Final device prototype

src/Components/IoT/Testing/revamp/client.py → src/Components/IoT/2025_T3_prototype/client.py

@@ -2,6 +2,7 @@
 import sounddevice as sd
 import numpy as np
 import paho.mqtt.client as mqtt
+from gps3 import gps3
 import time
 import wave
 import psutil
@@ -15,7 +16,7 @@
 
 os.makedirs(AUDIO_DIR, exist_ok=True)
 
-sd.default.device = (2, None)
+sd.default.device = (1, None)
 
 print("Connecting to MQTT Broker...")
 client = mqtt.Client()
@@ -43,14 +44,36 @@ def record_audio(duration=5, samplerate=44100):
     print("Recording complete", filename)
     return filepath
 
+
 def get_health_report():
     return {
         "cpu": psutil.cpu_percent(),
         "ram": psutil.virtual_memory().percent,
         "disk": psutil.disk_usage("/").percent,
-        "uptime": psutil.boot_time()
+        "uptime": time.time() - psutil.boot_time()
     }
 
+def get_gps_reading():
+    gps_socket = gps3.GPSDSocket()
+    data_stream = gps3.DataStream()
+
+    gps_socket.connect()
+    gps_socket.watch()
+
+    for new_data in gps_socket:
+        if new_data:
+            data_stream.unpack(new_data)
+
+            lat = data_stream.TPV.get('lat')
+            lon = data_stream.TPV.get('lon')
+            mode = data_stream.TPV.get('mode')
+
+            if mode == 3 and lat != 'n/a' and lon != 'n/a':
+                print("GPS FIX:", lat, lon)
+                return {"lat": lat, "lon": lon}
+
+    return None
+
 def send_data():
     audio_file = record_audio()
 
@@ -59,10 +82,13 @@ def send_data():
 
     health_data = get_health_report()
 
+    gps_data = get_gps_reading()
+
     audio_b64 = base64.b64encode(audio_bytes).decode('ascii')
 
     payload = {
         "health_data": health_data,
+        "gps_data": gps_data,
         "audio_file": audio_b64
     }
 
@@ -75,4 +101,4 @@ def send_data():
 if __name__ == "__main__":
     while True:
         send_data()
-        time.sleep(20)
+        time.sleep(20)

src/Components/IoT/Testing/revamp/server.py → src/Components/IoT/2025_T3_prototype/server.py

@@ -33,6 +33,7 @@ def on_message(client, userdata, msg):
     json_filename = f"data_{timestamp}.json"
     with open(os.path.join(JSON_DIR, json_filename), "w") as f:
         json.dump(payload["health_data"], f)
+        json.dump(payload["gps_data"], f)
     print(f"Saved JSON: {json_filename}")
 
     print("Writing audio data")

src/Components/IoT/previous_implementation/Dockerfile

@@ -0,0 +1,22 @@
+
+FROM python:3.9-slim
+WORKDIR /app
+# Install system libraries for sounddevice, OpenCV, numpy, and scientific packages
+RUN apt update && apt install -y \
+	portaudio19-dev \
+	libgl1 \
+	libglib2.0-0 \
+	libsm6 \
+	libxext6 \
+	libxrender1 \
+	libsndfile1 \
+	ffmpeg \
+	libatlas3-base \
+	liblapack-dev \
+	gfortran \
+	build-essential \
+	gcc \
+	--no-install-recommends
+COPY . /app
+RUN pip install --no-cache-dir -r requirements.txt
+CMD ["python", "client_pi.py"]

