初衷是为了求裂缝的最大宽度
直接上代码
import random
import cv2
import math
import numpy as np
from numpy.ma import cos, sin
import matplotlib.pyplot as plt
def max_circle(f):
img = cv2.imread(f, cv2.IMREAD_COLOR)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# _, img_gray = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
contous, hierarchy = cv2.findContours(img_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
"""
第二个参数表示轮廓的检索模式，有四种（本文介绍的都是新的cv2接口）：
cv2.RETR_EXTERNAL表示只检测外轮廓
cv2.RETR_LIST检测的轮廓不建立等级关系
cv2.RETR_CCOMP建立两个等级的轮廓，上面的一层为外边界，里面的一层为内孔的边界信息。如果内孔内还有一个连通物体，这个物体的边界也在顶层。
cv2.RETR_TREE建立一个等级树结构的轮廓。
第三个参数method为轮廓的近似办法
cv2.CHAIN_APPROX_NONE存储所有的轮廓点，相邻的两个点的像素位置差不超过1，即max（abs（x1-x2），abs（y2-y1））==1
cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素，只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息
cv2.CHAIN_APPROX_TC89_L1，CV_CHAIN_APPROX_TC89_KCOS使用teh-Chinl chain 近似算法
"""
for c in contous:
left_x = min(c[:, 0, 0])
right_x = max(c[:, 0, 0])
down_y = max(c[:, 0, 1])
up_y = min(c[:, 0, 1])
upper_r = min(right_x - left_x, down_y - up_y) / 2
# 定义相切二分精度
precision = math.sqrt((right_x - left_x) ** 2 + (down_y - up_y) ** 2) / (2 ** 13)
# 构造包含轮廓的矩形的所有像素点
Nx = 2 ** 8
Ny = 2 ** 8
pixel_X = np.linspace(left_x, right_x, Nx)
pixel_Y = np.linspace(up_y, down_y, Ny)
# [pixel_X, pixel_Y] = ndgrid(pixel_X, pixel_Y);
# pixel_X = reshape(pixel_X, numel(pixel_X), 1);
# pixel_Y = reshape(pixel_Y, numel(pixel_Y), 1);
xx, yy = np.meshgrid(pixel_X, pixel_Y)
# % 筛选出轮廓内所有像素点
in_list = []
for c in contous:
for i in range(pixel_X.shape[0]):
for j in range(pixel_X.shape[0]):
if cv2.pointPolygonTest(c, (xx[i][j], yy[i][j]), False) > 0:
in_list.append((xx[i][j], yy[i][j]))
in_point = np.array(in_list)
pixel_X = in_point[:, 0]
pixel_Y = in_point[:, 1]
# 随机搜索百分之一像素提高内切圆半径下限
N = len(in_point)
rand_index = random.sample(range(N), N // 100)
rand_index.sort()
radius = 0
big_r = upper_r
center = None
for id in rand_index:
tr = iterated_optimal_incircle_radius_get(c, in_point[id][0], in_point[id][1], radius, big_r, precision)
if tr > radius:
radius = tr
center = (in_point[id][0], in_point[id][1]) # 只有半径变大才允许位置变更，否则保持之前位置不变
# 循环搜索剩余像素对应内切圆半径
loops_index = [i for i in range(N) if i not in rand_index]
for id in loops_index:
tr = iterated_optimal_incircle_radius_get(c, in_point[id][0], in_point[id][1], radius, big_r, precision)
if tr > radius:
radius = tr
center = (in_point[id][0], in_point[id][1]) # 只有半径变大才允许位置变更，否则保持之前位置不变
# 效果测试
plot_x = np.linspace(0, 2 * math.pi, 100)
circle_X = center[0] + radius * cos(plot_x)
circle_Y = center[1] + radius * sin(plot_x)
print(radius * 2)
plt.figure()
plt.imshow(img_gray)
plt.plot(circle_X, circle_Y)
plt.show()
def iterated_optimal_incircle_radius_get(contous, pixelx, pixely, small_r, big_r, precision):
radius = small_r
L = np.linspace(0, 2 * math.pi, 360) # 确定圆散点剖分数360, 720
circle_X = pixelx + radius * cos(L)
circle_Y = pixely + radius * sin(L)
for i in range(len(circle_Y)):
if cv2.pointPolygonTest(contous, (circle_X[i], circle_Y[i]), False) < 0: # 如果圆散集有在轮廓之外的点
return 0
while big_r - small_r >= precision: # 二分法寻找最大半径
half_r = (small_r + big_r) / 2
circle_X = pixelx + half_r * cos(L)
circle_Y = pixely + half_r * sin(L)
if_out = False
for i in range(len(circle_Y)):
if cv2.pointPolygonTest(contous, (circle_X[i], circle_Y[i]), False) < 0: # 如果圆散集有在轮廓之外的点
big_r = half_r
if_out = True
if not if_out:
small_r = half_r
radius = small_r
return radius
if __name__ == '__main__':
max_circle('thresh_crack.png')