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test_IRN-Compression.py
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test_IRN-Compression.py
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import os.path as osp
import logging
import time
import argparse
from collections import OrderedDict
import numpy as np
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
import cv2
#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, required=True, help='Path to options YMAL file.')
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'] = []
test_results['psnr_lr'] = []
test_results['ssim_lr'] = []
test_results['psnr_y_lr'] = []
test_results['ssim_y_lr'] = []
test_results['bpp'] = []
test_results['psnr_restore'] = []
for data in test_loader:
model.feed_data(data)
img_path = data['GT_path'][0]
img_name = osp.splitext(osp.basename(img_path))[0]
model.test()
visuals = model.get_current_visuals()
sr_img = util.tensor2img(visuals['SR']) # uint8
srgt_img = util.tensor2img(visuals['GT']) # uint8
lr_img = util.tensor2img(visuals['LR']) # uint8
lrgt_img = util.tensor2img(visuals['LR_ref']) # uint8
rlr_img = util.tensor2img(visuals['RLR']) # 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)
if suffix:
save_img_path = osp.join(dataset_dir, img_name + suffix + '_GT.png')
else:
save_img_path = osp.join(dataset_dir, img_name + '_GT.png')
util.save_img(srgt_img, save_img_path)
if suffix:
save_img_path = osp.join(dataset_dir, img_name + suffix + '_LR.jpg')
else:
save_img_path = osp.join(dataset_dir, img_name + '_LR.jpg')
quality = opt['test']['jpg_quality']
cv2.imwrite(save_img_path, lr_img, [int(cv2.IMWRITE_JPEG_QUALITY),quality])
# get lr image size
lr_size = osp.getsize(save_img_path)
if suffix:
save_img_path = osp.join(dataset_dir, img_name + suffix + '_LR_ref.png')
else:
save_img_path = osp.join(dataset_dir, img_name + '_LR_ref.png')
util.save_img(lrgt_img, save_img_path)
# calculate PSNR, SSIM and bpp
h, w = srgt_img.shape[0], srgt_img.shape[1]
bpp = lr_size * 8. / (h * w)
test_results['bpp'].append(bpp)
sr_img = sr_img / 255.
srgt_img = srgt_img / 255.
lr_img = lr_img / 255.
lrgt_img = lrgt_img / 255.
crop_border = opt['crop_border'] if opt['crop_border'] else opt['scale']
if crop_border == 0:
cropped_sr_img = sr_img
cropped_srgt_img = srgt_img
else:
cropped_sr_img = sr_img[crop_border:-crop_border, crop_border:-crop_border, :]
cropped_srgt_img = srgt_img[crop_border:-crop_border, crop_border:-crop_border, :]
psnr = util.calculate_psnr(cropped_sr_img * 255, cropped_srgt_img * 255)
ssim = util.calculate_ssim(cropped_sr_img * 255, cropped_srgt_img * 255)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
# PSNR and SSIM for LR
psnr_lr = util.calculate_psnr(lr_img * 255, lrgt_img * 255)
ssim_lr = util.calculate_ssim(lr_img * 255, lrgt_img * 255)
test_results['psnr_lr'].append(psnr_lr)
test_results['ssim_lr'].append(ssim_lr)
psnr_restore = util.calculate_psnr(lr_img * 255, rlr_img)
test_results['psnr_restore'].append(psnr_restore)
if srgt_img.shape[2] == 3: # RGB image
sr_img_y = bgr2ycbcr(sr_img, only_y=True)
srgt_img_y = bgr2ycbcr(srgt_img, only_y=True)
if crop_border == 0:
cropped_sr_img_y = sr_img_y
cropped_srgt_img_y = srgt_img_y
else:
cropped_sr_img_y = sr_img_y[crop_border:-crop_border, crop_border:-crop_border]
cropped_srgt_img_y = srgt_img_y[crop_border:-crop_border, crop_border:-crop_border]
psnr_y = util.calculate_psnr(cropped_sr_img_y * 255, cropped_srgt_img_y * 255)
ssim_y = util.calculate_ssim(cropped_sr_img_y * 255, cropped_srgt_img_y * 255)
test_results['psnr_y'].append(psnr_y)
test_results['ssim_y'].append(ssim_y)
lr_img_y = bgr2ycbcr(lr_img, only_y=True)
lrgt_img_y = bgr2ycbcr(lrgt_img, only_y=True)
psnr_y_lr = util.calculate_psnr(lr_img_y * 255, lrgt_img_y * 255)
ssim_y_lr = util.calculate_ssim(lr_img_y * 255, lrgt_img_y * 255)
test_results['psnr_y_lr'].append(psnr_y_lr)
test_results['ssim_y_lr'].append(ssim_y_lr)
logger.info(
'{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}. LR PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}. PSNR Restore: {:.6f}, bpp: {:.6f}.'.
format(img_name, psnr, ssim, psnr_y, ssim_y, psnr_lr, ssim_lr, psnr_y_lr, ssim_y_lr, psnr_restore, bpp))
else:
logger.info('{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}. LR PSNR: {:.6f} dB; SSIM: {:.6f}. PSNR Restore: {:.6f}, bpp: {:.6f}.'.format(img_name, psnr, ssim, psnr_lr, ssim_lr, psnr_restore, bpp))
# Average PSNR/SSIM results
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
ave_psnr_lr = sum(test_results['psnr_lr']) / len(test_results['psnr_lr'])
ave_ssim_lr = sum(test_results['ssim_lr']) / len(test_results['ssim_lr'])
ave_psnr_restore = sum(test_results['psnr_restore']) / len(test_results['psnr_restore'])
ave_bpp = sum(test_results['bpp']) / len(test_results['bpp'])
logger.info(
'----Average PSNR/SSIM results for {}----\n\tpsnr: {:.6f} db; ssim: {:.6f}. LR psnr: {:.6f} db; ssim: {:.6f}.\n'.format(
test_set_name, ave_psnr, ave_ssim, ave_psnr_lr, ave_ssim_lr))
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'])
ave_psnr_y_lr = sum(test_results['psnr_y_lr']) / len(test_results['psnr_y_lr'])
ave_ssim_y_lr = sum(test_results['ssim_y_lr']) / len(test_results['ssim_y_lr'])
logger.info(
'----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}. LR PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.\n'.
format(ave_psnr_y, ave_ssim_y, ave_psnr_y_lr, ave_ssim_y_lr))
logger.info(
'----Average PSNR Restore and bpp----\n\tpsnr_restore: {:.6f} db; bpp: {:.6f}.\n'.format(
ave_psnr_restore, ave_bpp))