opencv python 基于KNN的手寫體識別的實(shí)例
OCR of Hand-written Data using kNN
OCR of Hand-written Digits
我們的目標(biāo)是構(gòu)建一個可以讀取手寫數(shù)字的應(yīng)用程序, 為此,我們需要一些train_data和test_data. OpenCV附帶一個images digits.png(在文件夾opencv\sources\samples\data\中),它有5000個手寫數(shù)字(每個數(shù)字500個,每個數(shù)字是20x20圖像).所以首先要將圖片切割成5000個不同圖片,每個數(shù)字變成一個單行400像素.前面的250個數(shù)字作為訓(xùn)練數(shù)據(jù),后250個作為測試數(shù)據(jù).
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 )
輸出:91.76
進(jìn)一步提高準(zhǔn)確率的方法是增加訓(xùn)練數(shù)據(jù),特別是錯誤的數(shù)據(jù).每次訓(xùn)練時最好是保存訓(xùn)練數(shù)據(jù),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 /文件夾中附帶一個數(shù)據(jù)文件letter-recognition.data.在每一行中,第一列是一個字母表,它是我們的標(biāo)簽. 接下來的16個數(shù)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 )
輸出:93.06
以上就是本文的全部內(nèi)容,希望對大家的學(xué)習(xí)有所幫助,也希望大家多多支持腳本之家。
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