src/Components/IoT/previous_implementation/client_pi.py

@@ -0,0 +1,146 @@
+import os 
+import psutil
+import sounddevice as sd
+import numpy as np
+import librosa
+import cv2
+import json
+import wave
+import time
+import requests
+import serial
+from gps3 import gps3
+from tflite_runtime.interpreter import Interpreter
+
+SERVER_URL = "http://192.168.1.122:5000/upload"
+SAMPLE_RATE = 16000
+DURATION = 5
+N_MFCC = 40
+MODEL_PATH = "models/checkpoint_MobileNetV3-Large.tflite" 
+CLASS_PATH = "models/class_names_MobileNetV3-Large.json"
+SAVE_DIR = "audioLocal"
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+sd.default.device = (2, None)
+
+print("Loading Model......")
+interpreter = Interpreter(model_path=MODEL_PATH)
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+input_index = input_details[0]['index']
+output_index = output_details[0]['index']
+
+with open(CLASS_PATH, "r") as f:
+    ID_TO_SPECIES = json.load(f)
+
+def get_health_report():
+    return {
+        "cpu": psutil.cpu_percent(),
+        "ram": psutil.virtual_memory().percent,
+        "disk": psutil.disk_usage("/").percent,
+        "uptime": psutil.boot_time()
+    }
+
+# sub-optimal, convert to using gps3 and gpsd
+def get_gps_location(port="/dev/ttyACM0",baudrate=9600,timeout=1):
+    try:
+        with serial.Serial(port, baudrate, timeout=timeout) as ser:
+            while True:
+                print("1")
+                line = ser.readline().decode("ascii", errors="replace").strip()
+                if line.startswith("$GPGGA") or line.startswith("$GPRMC"):
+                    print("2")
+                    parts = line.split(",")
+                    if len(parts) > 5 and parts[2] and parts[4]:
+                        lat = convert_to_decimal(parts[2], parts[3])
+                        lon = convert_to_decimal(parts[4], parts[5])
+                        print("lat: ", lat)
+                        return {"latitude": lat, "longitude": lon}
+
+    except Exception as e:
+        print("GPS error: ", e)
+
+    return  {"latitude": None, "longitude": None}
+
+
+#Check audio recording duration and quality, method for sending .wav file to server
+def record_audio(duration=5, samplerate=44100):
+    timestamp = time.strftime("%Y-%m-%d_%H-%M-%S")
+    filename = f"audio_{timestamp}.wav"
+    filepath = os.path.join(SAVE_DIR, filename)
+
+    print("Recording...")
+    audio_data = sd.rec(int(duration * samplerate), samplerate=samplerate, channels=1, dtype='float32')
+    sd.wait()
+    with wave.open(filepath, 'wb') as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(samplerate)
+        wf.writeframes((audio_data * 32767).astype(np.int16).tobytes())
+    print("Recording complete", filename)
+    return filepath, np.squeeze(audio_data)
+
+# Is processing neccessary? How does the model expect input?
+def preprocess_audio(audio, sr=SAMPLE_RATE):
+    mel_spec = librosa.feature.melspectrogram(y=audio, sr=sr, n_mels=224, fmax=sr//2)
+    mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)
+    mel_norm = 255 * (mel_spec_db - mel_spec_db.min()) / (mel_spec_db.max() - mel_spec_db.min())
+    mel_norm = mel_norm.astype(np.uint8)
+    mel_resized = cv2.resize(mel_norm, (224, 224))
+    mel_rgb = np.stack([mel_resized]*3, axis=-1)
+    mel_rgb = np.expand_dims(mel_rgb, axis=0)
+
+    return mel_rgb.astype(np.float32)
+
+def softmax(x):
+    e_x = np.exp(x - np.max(x))
+    return e_x / e_x.sum()
+
+
+# Does this prediction help the actual model in any way?
+def predict_species(audio):
+    input_data = preprocess_audio(audio)
+
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_index)[0]
+
+    probs = softmax(output_data)
+
+    class_id = int(np.argmax(probs))
+    confidence = float(probs[class_id])
+
+    species = ID_TO_SPECIES[class_id]
+    return species, confidence
+
+def cleanup_files(max_files=100):
+    files = sorted([os.path.join(SAVE_DIR, f) for f in os.listdir(SAVE_DIR)], key=os.path.getmtime)
+    if len(files) > max_files:
+        for f in files[:-max_files]:
+            os.remove(f)
+            print(f"Deleted old file: {f}")
+
+def send_data():
+    filepath, audio = record_audio()
+
+    species, confidence = predict_species(audio)
+    print(f"Predicted: {species} ({confidence:.2f})")
+
+    cleanup_files(max_files=100)
+
+    health_data = get_health_report()
+    # gps_data = get_gps_location()
+
+    payload = {
+        **health_data,
+        # **gps_data,
+        "species": species,
+        "confidence": confidence
+    }
+
+    response = requests.post(SERVER_URL, json=payload)
+
+if __name__ == "__main__":
+    while True:
+        send_data()

