检测这些圆，先找轮廓后通过轮廓点拟合椭圆
import cv2
import numpy as np
import matplotlib.pyplot as plt
import math
from Ransac_Process import RANSAC
def lj_img(img):
wlj, hlj = img.shape[1], img.shape[0]
lj_dis = 7 # 连接白色区域的判定距离
for ilj in range(wlj):
for jlj in range(hlj):
if img[jlj, ilj] == 255: # 判断上下左右是否存在白色区域并连通
for im in range(1, lj_dis):
for jm in range(1, lj_dis):
if ilj - im >= 0 and jlj - jm >= 0 and img[jlj - jm, ilj - im] == 255:
cv2.line(img, (jlj, ilj), (jlj - jm, ilj - im), (255, 255, 255), thickness=1)
if ilj + im < wlj and jlj + jm < hlj and img[jlj + jm, ilj + im] == 255:
cv2.line(img, (jlj, ilj), (jlj + jm, ilj + im), (255, 255, 255), thickness=1)
return img
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):
# params = cv2.SimpleBlobDetector_Params() # 黑色斑点面积大小：1524--1581--1400--周围干扰面积: 1325--1695--1688--
# # Filter by Area. 设置斑点检测的参数
# params.filterByArea = True # 根据大小进行筛选
# params.minArea = 10e2
# params.maxArea = 10e4
# params.minDistBetweenBlobs = 40 # 设置两个斑点间的最小距离 10*7.5
# # params.filterByColor = True # 跟据颜色进行检测
# params.filterByConvexity = False # 根据凸性进行检测
# params.minThreshold = 30 # 二值化的起末阈值,只有灰度值大于当前阈值的值才会被当成特征值
# params.maxThreshold = 30 * 2.5 # 75
# params.filterByColor = True # 检测颜色限制，0黑色，255白色
# params.blobColor = 255
# params.filterByCircularity = True
# params.minCircularity = 0.3
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:
# print(contours)
# 去掉离图中心最远的圆
max_idex, dis_max = 0, 0
for c_i in range(len(contours)):
c = contours[c_i]
cx, cy, cw, ch = cv2.boundingRect(c)
dis = math.sqrt(pow((cx + cw / 2 - w / 2), 2) + pow((cy + ch / 2 - h / 2), 2))
if dis > dis_max:
dis_max = dis
max_idex = c_i
contours.pop(max_idex)
# print(contours)
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()
else:
contours_merge = contours[0]
else:
contours_merge = contours[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=0.5, P=.99, S=.5, N=20)
# Ransac = RANSAC(data=points_data, threshold=0.05, P=.99, S=.618, N=25)
(X, Y), (LAxis, SAxis), Angle = Ransac.execute_ransac()
# 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))
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))
cv2.imshow("fit circle", img)
cv2.waitKey()
# cv2.imwrite("./test2/fitcircle.png", img)
# # 尝试寻找轮廓
# contours, hierarchy = cv2.findContours(grayImage, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# # print('初次检测数量: ', len(contours))
# if len(contours) == 1:
# cv2.drawContours(mask, contours[0], -1, (255, 255, 255), 1)
# cv2.imwrite('./mask.png', mask)
# x, y, w, h = cv2.boundingRect(contours[0])
# cv2.circle(img, (int(x+w/2), int(y+h/2)), 1, (0, 0, 255), -1)
# cv2.rectangle(img, (x, y), (x + w + 1, y + h + 1), (0, 255, 255), 1)
# point_center.append(x + w / 2 + point0[0])
# point_center.append(y + h / 2 + point0[1])
# cv2.imwrite('./center1.png', img)
# else:
# # 去除小面积杂点， 连接轮廓，求最小包围框
# kernel1 = np.ones((3, 3), dtype=np.uint8)
# kernel2 = np.ones((2, 2), dtype=np.uint8)
# grayImage = cv2.dilate(grayImage, kernel1, 1) # 1:迭代次数，也就是执行几次膨胀操作
# grayImage = cv2.erode(grayImage, kernel2, 1)
# cv2.imwrite('./img_dilate_erode.png', grayImage)
