如果在没有找到轮廓或轮廓的点集数很小无法拟合椭圆或在RANSAC中寻找最优解时会死循环中，优化后的代码
import cv2
import os
import numpy as np
import matplotlib.pyplot as plt
import math
from Ransac_Process import RANSAC
def cul_area(x_mask, y_mask, r_circle, mask):
mask_label = mask.copy()
num_area = 0
for xm in range(x_mask+r_circle-10, x_mask+r_circle+10):
for ym in range(y_mask+r_circle-10, y_mask+r_circle+10):
# print(mask[ym, xm])
if (pow((xm-x_mask), 2) + pow((ym-y_mask), 2) - pow(r_circle, 2)) == 0 and mask[ym, xm][0] == 255:
num_area += 1
mask_label[ym, xm] = (0, 0, 255)
cv2.imwrite('./test2/mask_label.png', mask_label)
print(num_area)
return num_area
def mainFigure(img, point0):
point_center = []
# cv2.imwrite('./test2/img_source.png', img)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# cv2.imwrite('./test2/img_hsv.png', img_hsv)
w, h = img.shape[1], img.shape[0]
w_hsv, h_hsv = img_hsv.shape[1], img_hsv.shape[0]
for i_hsv in range(w_hsv):
for j_hsv in range(h_hsv):
if img_hsv[j_hsv, i_hsv][0] < 200 and img_hsv[j_hsv, i_hsv][1] < 130 and img_hsv[j_hsv, i_hsv][2] > 120:
# if hsv[j_hsv, i_hsv][0] < 100 and hsv[j_hsv, i_hsv][1] < 200 and hsv[j_hsv, i_hsv][2] > 80:
img_hsv[j_hsv, i_hsv] = 255, 255, 255
else:
img_hsv[j_hsv, i_hsv] = 0, 0, 0
# cv2.imwrite('./test2/img_hsvhb.png', img_hsv)
# cv2.imshow("hsv", img_hsv)
# cv2.waitKey()
# 灰度化处理图像
grayImage = cv2.cvtColor(img_hsv, cv2.COLOR_BGR2GRAY)
# mask = np.zeros((grayImage.shape[0], grayImage.shape[1]), np.uint8)
# mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# cv2.imwrite('./mask.png', mask)
# 尝试寻找轮廓
contours, hierarchy = cv2.findContours(grayImage, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# 合并轮廓
if len(contours) > 1:
contours_merge = np.vstack([contours[0], contours[1]])
for i in range(2, len(contours)):
contours_merge = np.vstack([contours_merge, contours[i]])
# cv2.drawContours(img, contours_merge, -1, (0, 255, 255), 1)
# # cv2.imwrite('./test2/img_res.png', img)
# cv2.imshow("contours_merge", img)
# cv2.waitKey()
elif len(contours) == 1:
contours_merge = contours[0]
else:
print("No contours!")
return 0,0
# RANSAC拟合
points_data = np.reshape(contours_merge, (-1, 2)) # ellipse edge points set
# print("points_data", len(points_data))
# 2.Ransac fit ellipse param
# Ransac = RANSAC(data=points_data, threshold=0.1, P=.99, S=.5, N=10)
Ransac = RANSAC(data=points_data, threshold=0.5, P=.98, S=.6, N=10)
ellipse_values = Ransac.execute_ransac()
# 检测到轮廓里数量太少（<5）则无法拟合椭圆
if ellipse_values is None:
return 0,0
(X, Y), (LAxis, SAxis), Angle = ellipse_values
# print( (X, Y), (LAxis, SAxis))
# 拟合圆
cv2.ellipse(img, ((X, Y), (LAxis, SAxis), Angle), (0, 0, 255), 1, cv2.LINE_AA) # 画圆
cv2.circle(img, (int(X), int(Y)), 3, (0, 0, 255), -1) # 画圆心
point_center.append(int(X))
point_center.append(int(Y))
# 直接拟合
# rrt = cv2.fitEllipse(contours_merge) # x, y）代表椭圆中心点的位置（a, b）代表长短轴长度，应注意a、b为长短轴的直径，而非半径,angle 代表了中心旋转的角度
# # print("rrt", rrt)
# cv2.ellipse(img, rrt, (255, 0, 0), 1, cv2.LINE_AA) # 画圆
# x, y = rrt[0]
# cv2.circle(img, (int(x), int(y)), 3, (255, 0, 0), -1) # 画圆心
# point_center.append(int(x))
# point_center.append(int(y))
# # print("no",(x,y))
#
# # 两种方法坐标的距离
# dis_two_method = math.sqrt(math.pow(X - x, 2) + math.pow(Y - y, 2))
# print("两种方法坐标的距离", dis_two_method)
cv2.imshow("fit circle", img)
