Different intensity values for same image in OpenCV and MATLAB - matlab

Question

I'm using Python 2.7 and OpenCV 3.x for my project for omr sheet evaluation using web camera.
While finding the number of white pixels in around the center of circle,I came to know that the intensity values are wrong, but it shows the correct values in MATLAB when I'm using imtool('a1.png').
I'm using .png image (datatype uint8).
just run the code and in the image go to [360:370,162:172] coordinate and see the intensity values.. it should not be 0.
find the images here -> a1.png a2.png
Why is this happening?
import numpy as np
import cv2
from matplotlib import pyplot as plt
#select radius of circle
radius = 10;
#function for finding white pixels
def thresh_circle(img,ptx,pty):
centerX = ptx;
centerY = pty;
cntOfWhite = 0;
for i in range((centerX - radius),(centerX + radius)):
for j in range((centerY - radius), (centerY + radius)):
if(j < img.shape[0] and i < img.shape[1]):
val = img[i][j]
if (val == 255):
cntOfWhite = cntOfWhite + 1;
return cntOfWhite
MIN_MATCH_COUNT = 10
img1 = cv2.imread('a1.png',0) # queryImage
img2 = cv2.imread('a2.png',0) # trainImage
sift = cv2.SIFT()# Initiate SIFT detector
kp1, des1 = sift.detectAndCompute(img1,None)# find the keypoints and descriptors with SIFT
kp2, des2 = sift.detectAndCompute(img2,None)
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1,des2,k=2)
good = []# store all the good matches as per Lowe's ratio test.
for m,n in matches:
if m.distance < 0.7*n.distance:
good.append(m)
if len(good)>MIN_MATCH_COUNT:
src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.LMEDS,5.0)
#print M
matchesMask = mask.ravel().tolist()
h,w = img1.shape
else:
print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
matchesMask = None
img3 = cv2.warpPerspective(img1, M, (img2.shape[1],img2.shape[0]))
blur = cv2.GaussianBlur(img3,(5,5),0)
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
ret,th2 = cv2.threshold(blur,ret3,255,cv2.THRESH_BINARY_INV)
print th2[360:370,162:172]#print a block of image
plt.imshow(th2, 'gray'),plt.show()
cv2.waitKey(0)
cv2.imwrite('th2.png',th2)
ptyc = np.array([170,200,230,260]);#y coordinates of circle center
ptxc = np.array([110,145,180,215,335,370,405,440])#x coordinates of circle center
pts_src = np.zeros(shape = (32,2),dtype=np.int);#x,y coordinates of circle center
ct = 0;
for i in range(0,4):
for j in range(0,8):
pts_src[ct][1] = ptyc[i];
pts_src[ct][0] = ptxc[j];
ct = ct+1;
boolval = np.zeros(shape=(8,4),dtype=np.bool)
ct = 0;
for j in range(0,8):
for i in range(0,4):
a1 = thresh_circle(th2,pts_src[ct][0],pts_src[ct][1])
ct = ct+1;
if(a1 > 50):
boolval[j][i] = 1
else:
boolval[j][i] = 0