说我们有一个像this one:的Delaunay三角剖分
从fillConvexPoly
的getVoronoiFacetList
产生
[里面有可以通过getTriangleList
获得的三角形。我想画Delaunay三角剖分像是由三角形组成的平滑渐变图像,如下所示:
如何在opencv中做这样的事情?
这是在Python / OpenCV中执行此操作的方法,但是它将比我之前介绍的Python / Wand版本慢,因为它必须循环并求解重心坐标的每个像素处的线性最小二乘方程。
import cv2
import numpy as np
# References:
# https://stackoverflow.com/questions/31442826/increasing-efficiency-of-barycentric-coordinate-calculation-in-python
# https://math.stackexchange.com/questions/81178/help-with-cramers-rule-and-barycentric-coordinates
# create black background image
result = np.zeros((500,500,3), dtype=np.uint8)
# Specify (x,y) triangle vertices
a = (250,100)
b = (100,400)
c = (400,400)
# Specify colors
red = (0,0,255)
green = (0,255,0)
blue = (255,0,0)
# Make array of vertices
# ax bx cx
# ay by cy
# 1 1 1
triArr = np.asarray([a[0],b[0],c[0], a[1],b[1],c[1], 1,1,1]).reshape((3, 3))
# Get bounding box of the triangle
xleft = min(a[0], b[0], c[0])
xright = max(a[0], b[0], c[0])
ytop = min(a[1], b[1], c[1])
ybottom = max(a[1], b[1], c[1])
# loop over each pixel, compute barycentric coordinates and interpolate vertex colors
for y in range(ytop, ybottom):
for x in range(xleft, xright):
# Store the current point as a matrix
p = np.array([[x], [y], [1]])
# Solve for least squares solution to get barycentric coordinates
(alpha, beta, gamma) = np.linalg.lstsq(triArr, p, rcond=-1)[0]
# The point is inside the triangle if all the following conditions are met; otherwise outside the triangle
if alpha > 0 and beta > 0 and gamma > 0:
# do barycentric interpolation on colors
color = (red*alpha + green*beta + blue*gamma)
result[y,x] = color
# show results
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()
# save results
cv2.imwrite('barycentric_triange.png', result)
结果:
在OpenCV中,我不认为有任何现成的功能可以做到这一点。您将必须遍历图像中的每个像素并计算重心(区域)插值。例如,请参见https://codeplea.com/triangular-interpolation
但是,在Python / Wand(基于ImageMagick)中,您可以执行以下操作:
import numpy as np
from wand.image import Image
from wand.color import Color
from wand.drawing import Drawing
from wand.display import display
# define vertices of triangle
p1 = (250, 100)
p2 = (100, 400)
p3 = (400, 400)
# define barycentric colors and vertices
colors = {
Color('RED'): p1,
Color('GREEN1'): p2,
Color('BLUE'): p3
}
# create black image
black = np.zeros([500, 500, 3], dtype=np.uint8)
with Image.from_array(black) as img:
with img.clone() as mask:
with Drawing() as draw:
points = [p1, p2, p3]
draw.fill_color = Color('white')
draw.polygon(points)
draw.draw(mask)
img.sparse_color('barycentric', colors)
img.composite_channel('all_channels', mask, 'multiply', 0, 0)
img.format = 'png'
img.save(filename='barycentric_image.png')
display(img)
结果: