def load_and_preprocess(image_file, size):
img = Image.open(image_file).convert('RGB')
if size != "None":
transform = transforms.Compose([
transforms.Resize(int(size)),
transforms.ToTensor(),
tensor_normalizer()])
else:
transform = transforms.Compose([
transforms.ToTensor(),
tensor_normalizer()])
img_tensor = transform(img).unsqueeze(0)
return img_tensor
import cv2
import skvideo.io
import numpy as np
import torch
from torchvision import transforms
from torch.autograd import Variable
from PIL import Image
from fast_neural_style.transformer_net import TransformerNet
from fast_neural_style.utils import recover_image, tensor_normalizer
from tqdm import tqdm
# Preprocess Pipeline
preprocess = transforms.Compose(
[transforms.Resize(1024),
transforms.ToTensor(),
tensor_normalizer()])
# Setup the Model Architecture
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transformer = TransformerNet()
transformer.to(device)
# Load the Model and Stylize the Content Video
data_path = '/tmp2/vincentwu929/DIP_final/Sky/content/sky2/Video2_HR.avi'
mask_path = '/tmp2/vincentwu929/DIP_final/Sky/mask/mask_HR2.png'
video_name = 'Video2_HR'
style_name = 'ZaoWouKi' #'crayon', 'fountainpen', 'ZaoWouKi'
BATCH_SIZE = 8
mask = cv2.imread(mask_path).astype(np.bool)
save_model_path = "./models/" + style_name + "_10000_unstable_vgg19.pth"
if torch.cuda.is_available():
torch.cuda.manual_seed(SEED)
kwargs = {'num_workers': 4, 'pin_memory': True}
else:
kwargs = {}
# In[3]:
IMAGE_SIZE = 224
BATCH_SIZE = 4
DATASET = "../coco_2017/"
transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.CenterCrop(IMAGE_SIZE),
transforms.ToTensor(),
tensor_normalizer()
])
# http://pytorch.org/docs/master/torchvision/datasets.html#imagefolder
train_dataset = datasets.ImageFolder(DATASET, transform)
# http://pytorch.org/docs/master/data.html#torch.utils.data.DataLoader
train_loader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
**kwargs)
# In[4]:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
with torch.no_grad():
loss_network = LossNetwork()
loss_network.to(device)
get_ipython().run_line_magic('matplotlib', 'inline')
from PIL import Image
import skvideo.io
import numpy as np
import torch
from torchvision import transforms
from tqdm import tqdm_notebook
from fast_neural_style.transformer_net import TransformerNet
from fast_neural_style.utils import recover_image, tensor_normalizer
# In[2]:
preprocess = transforms.Compose([transforms.ToTensor(), tensor_normalizer()])
# In[3]:
transformer = TransformerNet()
# ## Low Resolution GIF Animation
# Convert gif file to video file:
# ```
# ffmpeg -f gif -i cat.gif cat.mp4
# ```
# In[ ]:
skvideo.io.ffprobe("videos/cat.mp4")