def test_net(model):
test_LR_dir = '../../dataset/test_imgs/youku_00150_00199_l'
test_HR_dir = '../../dataset/test_imgs/youku_00150_00199_h_GT'
video_list = []
for _ in os.listdir(test_LR_dir):
if osp.isdir(osp.join(test_LR_dir, _ )):
video_list.append(_)
for video_name in video_list:
print('testing ', video_name)
imglist = glob.glob(osp.join(test_LR_dir, video_name, '*.bmp'))
result_img_dir = osp.join(result_dir, video_name)
if not osp.exists(result_img_dir):
os.mkdir(result_img_dir)
for img_path in imglist:
img = cv2.imread(img_path) / 255
img_name = img_path.split('/')[-1]
img = torch.as_tensor(img.transpose(2, 0 ,1)[None]).float().cuda()
HR_img = model(img) * 255
HR_img = HR_img.cpu().detach().numpy().squeeze().transpose(1,2,0)
HR_img = np.clip(HR_img, 0, 255)
cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
video_path = frames_to_video(result_img_dir)
# embed()
video_path = y4m2yuv(video_path)
HR_video_path = osp.join(test_HR_dir, video_name.replace('l','h_GT')+ '.yuv')
cmd = 'vmafossexec yuv420p {} {} {} {} ../../vmaf/model/vmaf_v0.6.1.pkl --log {}.xml --psnr --ssim --ms-ssim --thread 2 --subsample 1'.format(1920, 1080, video_path, HR_video_path, osp.join(result_dir,video_name))
os.system(cmd)
def test_net(gpu_id, model_path, video_list, timeline, nframes):
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_id)
device = torch.device('cuda:0')
model = construct_model(model_path, device)
for video_name in video_list:
print('testing ', video_name)
imglist = glob.glob(osp.join(test_LR_dir, video_name, '*.bmp'))
imglist.sort(key=lambda x: (x.split('/')[-1][:-4]))
result_img_dir = osp.join(result_dir, video_name)
if not osp.exists(result_img_dir):
os.mkdir(result_img_dir)
for i in range(len(imglist)):
img = cv2.imread(imglist[i]) / 255
img_name = imglist[i].split('/')[-1]
img = torch.as_tensor(img.transpose(2, 0, 1)[None]).float().cuda()
HR_img = model(img).cpu().detach().numpy() * 255
HR_img = np.squeeze(HR_img).transpose(1, 2, 0)
HR_img = np.clip(HR_img, 0, 255)
cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
video_path = frames_to_video(result_img_dir)
# embed()
video_path = y4m2yuv(video_path)
HR_video_path = osp.join(test_HR_dir,
video_name.replace('l', 'h_GT') + '.yuv')
cmd = 'vmafossexec yuv420p {} {} {} {} ../../vmaf/model/vmaf_v0.6.1.pkl --log {}_{}.xml --psnr --ssim --ms-ssim --thread 2 --subsample 1'.format(
1920, 1080, video_path, HR_video_path,
osp.join(result_dir, video_name), timeline)
os.system(cmd)
def test_net(gpu_id, model_path, video_list, timeline, nframes):
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_id)
device = torch.device('cuda:0')
model = construct_model(model_path, device)
for video_name in video_list:
print('testing ', video_name)
imglist = glob.glob(osp.join(test_LR_dir, video_name, '*.bmp'))
result_img_dir = osp.join(result_dir, video_name)
if not osp.exists(result_img_dir):
os.mkdir(result_img_dir)
for i in range(len(imglist)):
img = cv2.imread(imglist[i]) / 255
img_name = imglist[i].split('/')[-1]
img = torch.as_tensor(img.transpose(2, 0, 1)[None]).float().cuda()
HR_img = model(img).cpu().detach().numpy() * 255
HR_img = np.squeeze(HR_img).transpose(1,2,0)
HR_img = np.clip(HR_img, 0, 255)
cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
video_path = frames_to_video(result_img_dir)
print('testing ', video_name)
imglist = glob.glob(osp.join(test_LR_dir, video_name, '*.bmp'))
result_img_dir = osp.join(result_dir, video_name)
if not osp.exists(result_img_dir):
os.mkdir(result_img_dir)
for img_path in imglist:
img = cv2.imread(img_path)
img_name = img_path.split('/')[-1]
HR_img = cv2.resize(img,
dsize=None,
fx=4,
fy=4,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
video_path = frames_to_video(result_img_dir)
# embed()
video_path = y4m2yuv(video_path)
HR_video_path = osp.join(test_HR_dir,
video_name.replace('l', 'h_GT') + '.yuv')
cmd = 'vmafossexec yuv420p {} {} {} {} ../../vmaf/model/vmaf_v0.6.1.pkl --log {}.xml --psnr --ssim --ms-ssim --thread 2 --subsample 1'.format(
1920, 1080, video_path, HR_video_path,
osp.join(result_dir, video_name))
os.system(cmd)
results_xml = glob.glob(osp.join(result_dir, '*.xml'))
psnr = 0.0
vmaf = 0.0
ssim = 0.0
ms_ssim = 0.0
def test_net(gpu_id, model_path, video_list, timeline, nframes):
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_id)
device = torch.device('cuda:0')
model = construct_model(model_path, device)
for video_name in video_list:
print('testing ', video_name)
imglist = glob.glob(osp.join(test_LR_dir, video_name, '*.bmp'))
result_img_dir = osp.join(result_dir, video_name)
if not osp.exists(result_img_dir):
os.mkdir(result_img_dir)
for i in range(nframes // 2):
img = cv2.imread(imglist[i])
img_name = imglist[i].split('/')[-1]
img = cv2.resize(img,
dsize=None,
fx=4,
fy=4,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(osp.join(result_img_dir, img_name), img)
img = cv2.imread(imglist[-i])
img_name = imglist[-i].split('/')[-1]
img = cv2.resize(img,
dsize=None,
fx=4,
fy=4,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(osp.join(result_img_dir, img_name), img)
for i in range(len(imglist) - nframes):
img = []
for j in range(nframes):
img.append(cv2.imread(imglist[i + j]) / 255)
img = np.stack(img, 0).transpose(3, 0, 1, 2)[None]
img_name = imglist[i + nframes // 2].split('/')[-1]
h = img.shape[3]
w = img.shape[4]
HR_img = np.zeros(shape=(1, 3, h * 4, w * 4))
start_row = [_ * 64 for _ in range(h // 64)]
start_row.append(h - 64)
start_col = [_ * 64 for _ in range(w // 64)]
start_col.append(w - 64)
for row in start_row:
for col in start_col:
patch = torch.as_tensor(img[:, :, :, row:row + 64,
col:col + 64]).float().cuda()
HR_img[:, :, row * 4:row * 4 + 64 * 4, col * 4:col * 4 +
64 * 4] = model(patch).cpu().detach().numpy() * 255
HR_img = np.squeeze(HR_img).transpose(1, 2, 0)
HR_img = np.clip(HR_img, 0, 255)
cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
video_path = frames_to_video(result_img_dir)
# embed()
video_path = y4m2yuv(video_path)
HR_video_path = osp.join(test_HR_dir,
video_name.replace('l', 'h_GT') + '.yuv')
cmd = 'vmafossexec yuv420p {} {} {} {} ../../vmaf/model/vmaf_v0.6.1.pkl --log {}_{}.xml --psnr --ssim --ms-ssim --thread 2 --subsample 1'.format(
1920, 1080, video_path, HR_video_path,
osp.join(result_dir, video_name), timeline)
os.system(cmd)
def test_net(gpu_id, model_path, video_list, timeline, nframes):
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_id)
