img_in = np.pad(np.rot90(img_lr, 2), ((0,1), (0,1), (0,0)), mode='reflect').transpose((2,0,1))
out_r2 = FourSimplexInterp(LUT, img_in, h, w, q, 2, upscale=UPSCALE)
img_in = np.pad(np.rot90(img_lr, 3), ((0,1), (0,1), (0,0)), mode='reflect').transpose((2,0,1))
out_r3 = FourSimplexInterp(LUT, img_in, w, h, q, 1, upscale=UPSCALE)
img_out = (out_r0/1.0 + out_r1/1.0 + out_r2/1.0 + out_r3/1.0) / 255.0
img_out = img_out.transpose((1,2,0))
img_out = np.round(np.clip(img_out, 0, 1) * 255).astype(np.uint8)
# Matching image sizes
if img_gt.shape[0] < img_out.shape[0]:
img_out = img_out[:img_gt.shape[0]]
if img_gt.shape[1] < img_out.shape[1]:
img_out = img_out[:, :img_gt.shape[1]]
if img_gt.shape[0] > img_out.shape[0]:
img_out = np.pad(img_out, ((0,img_gt.shape[0]-img_out.shape[0]),(0,0),(0,0)))
if img_gt.shape[1] > img_out.shape[1]:
img_out = np.pad(img_out, ((0,0),(0,img_gt.shape[1]-img_out.shape[1]),(0,0)))
# Save to file
Image.fromarray(img_out).save('./output_S_x{}_{}bit/{}_LUT_interp_{}bit.png'.format(UPSCALE, SAMPLING_INTERVAL, fn.split('/')[-1][:-4], SAMPLING_INTERVAL))
CROP_S = 4
psnr = PSNR(_rgb2ycbcr(img_gt)[:,:,0], _rgb2ycbcr(img_out)[:,:,0], CROP_S)
psnrs.append(psnr)
print('AVG PSNR: {}'.format(np.mean(np.asarray(psnrs))))
if img_gt.shape[1] < batch_out.shape[1]:
batch_out = batch_out[:, :img_gt.shape[1]]
elif img_gt.shape[1] > batch_out.shape[1]:
batch_out = np.pad(batch_out,
((0, 0),
(0, img_gt.shape[1] - batch_out.shape[1]),
(0, 0)))
# handling single channel images
if len(img_gt.shape) < 3:
img_gt = np.tile(img_gt[:, :, np.newaxis], [1, 1, 3])
img_gt_y = np.copy(img_gt)[:, :, 0]
batch_out_y = np.copy(batch_out)[:, :, 0]
# RGB to Y
else:
img_gt_y = _rgb2ycbcr(np.copy(img_gt))[:, :, 0]
batch_out_y = _rgb2ycbcr(np.copy(batch_out))[:, :, 0]
# Save to file
out_fn = files_gt[i].split("/")[-1][:-4]
Image.fromarray(batch_out).save('./output/Bic/{}/{}.png'.format(
testset, out_fn))
# Evaluation
CROP_S = 4
# PSNR
psnrs.append(PSNR(img_gt_y, batch_out_y, CROP_S))
# SSIM
ssims.append(
batch_S3 = model_G(F.pad(torch.rot90(val_L, 2, [2,3]), (0,1,0,1), mode='reflect'))
batch_S3 = torch.rot90(batch_S3, 2, [2,3])
batch_S4 = model_G(F.pad(torch.rot90(val_L, 3, [2,3]), (0,1,0,1), mode='reflect'))
batch_S4 = torch.rot90(batch_S4, 1, [2,3])
batch_S = ( torch.clamp(batch_S1,-1,1)*127 + torch.clamp(batch_S2,-1,1)*127 )
batch_S += ( torch.clamp(batch_S3,-1,1)*127 + torch.clamp(batch_S4,-1,1)*127 )
batch_S /= 255.0
# Output
image_out = (batch_S).cpu().data.numpy()
image_out = np.clip(image_out[0], 0. , 1.) # CxHxW
image_out = np.transpose(image_out, [1, 2, 0]) # HxWxC
# Save to file
image_out = ((image_out)*255).astype(np.uint8)
Image.fromarray(image_out).save('result/{}/{}.png'.format(str(VERSION), fn.split('/')[-1]))
# PSNR on Y channel
img_gt = (val_H*255).astype(np.uint8)
CROP_S = 4
psnrs.append(PSNR(_rgb2ycbcr(img_gt)[:,:,0], _rgb2ycbcr(image_out)[:,:,0], CROP_S))
print('AVG PSNR: Validation: {}'.format(np.mean(np.asarray(psnrs))))
writer.add_scalar('PSNR_valid', np.mean(np.asarray(psnrs)), i)
writer.flush()
np.uint8)).save('{}/result/v{}/{}.png'.format(
EXP_NAME, str(VERSION),
fn.split('/')[-1]))
# PSNR
img_gt = (val_H * 255).astype(np.uint8)
img_target = ((batch_Out) * 255).astype(np.uint8)
CROP_S = 16
if CROP_S > 0:
img_gt = img_gt[CROP_S:-CROP_S, CROP_S:-CROP_S]
img_target = img_target[CROP_S:-CROP_S, CROP_S:-CROP_S]
psnrs.append(
PSNR(
_rgb2ycbcr(img_gt)[:, :, 0],
_rgb2ycbcr(img_target)[:, :, 0], 0))
# LPIPS
dist = model_LPIPS.forward(
im2tensor(img_target),
im2tensor(img_gt)) # RGB image from [0,255] to [-1,1]
lpips.append(dist)
print('AVG PSNR/LPIPS: Validation: {}/{}'.format(
np.mean(np.asarray(psnrs)), np.mean(np.asarray(lpips))))
# Save best model
if i % I_SAVE == 0:
if np.mean(np.asarray(lpips)) < best_avg_lpips:
best_avg_lpips = np.mean(np.asarray(lpips))
img_out = img_out.transpose((1, 2, 0))
img_out = np.round(np.clip(img_out, 0, 1) * 255).astype(np.uint8)
# Matching image sizes
if img_gt.shape[0] < img_out.shape[0]:
img_target = img_out[:img_gt.shape[0]]
if img_gt.shape[1] < img_out.shape[1]:
img_target = img_out[:, :img_gt.shape[1]]
if img_gt.shape[0] > img_out.shape[0]:
img_target = np.pad(img_out, ((0, img_gt.shape[0] - img_out.shape[0]),
(0, 0), (0, 0)))
if img_gt.shape[1] > img_out.shape[1]:
img_target = np.pad(img_out,
((0, 0), (0, img_gt.shape[1] - img_out.shape[1]),
(0, 0)))
# Save to file
Image.fromarray(img_out).save(
'./output_F_x{}_{}bit/{}_LUT_interp_{}bit.png'.format(
UPSCALE, SAMPLING_INTERVAL,
fn.split('/')[-1][:-4], SAMPLING_INTERVAL))
CROP_S = 4
psnr = PSNR(
_rgb2ycbcr(img_gt)[:, :, 0],
_rgb2ycbcr(img_out)[:, :, 0], CROP_S)
psnrs.append(psnr)
print('AVG PSNR: {}'.format(np.mean(np.asarray(psnrs))))