边缘检测与轮廓提取：Canny 算子、霍夫变换

📂 所属阶段：第一阶段 — 图像处理基石（传统 CV 篇）
🔗 相关章节：图像增强与滤波 · 特征匹配实战

1. Canny 边缘检测

import cv2
import numpy as np

img = cv2.imread("photo.jpg", 0)

# Canny 边缘检测
edges = cv2.Canny(img, 100, 200)

cv2.imshow("Edges", edges)
cv2.waitKey(0)

2. 霍夫变换

2.1 直线检测

import cv2
import numpy as np

img = cv2.imread("photo.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)

# 霍夫直线变换
lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100, minLineLength=100, maxLineGap=10)

# 绘制直线
for line in lines:
    x1, y1, x2, y2 = line[0]
    cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)

cv2.imshow("Lines", img)
cv2.waitKey(0)

2.2 圆检测

import cv2
import numpy as np

img = cv2.imread("photo.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 霍夫圆检测
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20,
                           param1=50, param2=30, minRadius=0, maxRadius=0)

if circles is not None:
    circles = np.uint16(np.around(circles))
    for i in circles[0, :]:
        cv2.circle(img, (i[0], i[1]), i[2], (0, 255, 0), 2)

cv2.imshow("Circles", img)
cv2.waitKey(0)

3. 轮廓提取

import cv2
import numpy as np

img = cv2.imread("photo.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 查找轮廓
contours, _ = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(img, contours, -1, (0, 255, 0), 2)

cv2.imshow("Contours", img)
cv2.waitKey(0)

4. 小结

边缘检测三大工具：

1. Canny：最常用的边缘检测算子
2. 霍夫变换：检测直线和圆
3. 轮廓提取：获取对象的轮廓

应用场景：
- 物体检测 → 轮廓提取
- 车道线检测 → 霍夫直线
- 圆形物体检测 → 霍夫圆

💡 记住：边缘检测是目标检测的前置步骤。

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