OpenCV简单标准数字识别的完整实例 OpenCV简单标准数字识别的完整

在学习openCV时，看到一个问答做数字识别，里面配有代码，应用到了openCV里面的ml包，很有学习价值。
https://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python#

import sysimport numpy as npimport cv2 im = cv2.imread('t.png')im3 = im.copy() gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)#先转换为灰度图才能够使用图像阈值化 thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)#自适应阈值化 ##################Now finding Contours#################### image,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)#边缘查找，找到数字框，但存在误判 samples =np.empty((0,900))#将每一个识别到的数字所有像素点作为特征，储存到一个30*30的矩阵内responses = []#labelkeys = [i for i in range(48,58)]#48-58为ASCII码count =0for cnt in contours:if cv2.contourArea(cnt)>80:#使用边缘面积过滤较小边缘框[x,y,w,h] = cv2.boundingRect(cnt)ifh>25 and h < 30:#使用高过滤小框和大框count+=1cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)roi = thresh[y:y+h,x:x+w]roismall = cv2.resize(roi,(30,30))cv2.imshow('norm',im)key = cv2.waitKey(0)if key == 27:# (escape to quit)sys.exit()elif key in keys:responses.append(int(chr(key)))sample = roismall.reshape((1,900))samples = np.append(samples,sample,0)if count == 100:#过滤一下过多边缘框，后期可能会尝试极大抑制breakresponses = np.array(responses,np.float32)responses = responses.reshape((responses.size,1))print ("training complete") np.savetxt('generalsamples.data',samples)np.savetxt('generalresponses.data',responses)#cv2.waitKey()cv2.destroyAllWindows()

【OpenCV简单标准数字识别的完整实例】训练数据为：

文章图片

测试数据为：

文章图片

使用openCV自带的ML包，KNearest算法

import sysimport cv2import numpy as np #######training part############### samples = np.loadtxt('generalsamples.data',np.float32)responses = np.loadtxt('generalresponses.data',np.float32)responses = responses.reshape((responses.size,1)) model = cv2.ml.KNearest_create()model.train(samples,cv2.ml.ROW_SAMPLE,responses) def getNum(path):im = cv2.imread(path)out = np.zeros(im.shape,np.uint8)gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)#预处理一下for i in range(gray.__len__()):for j in range(gray[0].__len__()):if gray[i][j] == 0:gray[i][j] == 255else:gray[i][j] == 0thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)image,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)count = 0 numbers = []for cnt in contours:if cv2.contourArea(cnt)>80:[x,y,w,h] = cv2.boundingRect(cnt)ifh>25:cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)roi = thresh[y:y+h,x:x+w]roismall = cv2.resize(roi,(30,30))roismall = roismall.reshape((1,900))roismall = np.float32(roismall)retval, results, neigh_resp, dists = model.findNearest(roismall, k = 1)string = str(int((results[0][0])))numbers.append(int((results[0][0])))cv2.putText(out,string,(x,y+h),0,1,(0,255,0))count += 1if count == 10:breakreturn numbers numbers = getNum('1.png')