参考:https://blog.csdn.net/wsp_1138886114/article/details/82935839
步骤:
彩色图像转换为灰度图像(以灰度图或者单通道图读入)
对图像进行高斯模糊(去噪)
计算图像梯度,根据梯度计算图像边缘幅值与角度
沿梯度方向进行非极大值抑制(边缘细化)
双阈值边缘连接处理
二值化图像输出结果
""" cv2.Canny(image, # 输入原图(必须为单通道图) threshold1, threshold2, # 较大的阈值2用于检测图像中明显的边缘 [, edges[, apertureSize[, # apertureSize:Sobel算子的大小 L2gradient ]]]) # 参数(布尔值): true: 使用更精确的L2范数进行计算(即两个方向的倒数的平方和再开放), false:使用L1范数(直接将两个方向导数的绝对值相加)。 """ import cv2 import numpy as np original_img = cv2.imread("qingwen.png", 0) # canny(): 边缘检测 img1 = cv2.GaussianBlur(original_img,(3,3),0) canny = cv2.Canny(img1, 50, 150) # 形态学:边缘检测 _,Thr_img = cv2.threshold(original_img,210,255,cv2.THRESH_BINARY)#设定红色通道阈值210(阈值影响梯度运算效果) kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5)) #定义矩形结构元素 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()
可调整阈值大小的程序
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()
欢迎分享,转载请注明来源:内存溢出
评论列表(0条)