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FER_Detection.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 29 14:22:48 2024
+
+@author: Dhrumit Patel
+"""
+
+"""
+Dataset: FER-2013
+https://www.kaggle.com/datasets/msambare/fer2013
+"""
+
+from keras_preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+
+IMG_HEIGHT = 48
+IMG_WIDTH = 48
+batch_size = 32
+
+train_data_dir = 'data/train/'
+validation_data_dir = 'data/test/'
+
+train_datagen = ImageDataGenerator(rescale=1./255,
+                                   rotation_range=30,
+                                   shear_range=0.3,
+                                   zoom_range=0.3,
+                                   horizontal_flip=True,
+                                   fill_mode='nearest')
+
+validation_datagen = ImageDataGenerator(rescale=1./255)
+
+train_generator = train_datagen.flow_from_directory(train_data_dir,
+                                                    color_mode='grayscale',
+                                                    target_size=(IMG_HEIGHT, IMG_WIDTH),
+                                                    batch_size=batch_size,
+                                                    class_mode='categorical',
+                                                    shuffle=True)
+
+validation_generator = validation_datagen.flow_from_directory(validation_data_dir,
+                                                              color_mode='grayscale',
+                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),
+                                                              batch_size=batch_size,
+                                                              class_mode='categorical',
+                                                              shuffle=True)
+
+class_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
+
+img, label = train_generator.__next__()
+
+import random
+i = random.randint(0, (img.shape[0])-1)
+image = img[i]
+labl = class_labels[label[i].argmax()]
+plt.imshow(image[:,:,0], cmap='gray')
+plt.title(labl)
+plt.show()
+
+# Define the model
+model = Sequential()
+
+model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
+
+model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.1))
+
+model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.1))
+
+model.add(Conv2D(256, kernel_size=(3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.1))
+
+model.add(Flatten())
+model.add(Dense(512, activation='relu'))
+model.add(Dropout(0.2))
+
+model.add(Dense(7, activation='softmax'))
+
+model.compile(optimizer = 'adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+model.summary()
+
+from keras.utils import plot_model
+plot_model(model, show_dtype=True, show_layer_names=True)
+
+train_path = "data/train/"
+test_path = "data/test"
+
+num_train_imgs = 0
+for root, dirs, files in os.walk(train_path):
+    num_train_imgs += len(files)
+    
+num_test_imgs = 0
+for root, dirs, files in os.walk(test_path):
+    num_test_imgs += len(files)
+
+history = model.fit(train_generator,
+                    steps_per_epoch=num_train_imgs//batch_size,
+                    epochs=50,
+                    validation_data=validation_generator,
+                    validation_steps=num_test_imgs//batch_size)
+
+model.save('models/emotion_detection_model_50epochs.h5')
+
+# Plot the training and validation accuracy and loss at each epoch
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+epochs = range(1, len(loss) + 1)
+plt.plot(epochs, loss, 'y', label='Training loss')
+plt.plot(epochs, val_loss, 'r', label='Validation loss')
+plt.title('Training and validation loss')
+plt.xlabel('Epochs')
+plt.ylabel('Loss')
+plt.legend()
+plt.show()
+
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+plt.plot(epochs, acc, 'y', label='Training acc')
+plt.plot(epochs, val_acc, 'r', label='Validation acc')
+plt.title('Training and validation accuracy')
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.show()
+
+from keras.models import load_model
+my_model = load_model('models/emotion_detection_model_50epochs.h5',compile=False)
+
+# Generate a batch of images
+test_img, test_lbl = validation_generator.__next__()
+predictions = my_model.predict(test_img)
+
+predictions = np.argmax(predictions, axis=1)
+test_labels = np.argmax(test_lbl, axis=1)
+
+from sklearn.metrics import accuracy_score, confusion_matrix
+print(f"Accuracy: {accuracy_score(y_true=test_labels, y_pred=predictions)}")
+
+cm = confusion_matrix(y_true=test_labels, y_pred=predictions)
+cm
+
+import seaborn as sns
+sns.heatmap(cm, annot=True, fmt='d')
+
+class_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
+
+n = random.randint(0, test_img.shape[0] - 1)
+image = test_img[n]
+original_label = class_labels[test_labels[n]]
+predicted_label = class_labels[predictions[n]]
+plt.imshow(image[:, :, 0], cmap='gray')
+plt.title(f"Original Label: {original_label} | Predicted Label: {predicted_label}")
+plt.axis("off")
+plt.show()

emotion_detection_model_50epochs.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dde478237261277246ed989749189f0eda75c8bfd9904928622ef59a8279f344
+size 29933488