Fixed some errors in probability calculations

src/app.py

@@ -67,8 +67,18 @@ def mqtt_on_command(client, userdata, message):
         logging.error(f"MQTT Invalid message recieved: {e}")
         return
 
-    if msg.get("command") == 'doAlert':
-        userdata.act()
+    cmd = msg.get("command")
+
+    if cmd == 'doAlert':
+        userdata[1].act()
+
+    elif cmd == 'offline':
+        userdata[0][0].soundpreprocessor.set_fail_on_purpose(True)
+        BirbnetesIoTPlatformStatusDriver.enqueue_pattern('red', [1])
+
+    elif cmd == 'online':
+        userdata[0][0].soundpreprocessor.set_fail_on_purpose(False)
+        BirbnetesIoTPlatformStatusDriver.enqueue_pattern('green', [1])
 
 
 def main() -> None:
@@ -83,7 +93,10 @@ def main() -> None:
     loopingtimer = LoopingTimer(function=timer_tick, tick_args=listofabcsignaprocessors, interval=config.TICK_INTERVAL)
     loopingtimer.start()
 
-    client = paho.mqtt.client.Client(userdata=Loudspeaker(config.ENEMY_SOUNDS), client_id=config.DEVICE_ID)
+    client = paho.mqtt.client.Client(
+        userdata=(listofabcsignaprocessors, Loudspeaker(config.ENEMY_SOUNDS)),
+        client_id=config.DEVICE_ID
+    )
     client.on_connect = mqtt_on_connect
     client.on_disconnect = mqtt_on_disconnect
     client.on_message = mqtt_on_command

src/preprocessor/soundpreprocessor.py

@@ -11,6 +11,7 @@ import logging
 from pyAudioAnalysis import audioBasicIO
 from pyAudioAnalysis import MidTermFeatures
 import numpy
+import random
 
 from birbnetes_iot_platform_raspberry import BirbnetesIoTPlatformStatusDriver
 
@@ -86,6 +87,8 @@ class SoundPreProcessor(AbcPreProcessor):
 
         self._target_id = self._classes.index(target_class_name)
 
+        self._fail_on_purpose = False
+
     def preprocesssignal(self, file_path: str) -> bool:
         """
         Classify a sound sample.
@@ -126,12 +129,19 @@ class SoundPreProcessor(AbcPreProcessor):
         )
         class_id = int(class_id)  # faszom
 
+        if self._fail_on_purpose:  # titkos hozzávaló
+            if class_id == self._target_id:
+                class_id = random.choice(list(set(range(len(self._classes))) - {self._target_id}))
+
         logging.debug(
             f"Sample {file_path} identified as {self._classes[class_id]} with the probablility of {probability[class_id]}"
         )
 
         return bool((class_id == self._target_id) and (probability[class_id] > 0.5))
 
+    def set_fail_on_purpose(self, val: bool):
+        self._fail_on_purpose = val
+
     def __del__(self):
         try:
             os.remove(self._temp_model_name)