OpenCV3.3-Python3.6.2中KNN使用举例

# reference: https://docs.opencv.org/3.1.0/d5/d26/tutorial_py_knn_understanding.html#            opencv-3.3.0/doc/py_tutorials/py_ml/py_knn/py_knn_opencvimport cv2import numpy as npimport matplotlib.pyplot as plt# Feature set containing (x,y) values of 25 known/training datatrainData = np.random.randint(0, 100, (25,2)).astype(np.float32)# Labels each one either Red or Blue with numbers 0 and 1responses = np.random.randint(0, 2, (25,1)).astype(np.float32)# Take Red families and plot themred = trainData[responses.ravel() == 0]plt.scatter(red[:,0], red[:,1], 80, 'r', '^')# Take Blue families and plot themblue = trainData[responses.ravel() == 1]plt.scatter(blue[:,0], blue[:,1], 80, 'b', 's')#plt.show()# New comer is marked in green colornewcomer = np.random.randint(0, 100, (1,2)).astype(np.float32)plt.scatter(newcomer[:,0], newcomer[:,1], 80, 'g', 'o')knn = cv2.ml.KNearest_create()knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)ret, results, neighbours ,dist = knn.findNearest(newcomer, 3)print("result: ", results,"\n")print("neighbours: ", neighbours,"\n")print("distance: ", dist)plt.show()

执行结果如下：

GitHub： https://github.com/fengbingchun/NN_Test