某位大佬开源了论文源码，github地址：https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency
里面有一部分CRF后处理预测图的代码，这里我就直接扒过来了。
使用方法：

if args['crf_refine']:
	prediction = crf_refine(np.array(img), np.array(prediction))

源码：

import pydensecrf.densecrf as dcrf

def crf_refine(img, annos):      #use crf to refine predict pic
    def _sigmoid(x):
        return 1 / (1 + np.exp(-x))

    assert img.dtype == np.uint8
    assert annos.dtype == np.uint8
    print(img.shape[:2],annos.shape)
    assert img.shape[:2] == annos.shape

    # img and annos should be np array with data type uint8

    EPSILON = 1e-8

    M = 2  # salient or not
    tau = 1.05
    # Setup the CRF model
    d = dcrf.DenseCRF2D(img.shape[1], img.shape[0], M)

    anno_norm = annos / 255.

    n_energy = -np.log((1.0 - anno_norm + EPSILON)) / (tau * _sigmoid(1 - anno_norm))
    p_energy = -np.log(anno_norm + EPSILON) / (tau * _sigmoid(anno_norm))

    U = np.zeros((M, img.shape[0] * img.shape[1]), dtype='float32') # set a U which is the same size as input pic
    U[0, :] = n_energy.flatten()
    U[1, :] = p_energy.flatten()

    d.setUnaryEnergy(U)

    d.addPairwiseGaussian(sxy=3, compat=3)
    d.addPairwiseBilateral(sxy=60, srgb=5, rgbim=img, compat=5)

    # Do the inference
    infer = np.array(d.inference(1)).astype('float32')
    res = infer[1, :]

    res = res * 255
    res = res.reshape(img.shape[:2])  # the same size with the input pic
    return res.astype('uint8')