# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.

import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms

from .utils import convert_to_pil


class REBNCONV(nn.Module):

    def __init__(self, in_ch=3, out_ch=3, dirate=1):
        super(REBNCONV, self).__init__()
        self.conv_s1 = nn.Conv2d(
            in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate)
        self.bn_s1 = nn.BatchNorm2d(out_ch)
        self.relu_s1 = nn.ReLU(inplace=True)

    def forward(self, x):
        hx = x
        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
        return xout


def _upsample_like(src, tar):
    """upsample tensor 'src' to have the same spatial size with tensor 'tar'."""
    src = F.upsample(src, size=tar.shape[2:], mode='bilinear')
    return src


class RSU7(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU7, self).__init__()
        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
        self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):
        hx = x
        hxin = self.rebnconvin(hx)
        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)
        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)
        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)
        hx4 = self.rebnconv4(hx)
        hx = self.pool4(hx4)
        hx5 = self.rebnconv5(hx)
        hx = self.pool5(hx5)
        hx6 = self.rebnconv6(hx)
        hx7 = self.rebnconv7(hx6)
        hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
        hx6dup = _upsample_like(hx6d, hx5)
        hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
        hx5dup = _upsample_like(hx5d, hx4)
        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
        hx4dup = _upsample_like(hx4d, hx3)
        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)
        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)
        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
        return hx1d + hxin


class RSU6(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU6, self).__init__()

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)
        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):
        hx = x
        hxin = self.rebnconvin(hx)
        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)
        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)
        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)
        hx4 = self.rebnconv4(hx)
        hx = self.pool4(hx4)
        hx5 = self.rebnconv5(hx)
        hx6 = self.rebnconv6(hx5)
        hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
        hx5dup = _upsample_like(hx5d, hx4)
        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
        hx4dup = _upsample_like(hx4d, hx3)
        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)
        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)
        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
        return hx1d + hxin


class RSU5(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU5, self).__init__()

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)
        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):
        hx = x
        hxin = self.rebnconvin(hx)
        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)
        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)
        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)
        hx4 = self.rebnconv4(hx)
        hx5 = self.rebnconv5(hx4)
        hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
        hx4dup = _upsample_like(hx4d, hx3)
        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)
        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)
        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
        return hx1d + hxin


class RSU4(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU4, self).__init__()

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)
        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):

        hx = x
        hxin = self.rebnconvin(hx)
        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)
        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)
        hx3 = self.rebnconv3(hx)
        hx4 = self.rebnconv4(hx3)
        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)
        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)
        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
        return hx1d + hxin


class RSU4F(nn.Module):

    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU4F, self).__init__()

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)
        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)
        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):

        hx = x
        hxin = self.rebnconvin(hx)
        hx1 = self.rebnconv1(hxin)
        hx2 = self.rebnconv2(hx1)
        hx3 = self.rebnconv3(hx2)
        hx4 = self.rebnconv4(hx3)
        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
        hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
        hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))
        return hx1d + hxin


class U2NET(nn.Module):

    def __init__(self, in_ch=3, out_ch=1):
        super(U2NET, self).__init__()

        # encoder
        self.stage1 = RSU7(in_ch, 32, 64)
        self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.stage2 = RSU6(64, 32, 128)
        self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.stage3 = RSU5(128, 64, 256)
        self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.stage4 = RSU4(256, 128, 512)
        self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.stage5 = RSU4F(512, 256, 512)
        self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
        self.stage6 = RSU4F(512, 256, 512)
        # decoder
        self.stage5d = RSU4F(1024, 256, 512)
        self.stage4d = RSU4(1024, 128, 256)
        self.stage3d = RSU5(512, 64, 128)
        self.stage2d = RSU6(256, 32, 64)
        self.stage1d = RSU7(128, 16, 64)
        self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
        self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
        self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
        self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
        self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
        self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)
        self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1)

    def forward(self, x):

        hx = x
        hx1 = self.stage1(hx)
        hx = self.pool12(hx1)
        hx2 = self.stage2(hx)
        hx = self.pool23(hx2)
        hx3 = self.stage3(hx)
        hx = self.pool34(hx3)
        hx4 = self.stage4(hx)
        hx = self.pool45(hx4)
        hx5 = self.stage5(hx)
        hx = self.pool56(hx5)
        hx6 = self.stage6(hx)
        hx6up = _upsample_like(hx6, hx5)

        hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
        hx5dup = _upsample_like(hx5d, hx4)
        hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
        hx4dup = _upsample_like(hx4d, hx3)
        hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)
        hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)
        hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))
        d1 = self.side1(hx1d)
        d2 = self.side2(hx2d)
        d2 = _upsample_like(d2, d1)
        d3 = self.side3(hx3d)
        d3 = _upsample_like(d3, d1)
        d4 = self.side4(hx4d)
        d4 = _upsample_like(d4, d1)
        d5 = self.side5(hx5d)
        d5 = _upsample_like(d5, d1)
        d6 = self.side6(hx6)
        d6 = _upsample_like(d6, d1)
        d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1))
        return torch.sigmoid(d0), torch.sigmoid(d1), torch.sigmoid(
            d2), torch.sigmoid(d3), torch.sigmoid(d4), torch.sigmoid(
                d5), torch.sigmoid(d6)



class SalientAnnotator:
    def __init__(self, cfg, device=None):
        self.return_image = cfg.get('RETURN_IMAGE', False)
        self.use_crop = cfg.get('USE_CROP', False)
        pretrained_model = cfg['PRETRAINED_MODEL']
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
        self.norm_mean = [0.485, 0.456, 0.406]
        self.norm_std = [0.229, 0.224, 0.225]
        self.norm_size = cfg.get('NORM_SIZE', [320, 320])
        self.model = U2NET(3, 1)
        self.model.load_state_dict(torch.load(pretrained_model, map_location='cpu'))
        self.model = self.model.to(self.device).eval()
        self.transform_input = transforms.Compose([
            transforms.Resize(self.norm_size),
            transforms.ToTensor(),
            transforms.Normalize(mean=self.norm_mean, std=self.norm_std)
        ])

    def forward(self, image, return_image=None):
        return_image = return_image if return_image is not None else self.return_image
        image = convert_to_pil(image)
        img_w, img_h = image.size
        input_image = self.transform_input(image).float().unsqueeze(0).to(self.device)
        with torch.no_grad():
            results = self.model(input_image)
        data = results[0][0, 0, :, :]
        data_norm = (data - torch.min(data)) / (
                torch.max(data) - torch.min(data))
        data_norm_np = (data_norm.cpu().numpy() * 255).astype('uint8')
        data_norm_rst = cv2.resize(data_norm_np, (img_w, img_h))
        if return_image:
            image_np = np.array(image)
            _, binary_mask = cv2.threshold(data_norm_rst, 1, 255, cv2.THRESH_BINARY)
            white_bg = np.ones_like(image) * 255
            ret_image = np.where(binary_mask[:, :, np.newaxis] == 255, image_np, white_bg).astype(np.uint8)
            ret_mask = np.where(binary_mask, 255, 0).astype(np.uint8)
            if self.use_crop:
                x, y, w, h = cv2.boundingRect(binary_mask)
                ret_image = ret_image[y:y + h, x:x + w]
                ret_mask = ret_mask[y:y + h, x:x + w]
            return {"image": ret_image, "mask": ret_mask}
        else:
            return data_norm_rst


class SalientVideoAnnotator(SalientAnnotator):
    def forward(self, frames, return_image=None):
        ret_frames = []
        for frame in frames:
            anno_frame = super().forward(frame)
            ret_frames.append(anno_frame)
        return ret_frames