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scipy插值网格数据

imagemagick scipy numpy python

fmw42 · 技术社区 · 6 年前

我主要是一个python/numpy/scipy新手,但对图像处理和imagemagick很熟悉。我的最终目标是使用python/numpy/scipy将两个稀疏的数据网格插入到图像中,然后在imagemagick中用作X和Y位移映射,将一个网格扭曲到另一个网格中。我有点追随 https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html .

我有一个图像(SRC):

我最终想要扭曲到(dst),尽管图像和扭曲过程与这里提出的问题(网格相交值除外)无关:

我有两组X、Y控制点,每个图像中有一个控制点,如中所示 http://www.fmwconcepts.com/misc_tests/grid16_control_points2.txt

我已经计算了zx=xsrc xdst+127.5和zy=ysrc ydst+127.5作为dstx、dsty坐标的值,我想在整个129x129图像上插入这些值。(请注意,附加的127.5用于imagemagick使用——上面的位移图图像中的任何值将朝一个方向移动,下面的值将朝相反的方向移动)

下面的“我的代码”不显示任何错误消息,也不显示任何图像。我想我工作最多,除了PLT节目。但是,我希望有人能指出我做错了什么。

#!/bin/python3.7

"""

Use interpolation on a grid of x,y,z values, where z is either xdiff or ydiff for use as imagemagick 2D displacement maps

"""

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata as griddata
from PIL import Image

# test grid data is from https://imagemagick.org/Usage/distorts/#polynomial (zx and zy are xsrc-xdst and ysrc-ydst)

# python lists of x,y dst control points and zx=xsrc-xdiff, zy=ysrc-ydiff offsets to be interpolated over full image of size 129x129
xy = [[8.5,7.5], [20.5,5.5], [33.5,3.5], [48.5,1.5], [64.5,1.5], [80.5,1.5], [95.5,3.5], [109.5,5.5], [121.5,7.5], [5.5,20.5], [17.5,16.5], [31.5,14.5], [46.5,12.5], [64.5,11.5], [81.5,12.5], [97.5,15.5], [111.5,16.5], [123.5,19.5], [2.5,33.5], [14.5,31.5], [29.5,28.5], [45.5,26.5], [64.5,24.5], [83.5,26.5], [99.5,28.5], [113.5,31.5], [125.5,33.5], [1.5,48.5], [12.5,47.5], [26.5,45.5], [43.5,42.5], [64.5,40.5], [85.5,42.5], [103.5,45.5], [116.5,46.5], [127.5,48.5], [0.5,64.5], [11.5,64.5], [24.5,64.5], [41.5,64.5], [64.5,64.5], [87.5,64.5], [103.5,64.5], [117.5,64.5], [128.5,64.5], [1.5,80.5], [12.5,81.5], [25.5,83.5], [42.5,86.5], [64.5,87.5], [86.5,86.5], [103.5,83.5], [116.5,81.5], [127.5,80.5], [2.5,95.5], [14.5,97.5], [28.5,100.5], [45.5,103.5], [64.5,104.5], [83.5,102.5], [100.5,100.5], [114.5,97.5], [125.5,95.5], [5.5,109.5], [17.5,111.5], [30.5,114.5], [46.5,116.5], [64.5,117.5], [81.5,116.5], [97.5,114.5], [111.5,111.5], [123.5,109.5], [8.5,121.5], [19.5,123.5], [33.5,125.5], [48.5,127.5], [64.5,127.5], [80.5,127.5], [95.5,125.5], [109.5,123.5], [121.5,120.5]]
zx = [119.5, 123.5, 126.5, 127.5, 127.5, 127.5, 128.5, 130.5, 134.5, 122.5, 126.5, 128.5, 129.5, 127.5, 126.5, 126.5, 128.5, 132.5, 125.5, 129.5, 130.5, 130.5, 127.5, 124.5, 124.5, 126.5, 130.5, 126.5, 131.5, 133.5, 132.5, 127.5, 122.5, 120.5, 123.5, 128.5, 127.5, 132.5, 135.5, 134.5, 127.5, 120.5, 120.5, 122.5, 127.5, 126.5, 131.5, 134.5, 133.5, 127.5, 121.5, 120.5, 123.5, 128.5, 125.5, 129.5, 131.5, 130.5, 127.5, 124.5, 123.5, 125.5, 130.5, 122.5, 126.5, 129.5, 129.5, 127.5, 126.5, 126.5, 128.5, 132.5, 119.5, 124.5, 126.5, 127.5, 127.5, 127.5, 128.5, 130.5, 134.5]
zy = [120.5, 122.5, 124.5, 126.5, 126.5, 126.5, 124.5, 122.5, 120.5, 123.5, 127.5, 129.5, 131.5, 132.5, 131.5, 128.5, 127.5, 124.5, 126.5, 128.5, 131.5, 133.5, 135.5, 133.5, 131.5, 128.5, 126.5, 127.5, 128.5, 130.5, 133.5, 135.5, 133.5, 130.5, 129.5, 127.5, 127.5, 127.5, 127.5, 127.5, 127.5, 127.5, 127.5, 127.5, 127.5, 127.5, 126.5, 124.5, 121.5, 120.5, 121.5, 124.5, 126.5, 127.5, 128.5, 126.5, 123.5, 120.5, 119.5, 121.5, 123.5, 126.5, 128.5, 130.5, 128.5, 125.5, 123.5, 122.5, 123.5, 125.5, 128.5, 130.5, 134.5, 132.5, 130.5, 128.5, 128.5, 128.5, 130.5, 132.5, 135.5]

