video.py

import cv2
import torch

# Load the video
video_path = "path_to_video.mp4"
cap = cv2.VideoCapture(video_path)

# Video properties
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)

# Create a VideoWriter object for the output video
out = cv2.VideoWriter("output_with_CAM.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (frame_width, frame_height))

# Process each frame
while True:
    ret, frame = cap.read()
    if not ret:
        break  # End of the video

    # Detect landmarks for left and right eye bounding boxes (example)
    left_eye_bbox = (x_left, y_left, width_left, height_left)
    right_eye_bbox = (x_right, y_right, width_right, height_right)

    # Crop the eyes
    left_eye = frame[y_left : y_left + height_left, x_left : x_left + width_left]
    right_eye = frame[y_right : y_right + height_right, x_right : x_right + width_right]

    # Generate CAMs for left and right eyes
    CAM_left = generate_CAM(left_eye)  # Use your model here
    CAM_right = generate_CAM(right_eye)  # Use your model here

    # Resize CAMs if necessary
    CAM_left_resized = cv2.resize(CAM_left, (width_left, height_left))
    CAM_right_resized = cv2.resize(CAM_right, (width_right, height_right))

    # Overlay the CAMs onto the original frame
    frame[y_left : y_left + height_left, x_left : x_left + width_left] = CAM_left_resized
    frame[y_right : y_right + height_right, x_right : x_right + width_right] = CAM_right_resized

    # Write the processed frame to the output video
    out.write(frame)

# Release resources
cap.release()
out.release()
cv2.destroyAllWindows()