Image matplotlib:jpg图像特效/转换，就像它在一本打开的书的一页上一样

我希望出现一个图形（和某些文本），就好像它们打印在一本打开的书上一样。是否可以通过编程或在matplotlib中转换jpg图像来产生这样的效果

您可以使用一个背景轴和一本开源书来做类似的事情

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax2 = fig.add_axes([0.2, 0.3, 0.25, 0.3]) 

#Plot page from a book
im = plt.imread("./book_page.jpg")
implot = ax1.imshow(im, origin='lower')

# Plot a graph and set background to transparent
x = np.linspace(0,4.*np.pi,40)
y = np.sin(x)
ax2.plot(x,y,'-ro',alpha=0.5)
ax2.set_ylim([-1.1,1.1])
ax2.patch.set_alpha(0.0)

from matplotlib import rc
rc('text', usetex=True)
margin = im.shape[0]*0.075
ytext = im.shape[1]/2.+10
ax1.text(margin, ytext, "The following text is an example")
ax1.text(margin, 90, "Figure 1. Showing a sine function")

plt.show()

看起来像这样

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax2 = fig.add_axes([0.2, 0.3, 0.25, 0.3]) 

#Plot page from a book
im = plt.imread("./book_page.jpg")
implot = ax1.imshow(im, origin='lower')

# Plot a graph and set background to transparent
x = np.linspace(0,4.*np.pi,40)
y = np.sin(x)
ax2.plot(x,y,'-ro',alpha=0.5)
ax2.set_ylim([-1.1,1.1])
ax2.patch.set_alpha(0.0)

from matplotlib import rc
rc('text', usetex=True)
margin = im.shape[0]*0.075
ytext = im.shape[1]/2.+10
ax1.text(margin, ytext, "The following text is an example")
ax1.text(margin, 90, "Figure 1. Showing a sine function")

plt.show()

我用了下面的书

更新：添加了基于scikit图像扭曲的非仿射变换，但具有麦克斯韦分布。该解决方案将matplotlib线保存为图像，以便应用逐点变换。矢量图形的映射可能是可能的，但我认为这将更加复杂

import numpy as np
import matplotlib.pyplot as plt

def maxwellian_transform_image(image):

    from skimage.transform import PiecewiseAffineTransform, warp

    rows, cols = image.shape[0], image.shape[1]

    src_cols = np.linspace(0, cols, 20)
    src_rows = np.linspace(0, rows, 10)
    src_rows, src_cols = np.meshgrid(src_rows, src_cols)
    src = np.dstack([src_cols.flat, src_rows.flat])[0]

    # add maxwellian to row coordinates
    x = np.linspace(0, 3., src.shape[0])
    dst_rows = src[:, 1] + (np.sqrt(2/np.pi)*x**2 * np.exp(-x**2/2)) * 50
    dst_cols = src[:, 0]
    dst_rows *= 1.5
    dst_rows -= 1.0 * 50
    dst = np.vstack([dst_cols, dst_rows]).T

    tform = PiecewiseAffineTransform()
    tform.estimate(src, dst)

    out_rows = image.shape[0] - 1.5 * 50
    out_cols = cols
    out = warp(image, tform, output_shape=(out_rows, out_cols))

    return out

#Create the new figure
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])

#Plot page from a book
im = plt.imread("./book_page.jpg")
implot = ax.imshow(im, origin='lower')

# Plot and save graph as image, will need some manipulation of location
temp, at = plt.subplots()
margin = im.shape[0]*0.1
x = np.linspace(margin,im.shape[0]/2.,40)
y = im.shape[1]/3. + 0.1*im.shape[1]*np.sin(12.*np.pi*x/im.shape[0])
at.plot(x,y,'-ro',alpha=0.5)
temp.savefig("lineplot.png",transparent=True)

#Read in plot as an image and apply transform
plot = plt.imread("./lineplot.png")
out = maxwellian_transform_image(plot)
ax.imshow(out, extent=[0,im.shape[1],0,im.shape[0]])

plt.show()

这个数字现在看起来像

---

@Smith，谢谢你的回答，这更接近了，但我正在努力解决的是——曲线形状与图形。比方说，如果我将曲线页面定义为具有类似于麦克斯韦函数的函数形式，我想缩放/变换图像以跟随该曲线，因此，它看起来像是打印在一个开放的曲线页面上。例如，你所说的是仿射变换。我已经相应地更新了我的答案，虽然我不确定你问的是无关紧要的问题，但这正是我想要的。这有帮助。基于此，现在我可以构造我想要的变换。