import numpy as np
import cv2

def pil_cv2_image_converter(image):
    numpy_image = np.array(image)
    # Convert to an OpenCV image; notice the COLOR_RGB2BGR flag, which means
    # that the color is converted from RGB to BGR format.
    opencv_image = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR)
    return opencv_image

def chi2_distance(histA, histB, eps = 1e-10):
  # compute the chi-squared distance
  d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)
    for (a, b) in zip(histA, histB)])
         # return the chi-squared distance
  return d

def euclidean_distance(x, y):
    # compute the Euclidean distance
    d = np.sqrt(np.sum((x - y) ** 2))
    # return the Euclidean distance
    return d

def merge_features(feature1, feature2):
    # pad or truncate hist_color and hist_lbp to the same length
    max_length = max(feature1.shape[0], feature2.shape[0])
    feature1_padded = np.pad(hist_color, (0, max_length - hist_color.shape[0]))
    feature2_padded = np.pad(hist_lbp, (0, max_length - hist_lbp.shape[0]))

    # concatenate the padded feature vectors
    return np.hstack((hist_color_padded, hist_lbp_padded))