OpenCV-Python实战|轮廓处理全解析:发现、特征、属性与分层(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、处理）pip install numpy matplotlib

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 读取图像（灰度图）img = cv2.imread("test.jpg", 0) # 0表示灰度图，替换为自己的图像路径# 2. 预处理：高斯平滑→Canny边缘检测→二值化（确保轮廓清晰）img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1) # 平滑降噪canny = cv2.Canny(img_blur, 50, 100) # Canny边缘检测（沿用前几篇推荐参数）# 二值化：将边缘图转为纯黑白二值图（轮廓检测必备）_, binary = cv2.threshold(canny, 127, 255, cv2.THRESH_BINARY)# 显示预处理效果plt.subplot(1, 3, 1)plt.imshow(img, cmap="gray")plt.title("原始灰度图像")plt.axis("off")plt.subplot(1, 3, 2)plt.imshow(canny, cmap="gray")plt.title("Canny边缘检测")plt.axis("off")plt.subplot(1, 3, 3)plt.imshow(binary, cmap="gray")plt.title("二值图像（轮廓检测必备）")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取并预处理图像（沿用前面的二值图像）img = cv2.imread("test.jpg", 0)img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)canny = cv2.Canny(img_blur, 50, 100)_, binary = cv2.threshold(canny, 127, 255, cv2.THRESH_BINARY)# 1. 轮廓发现（只检索最外层轮廓，压缩冗余点）contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)print(f"找到的轮廓数量：{len(contours)}") # 打印轮廓数量# 2. 轮廓绘制（绘制所有轮廓，颜色为红色，线条粗细2）# 注意：绘制轮廓需用彩色图，先将灰度图转为彩色图img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)cv2.drawContours(img_color, contours, -1, (0, 0, 255), 2) # 红色轮廓# 显示效果plt.subplot(1, 2, 1)plt.imshow(binary, cmap="gray")plt.title("二值图像（轮廓来源）")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB)) # 转换为RGB显示plt.title(f"轮廓绘制（共{len(contours)}个轮廓）")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取并预处理，获取轮廓（沿用前面的代码）img = cv2.imread("test.jpg", 0)img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)canny = cv2.Canny(img_blur, 50, 100)_, binary = cv2.threshold(canny, 127, 255, cv2.THRESH_BINARY)contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# 取第一个轮廓（若有多个轮廓，可循环遍历）contour = contours[0]# 1. 计算面积与周长area = cv2.contourArea(contour)perimeter = cv2.arcLength(contour, closed=True)print(f"轮廓面积：{area:.2f} 像素")print(f"轮廓周长：{perimeter:.2f} 像素")# 2. 轮廓近似（epsilon=周长的0.02，精度可调整）epsilon = 0.02 * cv2.arcLength(contour, closed=True)approx_contour = cv2.approxPolyDP(contour, epsilon, closed=True)# 3. 凸包检测hull = cv2.convexHull(contour)# 绘制所有特征（彩色图绘制）img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)cv2.drawContours(img_color, [contour], -1, (0, 0, 255), 2) # 原轮廓（红色）cv2.drawContours(img_color, [approx_contour], -1, (0, 255, 0), 2) # 近似轮廓（绿色）cv2.drawContours(img_color, [hull], -1, (255, 0, 0), 2) # 凸包（蓝色）# 标注面积与周长cv2.putText(img_color, f"Area: {area:.0f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)cv2.putText(img_color, f"Perimeter: {perimeter:.0f}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)# 显示效果plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("轮廓特征：面积、周长、近似、凸包")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取并预处理，获取轮廓（沿用前面的代码）img = cv2.imread("test.jpg", 0)img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)canny = cv2.Canny(img_blur, 50, 100)_, binary = cv2.threshold(canny, 127, 255, cv2.THRESH_BINARY)contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)contour = contours[0]# 绘制基础图（彩色图）img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)cv2.drawContours(img_color, [contour], -1, (0, 0, 255), 2) # 原轮廓（红色）# 1. 边界矩形x, y, w, h = cv2.boundingRect(contour)cv2.rectangle(img_color, (x, y), (x+w, y+h), (0, 255, 0), 2) # 绿色矩形# 2. 最小闭合圆(x_circle, y_circle), radius = cv2.minEnclosingCircle(contour)cv2.circle(img_color, (int(x_circle), int(y_circle)), int(radius), (255, 0, 0), 2) # 蓝色圆形# 3. 