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main_dpir_deblocking_color.py
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main_dpir_deblocking_color.py
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import os.path
import logging
import numpy as np
from collections import OrderedDict
import torch
from utils import utils_logger
from utils import utils_image as util
import cv2
def main():
# ----------------------------------------
# Preparation
# ----------------------------------------
model_name = 'drunet_color'
quality_factors = [10, 20, 30, 40]
testset_name = 'LIVE1' # test set, 'LIVE1'
need_degradation = True # default: True
task_current = 'db' # 'dn' for deblocking
n_channels = 3 # fixed
model_pool = 'model_zoo' # fixed
testsets = 'testsets' # fixed
results = 'results' # fixed
noise_level_img = 0 # fixed: 0, noise level for LR image
result_name = testset_name + '_' + model_name + '_' + task_current
border = 0 # shave boader to calculate PSNR and SSIM
# ----------------------------------------
# L_path, E_path, H_path
# ----------------------------------------
L_path = os.path.join(testsets, testset_name) # L_path, for Low-quality images
E_path = os.path.join(results, result_name) # E_path, for Estimated images
util.mkdir(E_path)
logger_name = result_name
utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
logger = logging.getLogger(logger_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ----------------------------------------
# load model
# ----------------------------------------
model_path = os.path.join('model_zoo', 'drunet_deblocking_color.pth')
from models.network_unet import UNetRes as net
model = net(in_nc=4, out_nc=3, nc=[64, 128, 256, 512], nb=4, act_mode='R', downsample_mode='strideconv', upsample_mode='convtranspose', bias=False) # define network
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
L_paths = util.get_image_paths(L_path)
for quality_factor in quality_factors:
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
logger.info('model_name:{}, quality factor:{}'.format(model_name, quality_factor))
logger.info(L_path)
for idx, img in enumerate(L_paths):
# ------------------------------------
# (1) img_L
# ------------------------------------
img_name, ext = os.path.splitext(os.path.basename(img))
logger.info('{:->4d}--> {:>10s}'.format(idx+1, img_name+ext))
img_L = util.imread_uint(img, n_channels=n_channels)
img_H = img_L.copy()
# ------------------------------------
# Do the JPEG compression
# ------------------------------------
if need_degradation:
img_L = cv2.cvtColor(img_L, cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img_L, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img_L = cv2.imdecode(encimg, 1)
img_L = cv2.cvtColor(img_L, cv2.COLOR_BGR2RGB)
img_L = util.uint2tensor4(img_L)
noise_level = (100-quality_factor)/100.0
noise_level = torch.FloatTensor([noise_level])
noise_level_map = torch.ones((1,1, img_L.shape[2], img_L.shape[3])).mul_(noise_level).float()
img_L = torch.cat((img_L, noise_level_map), 1)
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
img_E = model(img_L)
img_E = util.tensor2uint(img_E)
if need_degradation:
img_H = img_H.squeeze()
# --------------------------------
# PSNR and SSIM
# --------------------------------
psnr = util.calculate_psnr(img_E, img_H, border=border)
ssim = util.calculate_ssim(img_E, img_H, border=border)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.4f}.'.format(img_name+ext, psnr, ssim))
# ------------------------------------
# save results
# ------------------------------------
util.imsave(img_E, os.path.join(E_path, img_name+'_'+model_name+'_'+str(quality_factor)+'.png'))
if need_degradation:
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(RGB) - {} - qf{} --PSNR: {:.2f} dB; SSIM: {:.4f}'.format(result_name, quality_factor, ave_psnr, ave_ssim))
if __name__ == '__main__':
main()