#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author ：hhx
@Date ：2022/5/13 9:53 
@Description ：
"""
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
import os

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False


# 将图片转为灰度图
# https://blog.csdn.net/weixin_43635647/article/details/99625105
# img = cv.imread('test.png', 0)
# cv.imshow("img", img)
# cv.waitKey()
def normalize(data):
    min = np.min(data)
    max = np.max(data)
    res = (data - min) / (max - min)
    return res, max, min


def OTSU(img_gray, GrayScale):
    u1 = 0.0  # 背景像素的平均灰度值
    u2 = 0.0  # 前景像素的平均灰度值
    th = 0.0

    # 总的像素数目
    PixSum = img_gray.size
    # 各个灰度值的像素数目
    PixCount = np.zeros(GrayScale)
    # 各灰度值所占总像素数的比例
    PixRate = np.zeros(GrayScale)
    # 统计各个灰度值的像素个数
    for i in range(PixSum):
        # 默认灰度图像的像素值范围为GrayScale
        Pixvalue = img_gray[i]
        PixCount[int(Pixvalue)] = PixCount[int(Pixvalue)] + 1

    # 确定各个灰度值对应的像素点的个数在所有的像素点中的比例。
    for j in range(GrayScale):
        PixRate[j] = PixCount[j] * 1.0 / PixSum
    Max_var = 0
    # 确定最大类间方差对应的阈值
    for i in range(1, GrayScale):  # 从1开始是为了避免w1为0.
        u1_tem = 0.0
        u2_tem = 0.0
        # 背景像素的比列
        w1 = np.sum(PixRate[:i])
        # 前景像素的比例
        w2 = 1.0 - w1
        if w1 == 0 or w2 == 0:
            pass
        else:  # 背景像素的平均灰度值
            for m in range(i):
                u1_tem = u1_tem + PixRate[m] * m
            u1 = u1_tem * 1.0 / w1
            # 前景像素的平均灰度值
            for n in range(i, GrayScale):
                u2_tem = u2_tem + PixRate[n] * n
            u2 = u2_tem / w2
            # print(u1)
            # 类间方差公式：G=w1*w2*(u1-u2)**2
            tem_var = w1 * w2 * np.power((u1 - u2), 2)
            # print(tem_var)
            # 判断当前类间方差是否为最大值。
            if Max_var < tem_var:
                Max_var = tem_var  # 深拷贝，Max_var与tem_var占用不同的内存空间。
                th = i
    return th


def preprocess_nor(data1):
    data = data1.ravel()
    id = np.where(data.astype('str') == 'nan')
    data = np.delete(data, id)
    data, max, min = normalize(data)
    data = data * 1000
    data = data.astype('int')
    return data, max, min


def preprocess_div(data1):
    data = data1.ravel()
    id = np.where((data.astype('str') == 'nan'))
    data = np.delete(data, id)
    id1 = np.where((data > 0))[0].shape[0]
    id2 = np.where((data < 0))[0].shape[0]
    print(id1, id2)
    print(data.shape)
    data = data * 1000
    min = np.min(data)
    data = data - min
    data = data.astype('int')


    return data, np.max(data), min

if __name__ == '__main__':
    data_1 = np.load('result/index_npy/2021-04_BLI.npy')
    data_2 = np.load('result/index_npy/2021-04_index2.npy')
    #
    # data_1 = np.load('result/index_npy/2016-12_BLI.npy')
    # data_2 = np.load('result/index_npy/2017-05_BLI.npy')
    # #
    # data_1 = np.load('result/index_npy/2021-07_index2.npy')
    # data_2 = np.load('result/index_npy/2021-12_index2.npy')



    choose = 1  # 0代表使用Maxmin归一化

    if choose == 0:
        data1, max1, min1 = preprocess_nor(data_1)
        data2, max2, min2 = preprocess_nor(data_2)
    else:
        data1, max1, min1 = preprocess_div(data_1)
        data2, max2, min2 = preprocess_div(data_2)
    print(data1)
    print(data2)
    if choose == 0:
        th_1 = (OTSU(data1, 1001))
        th_2 = (OTSU(data2, 1001))
    else:
        th_1 = (OTSU(data1, max1 + 1))
        th_2 = (OTSU(data2, max2 + 1))


    if choose == 0:
        th1 = (th_1 / 1000) * (max1 - min1) + min1
        th2 = (th_2 / 1000) * (max2 - min2) + min2
    else:
        th1 = (th_1 + min1) / 1000
        th2 = (th_2 + min2) / 1000

        print('阈值1为', th_1, th1, -min1)
        print('阈值2为', th_2, th2, -min2)

    fig = plt.figure(figsize=(8, 8))
    ax = plt.subplot(321)
    n1, bins1, patches1 = plt.hist(data1, bins=100, density=True, color='g', alpha=1)
    plt.axvline(th_1)
    plt.axvline(-min1, color='r')
    ax = plt.subplot(323)
    cax = ax.matshow(data_1, cmap='coolwarm', vmin=-0.5, vmax=0.5)
    fig.colorbar(cax)
    ax = plt.subplot(325)
    cax = ax.matshow(data_1 > th1, cmap='coolwarm')
    fig.colorbar(cax)

    ax = plt.subplot(322)
    n1, bins1, patches1 = plt.hist(data2, bins=100, density=True, color='g', alpha=1)
    plt.axvline(th_2)
    plt.axvline(-min2, color='r')
    ax = plt.subplot(324)
    cax = ax.matshow(data_2, cmap='coolwarm', vmin=-0.5, vmax=0.5)
    fig.colorbar(cax)
    ax = plt.subplot(326)
    cax = ax.matshow(data_2 > th2, cmap='coolwarm')
    fig.colorbar(cax)
    plt.tight_layout()
    plt.show()

    # fig = plt.figure(figsize=(8, 8))
    # ax = plt.subplot(111)
    # cax = ax.matshow(data_2, cmap='coolwarm', vmin=-0.5, vmax=0.5)
    # for key, spine in ax.spines.items():
    #     # 'left', 'right', 'bottom', 'top'
    #     if key == 'right' or key == 'top' or key == 'bottom' or key == 'left':
    #         spine.set_visible(False)
    # # fig.colorbar(cax)
    # plt.xticks([])
    # plt.yticks([])
    # plt.tight_layout()
    # plt.savefig('test.jpg')