slow_guided_filter.py

import cv2
import numpy as np


def guided_filter(im,p,r,eps):
    mean_I = cv2.boxFilter(im,cv2.CV_64F,(r,r))
    mean_p = cv2.boxFilter(p, cv2.CV_64F,(r,r))
    mean_Ip = cv2.boxFilter(im*p,cv2.CV_64F,(r,r))
    cov_Ip = mean_Ip - mean_I*mean_p

    mean_II = cv2.boxFilter(im*im,cv2.CV_64F,(r,r))
    var_I   = mean_II - mean_I*mean_I

    a = cov_Ip/(var_I + eps)
    b = mean_p - a*mean_I

    mean_a = cv2.boxFilter(a,cv2.CV_64F,(r,r))
    mean_b = cv2.boxFilter(b,cv2.CV_64F,(r,r))

    q = mean_a*im + mean_b
    return q


def soft_matting (im,et):
    gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
    gray = np.float64(gray)/255
    r = 60
    eps = 0
    t = guided_filter(gray,et,r,eps)

    return t