diff --git a/constants.py b/constants.py
index 011fcc12d306bf2160ca7d3fd347ed89e11461f0..2f0b6dcc72d1f027a0259acc6c67ea950b4373dd 100644
--- a/constants.py
+++ b/constants.py
@@ -1,10 +1,10 @@
-from pandas import datetime
+from _datetime import datetime
-MFCC_FEATURE_START = 1
-MFCC_FEATURE_END = 21
+MFCC_RANGE_START = 1
+MFCC_RANGE_END = 21
TRAINING_DATA_DIRECTORY_NAME = 'DemoTrainingDataset'
-TESTING_DATA_DIRECTORY_NAME = 'TEST'
+TESTING_DATA_DIRECTORY_NAME = 'Test'
CAR = 'Car'
@@ -36,4 +36,6 @@ ACCURACY_METRICS = 'accuracy'
LOG_DIR_PATH = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
-TRAINED_MODEL = 'trained_model.h5'
+TRAINED_MODEL = 'Trained_Model/trained_model.h5'
+
+TEST_DATA_SPLIT = 0.20
diff --git a/main.py b/main.py
index 124ce5c14c81b23452dcd521fd3dd8d30735b34e..defe61c48e24afd3c3849dc149fdae613af76c96 100644
--- a/main.py
+++ b/main.py
@@ -10,36 +10,43 @@ from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from keras import models
from keras import layers
-import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
import constants
+import sys
def create_csv_header():
- header = 'filename '
- for i in range(constants.MFCC_FEATURE_START, constants.MFCC_FEATURE_END):
- 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)
+ if os.path.isfile(constants.TRAINED_MODEL):
+ sys.exit("Trained model file already exists, "
+ "remove/move trained_model.h5 to another location and start training again")
+ if os.path.isfile(constants.FEATURES_CSV_NAME):
+ sys.exit("features.csv already exist, please remove/move the file to another location and run main.py again")
+ else:
+ header = 'filename '
+ for i in range(constants.MFCC_RANGE_START, constants.MFCC_RANGE_END):
+ header += f' mfcc{i}'
+ header += ' label'
+ header = header.split()
+ file = open(constants.FEATURES_CSV_NAME, 'x', 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=sr, n_mfcc=(constants.MFCC_FEATURE_END - constants.MFCC_FEATURE_START))
- to_append = f'{audioFile.name}'
- for g in mfcc:
- to_append += f' {np.mean(g)}'
+ for audio_file_name in os.listdir(trainingDataDir/f'{trainingDataSubDir}'):
+ if audio_file_name.endswith(".wav"):
+ audio_file = trainingDataDir/f'{trainingDataSubDir}/{audio_file_name}'
+ print("Extracting Features from Directory "+trainingDataSubDir+" and file "+audio_file.name)
+ y, sr = librosa.load(audio_file, mono=True)
+ mfcc_features = librosa.feature.mfcc(y=y, sr=sr,
+ n_mfcc=(constants.MFCC_RANGE_END - constants.MFCC_RANGE_START))
+ to_append = f'{audio_file.name}'
+ for mfcc_segment in mfcc_features:
+ to_append += f' {np.mean(mfcc_segment)}'
if trainingDataSubDir == constants.CAR:
to_append += f' {constants.LIGHT_WEIGHT}'
elif trainingDataSubDir == constants.BUS:
@@ -58,104 +65,84 @@ def extract_features(trainingDataDir, trainingDataSubDirs):
def preprocessing_csv_data():
- print("Reading Features... ")
- data = pd.read_csv(constants.FEATURES_CSV_NAME)
- data.head()
+ features_data = pd.read_csv(constants.FEATURES_CSV_NAME)
+ features_data.head()
# Dropping unnecessary columns (Column Filename is dropped)
- data = data.drop(['filename'], axis=1)
- data.head()
- return data
+ updated_features_data = features_data.drop(['filename'], axis=1)
+ updated_features_data.head()
+ return updated_features_data
-def encode_labels(data):
+def encode_labels(processedFeaturesData):
# 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
+ audio_labels_list = processedFeaturesData.iloc[:, -1]
+ encode_object = LabelEncoder()
+ encoded_target_audio_labels = encode_object.fit_transform(audio_labels_list)
+ return encoded_target_audio_labels, encode_object
-def normalize_data(data):
+def normalize_data(processedData):
# normalizing - Extracting Remaining Columns as X and normalizing them to a common scale
- scaler = StandardScaler()
