51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
|
# -*- 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()
|
|||
|
|
|||
|
|
|||
|
|