def test_data():
for i in range(81):
image_path = "./data/image/" + str(i) + ".jpg"
label_path = "./data/label/" + str(i) + ".jpg"
image_save_path = "./data/image0/" + str(i) + ".jpg"
label_save_path = "./data/label0/" + str(i) + ".jpg"
from PIL import Image
image = Image.open(image_path)
label = Image.open(label_path)
# from torchvision.transforms import ToTensor
# image = ToTensor()(image)
# label = ToTensor()(label)
# print(image.shape, label.shape)
from torchvision.transforms import Scale
image = Scale((450, 300))(image)
label = Scale((450, 300))(label)
image.save(image_save_path)
label.save(label_save_path)
model.cuda()
model.load_state_dict(torch.load('epochs/' + MODEL_NAME))
else:
model.load_state_dict(
torch.load('epochs/' + MODEL_NAME,
map_location=lambda storage, loc: storage))
dir = '/home/lucas/Lab/SRGAN/SRGAN/data/test2'
files = os.listdir(dir)
for file in files:
image = Image.open(dir + '/' + file)
print(image.size)
print(image.size[0])
min_edge = image.size[0] if image.size[0] < image.size[1] else image.size[1]
image_lr = Scale(min_edge // 2, interpolation=Image.BICUBIC)(image)
image = Variable(ToTensor()(image_lr), volatile=True).unsqueeze(0)
if TEST_MODE:
image = image.cuda()
start = time.clock()
out = model(image)
elapsed = (time.clock() - start)
print('cost' + str(elapsed) + 's')
out_img = ToPILImage()(out[0].data.cpu())
#out_img.save(dir + '/' + file[:-4] + '_generate' + str(UPSCALE_FACTOR) + '_init.jpg')
#out_img = Scale(256, interpolation=Image.BICUBIC)(out_img)
out_img.save(dir + '/' + file[:-4] + '_generate' + '.jpg')
image_lr = Scale(min_edge * 2, interpolation=Image.BICUBIC)(image_lr)
image_lr.save(dir + '/' + file[:-4] + '_init' + '.jpg')
