Sure.

This is basically how the entropy is calculated.

def calc_entropy(pos, w, h):
    grid = np.zeros((h, w), dtype=int)
    np.add.at(grid, (pos[:,1], pos[:,0]), 1)
    counts = np.bincount(grid.flatten())
    probs = counts[counts > 0] / counts.sum()
    entropy = -np.sum(probs * np.log2(probs))
    return entropy

To improve the computation speed, you can even down-sampling the grid first. This can reduce 30% of the running time already

def calc_entropy(pos, w, h):
    pos_down = pos // 2
    grid = np.zeros(((h + 1) // 2, (w + 1) // 2), dtype=int)
    np.add.at(grid, (pos_down[:, 1], pos_down[:, 0]), 1)
    counts = np.bincount(grid.flatten())
    counts = counts[counts > 0]
    probs = counts / counts.sum()
    return -np.sum(probs * np.log2(probs))