# -*- coding: utf-8 -*-

"""Test_LSTM.ipynb

**

 * This file is part of Hybrid CNN-LSTM for COVID-19 Severity Score Prediction paper.

 *

 * Written by Ankan Ghosh Dastider and Farhan Sadik.

 *

 * Copyright (c) by the authors under Apache-2.0 License. Some rights reserved, see LICENSE.

 */

"""

model = load_model('') #Link CNN model weight directory

model_lstm = load_model('') #Link LSTM model weight directory

data_dir_test = '' #Link test video

test_dir = os.path.join(data_dir_test)

test_data = []

for file in sorted(os.listdir(test_dir)):

    # print(file)

    test_data.append(['{}'.format(file)])

test_on = pd.DataFrame(test_data, columns=['File'])

test_on.head()

IMAGE_SIZE = 128

NUM_FRAMES = test_on.shape[0]

def read_image_test(filepath):

    return cv2.imread(os.path.join(data_dir_test, filepath)) # Loading a color image is the default flag

# Resize image to target size

def resize_image(newimage, image_size):

    return cv2.resize(newimage.copy(), image_size, interpolation=cv2.INTER_AREA)

X_Test = np.zeros((NUM_FRAMES, IMAGE_SIZE, IMAGE_SIZE, 3))

Y_Test = np.zeros((NUM_FRAMES, 1))

for i, file in tqdm(enumerate(test_on['File'].values)):

    newimage = read_image_test(file)

    if newimage is not None:

        X_Test[i] = resize_image(newimage, (IMAGE_SIZE, IMAGE_SIZE))

        match = re.search('Score(\d)',file)

        score = int(match.group(1))

        Y_Test[i] = score

        #print(file)

        #print(score)

Y_Test = to_categorical(Y_Test, num_classes=4)

# print(Y_Test)

# Normalize the data

X_Test = X_Test / 255.

print('X_Test Shape: {}'.format(X_Test.shape))

print('Y_Test Shape: {}'.format(Y_Test.shape))

output = np.zeros((1, NUM_FRAMES, 64))

specific_layer_output = K.function([model.layers[0].input], [model.get_layer('dropout_35').output])

layer_output = specific_layer_output([X_Test])[0]

#print(layer_output.shape)

#print(layer_output)

output[0] = layer_output

print('Output from CNN Shape: {}'.format(output.shape))

#custom3 = model.predict(X_Test)

#print(custom3)

score_types = ['Score 0', 'Score 1', 'Score 2', 'Score 3']

Y_pred_test = model_lstm.predict(output)

Y_pred_test = np.reshape(Y_pred_test, (Y_pred_test.shape[1], Y_pred_test.shape[2]))

Y_pred_test = np.argmax(Y_pred_test, axis=1)

Y_true_test = np.argmax(Y_Test, axis=1)

#print(Y_pred_lstm.shape)

#print(Y_Val_LSTM.shape)

cm = confusion_matrix(Y_true_test, Y_pred_test)

plt.figure(figsize=(12, 12))

ax = sns.heatmap(cm, cmap=plt.cm.Greens, annot=True, square=True, xticklabels=score_types, yticklabels=score_types)

ax.set_ylabel('Actual', fontsize=40)

ax.set_xlabel('Predicted', fontsize=40)

final_loss, final_accuracy = model.evaluate(X_Test, Y_Test)

print('Final Loss: {}, Final Accuracy: {}'.format(final_loss, final_accuracy))