device = torch.device('cuda:0')
model = construct_model(model_path, device, nframes)
for video_name in video_list:
if osp.isfile(osp.join(test_LR_dir, video_name)):
continue
print('testing ', video_name)
imglist = glob.glob(osp.join(test_LR_dir, video_name, '*.bmp'))
imglist.sort(key=lambda x: (x.split('/')[-1][:-4]))
result_img_dir = osp.join(result_dir, video_name)
if not osp.exists(result_img_dir):
os.mkdir(result_img_dir)
for i in range(nframes // 2):
img = cv2.imread(imglist[i]) / 255
img_name = imglist[i].split('/')[-1]
img = cv2.resize(img,
dsize=None,
fx=4,
fy=4,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(osp.join(result_img_dir, img_name), img * 255)
img = cv2.imread(imglist[-i]) / 255
img_name = imglist[-i].split('/')[-1]
img = cv2.resize(img,
dsize=None,
fx=4,
fy=4,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(osp.join(result_img_dir, img_name), img * 255)
for i in range(len(imglist) - nframes):
img = []
for j in range(nframes):
img.append(cv2.imread(imglist[i + j]) / 255)
img = np.stack(img, 0).transpose(3, 0, 1, 2)[None]
img_name = imglist[i + nframes // 2].split('/')[-1]
h = img.shape[3]
w = img.shape[4]
HR_img = np.zeros(shape=(1, 3, h * 4, w * 4))
start_row = [_ * 100 for _ in range(h // 100)]
start_row.append(h - 100)
start_col = [_ * 100 for _ in range(w // 100)]
start_col.append(w - 100)
for row in start_row:
for col in start_col:
patch = torch.as_tensor(img[:, :, :, row:row + 100,
col:col + 100]).float().cuda()
HR_img[:, :, row * 4:row * 4 + 100 * 4, col * 4:col * 4 +
100 * 4] = model(patch).cpu().detach().numpy() * 255
HR_img = np.squeeze(HR_img).transpose(1, 2, 0)
HR_img = np.clip(HR_img, 0, 255)
cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
video_path = frames_to_video(result_img_dir)
new_video_path = video_path.split('/')
new_video_path[-1] = video_name.replace('_l', '_h_Res.y4m')
os.rename(video_path, osp.join(*new_video_path))
def test_net(gpu_id, model_path, video_list, timeline, nframes, patch_size):
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_id)
device = torch.device('cuda:0')
model = construct_model(model_path, device)
for video_name in video_list:
if osp.isfile(osp.join(test_LR_dir, video_name)):
continue
print('testing ', video_name)
imglist = glob.glob(osp.join(test_LR_dir, video_name, '*.bmp'))
imglist.sort(key=lambda x: (x.split('/')[-1][:-4]))
result_img_dir = osp.join(result_dir, video_name)
if not osp.exists(result_img_dir):
os.mkdir(result_img_dir)
# for i in range(nframes//2):
# img = cv2.imread(imglist[i]) / 255
# img_name = imglist[i].split('/')[-1]
# img = np.stack([img]*nframes,0).transpose(0, 3, 1, 2)[None]
# HR_img = compute_hr(img, model)
# cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
# img = cv2.imread(imglist[-i]) / 255
# img_name = imglist[-i].split('/')[-1]
# img = np.stack([img]*nframes,0).transpose(0, 3, 1, 2)[None]
# HR_img = compute_hr(img, model)
# cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
if ('200' in video_name) or ('201' in video_name) or (
'202' in video_name) or ('203'
in video_name) or ('204'
in video_name):
space = 1
else:
space = 25
count = 1
for i in range(0, len(imglist), space):
img = []
