Merge branch 'main' into notifications

scripts/server.py

@@ -48,6 +48,7 @@ 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():
@@ -165,14 +166,20 @@ def predict(sample, sensitivity):
 
     # 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))
 
-    # 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)
+        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')
 
-    # Only return first the top ten results
-    return p_sorted[:10]
+    return p_sorted[:human_cutoff]
 
 def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap,):
     global INTERPRETER
@@ -194,14 +201,27 @@ 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):