Question

我正在尝试使用从平板扫描仪获得的图像来生成“皱缩的”图像。

按照3.1节中的论文[Link]中所述的方法进行操作。我已经编写了代码来生成受干扰的网格，但是我不知道如何将源图像中的这些像素映射到该网格上以形成受干扰的图像。

这是生成扰动网格的代码。

import numpy as np
import matplotlib.pyplot as plt

mr = 88
mc = 68

xx = np.arange(mr-1, -1, -1)
yy = np.arange(0, mc, 1)
[Y, X] = np.meshgrid(xx, yy)
ms = np.transpose(np.asarray([X.flatten('F'), Y.flatten('F')]), (1,0))

perturbed_mesh = ms
nv = np.random.randint(20) - 1
for k in range(nv):
    #Choosing one vertex randomly
    vidx = np.random.randint(np.shape(ms)[0])
    vtex = ms[vidx, :]
    #Vector between all vertices and the selected one
    xv  = perturbed_mesh - vtex
    #Random movement 
    mv = (np.random.rand(1,2) - 0.5)*20
    hxv = np.zeros((np.shape(xv)[0], np.shape(xv)[1] +1) )
    hxv[:, :-1] = xv
    hmv = np.tile(np.append(mv, 0), (np.shape(xv)[0],1))
    d = np.cross(hxv, hmv)
    d = np.absolute(d[:, 2])
    d = d / (np.linalg.norm(mv, ord=2))
    wt = d

    curve_type = np.random.rand(1)
    if curve_type > 0.3:
        alpha = np.random.rand(1) * 50 + 50
        wt = alpha / (wt + alpha)
    else:
        alpha = np.random.rand(1) + 1
        wt = 1 - (wt / 100 )**alpha
    msmv = mv * np.expand_dims(wt, axis=1)
    perturbed_mesh = perturbed_mesh + msmv

plt.scatter(perturbed_mesh[:, 0], perturbed_mesh[:, 1], c=np.arange(0, mr*mc))
plt.show()

这是扰动的网格的样子：

这是论文中的屏幕快照，显示了合成图像的生成：

用于测试的示例源图像： https://i.stack.imgur.com/26KN4.jpg

我一直坚持将源图像像素映射到网格上。如果有人可以提供帮助，我将不胜感激。

Answer 1

（1）使用cv2.copyMakeBorder放大图像，以避免扭曲点超出原始图像尺寸的范围。

cv2.copyMakeBorder(...)
    copyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value]]) -> dst
    .   @brief Forms a border around an image.
    .
    .   The function copies the source image into the middle of the destination image. The areas to the
    .   left, to the right, above and below the copied source image will be filled with extrapolated
    .   pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but
    .   what other more complex functions, including your own, may do to simplify image boundary handling.

用法：

img = cv2.copyMakeBorder(img, dh, dh, dw, dw, borderType=cv2.BORDER_CONSTANT, value=(0,0,0))

设置dw=nw//2, dh=nh//2可能没问题，必要时进行调整。 nh, nw是源图像的高度和宽度。

（2）使用the method from the paper

创建扰动的网格

xs, ys = create_grid() # the result is like np.meshgrid

请注意确保类型和大小。

# xs = xs.reshape(nh, nw).astype(np.float32)
# nh, nw is the height and width of the coppied image

（3）使用cv2.remap重新映射：

cv2.remap(...)
    remap(src, map1, map2, interpolation[, dst[, borderMode[, borderValue]]]) -> dst
    .   @brief Applies a generic geometrical transformation to an image.
    .
    .   The function remap transforms the source image using the specified map:
    .   \f[\texttt{dst} (x,y) =  \texttt{src} (map_x(x,y),map_y(x,y))\f]

用法：

dst= cv2.remap(img, xs, ys, cv2.INTER_CUBIC)

这是演示结果：

（4）裁剪非零区域并在必要时调整大小：

如何使用变形的网格变形图像

1 个答案: