"""
Example 7: Five Sensors with Async Pipeline

Demonstrates scaling to 5 sensors running in parallel with async mode.
Each sensor runs in its own process with 3-stage async pipeline.
"""

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

import cv2
import orisys
import multiprocessing as mp
import numpy as np


def main():
    # Configure 5 sensors (using same video for demo - replace with actual sources)
    configs = [
        {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
        {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
        {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
        {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
        {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
    ]
    
    with orisys.MultiSensor.async_context(configs) as multi:
        print(f"\n{len(configs)} sensors started. Press 'q' to quit")
        
        for all_data in multi:
            valid_data = [d for d in all_data if d and d['img'] is not None]
            
            # Print stats for all sensors
            for data in valid_data:
                print(f"S{data['sensor_id']}: FPS={data['fps']:4.1f} Fn={data['fnormal']:7.1f}", end=" | ")
            if valid_data:
                print()
            
            # Create grid display (2 rows: 3 + 2)
            imgs = []
            for data in valid_data:
                if data['flow'] is not None:
                    img = orisys.util.draw_arrows(data['img'].copy(), data['flow'], threshold=5)
                else:
                    img = data['img']
                # Add sensor ID label
                cv2.putText(img, f"S{data['sensor_id']}", (10, 30), 
                           cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
                imgs.append(img)
            
            if len(imgs) >= 5:
                # Create 2x3 grid (pad with black if needed)
                h, w = imgs[0].shape[:2]
                black = np.zeros((h, w, 3), dtype=np.uint8) if len(imgs[0].shape) == 3 else np.zeros((h, w), dtype=np.uint8)
                
                row1 = np.hstack([imgs[0], imgs[1], imgs[2]])
                row2 = np.hstack([imgs[3], imgs[4], black])
                grid = np.vstack([row1, row2])
                
                # Resize for display
                grid = cv2.resize(grid, (1200, 800))
                cv2.imshow("5 Sensors Grid", grid)
            
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
    
    cv2.destroyAllWindows()


if __name__ == '__main__':
    mp.freeze_support()
    main()