# contours, hierarchy = cv2.findContours(grayImage, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# if len(contours) == 1:
# cv2.drawContours(mask, contours[0], -1, (255, 255, 255), 1)
# cv2.imwrite('./mask.png', mask)
# x, y, w, h = cv2.boundingRect(contours[0])
# cv2.circle(img, (int(x + w / 2), int(y + h / 2)), 1, (0, 0, 255), -1)
# cv2.rectangle(img, (x, y), (x + w + 1, y + h + 1), (0, 255, 255), 1)
# point_center.append(x + w / 2 + point0[0])
# point_center.append(y + h / 2 + point0[1])
# cv2.imwrite('./center1.png', img)
# else:
# gray_circles = cv2.HoughCircles(grayImage, cv2.HOUGH_GRADIENT, 4, 10000, param1=100, param2=81, minRadius=10, maxRadius=19)
# # cv2.imwrite('./img_gray_circles.jpg', gray_circles)
# if len(gray_circles[0]) > 0:
# print('霍夫圆个数:', len(gray_circles[0]))
# for (x, y, r) in gray_circles[0]:
# x = int(x)
# y = int(y)
# cv2.circle(grayImage, (x, y), int(r), (255, 255, 255), -1)
# cv2.imwrite('./img_hf.jpg', grayImage)
#
# detector = cv2.SimpleBlobDetector_create(params)
# keypoints = list(detector.detect(grayImage))
# for poi in keypoints: # 回归到原大图坐标系
# x_poi, y_poi = poi.pt[0], poi.pt[1]
# cv2.circle(img, (int(x_poi), int(y_poi)), 20, (255, 255, 255), -1)
# point_center.append(poi.pt[0] + point0[0])
# point_center.append(poi.pt[1] + point0[1])
# cv2.imwrite('./img_blob.png', img)
# else:
# for num_cont in range(len(contours)):
# cont = cv2.contourArea(contours[num_cont])
# # if cont > 6:
# # contours2.append(contours[num_cont])
# if cont <= 6:
# x, y, w, h = cv2.boundingRect(contours[num_cont])
# cv2.rectangle(grayImage, (x, y), (x + w, y + h), (0, 0, 0), -1)
# cv2.imwrite('./img_weilj.png', grayImage)
# grayImage = lj_img(grayImage)
# cv2.imwrite('./img_lj.png', grayImage)
# contours, hierarchy = cv2.findContours(grayImage, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# # print('再次检测数量: ', len(contours))
#
# cv2.drawContours(mask, contours[0], -1, (255, 255, 255), 1)
# cv2.imwrite('./mask.png', mask)
# x, y, w, h = cv2.boundingRect(contours[0])
# cv2.circle(img, (int(x + w / 2), int(y + h / 2)), 1, (0, 0, 255), -1)
# cv2.rectangle(img, (x, y), (x + w + 1, y + h + 1), (0, 255, 255), 1)
# point_center.append(x + w / 2 + point0[0])
# point_center.append(y + h / 2 + point0[1])
# cv2.imwrite('./center1.png', img)
return point_center[0], point_center[1]
if __name__ == "__main__":
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)
# img = cv2.imread('./Images/danHoles/s2.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 = 10
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
select_point = np.asarray(random.sample(list(all_point), n))
return select_point
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)
# 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:
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)))
self.items = math.log(1 - self.P) / math.log(1 - pow(inliers_count / self.length, self.N))
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()检测对象
检测结果
蓝色是直接椭圆拟合的结果
红色是Ransc之后的结果
到此这篇关于Python基于随机采样一至性实现拟合椭圆的文章就介绍到这了,更多相关Python拟合椭圆内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网！
