51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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# -*- coding: utf-8 -*-
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# @Time : 2022-7-26 0026 19:09
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# @Author : Qing
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# @Email : derighoid@gmail.com
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# @File : Blue_noise_sampling.py
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# @Software: PyCharm
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'''
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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。
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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。
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'''
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from PIL import Image
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import matplotlib.pyplot as plt
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import numpy as np
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import math
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# # 最近邻插值算法
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# # dstH为新图的高;dstW为新图的宽
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# def blueNoiseSampl(img, dstH, dstW):
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# scrH, scrW, t = img.shape # src原图的长宽
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# retimg = np.zeros((dstH, dstW, 3), dtype=np.uint8)
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# for i in range(dstH - 1):
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# for j in range(dstW - 1):
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# scrx = round(i * (scrH / dstH))
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# scry = round(j * (scrW / dstW))
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# retimg[i, j] = img[scrx, scry]
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#
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# return retimg
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#
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#
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# im_path = './data/th.png'
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# image = np.array(Image.open(im_path))
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#
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# plt.figure(figsize=(16, 8))
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#
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# plt.subplot(1, 2, 1)
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# plt.imshow(image)
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#
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# image1 = blueNoiseSampl(image, image.shape[0] * 2, image.shape[1] * 2)
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# # 从array转换成image
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# image1 = Image.fromarray(image1.astype('uint8')).convert('RGB')
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# image1.save('./data/picture13.png')
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# plt.subplot(1, 2, 2)
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# plt.imshow(image1)
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# plt.show()
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