# convert python lists to numpy arrays
axy = np.asarray(xy, dtype=float)
azx = np.asarray(zx, dtype=float)
azy = np.asarray(zy, dtype=float)

# define integer grid onto which to interpolate
grid_x, grid_y = np.mgrid[0:129, 0:129]

# do interpolations
xdisplace = griddata(axy, azx, (grid_x, grid_y), method='cubic')
ydisplace = griddata(axy, azy, (grid_x, grid_y), method='cubic')

# replace all NAN with zero
xxdisplace = np.nan_to_num(xdisplace, copy=True)
yydisplace = np.nan_to_num(ydisplace, copy=True)

# Note: 3rd order polynomial interpolation can go wild and produce NAN values outside the normal range
# replace all NAN with zero
xxdisplace = np.nan_to_num(xdisplace, copy=True)
yydisplace = np.nan_to_num(ydisplace, copy=True)

# replace zero with 127.5
xxdisplace[xxdisplace == 0] = 127.5
yydisplace[yydisplace == 0] = 127.5

# display a subsection to examine interpolation
xxdsub = xxdisplace[0:5, 0:5]
print(xxdsub)

# display another subsection to examine interpolation
xxdsub = xxdisplace[60:65, 60:65]
print(xxdsub)

# save images
xdimg = Image.fromarray(xxdisplace.astype('uint8'))
ydimg = Image.fromarray(yydisplace.astype('uint8'))
xdimg.save('xdimg.png')
ydimg.save('ydimg.png')

# display images
plt.figure()
plt.imshow(xxdisplace.astype('uint8'))
plt.imshow(yydisplace.astype('uint8'))
plt.show()

我修改了我的原始代码来添加代码,用零替换NaN,然后用127.5替换零。但我没有收到错误信息,也没有显示图像。(在其他脚本中,plt.imshow()工作正常)。

第一个xdsub现在只包含127.5的值(从nan开始)

[[127.5 127.5 127.5 127.5 127.5]
 [127.5 127.5 127.5 127.5 127.5]
 [127.5 127.5 127.5 127.5 127.5]
 [127.5 127.5 127.5 127.5 127.5]
 [127.5 127.5 127.5 127.5 127.5]]

第二个xdsub数据显示了合理的插值数据。

【129.129950391 129.13466567 129.14438372 129.15604101 129.16453265】【128.77872804 128.78027629 128.78368175 128.79182482 128.7988161】 [128.41763609 128.4225153 128.4226885 128.42491114 128.43014312] 【128.0492988 128.05418802 128.05800038 128.05793775 128.05925686】【127.68687259 127.68699624 127.68719118 127.68750429 127.68722127】】

关于我的代码的任何建议都将是最受欢迎的。

我使用的是python 3.7.2、numpy 1.16.1、scipy 1.2.1和matplotlib 3.0.2(所有这些都是通过MacPorts安装在Mac OSX Sierra上的)

0 回复 | 直到 6 年前

bollence 6 年前

默认 griddata() 的参数 填充值 设置为南 . 大量负数的原因是转换的结果南到 int . 您可以尝试删除 .astype(int) .