from glob import glob
import os
from os.path import join
from pathlib import Path

import click




@click.command()
@click.option('-i', '--input', 'opt_fp_in', required=True,
  help='Input directory to glob')
@click.option('-o', '--output', 'opt_fp_out', required=True,
  help='Output directory for face frames')
@click.option('--size', 'opt_size', 
  type=(int, int), default=(300, 300),
  help='Output image size')
@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
  help='Slice list of files')
@click.pass_context
def cli(ctx, opt_fp_in, opt_fp_out, opt_size, opt_slice):
  """Split video to face frames"""

  from tqdm import tqdm
  import dlib
  import pandas as pd
  from PIL import Image, ImageOps, ImageFilter
  import cv2 as cv
  import numpy as np

  from app.processors import face_detector
  from app.utils import logger_utils, file_utils, im_utils
  from app.settings import types
  from app.utils import click_utils
  from app.settings import app_cfg as cfg
  from app.models.bbox import BBox

  log = logger_utils.Logger.getLogger()

  # -------------------------------------------------
  # process  
   
  detector = face_detector.DetectorDLIBCNN()

  # get file list
  fp_videos = glob(join(opt_fp_in, '*.mp4'))
  fp_videos += glob(join(opt_fp_in, '*.webm'))
  fp_videos += glob(join(opt_fp_in, '*.mkv'))

  min_distance_per = .025  # minimum distance percentage to save new face image
  face_interval = 5
  frame_interval_count = 0
  frame_count = 0
  bbox_prev = BBox(0,0,0,0)
  file_utils.mkdirs(opt_fp_out)
  dnn_size = opt_size
  max_dim = max(dnn_size)
  px_thresh = int(max_dim * min_distance_per)

  for fp_video in tqdm(fp_videos):
    # load video  
    video = cv.VideoCapture(fp_video)
    # iterate through frames
    while video.isOpened():
      res, frame = video.read()
      if not res:
        break
      # increment frames, save frame if interval has passed
      frame_count += 1  # for naming
      frame_interval_count += 1  # for interval
      bboxes = detector.detect(frame, opt_size=dnn_size, opt_pyramids=0)
      if len(bboxes) > 0 and frame_interval_count >= face_interval:
        dim = frame.shape[:2][::-1]
        d = bboxes[0].to_dim(dim).distance(bbox_prev)
        if d > px_thresh:
          # save frame
          zfc = file_utils.zpad(frame_count)
          fp_frame = join(opt_fp_out, '{}_{}.jpg'.format(Path(fp_video).stem, zfc))
          cv.imwrite(fp_frame, frame)
          frame_interval_count = 0
          bbox_prev = bboxes[0]