- X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype=float))
- return X
+ scale_object = StandardScaler()
+ X_normalized_features = scale_object.fit_transform(np.array(processedData.iloc[:, :-1], dtype=float))
+ return X_normalized_features
-def train_test_data_split(X, y):
+def train_test_data_split(XInput, yLabels):
# 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)
- return X_train, X_test, y_train, y_test
+ X_split_train, X_split_test, y_split_train, y_split_test = train_test_split(XInput, yLabels,
+ test_size=constants.TEST_DATA_SPLIT)
+ return X_split_train, X_split_test, y_split_train, y_split_test
def create_and_compile_model():
print("Creating a Model")
- # creating a model
- model = models.Sequential()
- model.add(layers.Dense(constants.HIDDEN_LAYER_1_DIMENSIONS, activation=constants.ACTIVATION_RELU, input_shape=(X.shape[1],)))
- model.add(layers.Dense(constants.HIDDEN_LAYER_2_DIMENSIONS, activation=constants.ACTIVATION_RELU))
- model.add(layers.Dense(constants.HIDDEN_LAYER_3_DIMENSIONS, activation=constants.ACTIVATION_RELU))
- model.add(layers.Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation=constants.ACTIVATION_SOFTMAX))
+ model_instance = models.Sequential()
+ model_instance.add(layers.Dense(constants.HIDDEN_LAYER_1_DIMENSIONS, activation=constants.ACTIVATION_RELU,
+ input_shape=(X_input_features.shape[1],)))
+ model_instance.add(layers.Dense(constants.HIDDEN_LAYER_2_DIMENSIONS, activation=constants.ACTIVATION_RELU))
+ model_instance.add(layers.Dense(constants.HIDDEN_LAYER_3_DIMENSIONS, activation=constants.ACTIVATION_RELU))
+ model_instance.add(layers.Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation=constants.ACTIVATION_SOFTMAX))
print("Compiling a Model")
- model.compile(optimizer= constants.OPTIMIZER_ADAM, loss= constants.LOSS_FUNCTION_SPARSE, metrics=[constants.ACCURACY_METRICS])
- return model
+ model_instance.compile(optimizer=constants.OPTIMIZER_ADAM,
+ loss=constants.LOSS_FUNCTION_SPARSE,
+ metrics=[constants.ACCURACY_METRICS])
+ return model_instance
-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)
+def train_and_save_model(compiledModel, X_train, y_train, X_test, y_test):
+ log_directory = constants.LOG_DIR_PATH
+ tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_directory)
+
print("Start Training...")
- history = model.fit(X_train, y_train, epochs=35, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])
+ training_history = compiledModel.fit(X_train, y_train, 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
+ #print(training_history)
+ compiledModel.save(constants.TRAINED_MODEL)
+ return training_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()
+ final_predictions = np.argmax(compiled_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, final_predictions, target_names=target_names))
# Changing Directory to Training Dataset Folder
chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
-trainingDataDir = Path.cwd()
-trainingDataSubDirs = os.listdir(trainingDataDir)
-
-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)
-
-
-
+training_data_directory = Path.cwd()
+training_data_sub_directories = os.listdir(training_data_directory)
+extract_features(training_data_directory, training_data_sub_directories)
+processed_features_data = preprocessing_csv_data()
+target_audio_labels, encoder_object = encode_labels(processed_features_data)
+X_input_features = normalize_data(processed_features_data)
+X_train_data, X_test_data, y_train_data, y_test_data = train_test_data_split(X_input_features, target_audio_labels)
+compiled_model = create_and_compile_model()
+model_training_history = train_and_save_model(compiled_model, X_train_data, y_train_data, X_test_data, y_test_data)
+predict(X_test_data, y_test_data)
diff --git a/test.py b/test.py
