import torch

import torch.nn as nn

import torch.nn.functional as F

from torch.nn import init

from features.feature import *

from gcn.layers import GConv

class Self_Attn(nn.Module):

    """ Self attention Layer"""

    def __init__(self,in_dim,activation):

        super(Self_Attn,self).__init__()

        self.chanel_in = in_dim

        self.activation = activation

        self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)

        self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)

        self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)

        self.gamma = nn.Parameter(torch.zeros(1))

        self.softmax  = nn.Softmax(dim=-1) #

    def forward(self,x):

        m_batchsize,C,width ,height = x.size()

        proj_query  = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N)

        proj_key =  self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H)

        energy =  torch.bmm(proj_query,proj_key) # transpose check

        attention = self.softmax(energy) # BX (N) X (N) 

        proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N

        out = torch.bmm(proj_value,attention.permute(0,2,1) )

        out = out.view(m_batchsize,C,width,height)

        out = self.gamma*out + x

        return out,attention

def init_weights(net, init_type='normal', gain=0.02):

    def init_func(m):

        classname = m.__class__.__name__

        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):

            if init_type == 'normal':

                init.normal_(m.weight.data, 0.0, gain)

            elif init_type == 'xavier':

                init.xavier_normal_(m.weight.data, gain=gain)

            elif init_type == 'kaiming':

                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')

            elif init_type == 'orthogonal':

                init.orthogonal_(m.weight.data, gain=gain)

            else:

                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)

            if hasattr(m, 'bias') and m.bias is not None:

                init.constant_(m.bias.data, 0.0)

        elif classname.find('BatchNorm2d') != -1:

            init.normal_(m.weight.data, 1.0, gain)

            init.constant_(m.bias.data, 0.0)

    print('initialize network with %s' % init_type)

    net.apply(init_func)

class conv_block(nn.Module):

    def __init__(self,ch_in,ch_out):

        super(conv_block,self).__init__()

        self.conv = nn.Sequential(

            nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True),

            nn.BatchNorm2d(ch_out),

            nn.ReLU(inplace=True),

            nn.Conv2d(ch_out, ch_out, kernel_size=3,stride=1,padding=1,bias=True),

            nn.BatchNorm2d(ch_out),

            nn.ReLU(inplace=True)

        )

    def forward(self,x):

        x = self.conv(x)

        return x

class up_conv(nn.Module):

    def __init__(self,ch_in,ch_out):

        super(up_conv,self).__init__()

        self.up = nn.Sequential(

            nn.Upsample(scale_factor=2),

            nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),

            nn.BatchNorm2d(ch_out),

            nn.ReLU(inplace=True)

        )

    def forward(self,x):

        x = self.up(x)

        return x

class Recurrent_block(nn.Module):

    def __init__(self,ch_out,t=2):

        super(Recurrent_block,self).__init__()

        self.t = t

        self.ch_out = ch_out

        self.conv = nn.Sequential(

            nn.Conv2d(ch_out,ch_out,kernel_size=3,stride=1,padding=1,bias=True),

            nn.BatchNorm2d(ch_out),

            nn.ReLU(inplace=True)

        )

    def forward(self,x):

        for i in range(self.t):

            if i==0:

                x1 = self.conv(x)

            x1 = self.conv(x+x1)

        return x1

class RRCNN_block(nn.Module):

    def __init__(self,ch_in,ch_out,t=2):

        super(RRCNN_block,self).__init__()

        self.RCNN = nn.Sequential(

            Recurrent_block(ch_out,t=t),

            Recurrent_block(ch_out,t=t)

        )

        self.Conv_1x1 = nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=1,padding=0)

    def forward(self,x):

        x = self.Conv_1x1(x)

        x1 = self.RCNN(x)

        return x+x1

class single_conv(nn.Module):

    def __init__(self,ch_in,ch_out):

        super(single_conv,self).__init__()

        self.conv = nn.Sequential(

            nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True),

            nn.BatchNorm2d(ch_out),

            nn.ReLU(inplace=True)

        )

    def forward(self,x):

        x = self.conv(x)

        return x

class Attention_block(nn.Module):

    def __init__(self,F_g,F_l,F_int):

        super(Attention_block,self).__init__()

        self.W_g = nn.Sequential(

            nn.Conv2d(F_g, F_int, kernel_size=1,stride=1,padding=0,bias=True),

            nn.BatchNorm2d(F_int)

            )

        self.W_x = nn.Sequential(

            nn.Conv2d(F_l, F_int, kernel_size=1,stride=1,padding=0,bias=True),

            nn.BatchNorm2d(F_int)

        )

        self.psi = nn.Sequential(

            nn.Conv2d(F_int, 1, kernel_size=1,stride=1,padding=0,bias=True),

            nn.BatchNorm2d(1),

            nn.Sigmoid()

        )

        self.relu = nn.ReLU(inplace=True)

    def forward(self,g,x):

        g1 = self.W_g(g)

        x1 = self.W_x(x)

        psi = self.relu(g1+x1)

        psi = self.psi(psi)

        return x*psi

def dootsu(img):

    se = img

    _, c, _, _ = img.size()

