KI-KNN

import librosa.feature
import pandas as pd
import numpy as np
from pathlib import Path
from os import chdir
import os
import csv
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from keras import models
from keras import layers
from keras import optimizers
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
import constants


max_len = 216


def create_csv_header():
    header = 'filename '
    for i in range(constants.MFCC_FEATURE_START, ((constants.MFCC_FEATURE_END - 1) * max_len) + 1):
        header += f' mfcc{i}'
    header += ' label'
    header = header.split()
    file = open(constants.FEATURES_CSV_NAME, 'w', newline='')
    with file:
        writer = csv.writer(file)
        writer.writerow(header)


def extract_features(trainingDataDir, trainingDataSubDirs):
    create_csv_header()
    # Looping over every file inside the subdirectories for feature extraction
    for trainingDataSubDir in trainingDataSubDirs:
        for fileName in os.listdir(trainingDataDir/f'{trainingDataSubDir}'):
            if fileName.endswith(".wav"):
                audioFile = trainingDataDir/f'{trainingDataSubDir}/{fileName}'
                print("Extracting Features from Directory "+trainingDataSubDir+" and file "+audioFile.name)
                y, sr = librosa.load(audioFile, mono=True)
                mfcc = librosa.feature.mfcc(y=y, sr=44100, n_mfcc=constants.N_MFCC)
                to_append = f'{audioFile.name}'

                print ("mfcc.shape before cutting/padding: ", mfcc.shape)
                #the mfcc extraction results in slighty different time frames per mfcc segment
                #in the case of 5seconds and 20 mfcc segments there are som time informations between 215 and 217
                #in order to unify the size the ones that stores under 216 timeinformation there will be some padding
                #for the ones that stores over 216 time information there will be a trim operation
                #IMPORTANT:
                #if the time of the audiofiles or the mfcc extraction number changes (current value is 20)
                #there needs to be an adjustment to the MAX_LEN parameter
                #1. check the new size of all the files
                #2. find a suitable average for padding or cutting
                #3. adjust the MAX_LEN variable in the constants.py file
                if (constants.MFCC_MAX_LEN > mfcc.shape[1]):
                    pad_width = max_len - mfcc.shape[1]
                    mfcc = np.pad(mfcc, pad_width=((0, 0), (0, pad_width)), mode='constant')
                elif constants.MFCC_MAX_LEN < mfcc.shape[1]:
                    mfcc = mfcc[:, :constants.MFCC_MAX_LEN]
                print ("mfcc.shape after cutting/padding: ", mfcc.shape)
                #store every single value in the csv file
                mfcc = np.reshape(mfcc, constants.MFCC_MAX_LEN * constants.N_MFCC)
                #print ("new total size: ", mfcc.shape)
                for g in mfcc:
                    to_append += f' {g}'
                if trainingDataSubDir == constants.CAR:
                    to_append += f' {constants.LIGHT_WEIGHT}'
                elif trainingDataSubDir == constants.BUS:
                    to_append += f' {constants.MEDIUM_WEIGHT}'
                elif trainingDataSubDir == constants.TRUCK:
                    to_append += f' {constants.HEAVY_WEIGHT}'
                elif trainingDataSubDir == constants.MOTORCYCLE:
                    to_append += f' {constants.TWO_WHEELED}'
                elif trainingDataSubDir == constants.TRAM:
                    to_append += f' {constants.RAIL_BOUND}'

                file = open(constants.FEATURES_CSV_NAME, 'a', newline='')
                with file:
                    writer = csv.writer(file)
                    writer.writerow(to_append.split())

def preprocessing_csv_data():
    print("Reading Features... ")
    data = pd.read_csv(constants.FEATURES_CSV_NAME)
    data.head()
    # Dropping unnecessary columns (Column Filename is dropped)
    data = data.drop(['filename'], axis=1)
    data.head()
    return data


def encode_labels(data):
    # Extracting classes/label column as y from csv and converting string labels to numbers using LabelEncoder
    audio_list = data.iloc[:, -1]
    encoder = LabelEncoder()
    target_labels = encoder.fit_transform(audio_list)
    return target_labels, encoder


def normalize_data(data):
    # normalizing - Extracting Remaining Columns as X and normalizing them to a common scale
    scaler = StandardScaler()
    print (data.iloc[:, :-1])
    X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype=float))
    X = X.reshape(-1, constants.N_MFCC, constants.MFCC_MAX_LEN, constants.CHANNELS)
    return X


def train_test_data_split(X, y):
    # splitting of dataset into train and test dataset
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
    print (X_train[0].shape)
    return X_train, X_test, y_train, y_test


def create_and_compile_model():
    print("Creating a Model")
    # creating a model
    from keras.models import Sequential
    from keras.layers import Conv2D, Dense, MaxPooling2D, Dropout, Flatten, BatchNormalization
    model = models.Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=(constants.N_MFCC, constants.MFCC_MAX_LEN, constants.CHANNELS)))
    model.add(Conv2D(32, kernel_size=(3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(128, activation="relu"))
    model.add(Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation='softmax'))
    print("Compiling a Model")
    optimizer = keras.optimizers.RMSprop()
    model.compile(optimizer=optimizer, loss=constants.LOSS_FUNCTION_SPARSE, metrics=[constants.ACCURACY_METRICS])
    print(model.summary())
    return model


def train_and_save_model(model, X_train, y_train, X_test, y_test):
    logdir = constants.LOG_DIR_PATH
    tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
    print("Start Training...")
    history = model.fit(X_train, y_train, batch_size=32, epochs=35, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])
    # Saving the trained model to avoid re-training
    model.save(constants.TRAINED_MODEL)
    return history


def predict(X_test, y_test):
    print("Predictions.....")
    predictions = np.argmax(model.predict(X_test), axis=-1)
    target_names = [constants.LIGHT_WEIGHT, constants.MEDIUM_WEIGHT, constants.HEAVY_WEIGHT,constants.TWO_WHEELED, constants.RAIL_BOUND]
    print(classification_report(y_test, predictions, target_names=target_names))


def plot_model_accuracy(history):
    # Plot graph Model Accuracy
    plt.plot(history.history['accuracy'])
    plt.plot(history.history['val_accuracy'])
    plt.title('Model Accuracy')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Test'], loc='upper left')
    plt.show()


def plot_model_loss(history):
    # Plot graph Model Loss
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('Model loss')
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(['Train', 'Test'], loc='upper right')
    plt.show()


# Changing Directory to Training Dataset Folder
chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
trainingDataDir = Path.cwd()
trainingDataSubDirs = os.listdir(trainingDataDir)
chdir("..")
if os.path.isfile(constants.FEATURES_CSV_NAME):
    print("features.csv already exists, skip extraction")
else:
    extract_features(trainingDataDir, trainingDataSubDirs)
#extract_features(trainingDataDir, trainingDataSubDirs)



data = preprocessing_csv_data()
target_labels, encoder = encode_labels(data)
X = normalize_data(data)
X_train, X_test, y_train, y_test = train_test_data_split(X, target_labels)
model = create_and_compile_model()
history = train_and_save_model(model, X_train, y_train, X_test, y_test)
predict(X_test, y_test)
plot_model_accuracy(history)
plot_model_loss(history)