From 5f5c320e61a5cc2dc6b38563bdbcebaa315a0c3a Mon Sep 17 00:00:00 2001 From: Jake Herbst Date: Wed, 11 May 2022 08:05:54 -0400 Subject: [PATCH] Updating script/privacy_server.py to satisfy pep8 style guide Used 'autopep8 --in-place --aggressive scripts/privacy_server.py' as initial style fixes. --- scripts/privacy_server.py | 295 ++++++++++++++++++++++++-------------- 1 file changed, 191 insertions(+), 104 deletions(-) diff --git a/scripts/privacy_server.py b/scripts/privacy_server.py index a103fff..63ae524 100755 --- a/scripts/privacy_server.py +++ b/scripts/privacy_server.py @@ -1,4 +1,19 @@ -import socket +from pathlib import Path +from tzlocal import get_localzone +import pytz +from time import sleep +import datetime +import sqlite3 +import requests +import json +from decimal import Decimal +import time +import math +import numpy as np +import librosa +import operator +import argparse +import socket import threading import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' @@ -6,25 +21,9 @@ os.environ['CUDA_VISIBLE_DEVICES'] = '' try: import tflite_runtime.interpreter as tflite -except: +except BaseException: 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 @@ -36,17 +35,18 @@ DISCONNECT_MESSAGE = "!DISCONNECT" server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: server.bind(ADDR) -except: +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]) + audiofmt = "." + \ + str(str(str([i for i in this_run if i.startswith( + 'AUDIOFMT')]).split('=')[1]).split('\\')[0]) def loadModel(): @@ -60,7 +60,7 @@ def loadModel(): # 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 = tflite.Interpreter(model_path=modelpath, num_threads=2) myinterpreter.allocate_tensors() # Get input and output tensors. @@ -82,6 +82,7 @@ def loadModel(): return myinterpreter + def loadCustomSpeciesList(path): slist = [] @@ -92,6 +93,7 @@ def loadCustomSpeciesList(path): return slist + def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5): # Split signal with overlap @@ -102,23 +104,25 @@ def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5): # 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') + 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) @@ -127,11 +131,12 @@ def readAudioData(path, overlap, sample_rate=48000): 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 + m[2] = math.cos(math.radians(m[2] * 7.5)) + 1 else: m[2] = -1 @@ -144,14 +149,20 @@ def convertMetadata(m): 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.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] @@ -162,26 +173,32 @@ def predict(sample, sensitivity): p_labels = dict(zip(CLASSES, p_sigmoid)) # Sort by score - p_sorted = sorted(p_labels.items(), key=operator.itemgetter(1), reverse=True) + 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 ['Non-bird_Non-bird', 'Noise_Noise']: p_sorted[i] = (p_sorted[i][0], 0.0) - if p_sorted[i][0]=='Human_Human': - print("HUMAN SCORE:",str(p_sorted[i])) - HUMAN_FLAG=True + if p_sorted[i][0] == 'Human_Human': + print("HUMAN SCORE:", str(p_sorted[i])) + HUMAN_FLAG = True with open(userDir + '/BirdNET-Pi/HUMAN.txt', 'a') as rfile: - rfile.write(str(datetime.datetime.now())+str(p_sorted[i])+ '\n') + rfile.write(str(datetime.datetime.now()) + + str(p_sorted[i]) + '\n') # date_stamp=datetime.datetime.now().strftime("%d_%m_%y_%H:%M:%S") -# -# sf.write('./home/*/human_sample.wav',np.random.randn(10,2) , 44100) #sample[0] +# +# sf.write('./home/*/human_sample.wav',np.random.randn(10,2) , 44100) +# #sample[0] # Only return first the top ten results - #INCREASE THIS TO SEE IF HUMAN IS DETECTED MORE RELIABLY + # INCREASE THIS TO SEE IF HUMAN IS DETECTED MORE RELIABLY # print('P_SORTED-------', p_sorted) return p_sorted[:100] + def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,): global INTERPRETER @@ -203,24 +220,24 @@ def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,): # Make prediction p = predict([sig, mdata], sensitivity) # print("PPPPP",p) - HUMAN_DETECTED=False - #Catch if Human is recognized + HUMAN_DETECTED = False + # Catch if Human is recognized for x in range(len(p)): if "Human" in p[x][0]: -# print("HUMAN DETECTED!!",p[x][0]) - #clear list - HUMAN_DETECTED=True - print("CHUNK -----",c) - + # print("HUMAN DETECTED!!",p[x][0]) + # clear list + HUMAN_DETECTED = True + print("CHUNK -----", c) + # Save result and timestamp pred_end = pred_start + 3.0 - + if HUMAN_DETECTED == True: - p=[('Human_Human',0.0)]*10 - print("HUMAN DETECTED!!!",p) + p = [('Human_Human', 0.0)] * 10 + print("HUMAN DETECTED!!!", p) detections[str(pred_start) + ';' + str(pred_end)] = p - + pred_start = pred_end - overlap print('DONE! Time', int((time.time() - start) * 10) / 10.0, 'SECONDS') @@ -233,15 +250,23 @@ 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') + 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') + 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 @@ -257,9 +282,9 @@ def handle_client(conn, addr): connected = False else: #print(f"[{addr}] {msg}") - + args = type('', (), {})() - + args.i = '' args.o = '' args.birdweather_id = '99999' @@ -270,8 +295,7 @@ def handle_client(conn, addr): args.sensitivity = 1.25 args.min_conf = 0.70 args.lat = -1 - args.lon = -1 - + args.lon = -1 for line in msg.split('||'): inputvars = line.split('=') @@ -298,14 +322,12 @@ def handle_client(conn, addr): 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: @@ -324,7 +346,8 @@ def handle_client(conn, addr): 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') + 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) @@ -332,44 +355,47 @@ def handle_client(conn, addr): 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)) + 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) + 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' - - + 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') - + 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('_') + Sci_Name, Com_Name = species.split('_') score = entry[1] - Confidence = str(round(score*100)) + Confidence = str(round(score * 100)) Lat = str(args.lat) Lon = str(args.lon) Cutoff = str(args.min_conf) @@ -378,66 +404,127 @@ def handle_client(conn, addr): Overlap = str(args.overlap) Com_Name = Com_Name.replace("'", "") File_Name = Com_Name.replace(" ", "_") + '-' + Confidence + '-' + \ - Date.replace("/", "-") + '-birdnet-' + Time + audiofmt + Date.replace( + "/", "-") + '-birdnet-' + Time + audiofmt - #Connect to SQLite Database - try: - con = sqlite3.connect(userDir + '/BirdNET-Pi/scripts/birds.db') + # Connect to SQLite Database + 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)) + 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() - except: + 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') + 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 - + 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) + 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" + 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) + "," + 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_commonName = "\"commonName\": \"" + \ + entry[0].split('_')[1] + "\"," + post_scientificName = "\"scientificName\": \"" + \ + entry[0].split('_')[0] + "\"," post_algorithm = "\"algorithm\": " + "\"alpha\"" + "," - post_confidence = "\"confidence\": " + str(entry[1]) + 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 + + 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: + 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) + # time.sleep(3) + + conn.close() - conn.close() def start(): # Load model