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NhanNT: Update some code to calculate task real_sr #163

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NhanNT: Update some code to calculate task real_sr #163

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1 change: 1 addition & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
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*.png
14 changes: 12 additions & 2 deletions main_test_swinir.py
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Expand Up @@ -6,6 +6,7 @@
import os
import torch
import requests
import pandas as pd

from models.network_swinir import SwinIR as net
from utils import util_calculate_psnr_ssim as util
Expand Down Expand Up @@ -56,6 +57,7 @@ def main():
test_results['psnrb'] = []
test_results['psnrb_y'] = []
psnr, ssim, psnr_y, ssim_y, psnrb, psnrb_y = 0, 0, 0, 0, 0, 0
results = list()

for idx, path in enumerate(sorted(glob.glob(os.path.join(folder, '*')))):
# read image
Expand Down Expand Up @@ -105,9 +107,15 @@ def main():
print('Testing {:d} {:20s} - PSNR: {:.2f} dB; SSIM: {:.4f}; PSNRB: {:.2f} dB;'
'PSNR_Y: {:.2f} dB; SSIM_Y: {:.4f}; PSNRB_Y: {:.2f} dB.'.
format(idx, imgname, psnr, ssim, psnrb, psnr_y, ssim_y, psnrb_y))
results.append([imgname, psnr, ssim, psnr_y, ssim_y])
else:
print('Testing {:d} {:20s}'.format(idx, imgname))
columns = ['Image Name', 'PSNR', 'SSIM', 'PSNR_Y', 'SSIM_Y']
df = pd.DataFrame(results, columns=columns)

csv_path = os.path.join(save_dir, 'results.csv')
df.to_csv(csv_path, index=False)
print(f'Results saved to {csv_path}')
# summarize psnr/ssim
if img_gt is not None:
ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
Expand Down Expand Up @@ -228,16 +236,18 @@ def setup(args):

def get_image_pair(args, path):
(imgname, imgext) = os.path.splitext(os.path.basename(path))
print(imgname)
print(imgext)

# 001 classical image sr/ 002 lightweight image sr (load lq-gt image pairs)
if args.task in ['classical_sr', 'lightweight_sr']:
img_gt = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.
img_lq = cv2.imread(f'{args.folder_lq}/{imgname}x{args.scale}{imgext}', cv2.IMREAD_COLOR).astype(
np.float32) / 255.

# 003 real-world image sr (load lq image only)
# 003 real-world image sr (load lq image and gt image)
elif args.task in ['real_sr']:
img_gt = None
img_gt = cv2.imread(f'{args.folder_gt}/{imgname}{imgext}', cv2.IMREAD_COLOR).astype(np.float32) / 255.
img_lq = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) / 255.

# 004 grayscale image denoising (load gt image and generate lq image on-the-fly)
Expand Down