OpenCV:适合检测到的边缘

Question

我通过使用canny边缘检测来检测水波的边缘.但是,我想为这条边拟合一条曲线.这可能在OpenCV中吗？

这是边缘检测前的图像:

这是边缘检测操作的结果:

代码是从OpenCV教程中的示例复制而来的:

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('BW.JPG',0)
edges = cv2.Canny(img,100,200)

plt.plot(1),plt.imshow(edges,cmap = 'gray')
plt.title('WAVE')
plt.show()

Answer 1

波非常简单,因此我们将多项式曲线拟合到由输出定义的主边cv2.首先,我们要获得积分的初级边缘.让我们假设你的原点就像它在左上角的图像上一样.看一下原始图像,我认为如果我们只取y一个范围内最大的点(750,1500),我们就会对我们的兴趣点有一个很好的近似.

import cv2
import numpy as np
from matplotlib import pyplot as plt
from numba import jit

# Show plot
img = cv2.imread('wave.jpg',0)
edges = cv2.Canny(img,100,200)

# http://stackoverflow.com/a/29799815/1698058
# Get index of matching value.
@jit(nopython=True)
def find_first(item, vec):
    """return the index of the first occurence of item in vec"""
    for i in range(len(vec)):
        if item == vec[i]:
            return i
    return -1

bounds = [750, 1500]
# Now the points we want are the lowest-index 255 in each row
window = edges[bounds[1]:bounds[0]:-1].transpose()

xy = []
for i in range(len(window)):
    col = window[i]
    j = find_first(255, col)
    if j != -1:
        xy.extend((i, j))
# Reshape into [[x1, y1],...]
data = np.array(xy).reshape((-1, 2))
# Translate points back to original positions.
data[:, 1] = bounds[1] - data[:, 1]

如果我们绘制这些点,我们可以看到它们与我们的目标非常接近.

plt.figure(1, figsize=(8, 16))
ax1 = plt.subplot(211)
ax1.imshow(edges,cmap = 'gray')
ax2 = plt.subplot(212)
ax2.axis([0, edges.shape[1], edges.shape[0], 0])
ax2.plot(data[:,1])
plt.show()

现在我们已经得到了一组坐标对,我们可以用它numpy.polyfit来生成最佳拟合多项式的系数,并numpy.poly1d从这些系数中生成函数.

xdata = data[:,0]
ydata = data[:,1]

z = np.polyfit(xdata, ydata, 5)
f = np.poly1d(z)

然后绘制验证

t = np.arange(0, edges.shape[1], 1)
plt.figure(2, figsize=(8, 16))
ax1 = plt.subplot(211)
ax1.imshow(edges,cmap = 'gray')
ax2 = plt.subplot(212)
ax2.axis([0, edges.shape[1], edges.shape[0], 0])
ax2.plot(t, f(t))
plt.show()