OpenCV-Python(25):Hough直线变换

目标

• 理解霍夫变换的概念
• 学习如何在一张图片中检测直线
• 学习函数cv2.HoughLines()和cv2.HoughLinesP()

OpenCV中的霍夫变换?

import cv2
import numpy as np

# 加载图像
image = cv2.imread('image.jpg')
# 灰度转换
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 边缘检测
edges = cv2.Canny(gray, 50, 150, apertureSize=3)

# 执行霍夫直线变换
lines = cv2.HoughLines(edges, 1, np.pi/180, 200)

# 绘制检测到的直线
if lines is not None:
    for rho, theta in lines[0]:
        a = np.cos(theta)
        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))
        cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 2)

# 显示结果
cv2.imshow('Hough Lines', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个示例中，首先加载图像并将其转换为灰度图像。然后使用Canny边缘检测算法检测图像的边缘。接下来，使用cv2.HoughLines函数执行霍夫直线变换，并设置了一些参数，例如距离分辨率、角度分辨率和阈值。最后，根据检测到的直线参数绘制直线，并显示结果图像。

结果如下:

概率霍夫变换(Probabilistic Hough Transform)?

import cv2
import numpy as np

img = cv2.imread('dave.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)

minLineLength = 100
maxLineGap = 10

lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength,maxLineGap)

for x1,y1,x2,y2 in lines[0]:
    cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)
cv2.imwrite('houghlines5.jpg',img)

结果如下：

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