#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import time

import torch
from PIL import Image
from executables.guiclient.utilities import DiceCoeff, rgb_segment
from neodroid.utilities.messaging_utilities.neodroid_camera_extraction import (
    extract_neodroid_camera,
)

__author__ = "Christian Heider Nielsen"

import neodroid as neo
from matplotlib import pyplot


def collect_states(_environments, num=4):
    states_c = []
    for _ in range(num):
        actions = _environments.action_space.sample()
        states = _environments.react(actions)
        state = next(iter(states.values()))
        terminated = state.terminated
        states_c.append(state)
    return states_c


def plot_images(
    rgb_im,
    seg_im,
    red_img,
    green_img,
    blue_img,
    red_masked_img,
    green_masked_img,
    blue_masked_img,
):
    fig = pyplot.figure()

    (ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8) = fig.subplots(4, 2)

    ax1.imshow(rgb_im)
    ax2.imshow(seg_im)

    ax3.imshow(red_img)
    ax4.imshow(red_masked_img)

    ax5.imshow(green_img)
    ax6.imshow(green_masked_img)

    ax7.imshow(blue_img)
    ax8.imshow(blue_masked_img)

    fig.tight_layout()
    pyplot.show()


i = 0


def save(rgb_im, seg_im):
    global i
    path = "/home/heider/Datasets/Neodroid/Segmentation/train"

    r_im = Image.fromarray(rgb_im)
    r_im.save(path + f"/image/image_{i}.png")
    s_im = Image.fromarray(seg_im)
    s_im.save(path + f"/mask/image_{i}_mask.png")
    i = i + 1


def process_states(states):
    rgb_imgs = []
    for state in states:
        rgb_img, seg_img, *_ = extract_neodroid_camera(state)

        (
            red_img,
            green_img,
            blue_img,
            red_masked_img,
            green_masked_img,
            blue_masked_img,
        ) = rgb_segment(rgb_img, seg_img)
        normed = red_img[..., 0] / 255.0
        rgb_imgs.append((rgb_img / 255.0, normed))
        # save(rgb_img, red_img)

        # plot_images(rgb_im,seg_im,red_img, green_img, blue_img, red_masked_img, green_masked_img,
        # blue_masked_img)

    return rgb_imgs


from torch import nn
from torch.nn import functional


class CNN(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        # self.bn1 = nn.BatchNorm2d(16)
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(16, 16, kernel_size=3, padding=1)
        # self.bn2 = nn.BatchNorm2d(16)
        # self.upsample1 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        # self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        self.tconv1 = nn.ConvTranspose2d(16, 1, kernel_size=2, stride=2)

    def forward(self, x):
        x = self.conv1(x)
        # x = self.bn1(x)
        x = functional.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        # x = self.bn1(x)
        x = functional.relu(x)
        # x = self.upsample1(x)
        x = self.tconv1(x)
        # x = self.upsample2(x)
        return x.transpose(1, -1).squeeze()


def main():
    _environments = neo.make(environment_name="seg", connect_to_running=True)
    _environments.reset()

    i = 0
    freq = 100
    time_s = time.time()

    net = CNN()
    optimiser = torch.optim.Adam(net.parameters())
    loss = DiceCoeff()

    while _environments.is_connected:
        states = collect_states(_environments)

        la = process_states(states)

        inputs, labels = zip(*la)
        inputs = torch.Tensor(inputs).transpose(1, -1)
        labels = torch.Tensor(labels).transpose(1, -1)

        preds = net(inputs).contiguous()

        los = loss(preds, labels.contiguous())

        los.backward(retain_graph=True)
        optimiser.step()
        optimiser.zero_grad()

        terminated = False

        time_now = time.time()
        if i % freq == 0:
            fps = 1 / (time_now - time_s)
            print(f"fps:[{fps}], loss:[{los}]")

            if i % (10 * freq) == 0:
                fig = pyplot.figure()

                (ax1, ax2) = fig.subplots(1, 2)

                preds2 = preds.detach().numpy()
                ax1.imshow(preds2[0])
                ax2.imshow(labels[0])

                fig.tight_layout()
                pyplot.show()

        i += 1
        time_s = time_now

        if terminated:
            _environments.reset()


if __name__ == "__main__":
    main()
