如何使用变形的网格变形图像 [英] How to warp an image using deformed mesh

问题描述

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.

I'm trying to generate "crumpled" images using images obtained from a flatbed bed scanner.

按照3.1节中的文件 [链接] 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.

By following the method described in the paper[Link] in the section 3.1. I've written the code generate the perturbed mesh but I don't know how to map this pixels from source image onto this mesh to form the perturbed image.

这是生成扰动网格的代码.

This is the code to generate the perturbed mesh.

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:

This is how the perturbed mesh looks like:

这是论文中显示合成图像生成的屏幕截图:

This is the screenshot from the paper illustrating the synthetic image generation:

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

Sample source image for testing: 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.

I'm stuck with mapping the source image pixels onto the mesh. I'll be grateful if someone can help.

推荐答案

(1)使用cv2.copyMakeBorder放大图像，以避免扭曲点超出原始图像大小的范围.

(1) Use cv2.copyMakeBorder to enlarge the image, to avoid the warpped points going out of range of the original image size.

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.

Set dw=nw//2, dh=nh//2 maybe ok, adjust if necessary. The nh, nw is the height and width of the source image.

(2)使用本文中的方法创建扰动的网格

(2) Create perturbed mesh grid using the method from the paper

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

请注意确保类型和大小.

Notice make sure the type and the size.

# 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:

(4) Crop the nonzero region and resize if necessary:

相关:

1. 从c ++转换opencv重映射代码到python

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