椭圆拟合（需轮廓点数量≥5）if len(contour) >= 5:ellipse = cv2.fitEllipse(contour)cv2.ellipse(img_color, ellipse, (0, 255, 255), 2) # 黄色椭圆# 标注属性名称cv2.putText(img_color, "Bounding Rect", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)cv2.putText(img_color, "Min Circle", (int(x_circle)-50, int(y_circle)-radius-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)if len(contour) >= 5:cv2.putText(img_color, "Ellipse", (int(x_circle)-50, int(y_circle)+radius+20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)# 显示效果plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("轮廓属性：边界矩形、最小闭合圆、椭圆拟合")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 读取并预处理，获取轮廓（主轮廓）img = cv2.imread("test.jpg", 0)img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)canny = cv2.Canny(img_blur, 50, 100)_, binary = cv2.threshold(canny, 127, 255, cv2.THRESH_BINARY)contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)contour1 = contours[0]# 2. 凸性缺陷hull = cv2.convexHull(contour1, returnPoints=False) # returnPoints=False，返回凸包索引defects = cv2.convexityDefects(contour1, hull)# 3. 点多边形测试（测试3个点：内部、外部、轮廓上）point1 = (50, 50) # 外部点point2 = (int(contour1[:, 0, 0].mean()), int(contour1[:, 0, 1].mean())) # 内部点（轮廓中心）point3 = tuple(contour1[0][0]) # 轮廓上的点# 计算点与轮廓的关系dist1 = cv2.pointPolygonTest(contour1, point1, True)dist2 = cv2.pointPolygonTest(contour1, point2, True)dist3 = cv2.pointPolygonTest(contour1, point3, True)# 4. 形状匹配（用轮廓近似后的轮廓进行匹配，简化计算）epsilon = 0.02 * cv2.arcLength(contour1, True)contour1_approx = cv2.approxPolyDP(contour1, epsilon, True)# 模拟第二个轮廓（可替换为自己的第二个目标轮廓）contour2 = contour1_approx.copy()match_score = cv2.matchShapes(contour1_approx, contour2, cv2.CONTOURS_MATCH_I1, 0.0)print(f"形状匹配值（越小越相似）：{match_score:.4f}")# 绘制所有效果img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)cv2.drawContours(img_color, [contour1], -1, (0, 0, 255), 2) # 原轮廓cv2.drawContours(img_color, [cv2.convexHull(contour1)], -1, (255, 0, 0), 2) # 凸包# 绘制凸性缺陷（若有缺陷）if defects is not None:for i in range(defects.shape[0]):s, e, f, d = defects[i][0]start = tuple(contour1[s][0])end = tuple(contour1[e][0])far = tuple(contour1[f][0])cv2.line(img_color, start, end, (0, 255, 0), 2)cv2.circle(img_color, far, 5, (0, 0, 255), -1) # 凹陷点标注# 绘制测试点cv2.circle(img_color, point1, 5, (255, 0, 0), -1) # 外部点（蓝）cv2.circle(img_color, point2, 5, (0, 255, 0), -1) # 内部点（绿）cv2.circle(img_color, point3, 5, (0, 0, 255), -1) # 轮廓上点（红）# 标注点的位置关系和匹配值cv2.putText(img_color, f"外: {dist1:.1f}", (point1[0]+10, point1[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)cv2.putText(img_color, f"内: {dist2:.1f}", (point2[0]+10, point2[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)cv2.putText(img_color, f"上: {dist3:.1f}", (point3[0]+10, point3[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)cv2.putText(img_color, f"匹配值: {match_score:.4f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)# 显示效果plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("进阶操作：凸性缺陷、点多边形测试、形状匹配")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取嵌套轮廓图像（如：一个大矩形内部包含一个小矩形）img = cv2.imread("nested_contour.jpg", 0) # 替换为嵌套轮廓图像路径_, binary = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV) # 二值化（反色，确保轮廓为白色）# 1. 检索所有轮廓，保留层级关系contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)print("轮廓层级信息：")print(hierarchy)# 2. 绘制不同层级的轮廓（父轮廓：红色，子轮廓：绿色）img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)# 遍历所有轮廓，根据层级判断父/子轮廓for i, contour in enumerate(contours):# 父轮廓：parent=-1（无父轮廓）if hierarchy[0][i][3] == -1:cv2.drawContours(img_color, [contour], -1, (0, 0, 255), 2)cv2.putText(img_color, f"父轮廓{i}", tuple(contour[0][0]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)# 子轮廓：parent≠-1（有父轮廓）else:cv2.drawContours(img_color, [contour], -1, (0, 255, 0), 2)cv2.putText(img_color, f"子轮廓{i}", tuple(contour[0][0]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)# 显示效果plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("轮廓分层结构：父轮廓（红）、子轮廓（绿）")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 环境验证（可选）print("OpenCV版本：", cv2.__version__)# 2. 读取并预处理图像（主图像，非嵌套）img = cv2.imread("test.jpg", 0) # 替换为自己的图像路径img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)canny = cv2.Canny(img_blur, 50, 100)_, binary = cv2.threshold(canny, 127, 255, cv2.THRESH_BINARY)# 3. 