Merge branch 'main' into python-lint

scripts/server.py

@@ -1,17 +1,20 @@
-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 argparse
 import operator
-import socket
-import threading
-import os
+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
+import apprise
+
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 os.environ['CUDA_VISIBLE_DEVICES'] = ''
 
@@ -20,7 +23,6 @@ try:
 except BaseException:
     from tensorflow import lite as tflite
 
-
 HEADER = 64
 PORT = 5050
 SERVER = socket.gethostbyname(socket.gethostname())
@@ -41,6 +43,7 @@ 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():
@@ -165,13 +168,19 @@ def predict(sample, sensitivity):
     # 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)
+#     #print("DATABASE SIZE:", len(p_sorted))
+#     #print("HUMAN-CUTOFF AT:", int(len(p_sorted)*priv_thresh)/10)
+#
+#     # Remove species that are on blacklist
 
-    # Only return first the top ten results
-    return p_sorted[:10]
+    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,):
@@ -194,16 +203,47 @@ 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
+        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 == 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,
+            )
 
 def writeResultsToFile(detections, min_conf, path):
 
@@ -213,8 +253,8 @@ def writeResultsToFile(detections, min_conf, path):
         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)):
+                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.')