如何使用變形網格扭曲圖像

Question

我正在嘗試使用從平板掃描儀獲得的圖像生成“皺巴巴”的圖像。

按照3.1節中論文[鏈接]中描述的方法。 我已經編寫了生成擾動網格的代碼，但我不知道如何將源圖像中的 map 這個像素添加到該網格上以形成擾動圖像。

這是生成擾動網格的代碼。

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()

這是擾動網格的樣子：

這是說明合成圖像生成的論文的屏幕截圖：

用於測試的示例源圖像： https://i.stack.imgur.com/26KN4.jpg

我堅持將源圖像像素映射到網格上。 如果有人可以提供幫助，我將不勝感激。

Answer 1

(1) 使用cv2.copyMakeBorder放大圖像，避免扭曲點超出原始圖像尺寸范圍。

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.

用途：

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

設置dw=nw//2, dh=nh//2可能沒問題，必要時調整。 nh, nw是源圖像的高度和寬度。

(2) 使用論文中的方法創建擾動網格

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

注意確定類型和尺寸。

# 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)

這是一個演示結果：

(4) 裁剪非零區域並在必要時調整大小：

---

有關的：

1. 將opencv重映射代碼從c++轉換為python

2. 分割掃描文檔中的文本行

Answer 2

我要為其他讀者留下一個最低限度的工作演示。

目前，試圖找出在哪種情況下perturbed_mesh變為None 。

def deform(img, perturbed_mesh):
    h,w = img.shape[:2]

    perturbed_mesh_x = perturbed_mesh[:,0]
    perturbed_mesh_y = perturbed_mesh[:,1]
    
    perturbed_mesh_x =perturbed_mesh_x.reshape((h,w))
    perturbed_mesh_y =perturbed_mesh_y.reshape((h,w))

    remapped = cv2.remap(img, perturbed_mesh_x, perturbed_mesh_y, cv2.INTER_LINEAR) 

    return remapped

def get_perturbed_mesh(img):
    """
        Author: papabiceps
        https://stackoverflow.com/q/53907633
    """

    # mesh row & col
    mr,mc = img.shape[:2]

    xx = np.arange(0, mr, 1)
    yy = np.arange(mc-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

        perturbed_mesh = perturbed_mesh.astype(np.float32)

        if perturbed_mesh is not None:
            result = deform(img, perturbed_mesh)

            flipped = cv2.flip(result,1)

            cv2.imshow("before",result)
            cv2.imshow("after",flipped)
            cv2.waitKey(0)
 

def main():
    img = cv2.imread("./sample.png")

    get_perturbed_mesh(img)