cv2.waitKey(3)
# cv2.imwrite("./test2/fitcircle.png", img)
return point_center[0], point_center[1]
if __name__ == "__main__":
# 测试所有图片
mainFolder = "./Images/save_img"
myFolders = os.listdir(mainFolder)
print("myFolders", myFolders)
myImageList = []
path = ''
for folder in myFolders:
path = mainFolder + '/' + folder
myImageList = os.listdir(path)
# print(myImageList)
# print(f'Tatal images deteted is {len(myImageList)}')
i = 0
for imagN in myImageList:
curImg = cv2.imread(f'{path}/{imagN}')
# images.append(curImg)
print(f'{path}/{imagN}')
point0 = [0, 0]
cir_x, cir_y = mainFigure(curImg, point0)
print("This is ", i, "圆心为",(cir_x, cir_y))
i += 1
# # 测试2
# for i in range(1,6):
# imageName = "s"
# imageName += str(i)
# path = './Images/danHoles/' + imageName + '.png'
# print(path)
# img = cv2.imread(path)
# point0 = [0, 0]
# cir_x, cir_y = mainFigure(img, point0)
# # 测试1
# img = cv2.imread('./Images/danHoles/s6.png')
# point0 = [0, 0]
# cir_x, cir_y = mainFigure(img, point0)
Ransac_Process.py
import cv2
import math
import random
import numpy as np
from numpy.linalg import inv, svd, det
import time
class RANSAC:
def __init__(self, data, threshold, P, S, N):
self.point_data = data # 椭圆轮廓点集
self.length = len(self.point_data) # 椭圆轮廓点集长度
self.error_threshold = threshold # 模型评估误差容忍阀值
self.N = N # 随机采样数
self.S = S # 设定的内点比例
self.P = P # 采得N点去计算的正确模型概率
self.max_inliers = self.length * self.S # 设定最大内点阀值
self.items = 8
self.count = 0 # 内点计数器
self.best_model = ((0, 0), (1e-6, 1e-6), 0) # 椭圆模型存储器
def random_sampling(self, n):
# 这个部分有修改的空间，这样循环次数太多了，可以看看别人改进的ransac拟合椭圆的论文
"""随机取n个数据点"""
all_point = self.point_data
if len(all_point) >= n:
select_point = np.asarray(random.sample(list(all_point), n))
return select_point
else:
print("轮廓点数太少，数量为", len(all_point))
return None
def Geometric2Conic(self, ellipse):
# 这个部分参考了GitHub中的一位大佬的，但是时间太久，忘记哪个人的了
"""计算椭圆方程系数"""
# Ax ^ 2 + Bxy + Cy ^ 2 + Dx + Ey + F
(x0, y0), (bb, aa), phi_b_deg = ellipse
a, b = aa / 2, bb / 2 # Semimajor and semiminor axes
phi_b_rad = phi_b_deg * np.pi / 180.0 # Convert phi_b from deg to rad
ax, ay = -np.sin(phi_b_rad), np.cos(phi_b_rad) # Major axis unit vector
# Useful intermediates
a2 = a * a
b2 = b * b
# Conic parameters
if a2 > 0 and b2 > 0:
A = ax * ax / a2 + ay * ay / b2
B = 2 * ax * ay / a2 - 2 * ax * ay / b2
C = ay * ay / a2 + ax * ax / b2
D = (-2 * ax * ay * y0 - 2 * ax * ax * x0) / a2 + (2 * ax * ay * y0 - 2 * ay * ay * x0) / b2
E = (-2 * ax * ay * x0 - 2 * ay * ay * y0) / a2 + (2 * ax * ay * x0 - 2 * ax * ax * y0) / b2
F = (2 * ax * ay * x0 * y0 + ax * ax * x0 * x0 + ay * ay * y0 * y0) / a2 + \
(-2 * ax * ay * x0 * y0 + ay * ay * x0 * x0 + ax * ax * y0 * y0) / b2 - 1
else:
# Tiny dummy circle - response to a2 or b2 == 0 overflow warnings
A, B, C, D, E, F = (1, 0, 1, 0, 0, -1e-6)
# Compose conic parameter array
conic = np.array((A, B, C, D, E, F))
return conic
def eval_model(self, ellipse):
# 这个地方也有很大修改空间，判断是否内点的条件在很多改进的ransac论文中有说明，可以多看点论文
"""评估椭圆模型，统计内点个数"""