    # squeezelayer = nn.Conv2d(c, c // 16, kernel_size=1)

    # squeezelayer.cuda()

    # img = squeezelayer(img)

    img = img.cpu().detach()

    channel = list(img.size())[1]

    batch = list(img.size())[0]

    imgfolder = img.chunk(batch, dim=0)

    chw_output = []

    for index in range(batch):

        bchw = imgfolder[index]

        chw = bchw.squeeze()

        chwfolder = chw.chunk(channel, dim=0)

        hw_output = []

        for i in range(channel):

            hw = chwfolder[i].squeeze()

            hw = np.transpose(hw.detach().numpy(), (0, 1))

            hw_otsu = otsu(hw)

            hw_otsu = torch.from_numpy(hw_otsu)

            hw_output.append(hw_otsu)

        chw_otsu = torch.stack(hw_output, dim=0)

        chw_output.append(chw_otsu)

    bchw_otsu = torch.stack(chw_output, dim=0).cuda()

    # result = torch.cat([se.float().cuda(), bchw_otsu.float().cuda()],dim=1)

    return bchw_otsu

class Self_Attn_OTSU(nn.Module):

    """ Self attention otsu Layer"""

    def __init__(self,in_dim,activation):

        super(Self_Attn_OTSU,self).__init__()

        self.chanel_in = in_dim

        self.activation = activation

        self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)

        self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)

        self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)

        self.gamma = nn.Parameter(torch.zeros(1))

        self.softmax  = nn.Softmax(dim=-1) #

    def forward(self,x):

        m_batchsize,C,width ,height = x.size()

        proj_query  = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N)

        proj_key =  self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H)

        energy =  torch.bmm(proj_query,proj_key) # transpose check

        attention = self.softmax(energy) # BX (N) X (N) 

        proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N

        out = torch.bmm(proj_value,attention.permute(0,2,1) )

        out = out.view(m_batchsize,C,width,height)

        out = dootsu(out)

        out = self.gamma*out + x

        return out,attention

def edge_conv2d(im):

    in_channel = list(im.size())[1]

    out_channel = in_channel

    # 用nn.Conv2d定义卷积操作

    conv_op = nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False)

    '''定义sobel算子参数，所有值除以3----有人觉得出来的图更好些；但我感觉应该是概率问题，没啥用'''

    # sobel_kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype='float32') / 3

    sobel_kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype='float32')

    # 将sobel算子转换为适配卷积操作的卷积核

    sobel_kernel = sobel_kernel.reshape((1, 1, 3, 3))

    # 卷积输出out_channle

    sobel_kernel = np.repeat(sobel_kernel, in_channel, axis=1)

    # 输入图的通道in_channle

    sobel_kernel = np.repeat(sobel_kernel, out_channel, axis=0)

    # 给卷积操作的卷积核赋值

    conv_op.weight.data = torch.from_numpy(sobel_kernel).cuda()

    # print(conv_op.weight.size())

    # print(im.size())

    # print(conv_op, '\n')

    edge_detect = conv_op(im)

    # print(torch.max(edge_detect))

    # 将输出转换为图片格式

    edge_detect = edge_detect.squeeze().detach()

    return edge_detect

class Self_Attn_EDGE(nn.Module):

    """ Self attention edge Layer"""

    def __init__(self,in_dim,activation):

        super(Self_Attn_EDGE,self).__init__()

        self.chanel_in = in_dim

        self.activation = activation

        self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)

        self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)

        self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)

        self.gamma = nn.Parameter(torch.zeros(1))

        self.softmax  = nn.Softmax(dim=-1) #

    def forward(self,x):

        m_batchsize,C,width ,height = x.size()

        proj_query  = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N)

        proj_key =  self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H)

        energy =  torch.bmm(proj_query,proj_key) # transpose check

        attention = self.softmax(energy) # BX (N) X (N) 

        proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N

        out = torch.bmm(proj_value,attention.permute(0,2,1) )

        out = out.view(m_batchsize,C,width,height)

        out = edge_conv2d(out)

        out = self.gamma*out + x

        return out,attention

class Self_Attn_GABOR(nn.Module):

    """ Self attention gabor Layer"""

    def __init__(self,in_dim,activation):

        super(Self_Attn_GABOR,self).__init__()

        self.chanel_in = in_dim

        self.activation = activation

        self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)

        self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)

        self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)

        self.gaborlayer = GConv(in_dim, out_channels = in_dim//4, kernel_size= 1, stride=1, M=4, nScale=1, bias=False, expand=True)

        self.gamma = nn.Parameter(torch.zeros(1))

        self.softmax  = nn.Softmax(dim=-1) #

    def forward(self,x):

        m_batchsize,C,width ,height = x.size()

        proj_query  = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N)

        proj_key =  self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H)

        energy =  torch.bmm(proj_query,proj_key) # transpose check

        attention = self.softmax(energy) # BX (N) X (N) 

        proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N

        out = torch.bmm(proj_value,attention.permute(0,2,1) )

        out = out.view(m_batchsize,C,width,height)

