如何使用OpenCV实现标准数字识别功能

这篇文章给大家分享的是有关如何使用OpenCV实现标准数字识别功能的内容。小编觉得挺实用的，因此分享给大家做个参考，一起跟随小编过来看看吧。

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)           if  h>25 and h < 30:        #使用高过滤小框和大框            count+=1            cv2.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()

训练数据为：

测试数据为：

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] == 255            else:                gray[i][j] == 0    thresh = 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)            if  h>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 += 1        if count == 10:            break    return numbers numbers = getNum('1.png')

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