import argparse
import os
import yaml
import utils
from PIL import Image
from tqdm import tqdm

import paddle
import paddle.vision.transforms.functional as TF

paddle.set_device('cpu')

parser = argparse.ArgumentParser(
    description='Apply the trained model to create a dataset')
parser.add_argument('--checkpoint',
                    default=None,
                    type=str,
                    help='checkpoint model to use')
parser.add_argument('--artifacts',
                    default='',
                    type=str,
                    help='selecting different artifacts type')
parser.add_argument('--name',
                    default='',
                    type=str,
                    help='additional string added to folder path')
parser.add_argument('--dataset',
                    default='df2k',
                    type=str,
                    help='selecting different datasets')
parser.add_argument('--track',
                    default='train',
                    type=str,
                    help='selecting train or valid track')
parser.add_argument('--num_res_blocks',
                    default=8,
                    type=int,
                    help='number of ResNet blocks')
parser.add_argument('--cleanup_factor',
                    default=2,
                    type=int,
                    help='downscaling factor for image cleanup')
parser.add_argument('--upscale_factor',
                    default=4,
                    type=int,
                    choices=[4],
                    help='super resolution upscale factor')
opt = parser.parse_args()

# define input and target directories
with open('./paths.yml', 'r') as stream:
    PATHS = yaml.load(stream)

if opt.dataset == 'df2k':
    path_sdsr = PATHS['datasets']['df2k'] + '/generated/sdsr/'
    path_tdsr = PATHS['datasets']['df2k'] + '/generated/tdsr/'
    input_source_dir = PATHS['df2k']['tdsr']['source']
    input_target_dir = PATHS['df2k']['tdsr']['target']
    source_files = [
        os.path.join(input_source_dir, x) for x in os.listdir(input_source_dir)
        if utils.is_image_file(x)
    ]
    target_files = [
        os.path.join(input_target_dir, x) for x in os.listdir(input_target_dir)
        if utils.is_image_file(x)
    ]
else:
    path_sdsr = PATHS['datasets'][
        opt.
        dataset] + '/generated/' + opt.artifacts + '/' + opt.track + opt.name + '_sdsr/'
    path_tdsr = PATHS['datasets'][
        opt.
        dataset] + '/generated/' + opt.artifacts + '/' + opt.track + opt.name + '_tdsr/'
    input_source_dir = PATHS[opt.dataset][opt.artifacts]['hr'][opt.track]
    input_target_dir = None
    source_files = [
        os.path.join(input_source_dir, x) for x in os.listdir(input_source_dir)
        if utils.is_image_file(x)
    ]
    target_files = []

tdsr_hr_dir = path_tdsr + 'HR'
tdsr_lr_dir = path_tdsr + 'LR'

assert not os.path.exists(PATHS['datasets'][opt.dataset])

if not os.path.exists(tdsr_hr_dir):
    os.makedirs(tdsr_hr_dir)
if not os.path.exists(tdsr_lr_dir):
    os.makedirs(tdsr_lr_dir)

# generate the noisy images
with paddle.no_grad():
    for file in tqdm(source_files, desc='Generating images from source'):
        # load HR image
        input_img = Image.open(file)
        input_img = TF.to_tensor(input_img)

        # Resize HR image to clean it up and make sure it can be resized again
        resize2_img = utils.imresize(input_img, 1.0 / opt.cleanup_factor, True)
        _, w, h = resize2_img.shape
        w = w - w % opt.upscale_factor
        h = h - h % opt.upscale_factor
        resize2_cut_img = resize2_img[:, :w, :h]

        # Save resize2_cut_img as HR image for TDSR
        path = os.path.join(tdsr_hr_dir, os.path.basename(file))
        utils.to_pil_image(resize2_cut_img).save(path, 'PNG')

        # Generate resize3_cut_img and apply model
        resize3_cut_img = utils.imresize(resize2_cut_img,
                                         1.0 / opt.upscale_factor, True)

        # Save resize3_cut_noisy_img as LR image for TDSR
        path = os.path.join(tdsr_lr_dir, os.path.basename(file))
        utils.to_pil_image(resize3_cut_img).save(path, 'PNG')

    for file in tqdm(target_files, desc='Generating images from target'):
        # load HR image
        input_img = Image.open(file)
        input_img = TF.to_tensor(input_img)

        # Save input_img as HR image for TDSR
        path = os.path.join(tdsr_hr_dir, os.path.basename(file))
        utils.to_pil_image(input_img).save(path, 'PNG')

        # generate resized version of input_img
        resize_img = utils.imresize(input_img, 1.0 / opt.upscale_factor, True)

        # Save resize_noisy_img as LR image for TDSR
        path = os.path.join(tdsr_lr_dir, os.path.basename(file))
        utils.to_pil_image(resize_img).save(path, 'PNG')
