import sys
import cv2
import numpy as np
import matplotlib.pyplot as plt

from comparisons import histogram_comparison, structural_similarity_index, euclidean_distance

# Allows imports from the style transfer submodule
sys.path.append('DCT-Net')

from source.cartoonize import Cartoonizer


def load_source(filename: str) -> np.ndarray:
  return cv2.imread(filename)[..., ::-1]


def find_and_crop_face(data: np.ndarray, classifier_file='haarcascades/haarcascade_frontalface_default.xml') -> np.ndarray:
  data_gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
  face_cascade = cv2.CascadeClassifier(classifier_file)
  face = face_cascade.detectMultiScale(data_gray, 1.1, 3)
  face = max(face, key=len)
  x, y, w, h = face
  face = data[y:y + h, x:x + w]
  return face


def plot_two_images(a: np.ndarray, b: np.ndarray):
  plt.figure(figsize=[10, 10])
  plt.subplot(121)
  plt.imshow(a)
  plt.title("A")
  plt.subplot(122)
  plt.imshow(b)
  plt.title("B")
  plt.show()


def compare_with_anime_characters(data: np.ndarray) -> int:
  # Example will be one face from anime dataset
  example = load_source('data/images/Aisaka, Taiga.jpg')
  # TODO: Use a different face detection method for anime images
  example_face = find_and_crop_face(example, 'haarcascades/lbpcascade_animeface.xml')
  data_rescaled = cv2.resize(data, example_face.shape[:2])
  plot_two_images(example_face, data_rescaled)
  print(histogram_comparison(data_rescaled, example_face))
  print(f'structural-similarity: {structural_similarity_index(data_rescaled, example_face)}')
  print(f'euclidean-distance: {euclidean_distance(data_rescaled, example_face)}')


def transfer_to_anime(img: np.ndarray):
  algo = Cartoonizer(dataroot='DCT-Net/damo/cv_unet_person-image-cartoon_compound-models')
  return algo.cartoonize(img).astype(np.uint8)


if __name__ == '__main__':
  source = load_source('UAM-Andre.jpg')
  source_anime = transfer_to_anime(source)
  source_face_anime = find_and_crop_face(source_anime)
  print(compare_with_anime_characters(source_face_anime))