轮廓发现（最外层轮廓）contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)if len(contours) == 0:print("未找到轮廓，请检查二值图像！")else:contour = contours[0]# 4. 轮廓特征（面积、周长、近似、凸包）area = cv2.contourArea(contour)perimeter = cv2.arcLength(contour, closed=True)epsilon = 0.02 * perimeterapprox_contour = cv2.approxPolyDP(contour, epsilon, closed=True)hull = cv2.convexHull(contour)# 5. 轮廓属性（边界矩形、最小闭合圆、椭圆拟合）x, y, w, h = cv2.boundingRect(contour)(x_circle, y_circle), radius = cv2.minEnclosingCircle(contour)ellipse = cv2.fitEllipse(contour) if len(contour) >= 5 else None# 6. 进阶操作（凸性缺陷、点多边形测试、形状匹配）hull_idx = cv2.convexHull(contour, returnPoints=False)defects = cv2.convexityDefects(contour, hull_idx) if hull_idx is not None else Nonepoint_inner = (int(contour[:, 0, 0].mean()), int(contour[:, 0, 1].mean()))point_outer = (50, 50)dist_inner = cv2.pointPolygonTest(contour, point_inner, True)dist_outer = cv2.pointPolygonTest(contour, point_outer, True)match_score = cv2.matchShapes(contour, approx_contour, cv2.CONTOURS_MATCH_I1, 0.0)# 7. 绘制所有效果（主图像）img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)# 绘制轮廓相关cv2.drawContours(img_color, [contour], -1, (0, 0, 255), 2)cv2.drawContours(img_color, [approx_contour], -1, (0, 255, 0), 2)cv2.drawContours(img_color, [hull], -1, (255, 0, 0), 2)# 绘制轮廓属性cv2.rectangle(img_color, (x, y), (x+w, y+h), (0, 255, 255), 2)cv2.circle(img_color, (int(x_circle), int(y_circle)), int(radius), (128, 0, 128), 2)if ellipse is not None:cv2.ellipse(img_color, ellipse, (255, 255, 0), 2)# 绘制凸性缺陷if defects is not None:for i in range(defects.shape[0]):s, e, f, d = defects[i][0]cv2.circle(img_color, tuple(contour[f][0]), 5, (0, 0, 255), -1)# 绘制测试点cv2.circle(img_color, point_inner, 5, (0, 255, 0), -1)cv2.circle(img_color, point_outer, 5, (255, 0, 0), -1)# 标注信息cv2.putText(img_color, f"Area: {area:.0f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)cv2.putText(img_color, f"Match: {match_score:.4f}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)cv2.putText(img_color, "Inner", (point_inner[0]+10, point_inner[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)cv2.putText(img_color, "Outer", (point_outer[0]+10, point_outer[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)# 8. 轮廓分层演示（嵌套图像）img_nested = cv2.imread("nested_contour.jpg", 0) # 替换为嵌套轮廓图像路径_, binary_nested = cv2.threshold(img_nested, 127, 255, cv2.THRESH_BINARY_INV)contours_nested, hierarchy_nested = cv2.findContours(binary_nested, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)img_nested_color = cv2.cvtColor(img_nested, cv2.COLOR_GRAY2BGR)for i, cnt in enumerate(contours_nested):if hierarchy_nested[0][i][3] == -1:cv2.drawContours(img_nested_color, [cnt], -1, (0, 0, 255), 2)cv2.putText(img_nested_color, f"Parent {i}", tuple(cnt[0][0]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)else:cv2.drawContours(img_nested_color, [cnt], -1, (0, 255, 0), 2)cv2.putText(img_nested_color, f"Child {i}", tuple(cnt[0][0]), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)# 9. 统一显示所有效果plt.figure(figsize=(15, 10))# 主轮廓效果plt.subplot(2, 2, 1)plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("轮廓特征+属性+进阶操作")plt.axis("off")# 二值图像plt.subplot(2, 2, 2)plt.imshow(binary, cmap="gray")plt.title("二值图像")plt.axis("off")# 嵌套轮廓分层plt.subplot(2, 2, 3)plt.imshow(cv2.cvtColor(img_nested_color, cv2.COLOR_BGR2RGB))plt.title("轮廓分层结构")plt.axis("off")# 轮廓近似对比plt.subplot(2, 2, 4)plt.imshow(cv2.cvtColor(cv2.drawContours(cv2.cvtColor(img, cv2.COLOR_GRAY2BGR), [contour, approx_contour], -1, (0,0,255, 0,255,0), 2), cv2.COLOR_BGR2RGB))plt.title("原轮廓（红）vs 近似轮廓（绿）")plt.axis("off")plt.tight_layout()plt.show()# 10. 保存结果（可选）cv2.imwrite("contour_all.jpg", img_color)cv2.imwrite("contour_nested.jpg", img_nested_color)