OpenCV-Python实战|霍夫变换全解析:轻松检测图像中的线与圆(附完整代码)

python# 安装/升级OpenCV-Python（核心依赖）pip install opencv-python -U# 安装辅助库（图像显示、数据处理）pip install numpy matplotlib

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 读取目标图（含直线、圆形，灰度图）img = cv2.imread("hough_test.jpg", 0) # 替换为自己的图像路径# 2. 图像预处理：高斯平滑（降噪）+ 边缘检测（突出线、圆的边缘，衔接前文）img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1) # 高斯平滑降噪（复用前文技巧）edges = cv2.Canny(img_blur, 50, 150) # Canny边缘检测（突出线、圆的轮廓）# 显示原始图与边缘图plt.subplot(1, 2, 1)plt.imshow(img, cmap="gray")plt.title("原始图像（含直线、圆形）")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(edges, cmap="gray")plt.title("边缘检测后图像（突出线、圆边缘）")plt.axis("off")plt.show()

pythoncv2.HoughLinesP(image, rho, theta, threshold, minLineLength, maxLineGap)

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 读取图像并预处理（复用前面的代码）img = cv2.imread("hough_test.jpg", 0)img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)edges = cv2.Canny(img_blur, 50, 150)# 2. 霍夫线变换（概率霍夫线变换，新手首选）lines = cv2.HoughLinesP(image=edges,rho=1, # 距离分辨率1像素theta=np.pi/180, # 角度分辨率1度threshold=50, # 阈值，可调整（50-100为宜）minLineLength=50, # 最小直线长度50像素maxLineGap=10 # 最大直线间隙10像素)# 3. 绘制检测到的直线（在原始图上标注）img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)if lines is not None: # 防止未检测到直线报错for line in lines:x1, y1, x2, y2 = line[0] # 提取直线的两个端点坐标# 绘制红色直线，线条粗细2cv2.line(img_color, (x1, y1), (x2, y2), (0, 0, 255), 2)# 显示效果（边缘图 vs 直线检测结果）plt.subplot(1, 2, 1)plt.imshow(edges, cmap="gray")plt.title("边缘检测图像")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("霍夫线变换检测结果")plt.axis("off")plt.show()# 输出检测到的直线数量line_count = len(lines) if lines is not None else 0print(f"共检测到{line_count}条直线")

pythoncv2.HoughCircles(image, method, dp, minDist, param1, param2, minRadius, maxRadius)

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 读取图像并预处理（平滑+边缘检测，提升检测精度）img = cv2.imread("hough_test.jpg", 0)img_blur = cv2.GaussianBlur(img, (5, 5), sigmaX=1) # 圆检测建议用5x5高斯核，降噪效果更好edges = cv2.Canny(img_blur, 50, 150)# 2. 霍夫圆变换（梯度法，新手首选）circles = cv2.HoughCircles(image=img_blur, # 建议用平滑后的灰度图，而非边缘图，检测更稳定method=cv2.HOUGH_GRADIENT,dp=1.5, # 累加器分辨率，1.5为宜minDist=50, # 两个圆心最小距离50像素，避免重叠param1=100, # Canny边缘检测高阈值，低阈值为50param2=30, # 圆检测阈值，可调整（20-50为宜）minRadius=20, # 最小圆半径20像素maxRadius=80 # 最大圆半径80像素)# 3. 绘制检测到的圆形（在原始图上标注）img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)if circles is not None: # 防止未检测到圆形报错# 将圆形参数转换为整数（返回的是浮点数）circles = np.uint16(np.around(circles))for circle in circles[0, :]:x, y, r = circle # 提取圆心坐标(x,y)和半径r# 绘制圆形：蓝色，线条粗细2cv2.circle(img_color, (x, y), r, (255, 0, 0), 2)# 标注圆心：红色圆点，半径3cv2.circle(img_color, (x, y), 3, (0, 0, 255), -1)# 显示效果（边缘图 vs 圆形检测结果）plt.subplot(1, 2, 1)plt.imshow(edges, cmap="gray")plt.title("边缘检测图像")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("霍夫圆变换检测结果")plt.axis("off")plt.show()# 输出检测到的圆形数量circle_count = len(circles[0]) if circles is not None else 0print(f"共检测到{circle_count}个圆形")

