Testing density map creation

[mrgloom]

Here is my test for density map creation, I draw 10 random points and than create density map, however sum over map is not 10, but 6.95563, for 100 points it's 91.6066 and it depens on point positions, how can we approximate it more precise?

import numpy as np
import scipy
import scipy.spatial
import scipy.ndimage
import cv2
from random import randint

def gaussian_filter_density(mask):

    density = np.zeros(mask.shape, dtype=np.float32)

    gt_count = np.count_nonzero(mask)
    if gt_count == 0:
        return density

    pts = np.array(zip(np.nonzero(mask)[1], np.nonzero(mask)[0]))

    leafsize = 2048
    # build kdtree
    tree = scipy.spatial.KDTree(pts.copy(), leafsize=leafsize)
    # query kdtree
    distances, locations = tree.query(pts, k=2, eps=10.)

    for i, pt in enumerate(pts):
        pt2d = np.zeros(mask.shape, dtype=np.float32)
        pt2d[pt[1],pt[0]] = 1.
        if gt_count > 1:
            sigma = distances[i][1]
        else:
            sigma = np.average(np.array(mask.shape))/2./2. #case: 1 point

        density += scipy.ndimage.filters.gaussian_filter(pt2d, sigma, mode='constant')

    print "np.sum(density): ", np.sum(density)

    return density

def test_density():
    h= 100
    w= 120
    mask= np.zeros((h,w))

    for i in range(0,10):
        y= randint(0,h-1)
        x= randint(0,w-1)
        mask[y,x]= 1

    density= gaussian_filter_density(mask)

    cv2.imwrite('mask.png', 255*mask)
    cv2.imwrite('density.png', 255*density)

test_density()

[ThanhNhann]

Sr, but now have you know the answer ? And one more, do you know why we have this line pt2d[pt[1],pt[0]] = 1. why not: pt2d[pt[0],pt[1]] = 1. Thanks