        # print(out.size())

        out = self.gaborlayer(out)

        # print(out.size())

        out = self.gamma*out + x

        return out,attention

class Mymodel(nn.Module):

    def __init__(self,img_ch=3,output_ch=1):

        super(Mymodel,self).__init__()

        self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2)

        self.Conv1 = conv_block(ch_in=img_ch,ch_out=64)

        self.Conv2 = conv_block(ch_in=64,ch_out=128)

        self.Conv3 = conv_block(ch_in=128,ch_out=256)

        self.Conv4 = conv_block(ch_in=256,ch_out=512)

        self.Conv5 = conv_block(ch_in=512,ch_out=1024)

        self.Up5 = up_conv(ch_in=1024,ch_out=512)

        self.Att5 = Attention_block(F_g=512,F_l=512,F_int=256)

        self.Up_conv5 = conv_block(ch_in=1024, ch_out=512)

        self.Up4 = up_conv(ch_in=512,ch_out=256)

        self.Att4 = Attention_block(F_g=256,F_l=256,F_int=128)

        self.Up_conv4 = conv_block(ch_in=512, ch_out=256)

        self.Up3 = up_conv(ch_in=256,ch_out=128)

        self.Att3 = Attention_block(F_g=128,F_l=128,F_int=64)

        self.Up_conv3 = conv_block(ch_in=256, ch_out=128)

        self.Up2 = up_conv(ch_in=128,ch_out=64)

        self.Att2 = Attention_block(F_g=64,F_l=64,F_int=32)

        self.Up_conv2 = conv_block(ch_in=128, ch_out=64)

        # # gabor

        # self.gaborlayer = GConv(in_channels=64, out_channels = 16, kernel_size= 1, stride=1, M=4, nScale=1, bias=False, expand=True)

        self.pred1 = nn.Conv2d(32,output_ch,kernel_size=1,stride=1,padding=0)

        self.pred2 = nn.Conv2d(32,output_ch,kernel_size=1,stride=1,padding=0)

        self.pred3 = nn.Conv2d(32,output_ch,kernel_size=1,stride=1,padding=0)

        # self-attention

        self.pred_1 = single_conv(ch_in=64, ch_out=32)

        self.pred_2 = single_conv(ch_in=64, ch_out=32)

        self.pred_3 = single_conv(ch_in=64, ch_out=32)

        self.conv_Atten1 = Self_Attn_OTSU(32,'relu')

        self.conv_Atten2 = Self_Attn_GABOR(32,'relu')

        # self.conv_Atten3 = Self_Attn(32,'relu')

        self.conv_Atten3 = Self_Attn_GABOR(32,'relu')

        # fusion module

        self.conv_fusion1 = conv_block(ch_in=160, ch_out=64)

        self.conv_fusion2 = nn.Conv2d(64, output_ch, kernel_size=1, stride=1, padding=0)   # v1

    def forward(self,x):

        # encoding path

        x1 = self.Conv1(x)

        x2 = self.Maxpool(x1)

        x2 = self.Conv2(x2)

        x3 = self.Maxpool(x2)

        x3 = self.Conv3(x3)

        x4 = self.Maxpool(x3)

        x4 = self.Conv4(x4)

        x5 = self.Maxpool(x4)

        x5 = self.Conv5(x5)

        # decoding + concat path

        d5 = self.Up5(x5)

        x4 = self.Att5(g=d5,x=x4)

        d5 = torch.cat((x4,d5),dim=1)        

        d5 = self.Up_conv5(d5)

        d4 = self.Up4(d5)

        x3 = self.Att4(g=d4,x=x3)

        d4 = torch.cat((x3,d4),dim=1)

        d4 = self.Up_conv4(d4)

        d3 = self.Up3(d4)

        x2 = self.Att3(g=d3,x=x2)

        d3 = torch.cat((x2,d3),dim=1)

        d3 = self.Up_conv3(d3)

        d2 = self.Up2(d3)

        x1 = self.Att2(g=d2,x=x1)

        d2 = torch.cat((x1,d2),dim=1)

        d2 = self.Up_conv2(d2)

        # d2_gabor = self.gaborlayer(d2)

        pred_1 = self.pred_1(d2)

        pred_2 = self.pred_2(d2)

        pred_3 = self.pred_3(d2)

        pred1 = self.pred1(pred_1)

        pred2 = self.pred2(pred_2)

        pred3 = self.pred3(pred_3)

        # self-attention

        attention_higher, _ = self.conv_Atten1(pred_1)

        attention_lower, _ = self.conv_Atten2(pred_2)

        attention_all, _ = self.conv_Atten3(pred_3)

        # fusion module

        y = torch.cat((attention_higher, attention_all, attention_lower, d2), dim=1)

        y = self.conv_fusion1(y)

        pred = self.conv_fusion2(y)

        return [pred1, pred2, pred3, pred]