HoughLines

代码：

# -*- coding=GBK -*-
import cv2 as cv
import numpy as np


# hough变换检测直线与圆 ：https://blog.csdn.net/on2way/article/details/47028969
def line_image(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    edges = cv.Canny(gray, 50, 150, apertureSize=3) # 第四个输入参数是Sobel算子的大小，3的话就是3*3
    cv.imshow("edges", edges)
    lines = cv.HoughLines(edges, 1, np.pi / 180, 200) # 霍夫变换进行形状检测
    # cv.HoughLines(edges, rho, theta, threshold)
    # img 为输入的图像，Canny提取后的边界数据;rho/ρ为距离精确度（理解为距离单位）;theta/θ为角度精确度（理解为角度单位单位）;threshold 为累加器阈值,当累加器中的值高于threshold时才认为是一条直线
    # 返回值每段直线(ρ,θ)的的二维矩阵
    for line in lines:
        rho, theta = line[0]
        a = np.cos(theta) # np.cos是便于计算数组所有theta的cos值，sin同理 ，而且三角函数是np库里面的
        b = np.sin(theta)
        x0 = a * rho
        y0 = b * rho
        x1 = int(x0 + 1000 * (-b))
        y1 = int(y0 + 1000 * (a))
        x2 = int(x0 - 1000 * (-b))
        y2 = int(y0 - 1000 * (a))
        cv.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2) # 输入参数： img， 起始点， 终， 颜色， 宽度
    cv.imshow("line", image)


src = cv.imread("Line.png")
cv.imshow("before", src)
line_image(src)
cv.waitKey(0)
cv.destroyAllWindows()

结果：

备注：

hough变换检测直线与圆参考资料：https://blog.csdn.net/on2way/article/details/47028969

HoughLinesP

代码：

# -*- coding=GBK -*-
import cv2 as cv
import numpy as np


def line_image(image):
   gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) # 灰度图像
   edges = cv.Canny(gray, 50, 200)
   lines = cv.HoughLinesP(edges, 1, np.pi/180, 30, minLineLength=60, maxLineGap=10)
   lines1 = lines[:, 0, :]  # 提取为二维

   for x1, y1, x2, y2 in lines1[:]:
     cv.line(image, (x1, y1), (x2, y2), (255, 0, 0), 1)
   cv.imshow("line", image)


src = cv.imread("Line.png")
cv.imshow("before", src)
line_image(src)
cv.waitKey(0)
cv.destroyAllWindows()

结果：

备注：

hough变换检测直线与圆参考资料：https://blog.csdn.net/on2way/article/details/47028969