import argparse

import logging

import os

import numpy as np

import torch

import torch.nn.functional as F

from PIL import Image

from torchvision import transforms

from utils.data_loading import BasicDataset

from unet.unet_model import UNet

from utils.utils import plot_img_and_mask

device = torch.device('cuda:4' if torch.cuda.is_available() else 'cpu')

directory = r'/home/data/spleen_blood/data/test/imgs'

def predict_img(net,

                full_img,

                device,

                scale_factor=1,

                out_threshold=0.5):

    net.eval()

    img = torch.from_numpy(BasicDataset.preprocess(None, full_img, scale_factor, is_mask=False))

    img = img.unsqueeze(0)

    img = img.to(device=device, dtype=torch.float32)

    with torch.no_grad():

        output = net(img).cpu()

        output = F.interpolate(output, (full_img.size[1], full_img.size[0]), mode='bilinear')

        if net.n_classes > 1:

            mask = output.argmax(dim=1)

        else:

            mask = torch.sigmoid(output) > out_threshold

    return mask[0].long().squeeze().numpy()

def get_output_filenames(in_files):

    return f'{os.path.splitext(in_files)[0]}_OUT.png'

def mask_to_image(mask: np.ndarray, mask_values):

    if isinstance(mask_values[0], list):

        out = np.zeros((mask.shape[-2], mask.shape[-1], len(mask_values[0])), dtype=np.uint8)

    elif mask_values == [0, 1]:

        out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=bool)

    else:

        out = np.zeros((mask.shape[-2], mask.shape[-1]), dtype=np.uint8)

    if mask.ndim == 3:

        mask = np.argmax(mask, axis=0)

    for i, v in enumerate(mask_values):

        out[mask == i] = v

    return Image.fromarray(out)

if __name__ == '__main__':

    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')

    model = './epoch_26_acc_0.90_best_val_acc.pth'

    for fn in os.listdir(directory):

        in_files = os.path.join(directory, fn)

        out_files = get_output_filenames(in_files)

        #print(fn, in_files, out_files)

        net = UNet(n_channels=1, n_classes=5, bilinear=True)

        logging.info(f'Loading model {model}')

        logging.info(f'Using device {device}')

        net.to(device=device)

        state_dict = torch.load(model, map_location=device)

        mask_values = state_dict.pop('mask_values', [0, 1])

        net.load_state_dict(state_dict)

        logging.info('Model loaded!')

        filename = in_files

        logging.info(f'Predicting image {filename} ...')

        img = Image.open(filename)

        mask = predict_img(net=net,

                           full_img=img,

                           scale_factor=0.5,

                           out_threshold=0.5,

                           device=device)

        out_filename = out_files

        result = mask_to_image(mask, mask_values)

        result.save(out_filename)

        logging.info(f'Mask saved to {out_filename}')

        #logging.info(f'Visualizing results for image {filename}, close to continue...')

        #plot_img_and_mask(img, mask)