diff --git a/mcu/hands_on_feature_vectors/uart-reader.py b/mcu/hands_on_feature_vectors/uart-reader.py index f1f1b9c4a434d199fafd9a671b0d4f9e495cb0da..c61428245b9938ed5ec4e399f3390c36abf6093d 100644 --- a/mcu/hands_on_feature_vectors/uart-reader.py +++ b/mcu/hands_on_feature_vectors/uart-reader.py @@ -3,12 +3,11 @@ uart-reader.py ELEC PROJECT - 210x """ import argparse -from pathlib import Path + import matplotlib.pyplot as plt import numpy as np import serial from serial.tools import list_ports -from sklearn.preprocessing import StandardScaler,MinMaxScaler, RobustScaler, PowerTransformer, Normalizer, QuantileTransformer import os @@ -63,13 +62,6 @@ if __name__ == "__main__": args = argParser.parse_args() print("uart-reader launched...\n") - filename = 'modelLDA.pickle' - model_dir = Path(__file__).parent.parent.parent/"classification" / "data" /"models" / filename - with open(model_dir, "rb") as file: - model = pickle.load(file) - - - if args.port is None: print( "No port specified, here is a list of serial communication port available" @@ -83,24 +75,17 @@ if __name__ == "__main__": else: input_stream = reader(port=args.port) - msg_counter = 0 + msg_counter = 351 classname = "" for melvec in input_stream: msg_counter += 1 - - melvec= melvec/np.abs(np.max(melvec)) - melvec = StandardScaler().fit_transform(melvec.reshape(-1,1)).reshape(-1) - melvec = RobustScaler().fit_transform(melvec.reshape(-1,1)).reshape(-1) - melvec = Normalizer().fit_transform(melvec.reshape(-1,1)).reshape(-1) - - prediction_lda = model.predict(melvec) - print(prediction_lda) + if (msg_counter <= 1500): + np.save("../../classification/data_mcu/{}_{}".format(classname,msg_counter),melvec) #print(msg_counter) #plt.figure() #plot_specgram(melvec.reshape((N_MELVECS, MELVEC_LENGTH)).T, ax=plt.gca(), is_mel=True, title="MEL Spectrogram #{}".format(msg_counter), xlabel="Mel vector") #plt.show() - - + \ No newline at end of file