KNN train() in cv2 with opencv 3.0

Question

I'm trying to run k-nearest neighbours using cv2 (python 2.7) and opencv 3.0. I've replicated the same error message using code like http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_ml/py_knn/py_knn_understanding/py_knn_understanding.html:

import cv2 import numpy as np import matplotlib.pyplot as plt # Feature set containing (x,y) values of 25 known/training data trainData = np.random.randint(0,100,(25,2)).astype(np.float32) # Labels each one either Red or Blue with numbers 0 and 1 responses = np.random.randint(0,2,(25,1)).astype(np.float32) # Take Red families and plot them red = trainData[responses.ravel()==0] plt.scatter(red[:,0],red[:,1],80,'r','^') # Take Blue families and plot them blue = trainData[responses.ravel()==1] plt.scatter(blue[:,0],blue[:,1],80,'b','s') plt.show() newcomer = np.random.randint(0,100,(1,2)).astype(np.float32) plt.scatter(newcomer[:,0],newcomer[:,1],80,'g','o')  #The following line is modified for OpenCV 3.0 knn = cv2.ml.KNearest_create() knn.train(trainData,responses) ret, results, neighbours ,dist = knn.find_nearest(newcomer, 3)  print "result: ", results,"\n" print "neighbours: ", neighbours,"\n" print "distance: ", dist  plt.show() 

I modified the line knn = cv2.ml.KNearest_create() for OpenCV 3, but the subsequent line produces an error "TypeError: only length-1 arrays can be converted to Python scalars" and I can't figure out what I should be using for the train function.

Answer 1

You are passing wrong length of array for KNN algorithm....glancing at your code, i found that you have missed the cv2.ml.ROW_SAMPLE parameter in knn.train function, passing this parameter considers the length of array as 1 for entire row. thus your corrected code would be as below:

import cv2 import numpy as np import matplotlib.pyplot as plt  trainData = np.random.randint(0,100,(51,2)).astype(np.float32) responses = np.random.randint(0,2,(51,1)).astype(np.float32)  red = trainData[responses.ravel()==0] plt.scatter(red[:,0],red[:,1],80,'r','^') blue = trainData[responses.ravel()==1] plt.scatter(blue[:,0],blue[:,1],80,'b','s')   newcomer = np.random.randint(0,100,(5,2)).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 ("results: ", results,"\n") print ("neighbours: ", neighbours,"\n") print ("distances: ", dist)  plt.show() 

Here is the result which i got from it....