import cv2
import numpy as np
import tensorflow as tf

# Load YOLOv5 model
model = YOLOv5()
model.load_weights('yolov5.h5')

# Load LSTM model
lstm_model = tf.keras.models.load_model('lstm.h5')

# Load video file
cap = cv2.VideoCapture('video.mp4')

# Initialize input sequence for LSTM
input_sequence = np.zeros((1, sequence_length, input_size))

# Loop through video frames
while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Run YOLOv5 on frame
    boxes, labels, scores = model.predict(frame)

    # Extract information from bounding boxes
    object_features = extract_features_from_boxes(boxes, labels)

    # Update input sequence for LSTM
    input_sequence[:, :-1, :] = input_sequence[:, 1:, :]
    input_sequence[:, -1, :] = object_features

    # Predict future object locations with LSTM
    future_object_locations = lstm_model.predict(input_sequence)

    # Draw bounding boxes and predicted future locations on frame
    draw_boxes_and_predictions(frame, boxes, future_object_locations)

    # Display updated frame
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Clean up
cap.release()
cv2.destroyAllWindows()