import torch

import numpy as np

def get_specificity(SR, GT, threshold=0.5):

    """

    cal each img specificity

    所有负例中被分对的概率

    结节在在输入图片中所占比例较少 所以该指标的值很高

    """

    # Sensitivity == Recall

    SR = SR > threshold

    GT = GT == torch.max(GT)

    # TP : True Positive

    # FN : False Negative

    # TP = ((SR == 1) + (GT == 1)) == 2

    # FN = ((SR == 0) + (GT == 1)) == 2

    # TN : True Negative

    # FP : False Positive

    TN = ((SR == 0) + (GT == 0)) == 2

    FP = ((SR == 1) + (GT == 0)) == 2

    SE = float(torch.sum(TN)) / (float(torch.sum(TN + FP)) + 1e-6)

    return SE

def meanspecificity_seg(pred, gt, spes):

    """

    :return save img' sensitivity values in sens

    """

    gt_tensor = gt

    gt_tensor = gt_tensor.cpu()

    pred[pred < 0.5] = 0

    pred[pred >= 0.5] = 1

    pred = pred.type(torch.LongTensor)

    # TO CPU

    # pred_np = pred.data.cpu().numpy()

    # gt = gt.data.cpu().numpy()

    for x in range(pred.size()[0]):

        spe = get_specificity(pred[x], gt_tensor[x])

        spes.append(spe)

    return spes