mooddetection.py

# -*- coding: utf-8 -*-
"""MoodDetection.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1OcR_u9n7rcWFKhNn569o2xyg7fngDIwv

# MOOD DETECTION
"""

#import libraries
import tensorflow as tf
from tensorflow.keras.preprocessing.image import img_to_array, array_to_img
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import cv2
import os
import numpy as np
import matplotlib.pyplot as plt

#import dataset's zip file
zip_file_path = '/content/drive/MyDrive/Kaggle/fer2013.zip'
extracted_dir = '/content/fer2013/'
os.makedirs(extracted_dir, exist_ok=True)

#extract zip file
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
    zip_ref.extractall(extracted_dir)

extracted_files = os.listdir(extracted_dir)
print(extracted_files)

TRAIN_DIR = "/content/fer2013/train"
TEST_DIR = "/content/fer2013/test"
BATCH_SIZE=64

# Function to load and preprocess the dataset
def load_and_preprocess_data(directory):
    images = []
    labels = []             #store preprocessed images and corresponding labels.

    label_encoder = LabelEncoder()

    for i, emotion_folder in enumerate(os.listdir(directory)):
        emotion_path = os.path.join(directory, emotion_folder)

        for filename in os.listdir(emotion_path):
            img_path = os.path.join(emotion_path, filename)
            img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
            img = cv2.resize(img, (48, 48))
            img = img / 255.0  # normalize to [0,1]
            images.append(img)
            labels.append(i)  # Use the numerical label from label_encoder

    return np.array(images), np.array(labels)

# Load and preprocess training and testing data
X_train, y_train = load_and_preprocess_data(TRAIN_DIR)
X_test, y_test = load_and_preprocess_data(TEST_DIR)

#Reshape and normalize the data(/255.0)
X_train = X_train.reshape(-1, 48, 48, 1)
X_test = X_test.reshape(-1, 48, 48, 1)

#Convert labels to one-hot encoding (represent categorical labels as binary vectors)
y_train = to_categorical(y_train, num_classes=7)  # 7 classes for emotions
y_test = to_categorical(y_test, num_classes=7)

plt.imshow(X_train[0].reshape(48, 48), cmap='gray')
plt.title(f'Class: {np.argmax(y_train[0])}')
plt.show()

model = keras.Sequential([
    keras.layers.Dense(100, input_shape=(48, 48, 1), activation='relu'),
    keras.layers.Flatten(),
    keras.layers.Dense(7, activation='softmax')  # Change the number of units to 7
])

# Compile the model
model.compile(optimizer='adam',
              loss='categorical_crossentropy',  # Use categorical crossentropy
              metrics=['accuracy'])

#checking no of samples
print(X_train.shape)
print(y_train.shape)

# Train the model
model.fit(X_train, y_train, epochs=5)

# Evaluate the model on the test set
model.evaluate(X_test, y_test)

# Make predictions on the test set
y_predicted = model.predict(X_test)
y_predicted_labels = [np.argmax(i) for i in y_predicted]

plt.imshow(X_test[1].reshape(48, 48), cmap='gray')
plt.title(f'Predicted Label: {y_predicted_labels[1]}')
plt.show()

y_test_labels = np.argmax(y_test, axis=1)

if len(y_test_labels) == len(y_predicted_labels):
    cm = tf.math.confusion_matrix(labels=y_test_labels, predictions=y_predicted_labels)
cm

import seaborn as sn

plt.figure(figsize=(10, 7))
sn.heatmap(cm, annot=True, fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Truth')
plt.show()