index b1b9f26c9f5e82c8831390154035aaed6eac84f1..5fa110e351be09166db357c0d0fb1b8b4565b783 100644
--- a/test.py
+++ b/test.py
@@ -7,31 +7,36 @@ import csv
from tensorflow import keras
from sklearn.preprocessing import LabelEncoder, StandardScaler
import constants
+import sys
def create_csv_header():
- header=''
- for i in range(constants.MFCC_FEATURE_START, constants.MFCC_FEATURE_END):
- header += f' mfcc{i}'
- header = header.split()
- file = open(constants.TEST_CSV_NAME, 'w', newline='')
- with file:
- writer = csv.writer(file)
- writer.writerow(header)
+ if os.path.isfile(constants.TEST_CSV_NAME):
+ sys.exit("test.csv already exist, please remove/move the file to another location and run test.py again")
+ else:
+ header = ''
+ for i in range(constants.MFCC_RANGE_START, constants.MFCC_RANGE_END):
+ header += f' mfcc{i}'
+ header = header.split()
+ file = open(constants.TEST_CSV_NAME, 'x', newline='')
+ with file:
+ writer = csv.writer(file)
+ writer.writerow(header)
def extract_features(workingDir, subDirectories):
create_csv_header()
for subDirectory in subDirectories:
if subDirectory == constants.TESTING_DATA_DIRECTORY_NAME:
- for fileName in os.listdir(workingDir/f'{subDirectory}'):
- if fileName.endswith(".wav"):
- audioFile = workingDir / f'{subDirectory}/{fileName}'
- y, sr = librosa.load(audioFile, mono=True)
- mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=(constants.MFCC_FEATURE_END - constants.MFCC_FEATURE_START))
+ for test_audio_file_name in os.listdir(workingDir/f'{subDirectory}'):
+ if test_audio_file_name.endswith(".wav"):
+ test_audio_file = workingDir / f'{subDirectory}/{test_audio_file_name}'
+ y, sr = librosa.load(test_audio_file, mono=True)
+ mfcc_features = librosa.feature.mfcc(y=y, sr=sr,
+ n_mfcc=(constants.MFCC_RANGE_END - constants.MFCC_RANGE_START))
to_append = ''
- for g in mfcc:
- to_append += f' {np.mean(g)}'
+ for mfcc_segment in mfcc_features:
+ to_append += f' {np.mean(mfcc_segment)}'
file = open(constants.TEST_CSV_NAME, 'a', newline='')
with file:
writer = csv.writer(file)
@@ -39,30 +44,29 @@ def extract_features(workingDir, subDirectories):
def preprocessing_csv_data():
- # reading dataset from csv
print("Reading Features... ")
- data = pd.read_csv(constants.TEST_CSV_NAME)
- data.head()
- return data
+ test_features_data = pd.read_csv(constants.TEST_CSV_NAME)
+ test_features_data.head()
+ return test_features_data
-def normalize_data(data):
+def normalize_data(processedData):
# # normalizing - Extracting Remaining Columns as X and normalizing them to a common scale
- scaler = StandardScaler()
- X = scaler.fit_transform(np.array(data.iloc[:, :], dtype=float))
- print(X)
- print(X.shape)
- return X
+ scale_object = StandardScaler()
+ X_test = scale_object.fit_transform(np.array(processedData.iloc[:, :], dtype=float))
+ return X_test
-WorkingDir = Path.cwd()
-subDirectories = os.listdir(WorkingDir)
-extract_features(WorkingDir, subDirectories)
-data = preprocessing_csv_data()
-X = normalize_data(data)
-model = keras.models.load_model('./DemoTrainingDataset/trained_model.h5')
-model.summary()
-predictions = np.argmax(model.predict(X), axis=-1)
+working_directory = Path.cwd()
+sub_directories = os.listdir(working_directory)
+extract_features(working_directory, sub_directories)
+processed_data = preprocessing_csv_data()
+X_test_data = normalize_data(processed_data)
+if os.path.isfile('./DemoTrainingDataset/Trained_Model/trained_model.h5'):
+ model = keras.models.load_model('./DemoTrainingDataset/Trained_Model/trained_model.h5')
+else:
+ sys.exit("Trained model file does not exists")
+predictions = np.argmax(model.predict(X_test_data), axis=-1)
encoder = LabelEncoder()
labels = ['Light-Weight', 'Medium-Weight', 'Heavy-Weight', 'Two-Wheeled', 'Rail-Bound']
encoder.fit_transform(labels)