OpenCV简单标准数字识别的完整实例

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://*.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python#

import sys
import numpy as np
import 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 = []                  #label
keys = [i for i in range(48,58)]    #48-58为ASCII码
count =0
for 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
              break
responses = 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 sys
import cv2
import 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')

总结

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原文链接：https://blog.csdn.net/huang_nansen/article/details/83241143