# -*- coding: utf-8 -*-
# @Time    : 2022-7-26 0026 19:09
# @Author  : Qing
# @Email   : derighoid@gmail.com
# @File    : Blue_noise_sampling.py
# @Software: PyCharm

'''
计算新图形（放大后或缩小后）的坐标点像素值对应于原图像中哪一个像素点填充的。
src是原图，dst是新图，原来的图像宽度/高度除以新图像的宽度/高度可以得到缩放比例，假如是缩小图片括号内的数字小于1，放大则大于1，相当于系数，再乘以新图片的宽度/高度，就实现了缩放。
'''

from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import math


# # 最近邻插值算法
# # dstH为新图的高;dstW为新图的宽
# def blueNoiseSampl(img, dstH, dstW):
#     scrH, scrW, t = img.shape  # src原图的长宽
#     retimg = np.zeros((dstH, dstW, 3), dtype=np.uint8)
#     for i in range(dstH - 1):
#         for j in range(dstW - 1):
#             scrx = round(i * (scrH / dstH))
#             scry = round(j * (scrW / dstW))
#             retimg[i, j] = img[scrx, scry]
#
#     return retimg
#
#
# im_path = './data/th.png'
# image = np.array(Image.open(im_path))
#
# plt.figure(figsize=(16, 8))
#
# plt.subplot(1, 2, 1)
# plt.imshow(image)
#
# image1 = blueNoiseSampl(image, image.shape[0] * 2, image.shape[1] * 2)
# # 从array转换成image
# image1 = Image.fromarray(image1.astype('uint8')).convert('RGB')
# image1.save('./data/picture13.png')
# plt.subplot(1, 2, 2)
# plt.imshow(image1)
# plt.show()