ｏpencv图像处理（八）canny 边缘检测、轮廓检测

最新推荐文章于 2025-05-10 08:58:54 发布

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最新推荐文章于 2025-05-10 08:58:54 发布 · 1.8k 阅读

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"""首先，由于Canny只能处理灰度图，所以将读取的图像转成灰度图。
用高斯平滑处理原图像降噪。
调用Canny函数，指定最大和最小阈值，其中apertureSize默认为3
edge = cv2.Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient ]]])
"""
import cv2
import numpy as np

img = cv2.imread("cat.jpg", 0)
img = cv2.GaussianBlur(img, (3, 3), 0)
canny = cv2.Canny(img, 50, 150)
cv2.imshow('Canny', canny)
cv2.waitKey(0)
cv2.destroyAllWindows()

#这个程序只是静态的，在github上有一个可以在运行时调整阈值大小的程序
def CannyThreshold(lowThreshold):
    detected_edges = cv2.GaussianBlur(gray, (3, 3), 0)
    detected_edges = cv2.Canny(detected_edges, lowThreshold, lowThreshold * ratio, apertureSize=kernel_size)
    dst = cv2.bitwise_and(img, img, mask=detected_edges)  # just add some colours to edges from original image.
    cv2.imshow('canny demo', dst)


lowThreshold = 0
max_lowThreshold = 100
ratio = 3
kernel_size = 3

img = cv2.imread('D:/lion.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

cv2.namedWindow('canny demo')

cv2.createTrackbar('Min threshold', 'canny demo