emotion-python/test_model.py at main · meher0704/emotion-python · GitHub

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import numpy as np
from keras.preprocessing import image
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization

import tensorflow as tf

# Define the model architecture
model = tf.keras.models.Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(48, 48, 1)))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(128, (5, 5), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.25))

model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.25))

model.add(Dense(7, activation='softmax'))

# Load the saved model weights
model.load_weights('FER_model.h5')

# Function to preprocess the image for prediction
def preprocess_image(image_path):
    img = image.load_img(image_path, target_size=(48, 48), grayscale=True)
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array /= 255.0  # Normalize pixel values to be between 0 and 1
    return img_array

# Path to the test image you want to use
test_image_path = "my_picture.jpg"  # Replace with the actual path

# Preprocess the image
preprocessed_image = preprocess_image(test_image_path)

# Make predictions
predictions = model.predict(preprocessed_image)

# Get the predicted class index
predicted_class_index = np.argmax(predictions)

# Mapping of class index to emotion label
emotion_labels = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}

# Get the emotion label for the predicted class
predicted_emotion = emotion_labels[predicted_class_index]

print("Predicted Emotion:", predicted_emotion)