一、OpenCV-Python 中 Canny() 引數

"""
cv2.Canny(image,            # 輸入原圖（必須為單通道圖）
          threshold1, 
          threshold2,       # 較大的閾值2用於檢測影象中明顯的邊緣
          [, edges[, 
          apertureSize[,    # apertureSize：Sobel運算元的大小
          L2gradient ]]])   # 引數(布林值)：
                              true： 使用更精確的L2範數進行計算（即兩個方向的倒數的平方和再開放），
                              false：使用L1範數（直接將兩個方向導數的絕對值相加）。
"""
 


import cv2
import numpy as np  
 
original_img = cv2.imread("qingwen.png", 0)

# canny(): 邊緣檢測
img1 = cv2.GaussianBlur(original_img,(3,3),0)
canny = cv2.Canny(img1, 50, 150)

# 形態學：邊緣檢測
_,Thr_img = cv2.threshold(original_img,210,255,cv2.THRESH_BINARY)#設定紅色通道閾值210（閾值影響梯度運算效果）
kernel = cv2.getStructuringElement(
 
cv2.MORPH_RECT,(5,5))         #定義矩形結構元素
gradient = cv2.morphologyEx(Thr_img, cv2.MORPH_GRADIENT, kernel) #梯度

cv2.imshow("original_img", original_img) 
cv2.imshow("gradient", gradient) 
cv2.imshow('Canny', canny)

cv2.waitKey(0)
cv2.destroyAllWindows()

可調整閾值大小的程式

import cv2
import numpy as np
 
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('qingwen.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
 
cv2.namedWindow('canny demo')
 
cv2.createTrackbar('Min threshold','canny demo',lowThreshold, max_lowThreshold, CannyThreshold)
 
CannyThreshold(0)  # initialization
if cv2.waitKey(0) == 27:
    cv2.destroyAllWindows()