# this an ellipse ?
a, b, c, d, e, f = self.Geometric2Conic(ellipse)
E = 4 * a * c - b * b
if E <= 0:
# print('this is not an ellipse')
return 0, 0
# which long axis ?
(x, y), (LAxis, SAxis), Angle = ellipse
LAxis, SAxis = LAxis / 2, SAxis / 2
if SAxis > LAxis:
temp = SAxis
SAxis = LAxis
LAxis = temp
# calculate focus
Axis = math.sqrt(LAxis * LAxis - SAxis * SAxis)
f1_x = x - Axis * math.cos(Angle * math.pi / 180)
f1_y = y - Axis * math.sin(Angle * math.pi / 180)
f2_x = x + Axis * math.cos(Angle * math.pi / 180)
f2_y = y + Axis * math.sin(Angle * math.pi / 180)
# identify inliers points
f1, f2 = np.array([f1_x, f1_y]), np.array([f2_x, f2_y])
f1_distance = np.square(self.point_data - f1)
f2_distance = np.square(self.point_data - f2)
all_distance = np.sqrt(f1_distance[:, 0] + f1_distance[:, 1]) + np.sqrt(f2_distance[:, 0] + f2_distance[:, 1])
Z = np.abs(2 * LAxis - all_distance)
delta = math.sqrt(np.sum((Z - np.mean(Z)) ** 2) / len(Z))
# Update inliers set
inliers = np.nonzero(Z < 0.8 * delta)[0]
inlier_pnts = self.point_data[inliers]
return len(inlier_pnts), inlier_pnts
def execute_ransac(self):
Time_start = time.time()
while math.ceil(self.items):
# print(self.max_inliers)
# 1.select N points at random
select_points = self.random_sampling(self.N)
# 当从轮廓中采集的点不够拟合椭圆时跳出循环
if select_points is None or len(select_points) < 5:
# print(select_points)
return None
else:
# 2.fitting N ellipse points
ellipse = cv2.fitEllipse(select_points)
# 3.assess model and calculate inliers points
inliers_count, inliers_set = self.eval_model(ellipse)
# 4.number of new inliers points more than number of old inliers points ?
if inliers_count > self.count:
if len(inliers_set) > 4:
ellipse_ = cv2.fitEllipse(inliers_set) # fitting ellipse for inliers points
self.count = inliers_count # Update inliers set
self.best_model = ellipse_ # Update best ellipse
# print("self.count", self.count)
# 5.number of inliers points reach the expected value
if self.count > self.max_inliers:
# print('the number of inliers: ', self.count)
break
# Update items
# print(math.log(1 - pow(inliers_count / self.length, self.N)))
if math.log(1 - pow(inliers_count / self.length, self.N)) != 0:
self.items = math.log(1 - self.P) / math.log(1 - pow(inliers_count / self.length, self.N))
Time_end = time.time()
# print(Time_end - Time_start )
if Time_end - Time_start >= 4:
# print("time is too long")
break
return self.best_model
if __name__ == '__main__':
# 1.find ellipse edge line
contours, hierarchy = cv2.findContours(grayImage, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
# 2.Ransac fit ellipse param
points_data = np.reshape(contours, (-1, 2)) # ellipse edge points set
Ransac = RANSAC(data=points_data, threshold=0.5, P=.99, S=.618, N=10)
(X, Y), (LAxis, SAxis), Angle = Ransac.execute_ransac()
THE END
喜欢就支持一下吧
