opencv python 基于KNN的手写体识别的实例

 import numpy as np import cv2 import matplotlib.pyplot as plt img = cv2.imread('digits.png-600') 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 )

 # 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'] 

 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 )