"""
Example 5: Async/Pipelined Processing

Overlaps image capture with deformation computation for higher throughput.
Pipeline: [Capture Thread] -> [Queue] -> [Compute Thread] -> [Queue] -> [Main]
"""

import os
os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"

import cv2
import orisys


def main():
    sensor = orisys.Sensor("./s1.mp4", isstitch=True, cuda=True, verbose=True)
    sensor.enable_async(queue_size=2)
    
    print("\nPress 'q' to quit, 'r' to reset")
    
    try:
        while True:
            data = sensor.get_async(timeout=0.05)
            if data is None:
                continue
            
            print(f"FPS: {data['fps']:5.1f}, Fn: {data['fnormal']:8.2f}, "
                  f"Fx: {data['fshearx']:8.2f}, Fy: {data['fsheary']:8.2f}")
            
            if data['img'] is not None and data['flow'] is not None:
                arrows = orisys.util.draw_arrows(data['img'].copy(), data['flow'], threshold=5)
                cv2.imshow("Deformation", arrows)
            
            if data['depth'] is not None:
                depth_img = cv2.applyColorMap((data['depth'] * 255).astype('uint8'), cv2.COLORMAP_HOT)
                cv2.imshow("Depth", depth_img)
            
            key = cv2.waitKey(1) & 0xFF
            if key == ord('q'):
                break
            elif key == ord('r'):
                sensor.reset()
    finally:
        sensor.disconnect()
        cv2.destroyAllWindows()


if __name__ == '__main__':
    main()