python_opencv_knn.py

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the data, converters convert the letter to a number
data= np.loadtxt("D:\OpenCV\opencv\sources\samples\data\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, responses)
# knn.train(trainData, responses)
knn.train(trainData,cv2.ml.ROW_SAMPLE,responses)
ret, result, neighbours, dist = knn.findNearest(testData, 5)

correct = np.count_nonzero(result == labels)
accuracy = correct*100.0/10000
print accuracy