opencv中给出了canny边缘检测的接口，直接调用：
ret = cv2.canny(img,t1,t2)
即可得到边缘检测的结果ret。其中，t1，t2是需要人为设置的阈值。有不少论文研究了自动化的阈值设置方法，即算法在运行过程中能够自适应地找到较佳的分割阈值t1，t2，但是缺乏开源代码，特别是基于python3的实现几乎没有。
本文基于python3，复现一种自适应的阈值分割方法。
输入图片是：
输出结果对比如下：左图是直接用canny，右图是用本文程序自适应分割。
比较不足的是，由于自底向上重新编写，包括非最大抑制等过程。。可能耗时比较久。上面输入图像耗时27.38s，不知道其他学者研究的自适应阈值canny边缘检测方法的耗时情况如何。。程序中预处理过程中已经做了降采样，如果没有降采样的话，耗时会更长。
下面上代码：
主程序.py:
import numpy as np import cv2, time, math from scipy.signal import convolve2d as conv2 from matplotlib import pyplot as plt from bilateralfilt import bilatfilt from dog import deroGauss import time #........................................................................................... def get_edges(I,sd): dim = I.shape Idog2d = np.zeros((nang,dim[0],dim[1])) for i in range(nang): dog2d = deroGauss(5,sd,angles[i]) Idog2dtemp = abs(conv2(I,dog2d,mode='same',boundary='fill')) Idog2dtemp[Idog2dtemp<0]=0 Idog2d[i,:,:] = Idog2dtemp return Idog2d #........................................................................................... def nonmaxsup(I,gradang): dim = I.shape Inms = np.zeros(dim) xshift = int(np.round(math.cos(gradang*np.pi/180))) yshift = int(np.round(math.sin(gradang*np.pi/180))) Ipad = np.pad(I,(1,),'constant',constant_values = (0,0)) for r in range(1,dim[0]+1): for c in range(1,dim[1]+1): maggrad = [Ipad[r-xshift,c-yshift],Ipad[r,c],Ipad[r+xshift,c+yshift]] if Ipad[r,c] == np.max(maggrad): Inms[r-1,c-1] = Ipad[r,c] return Inms #........................................................................................... def calc_sigt(I,threshval): M,N = I.shape ulim = np.uint8(np.max(I)) N1 = np.count_nonzero(I>threshval) N2 = np.count_nonzero(I<=threshval) w1 = np.float64(N1)/(M*N) w2 = np.float64(N2)/(M*N) #print N1,N2,w1,w2 try: u1 = np.sum(i*np.count_nonzero(np.multiply(I>i-0.5,I<=i+0.5))/N1 for i in range(threshval+1,ulim)) u2 = np.sum(i*np.count_nonzero(np.multiply(I>i-0.5,I<=i+0.5))/N2 for i in range(threshval+1)) uT = u1*w1+u2*w2 sigt = w1*w2*(u1-u2)**2 #print u1,u2,uT,sigt except: return 0 return sigt #........................................................................................... def get_threshold(I): max_sigt = 0 opt_t = 0 ulim = np.uint8(np.max(I)) print(ulim) for t in range(ulim+1): sigt = calc_sigt(I,t) #print t, sigt if sigt > max_sigt: max_sigt = sigt opt_t = t print ('optimal high threshold: ',opt_t) return opt_t #........................................................................................... def threshold(I,uth): lth = uth/2.5 Ith = np.zeros(I.shape) Ith[I>=uth] = 255 Ith[I<lth] = 0 Ith[np.multiply(I>=lth, I<uth)] = 100 return Ith #........................................................................................... def hysteresis(I): r,c = I.shape #xshift = int(np.round(math.cos(gradang*np.pi/180))) #yshift = int(np.round(math.sin(gradang*np.pi/180))) Ipad = np.pad(I,(1,),'edge') c255 = np.count_nonzero(Ipad==255) imgchange = True for i in range(1,r+1): for j in range(1,c+1): if Ipad[i,j] == 100: #if Ipad[i-xshift,j+yshift]==255 or Ipad[i+xshift,j-yshift]==255: if np.count_nonzero(Ipad[r-1:r+1,c-1:c+1]==255)>0: Ipad[i,j] = 255 else: Ipad[i,j] = 0 Ih = Ipad[1:r+1,1:c+1] return Ih #........................................................................................... #Reading the image img = cv2.imread('img0030.jpg') while img.shape[0] > 1100 or img.shape[1] > 1100: img = cv2.resize(img,None, fx=0.5,fy=0.5,interpolation = cv2.INTER_AREA) #tic = time.time() gimg = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) dim = img.shape #........................................................................................... #Bilateral filtering print ('Bilateral filtering...