def connectToWorkers(debug, workers, wport): loadClients = [] for worker in workers: common.debugPrint(debug, 'Connecting to Worker at ' + worker + ':' + str(wport)) s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((worker, wport)) loadClients.append(s) return loadClients
def predict_emotion(data: deque) -> None:
debugPrint("Loading model....")
model_path = f"{Path.cwd()}/model.h5"
cpu = False
model_path
if cpu:
with tf.device('/cpu:0'):
model = keras.models.load_model(model_path)
else:
model = build_model()
prediction = model.predict(np.array(list(data)).reshape(150, 1))
emotions = ["happy", "melancholy", "surprised", "calm"]
choice = np.argmax(prediction[np.argmax(prediction)])
try:
emotion = emotions[choice]
return emotion
except Exception as e:
debugPrint(
f"An error occured when trying to return the emotion: {e}, with choice index of: {choice}"
)
return random.choice(emotions)
sys.exit(1)
delay = '5' # Milliseconds in between LoadClient requests.
# Main.
common.printAndFlush('Configuration: server=' + serverName + ', worker=' + hostname)
common.printAndFlush('Global: versions=' + str(versions) + ', numClients=' + str(numClients) + ', messageSizes=' + str(messageSizes) + ', sessionLengths=' + str(sessionLengths))
serverSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
serverSocket.bind((hostname, int(wport)))
serverSocket.listen(5) # 5 seems to be a kind of default.
common.debugPrint(debug, 'Listening on port: ' + wport)
(s, address) = serverSocket.accept()
common.debugPrint(debug, 'Server script connected, starting main loop...')
for v in versions:
for clients in numClients:
clients = str(int(clients) / len(workers))
for size in messageSizes:
for length in sessionLengths:
for i in range(0, repeats):
common.printAndFlush('Parameters: version=' + v + ', clients=' + clients + ', size=' + size + ', length=' + length + ', trial=' + str(i))
s.recv(1024);
# Seconds.
serverWarmup = 3
workerWarmup = 3
coolDown = 3
loadSpinStart = 10
# Main.
common.printAndFlush('Configuration: server=' + socket.gethostname() + ', workers=' + str(workers) + ', client=' + client)
common.printAndFlush('Global: versions=' + str(versions) + ', numClients=' + str(numClients) + ', messageSizes=' + str(messageSizes) + ', sessionLengths=' + str(sessionLengths))
loadClients = connectToWorkers(debug, workers, wport)
common.debugPrint(debug, 'Connecting to Client at ' + client + ':' + str(cport))
timerClient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
timerClient.connect((client, cport))
common.debugPrint(debug, 'Starting main loop...')
for v in versions:
for clients in numClients:
for size in messageSizes:
for length in sessionLengths:
for i in range(0, repeats):
common.printAndFlush('Parameters: version=' + v + ', size=' + size + ', length=' + length + ', trial=' + str(i))
command = renv
import random
import numpy as np
from common import debugPrint
from collections import deque
from pathlib import Path
from train import build_model
try:
import tensorflow as tf
import tensorflow.keras as keras
except ImportError:
debugPrint("Unable to import Tensorflow! Is it installed?")
def predict_emotion(data: deque) -> None:
debugPrint("Loading model....")
model_path = f"{Path.cwd()}/model.h5"
cpu = False
model_path
if cpu:
with tf.device('/cpu:0'):
model = keras.models.load_model(model_path)
else:
model = build_model()
prediction = model.predict(np.array(list(data)).reshape(150, 1))
emotions = ["happy", "melancholy", "surprised", "calm"]
choice = np.argmax(prediction[np.argmax(prediction)])
try:
emotion = emotions[choice]
window = 3
else:
window = 40
# Main.
common.printAndFlush('Configuration: server=' + serverName + ', client=' + hostname)
common.printAndFlush('Global: window=' + str(window) + ', versions=' + str(versions) + ', numClients=' + str(numClients) + ', messageSizes=' + str(messageSizes) + ', sessionLengths=' + str(sessionLengths))
serverSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
serverSocket.bind((hostname, cport))
serverSocket.listen(5) # 5 seems to be a kind of default.
common.debugPrint(debug, 'Listening on port: ' + str(cport))
(s, address) = serverSocket.accept()
common.debugPrint(debug, 'Server script connected, starting main loop...')
for v in versions:
for clients in numClients:
for size in messageSizes:
for length in sessionLengths:
for i in range(0, outers):
common.printAndFlush('Parameters: version=' + v + ', clients=' + clients + ', size=' + size + ', length=' + length + ', trial=' + str(i))
s.recv(1024)
prefix = renv + ' -cp tests/classes aplas.bmarks2.micro.SignalClient ' + serverName + ' ' + sport
import time
import json
import random
from common import debugPrint
from LSL_helper import connectToEEG, recordEEG
from model_helper import predict_emotion
## Call this function to send the predicted emotion to the desktop
## Calling this function will end this process
def sendPredictedEmotion(emotion: str) -> None:
print(json.dumps({ 'emotion' : emotion }))
## Call to signal to desktop that a connection has been established
def confirmConnection() -> None:
print(json.dumps({ 'hasConfirmed' : True }))
# TODO: Error handling for None
# TODO: Add remaining model code
stream_connection = connectToEEG()
confirmConnection()
data = recordEEG(stream_connection)
emotion = predict_emotion(data)
debugPrint(f"the emotion was: {emotion}")
sendPredictedEmotion(emotion)
