Moments as feature and KNN classifier using OpenCV and Python - Knearest doesn't classify

Question

I have a set of twelve shapes and I'm trying to determine the shape of a query image with the my database For convinience I have done all the preprocessing in MATLAB and have stored the shape contours, as binary image separately for training and testing prupose

I have done the Feature extraction and Implementation phase in Python In the training phase, I read the binary image, extract HU MOMETS (7 Vector) as features for all the train images(285 in total) So my samples (train samples) dimension is [285, 7] My responses dimension is [285]

Now I follow a similar strategy for testing as well. Test images consititue 541 in total Read image -> extract hu moments -> feed it to knn.find_nearest Dimension of the test moments is [1, 7]

The problem that I'm facing here is, irrespective of the test image that I give, I'm getting a return value in the knn.find_nearest as 76. It is consistently giving the same value for every image

What have I done to debug

• Checked with code to ensure if the dimens that I'm providing to KNN is correct
• Checked the Hu-moments values in matlab as well, and found nearly similar with opencv
• Also made sure that hu-moments for test image are computing properly
• Tested with changing the k value (when k=1 retval is 76, when k=3 retval is 75)

I don't know what I have done wrong here. please help

import os
import sys
import numpy
import cv2
import cv2.cv as cv
import xlwt


def main():

    pth = sys.argv[1]
    lsfolds = os.listdir(pth)

    files_path = []
    all_moments = numpy.empty((0,7))
    train_samples = []
    responses = []
    cnt=0

    for di in lsfolds:
        img_path = os.path.join(pth,di)
        lsfiles = os.listdir(img_path)  

        for ls in lsfiles:

            comp_path = os.path.join(img_path,ls)
            files_path.append(comp_path)
            if (comp_path[-4:] == 's.db'):
                continue

            img_bin = cv2.imread(comp_path,-1)
            #cv2.imshow('Image', img_bin)
            #cv2.waitKey()

            #################### Moments as Feature ###############################
            moments = cv2.moments(img_bin,1)
            hu_moments = cv2.HuMoments(moments)
            hu_moments = hu_moments.reshape((1,7))
            all_moments = numpy.append(all_moments,hu_moments,0)
            train_samples.append(comp_path)
            responses.append(int(cnt))

            ########################################################################

            cnt += 1

    responses = numpy.float32(responses)
    all_moments = numpy.float32(all_moments)

    ################## KNN #####################################

    knn_train_eng = cv2.KNearest()
    knn_train_eng.train(all_moments,responses)


    #######################################Testing######################################
    timg_pth = sys.argv[2]
    tfolds = os.listdir(timg_pth)

    wb = xlwt.Workbook()
    ws = wb.add_sheet('Test Results')
    c=0

    for tdi in tfolds:
        timg_dir = os.path.join(timg_pth,tdi)
        tfiles = os.listdir(timg_dir)

        for fl in tfiles:

            timg_path = os.path.join(timg_dir,fl)

            if (timg_path[-4:] == 's.db'):
                continue

            timg = cv2.imread(timg_path,-1)
            timg_bin = timg;
            #cv2.imshow('test_bin',timg_bin)

            tmoments = cv2.moments(timg_bin)
            thu_moments = cv2.HuMoments(tmoments)

            thu_moments = thu_moments.reshape((1,7))
            thu_moments = numpy.float32(thu_moments)

            retval, results, neigh_resp, dists = knn_train_eng.find_nearest(thu_moments,1) # Predict using KNN

            res,dist =  int(results[0][0]),dists[0][0]
            op_answ = str(int((results[0][0])))

            print op_answ
            print train_samples[int(op_answ)]
            op = cv2.imread(train_samples[int(op_answ)])

            c+=1
            cv2.destroyAllWindows()



    ##############################################################################

    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()