将图像切片到两个数组点上

Question

我有困难切片图像使用坐标作为起点和终点。到目前为止，我有以下代码

hdulist = fits.open(filename)
hdr= hdulist[0].header 

import numpy as np
import scipy
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure, show
from astropy.io import fits
from scipy import interpolate

data=hdulist[0].data

#Make a line with "num" points
D, B = input('Enter the coordinates of the starting point:').split(',')
E, C = input("Enter the coordinates of the stopping point: ").split(',')
x0= float(D)
x1= float(E)
y0= float(B)
y1= float(C)

x = np.arange(data.shape[1])
y = np.arange(data.shape[0])
#length = int((np.hypot(x1-x0, y1-y0))) (can be used instead of num_points)
num_points = 1000
xvalues = np.linspace(x0, x1, num_points)
yvalues = np.linspace(y0, y1, num_points)
f = scipy.interpolate.interp2d(x, y, data) #default is linear


# Extract the values along the line
profile = f(xvalues, yvalues) #this gives me a 2D array, I think it needs to be 1D
#c = profile.flatten()
print(profile.shape)

“轮廓”不是线性的而是立方的。有没有一种方法可以使轮廓线性化，这样我就可以在起点和终点之间的点上切割图像？我只想让'个人资料'1D而不是2D

我想把它画成这样：

^{pr2}$

我的代码不工作，因为我得到的绘图不是显示一条直线的一段，而是一个立方体的一部分。在

Answer 1

从interp2D的文档中可以看出，网格是由插值构建的。凭直觉，我觉得你需要网格的diagonal。快速修改代码：

import numpy as np
import scipy
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure, show
from scipy import interpolate

X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
data = np.sin(R)

x0 = 2
x1 = 23
y0 = 1
y1 = 36

rx0 = 4
rx1 = 2
ry0 = 7
ry1 = 32

x = np.arange(data.shape[1])
y = np.arange(data.shape[0])
num_points = 1000
xvalues = np.linspace(x0, x1, num_points)
yvalues = np.linspace(y0, y1, num_points)
f = scipy.interpolate.interp2d(x, y, data) #default is linear


# Extract the values along the line
profile = f(xvalues, yvalues)

xvalues2 = np.linspace(rx0, rx1, num_points)
yvalues2 = np.linspace(ry0, ry1, num_points)
profile2 = f(xvalues2, yvalues2)

plt.subplot(121)
plt.imshow(data.T, origin="lower", interpolation="nearest")
plt.scatter([x0, x1], [y0, y1])
plt.plot([x0, x1], [y0, y1])
plt.scatter([rx0, rx1], [ry0, ry1], c="r")
plt.plot([rx0, rx1], [ry0, ry1], c="r")
# plt.show()

diag = np.diag(profile)
diag2 = np.diag(profile2)
plt.subplot(122)
plt.plot(np.arange(diag.shape[0]), diag)
plt.plot(np.arange(diag2.shape[0]), diag2, c="r")
plt.show()

这将返回以下结果：

注意：我没有考虑二维图中的坐标（这就是为什么两条线看起来一样大）。在