import os.path as osp
import logging
import time
import argparse
from collections import OrderedDict

import options.options as option
import utils.util as util
from data.util import bgr2ycbcr
from data import create_dataset, create_dataloader
from models import create_model

#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='options/test/test_ESRGAN_vrp.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)

util.mkdirs(
    (path for key, path in opt['path'].items()
     if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
                  screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt))

#### Create test dataset and dataloader
test_loaders = []
for phase, dataset_opt in sorted(opt['datasets'].items()):
    test_set = create_dataset(dataset_opt)
    test_loader = create_dataloader(test_set, dataset_opt)
    logger.info('Number of test images in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
    test_loaders.append(test_loader)

model = create_model(opt)
for test_loader in test_loaders:
    test_set_name = test_loader.dataset.opt['name']
    logger.info('\nTesting [{:s}]...'.format(test_set_name))
    test_start_time = time.time()
    dataset_dir = osp.join(opt['path']['results_root'], test_set_name)
    util.mkdir(dataset_dir)

    test_results = OrderedDict()
    test_results['psnr'] = []
    test_results['ssim'] = []
    test_results['psnr_y'] = []
    test_results['ssim_y'] = []

    for data in test_loader:
        need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
        model.feed_data(data, need_GT=need_GT)
        img_path = data['GT_path'][0] if need_GT else data['LQ_path'][0]
        img_name = osp.splitext(osp.basename(img_path))[0]

        model.test()
        visuals = model.get_current_visuals(need_GT=need_GT)

        sr_img = util.tensor2img(visuals['rlt'])  # uint8

        # save images
        suffix = opt['suffix']
        if suffix:
            save_img_path = osp.join(dataset_dir, img_name + suffix + '.png')
        else:
            save_img_path = osp.join(dataset_dir, img_name + '.png')
        util.save_img(sr_img, save_img_path)

        # calculate PSNR and SSIM
        if need_GT:
            gt_img = util.tensor2img(visuals['GT'])
            sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
            psnr = util.calculate_psnr(sr_img, gt_img)
            ssim = util.calculate_ssim(sr_img, gt_img)
            test_results['psnr'].append(psnr)
            test_results['ssim'].append(ssim)

            if gt_img.shape[2] == 3:  # RGB image
                sr_img_y = bgr2ycbcr(sr_img / 255., only_y=True)
                gt_img_y = bgr2ycbcr(gt_img / 255., only_y=True)

                psnr_y = util.calculate_psnr(sr_img_y * 255, gt_img_y * 255)
                ssim_y = util.calculate_ssim(sr_img_y * 255, gt_img_y * 255)
                test_results['psnr_y'].append(psnr_y)
                test_results['ssim_y'].append(ssim_y)
                logger.info(
                    '{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
                    format(img_name, psnr, ssim, psnr_y, ssim_y))
            else:
                logger.info('{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}.'.format(img_name, psnr, ssim))
        else:
            logger.info(img_name)

    if need_GT:  # metrics
        # Average PSNR/SSIM results
        ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
        ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
        logger.info(
            '----Average PSNR/SSIM results for {}----\n\tPSNR: {:.6f} dB; SSIM: {:.6f}\n'.format(
                test_set_name, ave_psnr, ave_ssim))
        if test_results['psnr_y'] and test_results['ssim_y']:
            ave_psnr_y = sum(test_results['psnr_y']) / len(test_results['psnr_y'])
            ave_ssim_y = sum(test_results['ssim_y']) / len(test_results['ssim_y'])
            logger.info(
                '----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}\n'.
                format(ave_psnr_y, ave_ssim_y))