#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File        :   affinity.py    
@Contact     :   zpyovo@hotmail.com
@License     :   (C)Copyright 2018-2019, Lab501-TransferLearning-SCUT
@Description :

@Modify Time      @Author    @Version    @Desciption
------------      -------    --------    -----------
2022/3/12 9:18 PM   Pengyu Zhang      1.0         None
'''

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn.functional as F
import numpy as np
import cv2
def eightway_total_diff(arr):
    # 对输入数组进行填充，每边填充1个元素
    padded_arr = np.pad(arr, pad_width=1, mode='edge')

    # 获取填充后数组的尺寸
    h, w = padded_arr.shape

    # 初始化输出数组，用于存储总差值
    diff_array = np.zeros((h-2, w-2))

    # 遍历每个元素（不包括填充的边界）
    for y in range(1, h-1):
        for x in range(1, w-1):
            total_diff = 0  # 初始化当前元素的总差值
            # 计算每个方向的差值
            for dy in range(-1, 2):
                for dx in range(-1, 2):
                    if dy == 0 and dx == 0:
                        # 排除中心点自身
                        continue
                    # 直接使用填充后的坐标计算差值
                    diff = abs(padded_arr[y, x] - padded_arr[y+dy, x+dx])
                    # 如果差值小于0，则置为0
                    total_diff += max(diff, 0)
            # 将总差值存储在输出数组中
            diff_array[y-1, x-1] = total_diff
    return diff_array.astype(int)

def roi_affinity_siml(X_ter,X_str):
    X_ter = np.array(X_ter)
    X_str = np.array(X_str)
    X_ter_diffs = eightway_total_diff(X_ter / X_ter.max())
    X_str_diffs = eightway_total_diff(X_str / X_str.max())
    # 计算差异
    difference = cv2.absdiff(X_ter_diffs, X_str_diffs)
    mean_diff = np.mean(difference)
    # 计算相似度
    similarity = max((1 - mean_diff) * 100, 0)
    return similarity