-' + \ + Date.replace("/", "-") + '-birdnet-' + Time + '.mp3' + + #Connect to SQLite Database + con = sqlite3.connect('/home/pi/BirdNET-Pi/scripts/birds2.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() + 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) + '.mp3' + '\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(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()