"""
Crop images to prepare for training
"""

import click
# from PIL import Image, ImageOps, ImageFilter, ImageDraw

from app.settings import types
from app.utils import click_utils
from app.settings import app_cfg as cfg

@click.command()
@click.option('-i', '--input', 'opt_dir_in', required=True,
  help='Input directory')
@click.option('-o', '--output', 'opt_fp_out', required=True,
  help='Output CSV')
@click.option('-e', '--ext', 'opt_ext',
  default='jpg', type=click.Choice(['jpg', 'png']),
  help='File glob ext')
@click.option('--size', 'opt_size', 
  type=(int, int), default=(300, 300),
  help='Output image size')
@click.option('-t', '--detector-type', 'opt_detector_type',
  type=cfg.FaceDetectNetVar,
  default=click_utils.get_default(types.FaceDetectNet.DLIB_CNN),
  help=click_utils.show_help(types.FaceDetectNet))
@click.option('-g', '--gpu', 'opt_gpu', default=0,
  help='GPU index')
@click.option('--conf', 'opt_conf_thresh', default=0.85, type=click.FloatRange(0,1),
  help='Confidence minimum threshold')
@click.option('--pyramids', 'opt_pyramids', default=0, type=click.IntRange(0,4),
  help='Number pyramids to upscale for DLIB detectors')
@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
  help='Slice list of files')
@click.option('--display/--no-display', 'opt_display', is_flag=True, default=False,
  help='Display detections to debug')
@click.pass_context
def cli(ctx, opt_dir_in, opt_fp_out, opt_ext, opt_size, opt_detector_type, 
  opt_gpu, opt_conf_thresh, opt_pyramids, opt_slice, opt_display):
  """Extrace face"""
  
  import sys
  import os
  from os.path import join
  from pathlib import Path
  from glob import glob
  from tqdm import tqdm
  import numpy as np
  import dlib  # must keep a local reference for dlib
  import cv2 as cv
  import pandas as pd

  from app.utils import logger_utils, file_utils, im_utils
  from app.processors import face_detector 

  # -------------------------------------------------
  # init here

  log = logger_utils.Logger.getLogger()
  
  if opt_detector_type == types.FaceDetectNet.CVDNN:
    detector = face_detector.DetectorCVDNN()
  elif opt_detector_type == types.FaceDetectNet.DLIB_CNN:
    detector = face_detector.DetectorDLIBCNN(opt_gpu)
  elif opt_detector_type == types.FaceDetectNet.DLIB_HOG:
    detector = face_detector.DetectorDLIBHOG()
  elif opt_detector_type == types.FaceDetectNet.HAAR:
    log.error('{} not yet implemented'.format(opt_detector_type.name))
    return

  
  # -------------------------------------------------
  # process here
  
  # get list of files to process
  fp_ims = glob(join(opt_dir_in, '*.{}'.format(opt_ext)))
  if opt_slice:
    fp_ims = fp_ims[opt_slice[0]:opt_slice[1]]
  log.debug('processing {:,} files'.format(len(fp_ims)))


  data = []

  for fp_im in tqdm(fp_ims):
    im = cv.imread(fp_im)
    bboxes = detector.detect(im, opt_size=opt_size, opt_pyramids=opt_pyramids)
    fpp_im = Path(fp_im)
    for bbox in bboxes:
      roi = {
        'fn': fpp_im.stem, 
        'ext': fpp_im.suffix, 
        'x': bbox.x, 
        'y': bbox.y, 
        'w': bbox.w, 
        'h': bbox.h}
      dim = bbox.to_dim(im.shape[:2][::-1])  # w,h
      data.append(roi)
    
    # debug display
    if opt_display and len(bboxes):
      im_md = im_utils.resize(im, width=opt_size[0])
      for bbox in bboxes:
        dim = bbox.to_dim(im_md.shape[:2][::-1])
        cv.rectangle(im_md, dim.pt_tl, dim.pt_br, (0,255,0), 3)
      cv.imshow('', im_md)
      while True:
        k = cv.waitKey(1) & 0xFF
        if k == 27 or k == ord('q'):  # ESC
          cv.destroyAllWindows()
          sys.exit()
        elif k != 255:
          # any key to continue
          break

  # save date
  df = pd.DataFrame.from_dict(data)
  df.to_csv(opt_fp_out)