for j in range(-(nframes // 2), (nframes // 2) + 1, 1):
index = abs(i +
j) if (i +
j) < 100 else 2 * len(imglist) - 1 - (i + j)
img.append(cv2.imread(imglist[index]) / 255)
img = np.stack(img, 0).transpose(0, 3, 1, 2)[None]
img_name = imglist[i].split('/')[-1]
HR_img = compute_hr(img, patch_size, model)
img_flip = img[:, ::-1] - np.zeros_like(img)
HR_img = HR_img + compute_hr(img_flip, patch_size, model)
img_flip = img[:, :, :, ::-1] - np.zeros_like(img)
HR_img = HR_img + compute_hr(img_flip, patch_size, model)[::-1]
img_flip = img[:, :, :, :, ::-1] - np.zeros_like(img)
HR_img = HR_img + compute_hr(img_flip, patch_size, model)[:, ::-1]
HR_img = HR_img / 4
cv2.imwrite(osp.join(result_img_dir,
str(count).zfill(3) + '.bmp'), HR_img)
count += 1
video_path = frames_to_video(result_img_dir)
new_video_path = video_path.split('/')
new_video_path[-1] = video_name.replace('_l', '_h_Res.y4m')
os.rename(video_path, osp.join(*new_video_path))
def test_net(gpu_id, model_path, video_list, timeline, nframes):
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_id)
device = torch.device('cuda:0')
model, single_model = construct_model(model_path, device)
for video_name in video_list:
print('testing ', video_name)
imglist = glob.glob(osp.join(test_LR_dir, video_name, '*.bmp'))
result_img_dir = osp.join(result_dir, video_name)
imglist.sort(key=lambda x: (x.split('/')[-1][:-4]))
if not osp.exists(result_img_dir):
os.mkdir(result_img_dir)
for i in range(nframes // 2):
img = cv2.imread(imglist[i])
img_name = imglist[i].split('/')[-1]
img = torch.as_tensor(img.transpose(2, 0,
1)[None]).float().cuda() / 255
img = single_model(img).detach().cpu().squeeze().numpy().transpose(
1, 2, 0) * 255
cv2.imwrite(osp.join(result_img_dir, img_name), img)
img = cv2.imread(imglist[-i])
img_name = imglist[-i].split('/')[-1]
img = torch.as_tensor(img.transpose(2, 0,
1)[None]).float().cuda() / 255
img = single_model(img).detach().cpu().squeeze().numpy().transpose(
1, 2, 0) * 255
cv2.imwrite(osp.join(result_img_dir, img_name), img)
for i in range(len(imglist) - nframes):
img = []
for j in range(nframes):
img.append(cv2.imread(imglist[i + j]) / 255)
img = np.stack(img, -1)
dis = np.max(
np.mean((img - img[:, :, :, nframes // 2:nframes // 2 + 1])**2,
(0, 1, 2)))
if dis > 100 / 255**2:
print("single model")
HR_img = single_model(
torch.as_tensor(img[:, :, :, nframes // 2].transpose(
2, 0,
1)[None]).float().cuda()).cpu().detach().numpy() * 255
else:
print("multi model")
img = img.reshape(img.shape[0], img.shape[1],
-1).transpose(2, 0, 1)[None]
HR_img = model(torch.as_tensor(
img).float().cuda()).cpu().detach().numpy() * 255
img_name = imglist[i + nframes // 2].split('/')[-1]
HR_img = np.squeeze(HR_img).transpose(1, 2, 0)
HR_img = np.clip(HR_img, 0, 255)
cv2.imwrite(osp.join(result_img_dir, img_name), HR_img)
video_path = frames_to_video(result_img_dir)
# embed()
video_path = y4m2yuv(video_path)
HR_video_path = osp.join(test_HR_dir,
video_name.replace('l', 'h_GT') + '.yuv')
cmd = 'vmafossexec yuv420p {} {} {} {} ../../vmaf/model/vmaf_v0.6.1.pkl --log {}_{}.xml --psnr --ssim --ms-ssim --thread 2 --subsample 1'.format(
1920, 1080, video_path, HR_video_path,
osp.join(result_dir, video_name), timeline)
os.system(cmd)
