读取图像computer.jpg及coins.jpg，基于Python实现基于霍夫变换的直线和圆检测，并显示原始图像及边缘图像python代码

在Python中，我们可以使用OpenCV库来进行图像处理，包括霍夫变换（Hough Transform）来检测直线和圆形。首先，需要安装`opencv-python`库。以下是一个简单的示例，演示如何读取图像、边缘检测以及应用霍夫变换：

import cv2
import numpy as np

# 读取图像
img1 = cv2.imread('computer.jpg')
img2 = cv2.imread('coins.jpg')

# 检查图像是否加载成功
if img1 is None or img2 is None:
    print("图片无法加载")
else:
    # 转换为灰度图像
    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

    # Canny边缘检测
    edges1 = cv2.Canny(gray1, 50, 150)  # 边缘阈值可以根据实际情况调整
    edges2 = cv2.Canny(gray2, 50, 150)

    # 对边缘图像应用霍夫变换寻找直线
    lines1 = cv2.HoughLinesP(edges1, 1, np.pi / 180, 100, minLineLength=100, maxLineGap=50)
    lines2 = cv2.HoughLinesP(edges2, 1, np.pi / 180, 100, minLineLength=100, maxLineGap=50)

    # 对边缘图像应用霍夫变换寻找圆形
    circles1 = cv2.HoughCircles(edges1, cv2.HOUGH_GRADIENT, dp=1, minDist=20, param1=50, param2=30, minRadius=0, maxRadius=0)
    circles2 = cv2.HoughCircles(edges2, cv2.HOUGH_GRADIENT, dp=1, minDist=20, param1=50, param2=30, minRadius=0, maxRadius=0)

    # 可视化结果
    for line in lines1:
        x1, y1, x2, y2 = line[0]
        cv2.line(img1, (x1, y1), (x2, y2), (0, 255, 0), 2)

    for circle in circles1:
        if circle is not None and len(circle):
            cv2.circle(img1, (circle[0][0], circle[0][1]), circle[0][2], (0, 0, 255), 2)

    for line in lines2:
        x1, y1, x2, y2 = line[0]
        cv2.line(img2, (x1, y1), (x2, y2), (0, 255, 0), 2)

    for circle in circles2:
        if circle is not None and len(circle):
            cv2.circle(img2, (circle[0][0], circle[0][1]), circle[0][2], (0, 0, 255), 2)

    # 显示原始图像和检测后的图像
    cv2.imshow("Computer Image with Lines", img1)
    cv2.imshow("Coins Image with Circles", img2)
    cv2.waitKey(0)
    cv2.destroyAllWindows()