src/Components/IoT/previous_implementation/iot-simulator-deployment.yaml

@@ -0,0 +1,19 @@
+
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: iot-simulator
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: iot-simulator
+  template:
+    metadata:
+      labels:
+        app: iot-simulator
+    spec:
+      containers:
+      - name: iot-simulator
+        image: ts-simulator:latest #for development, use <registry-name>/ts-simulator:latest
+        restartPolicy: Always

src/Components/IoT/previous_implementation/requirements.txt

@@ -0,0 +1,10 @@
+psutil
+sounddevice
+numpy
+librosa
+opencv-python
+requests
+pyserial
+gps3
+tflite-runtime
+flask

src/Prototypes/Iot/Component_Testing/audio.py

@@ -0,0 +1,54 @@
+import os
+import sounddevice as sd
+import numpy as np
+import paho.mqtt.client as mqtt
+import time
+import wave
+
+SAVE_DIR = "audioLocal"
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+sd.default.device = (1, None)
+
+print("Connecting to MQTT Broker...")
+client = mqtt.Client()
+client.connect("broker.hivemq.com", 1883, 60)
+client.loop_start()
+topic = "iot/data/test"
+print("Connected.")
+
+def record_audio(duration=5, samplerate=44100):
+    timestamp = time.strftime("%Y-%m-%d_%H-%M-%S")
+    filename = f"audio_{timestamp}.wav"
+    filepath = os.path.join(SAVE_DIR, filename)
+
+    print("Recording...")
+    audio_data = sd.rec(int(duration * samplerate), samplerate=samplerate, channels=1, dtype='float32')
+    sd.wait()
+    with wave.open(filepath, 'wb') as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(samplerate)
+        wf.writeframes((audio_data * 32767).astype(np.int16).tobytes())
+    print("Recording complete", filename)
+    return filepath
+
+def on_publish(client, userdata, mid):
+    print(f"Message {mid} published.")
+
+def send_data():
+    audio_file = record_audio()
+
+    with open(audio_file, "rb") as f:
+        imagestring = f.read()
+    audio_data = bytearray(imagestring)
+
+
+    client.on_publish = on_publish
+    client.publish(topic, audio_data, qos=1)
+    print(f"Published audio to {topic}: {audio_file}")
+
+if __name__ == "__main__":
+    while True:
+        send_data()
+        time.sleep(20)

src/Prototypes/Iot/Component_Testing/audio_server.py

@@ -0,0 +1,35 @@
+import paho.mqtt.client as mqtt
+import os
+import time
+
+SAVE_DIR = "audioLocal"
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+def on_connect(client, userdata, flags, rc):
+    if rc == 0:
+        print("Subscriber connected successfully")
+        client.subscribe(topic, qos=1)
+    else:
+        print(f"Subscriber failed to connect, code {rc}")
+
+def on_subscribe(client, userdata, mid, granted_qos):
+    print(f"Subscribed with QoS {granted_qos}")
+
+def on_message(client, userdata, msg):
+    print(f"Received message on {msg.topic}")
+    print("Writing")
+    timestamp = time.strftime("%Y-%m-%d_%H-%M-%S")
+    filename = f"audio_{timestamp}.wav"
+    filepath = os.path.join(SAVE_DIR, filename)
+    with open(filepath, "wb") as f:
+        f.write(msg.payload)
+
+client = mqtt.Client()
+client.on_connect = on_connect
+client.on_subscribe = on_subscribe
+client.on_message = on_message
+client.connect("broker.hivemq.com", 1883, 60)
+topic = "iot/data/test"
+client.subscribe(topic, qos=1)
+print(f"Subscribed to {topic}")
+client.loop_forever()