"""
    Author: Yassin Riyazi
    Date: 04-08-2025
    Description:
        This script processes directories containing images of drops, extracting and analyzing them using the DropDetection_Sum module.
        It utilizes multiprocessing to handle multiple directories concurrently and employs tqdm for progress tracking.
        It is designed to work with a specific directory structure and image processing requirements.
        It is part of the P2NeuralNetwork project under the Viscosity project umbrella.
"""
import  glob
import  os
# import  cv2
# import  numpy                   as      np
# import  matplotlib.pyplot       as      plt
from    multiprocessing         import  Pool
from    tqdm                    import  tqdm
from   typing                  import  Dict,Union
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../', 'src/PyThon/ContactAngle/DropDetection')))
from DropDetection_Sum import Main # type: ignore


def worker(args:Dict[str, Union[str, bool]]):
    """Worker function to call Main with the given arguments."""
    try:
        Main(
            experiment=args['experiment'],
            SaveAddress=args['SaveAddress'],
            SaveAddressCSV=args['SaveAddressCSV'],
            extension=args['extension'],
            _morphologyEx=args['_morphologyEx']
        )
    except Exception as e:
        print(f"Error processing {args['experiment']}: {e}")

if __name__ == "__main__":

    # for tilt in glob.glob("/media/d2u25/Dont/frames_Process_15/*"):
    #     for experiment in glob.glob(os.path.join(tilt,'*')):
    #         for _idx, rep in enumerate(glob.glob(os.path.join(experiment,'*'))):
    #             if _idx < 5:
    #                 _SaveAddresses = (rep.replace('frames_Process_15', 'frames_Process_15_Patch'))
    #                 Main(experiment = rep,
    #                     SaveAddress = _SaveAddresses,
    #                     SaveAddressCSV= _SaveAddresses,
    #                     extension = '.png',
    #                     _morphologyEx = True,)

    
    # Set the number of processes (adjust as needed)
    fps = 30
    num_processes = 14  # Example: Use 4 processes

    # Collect all tasks
    tasks = []
    # f"/media/d2u25/Dont/frames_Process_{fps}"
    HauptAddress = '/media/d2u25/Dont/frames_Process_30'
    for tilt in glob.glob(f"{HauptAddress}/*"):
        for experiment in glob.glob(os.path.join(tilt, '*')):
            for _idx, rep in enumerate(glob.glob(os.path.join(experiment, '*'))):
                # if _idx < 5:
                _SaveAddresses = rep.replace(f'frames_Process_{fps}', f'frames_Process_{fps}_Position')
                tasks.append({
                    'experiment': rep,
                    'SaveAddress': _SaveAddresses,
                    'SaveAddressCSV': _SaveAddresses,
                    'extension': '.png',
                    '_morphologyEx': True
                })

    # Run tasks in parallel with progress bar
    with Pool(processes=num_processes) as pool:
        list(tqdm(pool.imap(worker, tasks), total=len(tasks), desc="Processing videos"))

    # """
    #     Check the YOLO result with OpenCV vcountors
    #     Normalize the white lines in bottom of the images
    #     save x1 in the textfile with same name as the image
    # """