opencv python 基于KNN的手写体识别的实例


            OCR of Hand-written Data using kNN 

OCR of Hand-written Digits



我们的目标是构建一个可以读取手写数字的应用程序, 为此，我们需要一些train_data和test_data. OpenCV附带一个images digits.png（在文件夹opencv\sources\samples\data\中），它有5000个手写数字（每个数字500个,每个数字是20x20图像）.所以首先要将图片切割成5000个不同图片,每个数字变成一个单行400像素.前面的250个数字作为训练数据，后250个作为测试数据.





import numpy as np

import cv2

import matplotlib.pyplot as plt



img = cv2.imread('digits.png')

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)



# Now we split the image to 5000 cells, each 20x20 size

cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]



# Make it into a Numpy array. It size will be (50,100,20,20)

x = np.array(cells)



# Now we prepare train_data and test_data.

train = x[:,:50].reshape(-1,400).astype(np.float32) # Size = (2500,400)

test = x[:,50:100].reshape(-1,400).astype(np.float32) # Size = (2500,400)



# Create labels for train and test data

k = np.arange(10)

train_labels = np.repeat(k,250)[:,np.newaxis]

test_labels = train_labels.copy()



# Initiate kNN, train the data, then test it with test data for k=1

knn = cv2.ml.KNearest_create()

knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)

ret,result,neighbours,dist = knn.findNearest(test,k=5)



# Now we check the accuracy of classification

# For that, compare the result with test_labels and check which are wrong

matches = result==test_labels

correct = np.count_nonzero(matches)

accuracy = correct*100.0/result.size

print( accuracy )



输出：91.76

进一步提高准确率的方法是增加训练数据,特别是错误的数据.每次训练时最好是保存训练数据,以便下次使用.





# save the data

np.savez('knn_data.npz',train=train, train_labels=train_labels)



# Now load the data

with np.load('knn_data.npz') as data:

  print( data.files )

  train = data['train']

  train_labels = data['train_labels']







OCR of English Alphabets



在opencv / samples / data /文件夹中附带一个数据文件letter-recognition.data.在每一行中,第一列是一个字母表,它是我们的标签. 接下来的16个数字是它的不同特征.





import numpy as np

import cv2

import matplotlib.pyplot as plt





# Load the data, converters convert the letter to a number

data= np.loadtxt('letter-recognition.data', dtype= 'float32', delimiter = ',',

          converters= {0: lambda ch: ord(ch)-ord('A')})



# split the data to two, 10000 each for train and test

train, test = np.vsplit(data,2)



# split trainData and testData to features and responses

responses, trainData = np.hsplit(train,[1])

labels, testData = np.hsplit(test,[1])



# Initiate the kNN, classify, measure accuracy.

knn = cv2.ml.KNearest_create()

knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)

ret, result, neighbours, dist = knn.findNearest(testData, k=5)



correct = np.count_nonzero(result == labels)

accuracy = correct*100.0/10000

print( accuracy )



输出：93.06



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