\n') gimg = bilatfilt(gimg,5,3,10) print ('after bilat: ',np.max(gimg),'\n') #........................................................................................... stime = time.time() angles = [0,45,90,135] nang = len(angles) #........................................................................................... #Gradient of Image print ('Calculating Gradient...\n') img_edges = get_edges(gimg,2) print ('after gradient: ',np.max(img_edges),'\n') #........................................................................................... #Non-max suppression print ('Suppressing Non-maximas...\n') for n in range(nang): img_edges[n,:,:] = nonmaxsup(img_edges[n,:,:],angles[n]) print ('after nms: ', np.max(img_edges)) img_edge = np.max(img_edges,axis=0) lim = np.uint8(np.max(img_edge)) plt.imshow(img_edge) plt.show() #........................................................................................... #Converting to uint8 #img_edges_uint8 = np.uint8(img_edges) #........................................................................................... #Thresholding print ('Calculating Threshold...\n') th = get_threshold(gimg) the = get_threshold(img_edge) #........................................................................................... print ('\nThresholding...\n') img_edge = threshold(img_edge, the*0.25) #cv2.imshow('afterthe',img_edge) #........................................................................................... #Hysteresis print ('Applying Hysteresis...\n') #for i in xrange(nang): img_edge = nonmaxsup(hysteresis(img_edge),90) #........................................................................................... #img_edge = np.max(img_edges,axis=0) #........................................................................................... #OpenCV Canny Function img_canny = cv2.Canny(np.uint8(gimg),th/3,th) #toc = time.time() #print('自适应耗时：',toc-tic) cv2.imshow('Uncanny',img_edge) cv2.imshow('Canny',img_canny) print( 'Time taken :: ', str(time.time()-stime)+' seconds...') cv2.waitKey(0) dog.py:
import numpy as np import math #Oriented Odd Symmetric Gaussian Filter :: First Derivative of Gaussian def deroGauss(w=5,s=1,angle=0): wlim = (w-1)/2 y,x = np.meshgrid(np.arange(-wlim,wlim+1),np.arange(-wlim,wlim+1)) G = np.exp(-np.sum((np.square(x),np.square(y)),axis=0)/(2*np.float64(s)**2)) G = G/np.sum(G) dGdx = -np.multiply(x,G)/np.float64(s)**2 dGdy = -np.multiply(y,G)/np.float64(s)**2 angle = angle*math.pi/180 #converting to radians dog = math.cos(angle)*dGdx + math.sin(angle)*dGdy return dog bilateralfilt.py：
import numpy as np #import cv2, time def bilatfilt(I,w,sd,sr): dim = I.shape Iout= np.zeros(dim) #If the window is 5X5 then w = 5 wlim = (w-1)//2 y,x = np.meshgrid(np.arange(-wlim,wlim+1),np.arange(-wlim,wlim+1)) #Geometric closeness g = np.exp(-np.sum((np.square(x),np.square(y)),axis=0)/(2*(np.float64(sd)**2))) #Photometric Similarity Ipad = np.pad(I,(wlim,),'edge') for r in range(wlim,dim[0]+wlim): for c in range(wlim,dim[1]+wlim): Ix = Ipad[r-wlim:r+wlim+1,c-wlim:c+wlim+1] s = np.exp(-np.square(Ix-Ipad[r,c])/(2*(np.float64(sr)**2))) k = np.multiply(g,s) Iout[r-wlim,c-wlim] = np.sum(np.multiply(k,Ix))/np.sum(k) return Iout 
参考资料：
https://github.com/sadimanna/canny（基于python2实现自适应阈值的canny，且个别地方会报错）
算法细节，可以网上搜索查看相关文献。
文章来源: https://blog.csdn.net/lyxleft/article/details/91558726