import numpy as np

import cv2

import matplotlib.pyplot as plt

import torch

import torchvision

mean_nums = [0.485, 0.456, 0.406]

std_nums = [0.229, 0.224, 0.225]

def load_image(img_path, resize=True):

  img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)

  if resize:

    img = cv2.resize(img, (64, 64), interpolation = cv2.INTER_AREA)

  return img

def show_image(img_path):

  img = load_image(img_path)

  plt.imshow(img)

  plt.axis('off')

def show_grid_image(image_paths):

  images = [load_image(img) for img in image_paths]

  images = torch.as_tensor(images)

  images = images.permute(0, 3, 1, 2)

  grid_img = torchvision.utils.make_grid(images, nrow=11)

  plt.figure(figsize=(24, 12))

  plt.imshow(grid_img.permute(1, 2, 0))

  plt.axis('off');

def image_show(inp, title=None):

  inp = inp.numpy().transpose((1, 2, 0))

  mean = np.array([mean_nums])

  std = np.array([std_nums])

  inp = std * inp + mean

  inp = np.clip(inp, 0, 1)

  plt.imshow(inp)

  if title is not None:

    plt.title(title)

  plt.axis('off')