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 读取图像并预处理img = cv2.imread("hough_test.jpg", 0)img_blur = cv2.GaussianBlur(img, (5, 5), sigmaX=1)edges = cv2.Canny(img_blur, 50, 150)# 2. 同时执行霍夫线变换和霍夫圆变换# 霍夫线变换lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, minLineLength=50, maxLineGap=10)# 霍夫圆变换circles = cv2.HoughCircles(img_blur, cv2.HOUGH_GRADIENT, 1.5, 50, param1=100, param2=30, minRadius=20, maxRadius=80)# 3. 绘制线和圆（在同一张图上标注）img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)# 绘制直线（红色）if lines is not None:for line in lines:x1, y1, x2, y2 = line[0]cv2.line(img_color, (x1, y1), (x2, y2), (0, 0, 255), 2)# 绘制圆形（蓝色）和圆心（红色）if circles is not None:circles = np.uint16(np.around(circles))for circle in circles[0, :]:x, y, r = circlecv2.circle(img_color, (x, y), r, (255, 0, 0), 2)cv2.circle(img_color, (x, y), 3, (0, 0, 255), -1)# 4. 显示综合检测效果plt.figure(figsize=(12, 6))# 原始图plt.subplot(1, 3, 1)plt.imshow(img, cmap="gray")plt.title("原始图像")plt.axis("off")# 边缘图plt.subplot(1, 3, 2)plt.imshow(edges, cmap="gray")plt.title("边缘检测图像")plt.axis("off")# 综合检测结果plt.subplot(1, 3, 3)plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("霍夫变换综合检测结果（线+圆）")plt.axis("off")plt.tight_layout()plt.show()# 输出检测数量line_count = len(lines) if lines is not None else 0circle_count = len(circles[0]) if circles is not None else 0print(f"共检测到{line_count}条直线，{circle_count}个圆形")

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 环境验证（可选）print("OpenCV版本：", cv2.__version__)# 2. 读取图像（替换为自己的图像路径）img = cv2.imread("hough_test.jpg", 0)if img is None:print("错误：未找到图像，请检查图像路径！")else:# 3. 图像预处理（高斯平滑+边缘检测）img_blur = cv2.GaussianBlur(img, (5, 5), sigmaX=1)edges = cv2.Canny(img_blur, 50, 150)# 4. 霍夫线变换lines = cv2.HoughLinesP(image=edges,rho=1,theta=np.pi/180,threshold=50,minLineLength=50,maxLineGap=10)# 5. 霍夫圆变换circles = cv2.HoughCircles(image=img_blur,method=cv2.HOUGH_GRADIENT,dp=1.5,minDist=50,param1=100,param2=30,minRadius=20,maxRadius=80)# 6. 绘制检测结果（分别绘制线、圆，以及综合结果）# 绘制直线img_lines = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)if lines is not None:for line in lines:x1, y1, x2, y2 = line[0]cv2.line(img_lines, (x1, y1), (x2, y2), (0, 0, 255), 2)# 绘制圆形img_circles = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)if circles is not None:circles = np.uint16(np.around(circles))for circle in circles[0, :]:x, y, r = circlecv2.circle(img_circles, (x, y), r, (255, 0, 0), 2)cv2.circle(img_circles, (x, y), 3, (0, 0, 255), -1)# 综合绘制（线+圆）img_combined = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)if lines is not None:for line in lines:x1, y1, x2, y2 = line[0]cv2.line(img_combined, (x1, y1), (x2, y2), (0, 0, 255), 2)if circles is not None:circles = np.uint16(np.around(circles))for circle in circles[0, :]:x, y, r = circlecv2.circle(img_combined, (x, y), r, (255, 0, 0), 2)cv2.circle(img_combined, (x, y), 3, (0, 0, 255), -1)# 7. 统一显示所有效果plt.figure(figsize=(15, 10))# 第一行：原始图、边缘图plt.subplot(2, 3, 1)plt.imshow(img, cmap="gray")plt.title("原始图像")plt.axis("off")plt.subplot(2, 3, 2)plt.imshow(edges, cmap="gray")plt.title("边缘检测图像")plt.axis("off")# 第二行：直线检测、圆形检测、综合检测plt.subplot(2, 3, 4)plt.imshow(cv2.cvtColor(img_lines, cv2.COLOR_BGR2RGB))plt.title(f"霍夫线变换（{len(lines) if lines is not None else 0}条直线）")plt.axis("off")plt.subplot(2, 3, 5)plt.imshow(cv2.cvtColor(img_circles, cv2.COLOR_BGR2RGB))plt.title(f"霍夫圆变换（{len(circles[0]) if circles is not None else 0}个圆形）")plt.axis("off")plt.subplot(2, 3, 6)plt.imshow(cv2.cvtColor(img_combined, cv2.COLOR_BGR2RGB))plt.title("综合检测结果（线+圆）")plt.axis("off")plt.tight_layout()plt.show()# 8. 保存结果（可选）cv2.imwrite("hough_lines.jpg", img_lines)cv2.imwrite("hough_circles.jpg", img_circles)cv2.imwrite("hough_combined.jpg", img_combined)