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main_test_fbcnn_color.py
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main_test_fbcnn_color.py
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import os.path
import logging
import numpy as np
from datetime import datetime
from collections import OrderedDict
import torch
import cv2
from utils import utils_logger
from utils import utils_image as util
import requests
def main():
quality_factor_list = [10, 20, 30, 40, 50, 60, 70, 80, 90]
testset_name = 'LIVE1_color' # 'LIVE1_color' 'BSDS500_color' 'ICB'
n_channels = 3 # set 1 for grayscale image, set 3 for color image
model_name = 'fbcnn_color.pth'
nc = [64,128,256,512]
nb = 4
show_img = False # default: False
testsets = 'testsets'
results = 'test_results'
for quality_factor in quality_factor_list:
result_name = testset_name + '_' + model_name[:-4]
H_path = os.path.join(testsets, testset_name)
E_path = os.path.join(results, result_name, str(quality_factor)) # E_path, for Estimated images
util.mkdir(E_path)
model_pool = 'model_zoo' # fixed
model_path = os.path.join(model_pool, model_name)
if os.path.exists(model_path):
print(f'loading model from {model_path}')
else:
os.makedirs(os.path.dirname(model_path), exist_ok=True)
url = 'https://github.com/jiaxi-jiang/FBCNN/releases/download/v1.0/{}'.format(os.path.basename(model_path))
r = requests.get(url, allow_redirects=True)
print(f'downloading model {model_path}')
open(model_path, 'wb').write(r.content)
logger_name = result_name + '_qf_' + str(quality_factor)
utils_logger.logger_info(logger_name, log_path=os.path.join(E_path, logger_name+'.log'))
logger = logging.getLogger(logger_name)
logger.info('--------------- quality factor: {:d} ---------------'.format(quality_factor))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
border = 0
# ----------------------------------------
# load model
# ----------------------------------------
from models.network_fbcnn import FBCNN as net
model = net(in_nc=n_channels, out_nc=n_channels, nc=nc, nb=nb, act_mode='R')
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))
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnrb'] = []
H_paths = util.get_image_paths(H_path)
for idx, img in enumerate(H_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)
if n_channels == 3:
img_L = cv2.cvtColor(img_L, cv2.COLOR_RGB2BGR)
_, encimg = cv2.imencode('.jpg', img_L, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img_L = cv2.imdecode(encimg, 0) if n_channels == 1 else cv2.imdecode(encimg, 3)
if n_channels == 3:
img_L = cv2.cvtColor(img_L, cv2.COLOR_BGR2RGB)
img_L = util.uint2tensor4(img_L)
img_L = img_L.to(device)
# ------------------------------------
# (2) img_E
# ------------------------------------
#img_E,QF = model(img_L, torch.tensor([[0.6]]))
img_E,QF = model(img_L)
QF = 1 - QF
img_E = util.tensor2single(img_E)
img_E = util.single2uint(img_E)
img_H = util.imread_uint(H_paths[idx], n_channels=n_channels).squeeze()
# --------------------------------
# PSNR and SSIM, PSNRB
# --------------------------------
psnr = util.calculate_psnr(img_E, img_H, border=border)
ssim = util.calculate_ssim(img_E, img_H, border=border)
psnrb = util.calculate_psnrb(img_H, img_E, border=border)
test_results['psnr'].append(psnr)
test_results['ssim'].append(ssim)
test_results['psnrb'].append(psnrb)
logger.info('{:s} - PSNR: {:.2f} dB; SSIM: {:.3f}; PSNRB: {:.2f} dB.'.format(img_name+ext, psnr, ssim, psnrb))
logger.info('predicted quality factor: {:d}'.format(round(float(QF*100))))
util.imshow(np.concatenate([img_E, img_H], axis=1), title='Recovered / Ground-truth') if show_img else None
util.imsave(img_E, os.path.join(E_path, img_name+'.png'))
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
ave_psnrb = sum(test_results['psnrb']) / len(test_results['psnrb'])
logger.info(
'Average PSNR/SSIM/PSNRB - {} -: {:.2f}$\\vert${:.4f}$\\vert${:.2f}.'.format(result_name+'_'+str(quality_factor), ave_psnr, ave_ssim, ave_psnrb))
if __name__ == '__main__':
main()