import cv2
import numpy as np
from skimage.metrics import structural_similarity

def histogram_comparison(data_a: np.ndarray, data_b: np.ndarray) -> dict:
  hsv_a = cv2.cvtColor(data_a, cv2.COLOR_BGR2HSV)
  hsv_b = cv2.cvtColor(data_b, cv2.COLOR_BGR2HSV)

  histSize = [50, 60]
  hue_ranges = [0, 180]
  sat_ranges = [0, 256]
  channels = [0, 1]
  ranges = hue_ranges + sat_ranges

  hist_a = cv2.calcHist([hsv_a], channels, None, histSize, ranges, accumulate=False)
  cv2.normalize(hist_a, hist_a, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
  hist_b = cv2.calcHist([hsv_b], channels, None, histSize, ranges, accumulate=False)
  cv2.normalize(hist_b, hist_b, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)

  return {
    'correlation': cv2.compareHist(hist_a, hist_b, 0),
    'chi-square': cv2.compareHist(hist_a, hist_b, 1),
    'intersection': cv2.compareHist(hist_a, hist_b, 2),
    'bhattacharyya-distance': cv2.compareHist(hist_a, hist_b, 3),
  }


def structural_similarity_index(data_a: np.ndarray, data_b: np.ndarray) -> float:
  return structural_similarity(cv2.cvtColor(data_a, cv2.COLOR_BGR2GRAY), cv2.cvtColor(data_b, cv2.COLOR_BGR2GRAY))


def euclidean_distance(data_a: np.ndarray, data_b: np.ndarray) -> float:
  gray_a = cv2.cvtColor(data_a, cv2.COLOR_BGR2GRAY)
  histogram_a = cv2.calcHist([gray_a], [0], None, [256], [0, 256])

  gray_b = cv2.cvtColor(data_b, cv2.COLOR_BGR2GRAY)
  histogram_b = cv2.calcHist([gray_b], [0],  None, [256], [0, 256])
  result, i = [0.], 0
  while i < len(histogram_a) and i < len(histogram_b):
    result += (histogram_a[i] - histogram_b[i]) ** 2
    i += 1
  return result[0] ** (1 / 2)