Canny 边缘提取相关知识学习，图像处理第 32 篇博客

edges = cv2.Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient]]])edges =  cv.Canny(dx, dy, threshold1, threshold2[, edges[, L2gradient]]

import numpy as npimport cv2 as cvfrom matplotlib import pyplot as pltsrc = cv.imread("test1.jpg", 0)# 图像降噪src = cv.GaussianBlur(src, (3, 3), 0)# 图像梯度xgrad = cv.Sobel(src, cv.CV_16SC1, 1, 0)ygrad = cv.Sobel(src, cv.CV_16SC1, 0, 1)
# Canny 边缘检测，50 为低阈值，150 为高阈值，参数必须符合1：3或者1：2canny = cv.Canny(xgrad, ygrad, 50, 150)# 直接用灰度图像# canny = cv.Canny(gray, 50, 150)
imgs = np.hstack([src, canny])plt.figure(figsize=(20, 10))plt.imshow(imgs, "gray")plt.axis('off')plt.show()

import numpy as npimport cv2 as cvfrom matplotlib import pyplot as pltsrc = cv.imread("test1.jpg")
cv.namedWindow("bar", cv.WINDOW_AUTOSIZE)
low_threshold = 0high_threshold = 0

def do(x):    global high_threshold    if x != 0:        high_threshold = 3 * x

cv.createTrackbar("low_threshold", "bar", 10, 100, do)
# 图像降噪src = cv.GaussianBlur(src, (3, 3), 0)
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)# 图像梯度xgrad = cv.Sobel(gray, cv.CV_16SC1, 1, 0)ygrad = cv.Sobel(gray, cv.CV_16SC1, 0, 1)
while True:    low_threshold = cv.getTrackbarPos("low_threshold", "bar")    canny = cv.Canny(gray, low_threshold, high_threshold)    cv.imshow("canny", canny)    if cv.waitKey(1) & 0xFF == 27:        break
cv.destroyAllWindows()