import datetime
from glob import glob
import cv2
import os
from tqdm import tqdm

def mp4_to_png(input_path: str, save_path: str) -> str: 
    """ Converts mp4 to pngs for each frame of the video.
        Args: input_path is the path to the mp4 file, save_path is the directory to save the frames.
        Returns: save_path, fps the number of frames per second.
    """  
    # get frames per second
    fps = int(cv2.VideoCapture(input_path).get(cv2.CAP_PROP_FPS))
    # run subprocess to convert mp4 to pngs 
    os.system(f"ffmpeg -i {input_path} -vf fps={fps} {save_path}/frame%08d.png")
    return fps

def frame_to_timestamp(frame_number: int, fps: int): 
    # Calculate the timestamp in seconds
    timestamp_seconds = frame_number / fps
    
    # Convert the timestamp to hh:mm:ss
    minutes, seconds = divmod(timestamp_seconds, 60)
    hours, minutes = divmod(minutes, 60)
    # print("Timestamp:", hours, ":", minutes, ":", seconds)
    timestamp = f"{int(hours):02d}:{int(minutes):02d}:{seconds:06.3f}"
    return timestamp

def vid_stitcher(frames_dir: str, output_path: str, fps: int = 30) -> str:
    """
    Takes a list of frames as numpy arrays and writes them to a video file.
    """
    # Get the list of frames
    frame_list = sorted(glob(os.path.join(frames_dir, 'frame*.png')))

    # Prepare the VideoWriter
    frame = cv2.imread(frame_list[0])
    height, width, _ = frame.shape
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))

    # Use multithreading to read frames faster
    from concurrent.futures import ThreadPoolExecutor
    with ThreadPoolExecutor() as executor:
        frames = list(executor.map(cv2.imread, frame_list))

    # Write frames to the video
    with tqdm(total=len(frame_list), desc='Stitching frames') as pbar:
        for frame in frames:
            out.write(frame)
            pbar.update(1)
    
    return output_path