import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
import os

from nets import Model

import wandb

from torch.utils.data import DataLoader
from dataset import CTDDataset
from train import normalize_and_colormap, parse_yaml, inference as ctd_inference

device = 'cuda'
wandb.init(project="crestereo", entity="cpt-captain")


def do_infer(left_img, right_img, gt_disp, model, attend_pattern=True):
    disp = ctd_inference(left_img, right_img, gt_disp, None, model, None, n_iter=20, wandb_log=False, attend_pattern=attend_pattern)

    disp_vis = normalize_and_colormap(disp)
    # gt_disp_vis = normalize_and_colormap(gt_disp)
    # if gt_disp.shape != disp.shape:
        # gt_disp = gt_disp.reshape(disp.shape)
    # disp_err = gt_disp - disp
    # disp_err = normalize_and_colormap(disp_err.abs())
    if isinstance(left_img, torch.Tensor):
        left_img = left_img.cpu().detach().numpy().astype('uint8')
        right_img = right_img.cpu().detach().numpy().astype('uint8')

    results = {
        'disp': wandb.Image(
            disp,
            caption=f"Pred. Disparity \n{disp.min():.{2}f}/{disp.max():.{2}f}",
        ),
        'disp_vis': wandb.Image(
            disp_vis,
            caption=f"Pred. Disparity \n{disp.min():.{2}f}/{disp.max():.{2}f}",
        ),
        # 'disp_err': wandb.Image(
            # disp_err,
            # caption=f"Disparity Error\n{disp_err.min():.{2}f}/{disp_err.max():.{2}f}",
        # ),
        'input_left': wandb.Image(
            left_img,
            caption=f"Input left",
        ),
        'input_right': wandb.Image(
            right_img,
            caption=f"Input right",
        ),
    }

    if gt_disp is not None:
        print('logging gt')
        print(f'gt: {gt_disp.max()}/{gt_disp.min()}/{gt_disp.mean()}')
        gt_disp_vis = normalize_and_colormap(gt_disp)
        results.update({
            'gt_disp_vis': wandb.Image(
                gt_disp_vis,
                caption=f"GT Disparity \n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}",
            )})
    wandb.log(results)


def downsample(img, half_height_out=480):
    downsampled = cv2.pyrDown(img)
    diff = (downsampled.shape[0] - half_height_out) // 2
    return downsampled[diff:downsampled.shape[0] - diff, 0:downsampled.shape[1]]


if __name__ == '__main__':
    # model_path = "models/crestereo_eth3d.pth"
    model_path = "train_log/models/latest.pth"
    # model_path = "train_log/models/epoch-120.pth"
    # model_path = "train_log/models/epoch-250.pth"

    print(model_path)
    # reference_pattern_path = '/home/nils/kinect_reference_high_res_scaled_down.png'
    # reference_pattern_path = '/home/nils/new_reference.png'
    # reference_pattern_path = '/home/nils/kinect_reference_high_res.png'
    reference_pattern_path = '/home/nils/miniprojekt/kinect_high_res_thresh_denoised.png'
    # reference_pattern_path = '/home/nils/miniprojekt/kinect_syn_ref.png'
    # reference_pattern_path = '/home/nils/kinect_reference_cropped.png'
    # reference_pattern_path = '/home/nils/orig_ctd/connecting_the_dots/data/kinect_pattern.png'

    # data_type = 'kinect'
    data_type = 'blender'
    augment = False

    args = parse_yaml("cfgs/train.yaml")

    wandb.config.update({'model_path': model_path, 'reference_pattern': reference_pattern_path, 'augment': augment, 'data_type': data_type, 'pattern_self_attention': args.pattern_attention})

    model = Model(max_disp=256, mixed_precision=False, test_mode=True)
    model = nn.DataParallel(model, device_ids=[device])
    state_dict = torch.load(model_path)['state_dict']
    model.load_state_dict(state_dict, strict=True)
    model.to(device)
    model.eval()

    # dataset = CTDDataset('/media/Data1/connecting_the_dots_data/ctd_data/', data_type=data_type,
                         # pattern_path=reference_pattern_path, augment=augment)
    # dataloader = DataLoader(dataset, args.batch_size, shuffle=True,
                            # num_workers=0, drop_last=False, persistent_workers=False, pin_memory=True)
    
    # for batch in dataloader:
    gt_disp = None
    right = downsample(cv2.imread(reference_pattern_path))

    if data_type == 'blender':
        img_path = '/media/Data1/connecting_the_dots_data/blender_renders/data/'
    elif data_type == 'kinect':
        img_path = '/home/nils/kinect_pngs/ir/'

    for img in sorted(list(entry for entry in os.scandir(img_path) if 'depth' not in entry.name), key=lambda x:x.name)[:25]:
        print(img.path)
        if data_type == 'blender':
            baseline = 0.075    # meters
            fl = 560.           # as per CTD

            gt_path = img.path.rsplit('.')[0] + '_depth0001.png'
            gt_depth = downsample(cv2.imread(gt_path))

            mystery_factor = 35     # we don't get reasonable disparities due to incorrect depth scaling (or something like that)
            gt_disp = (baseline * fl * mystery_factor) / gt_depth

        left = downsample(cv2.imread(img.path))

        do_infer(left, right, gt_disp, model, attend_pattern=args.pattern_attention)