[英]How to warp an image using deformed mesh
我正在尝试使用从平板扫描仪获得的图像生成“皱巴巴”的图像。
按照3.1节中论文[链接]中描述的方法。 我已经编写了生成扰动网格的代码,但我不知道如何将源图像中的 map 这个像素添加到该网格上以形成扰动图像。
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()
用于测试的示例源图像: https://i.stack.imgur.com/26KN4.jpg
我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。
(1) 使用cv2.copyMakeBorder
放大图像,避免扭曲点超出原始图像尺寸范围。
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.
用途:
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,必要时调整。 nh, nw
是源图像的高度和宽度。
(2) 使用论文中的方法创建扰动网格
xs, ys = create_grid() # the result is like np.meshgrid
注意确定类型和尺寸。
# 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)
这是一个演示结果:
(4) 裁剪非零区域并在必要时调整大小:
有关的:
我要为其他读者留下一个最低限度的工作演示。
目前,试图找出在哪种情况下perturbed_mesh
变为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)
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