Predicting the frames

prediction.py

# pick a random index from validation dataset
random_index = np.random.choice(range(len(X_val)), size=1)
test_serie_X = X_val[random_index[0]]
test_serie_Y = y_val[random_index[0]]

first_frames = test_serie_X
original_frames = test_serie_Y
# predict the next 18 fames
new_prediction = model.predict(np.expand_dims(first_frames, axis=0))
new_prediction = np.squeeze(new_prediction, axis=0)

fig, axes = plt.subplots(2, 18, figsize=(20, 4))

# Plot the ground truth frames.
for idx, ax in enumerate(axes[0]):
    ax.imshow(np.squeeze(original_frames[idx]), cmap="viridis")
    ax.set_title(f"Frame {idx + 18}")
    ax.axis("off")

# Plot the predicted frames.
for idx, ax in enumerate(axes[1]):
    ax.imshow((new_prediction[idx]).reshape((344,315)), cmap="viridis")
    ax.set_title(f"Frame {idx + 18}")
    ax.axis("off")

plt.show()