def crf(original_image, mask_img):

    # Converting annotated image to RGB if it is Gray scale
    if(len(mask_img.shape)<3):
        mask_img = gray2rgb(mask_img)
#     #Converting the annotations RGB color to single 32 bit integer
    annotated_label = mask_img[:,:,0] + (mask_img[:,:,1]<<8) + (mask_img[:,:,2]<<16)
#     # Convert the 32bit integer color to 0,1, 2, ... labels.
    colors, labels = np.unique(annotated_label, return_inverse=True)
    n_labels = 2
    #Setting up the CRF model
    d = dcrf.DenseCRF2D(original_image.shape[1], original_image.shape[0], n_labels)
    # get unary potentials (neg log probability)
    U = unary_from_labels(labels, n_labels, gt_prob=0.7, zero_unsure=False)
    d.setUnaryEnergy(U)
    # This adds the color-independent term, features are the locations only.
    d.addPairwiseGaussian(sxy=(3, 3), compat=3, kernel=dcrf.DIAG_KERNEL,
                      normalization=dcrf.NORMALIZE_SYMMETRIC)
    #Run Inference for 10 steps
    Q = d.inference(10)
    # Find out the most probable class for each pixel.
    MAP = np.argmax(Q, axis=0)
    return MAP.reshape((original_image.shape[0],original_image.shape[1]))

def crf(original_image, mask_img):
    labels = mask_img
    n_labels = 4
    #Setting up the CRF model
    d = dcrf.DenseCRF2D(original_image.shape[1], original_image.shape[0], n_labels)
    # get unary potentials (neg log probability)
    U = unary_from_labels(labels, n_labels, gt_prob=0.7, zero_unsure=False)
    d.setUnaryEnergy(U)
    # This adds the color-independent term, features are the locations only.
    d.addPairwiseGaussian(sxy=(3, 3), compat=3, kernel=dcrf.DIAG_KERNEL,
                      normalization=dcrf.NORMALIZE_SYMMETRIC)
    #Run Inference for 10 steps
    Q = d.inference(10)
    # Find out the most probable class for each pixel.
    MAP = np.argmax(Q, axis=0)
    return MAP.reshape((original_image.shape[0],original_image.shape[1]))