"""
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_fp_in', default=None, required=True,
  help='Image filepath')
@click.option('-o', '--output', 'opt_fp_out', default=None,
  help='GIF output path')
@click.option('--size', 'opt_size', 
  type=(int, int), default=(300, 300),
  help='Output image size')
@click.option('-g', '--gpu', 'opt_gpu', default=0,
  help='GPU index')
@click.option('-f', '--force', 'opt_force', is_flag=True,
  help='Force overwrite file')
@click.option('--display/--no-display', 'opt_display', is_flag=True, default=True,
  help='Display detections to debug')
@click.pass_context
def cli(ctx, opt_fp_in, opt_fp_out, opt_gpu, opt_size, opt_force, opt_display):
  """Face pose demo"""
  
  import sys
  import os
  from os.path import join
  from pathlib import Path
  import time
  
  from tqdm import tqdm
  import numpy as np
  import pandas as pd
  import cv2 as cv
  import dlib
  from PIL import Image
  import matplotlib.pyplot as plt

  from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
  from app.utils import plot_utils
  from app.processors import face_detector, face_landmarks
  from app.models.data_store import DataStore
  

  log = logger_utils.Logger.getLogger()

  
  # -------------------------------------------------
  # load image

  im = cv.imread(opt_fp_in)
  im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])


  # ----------------------------------------------------------------------------
  # detect face

  face_detector = face_detector.DetectorCVDNN()
  bboxes = face_detector.detect(im_resized, largest=True)
  bbox_norm = bboxes[0]
  dim = im_resized.shape[:2][::-1]
  bbox_dim = bbox_norm.to_dim(dim)
  if not bbox_norm:
    log.error('no face detected')
    return


  # ----------------------------------------------------------------------------
  # generate 68 point landmarks using dlib
  
  from app.processors import face_landmarks
  landmark_detector_2d_68 = face_landmarks.Dlib2D_68()
  points_2d_68 = landmark_detector_2d_68.landmarks(im_resized, bbox_norm)


  # ----------------------------------------------------------------------------
  # generate pose from 68 point 2D landmarks
  
  from app.processors import face_pose
  pose_detector = face_pose.FacePoseDLIB()
  pose_data = pose_detector.pose(points_2d_68, dim)

  # ----------------------------------------------------------------------------
  # output
  
  log.info(f'Face coords: {bbox_dim} face')
  log.info(f'pitch: {pose_data["pitch"]}, roll: {pose_data["roll"]}, yaw: {pose_data["yaw"]}')


  # ----------------------------------------------------------------------------
  # draw

  # draw 2d landmarks
  im_landmarks = im_resized.copy()
  im_landmarks = draw_utils.draw_landmarks2D(im_landmarks, points_2d_68)
  im_landmarks = draw_utils.draw_bbox(im_landmarks, bbox_norm)

  # draw pose
  im_pose = im_resized.copy()
  im_pose = draw_utils.draw_pose(im_pose, pose_data['point_nose'], pose_data['points'])
  im_pose = draw_utils.draw_degrees(im_pose, pose_data)    


  # ----------------------------------------------------------------------------
  # save
  
  if opt_fp_out:
    # save pose only
    cv.imwrite(opt_fp_out, im_pose)


  # ----------------------------------------------------------------------------
  # display

  if opt_display:
      
    # show all images here
    cv.imshow('Original', im_resized)
    cv.imshow('2D 68PT Landmarks', im_landmarks)
    cv.imshow('Pose', im_pose)
    display_utils.handle_keyboard()