例如,如何在 opencv 加载的图像上应用
RBFInterpolator
?
我需要使用 numpy 的向量运算来应用插值(速度很快)
我需要通过定义图像点之间的插值对图像进行非仿射变换。
我该怎么做?
import cv2
import numpy as np
from scipy.interpolate import RBFInterpolator
# Load the image
image = cv2.imread('image.png')
width,height = image.shape[:2]
w=width
h=height
# Your source points and corresponding destination points
src_points = np.array([
[0, 0],
[w,h],
[w,0],
[0,h],
])
dst_points = np.array([
[0, 0],
[w,h],
[w,0],
[0,h],
])
# Create the RBF interpolator instance
rbfx = RBFInterpolator(src_points,dst_points[:,0],kernel="thin_plate_spline")
rbfy = RBFInterpolator(src_points,dst_points[:,1],kernel="thin_plate_spline")
# Create a meshgrid to interpolate over the entire image
img_grid = np.mgrid[0:width, 0:height]
grid_x, grid_y = img_grid
# flatten grid so it could be feed into interpolation
flatten=img_grid.reshape(2, -1).T
# Interpolate the displacement using the RBF interpolators
map_x = rbfx(flatten).reshape(width,height).astype(np.float32)
map_y = rbfy(flatten).reshape(width,height).astype(np.float32)
# Apply the remapping to the image using OpenCV
warped_image = cv2.remap(image, map_y, map_x, cv2.INTER_LINEAR)
# Save or display the result
cv2.imwrite('remap.png', warped_image)
上面的代码应该生成与输入图像相同的图像。
改变源点和目标点进行图像变换。
我相信有一种更快的方法来进行插值,但这就是我想出的