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import numpy as np
import shutil
import os
import argparse
import torch
import glob
from tqdm import tqdm
from PIL import Image
from collections import OrderedDict
from src.models.vit.config import load_config
import torchvision.transforms as transforms
import cv2
from skimage import io
from src.models.CNN.ColorVidNet import GeneralColorVidNet
from src.models.vit.embed import GeneralEmbedModel
from src.models.CNN.NonlocalNet import GeneralWarpNet
from src.models.CNN.FrameColor import frame_colorization
from src.utils import (
RGB2Lab,
ToTensor,
Normalize,
uncenter_l,
tensor_lab2rgb,
SquaredPadding,
UnpaddingSquare
)
def load_params(ckpt_file):
params = torch.load(ckpt_file)
new_params = []
for key, value in params.items():
new_params.append((key, value))
return OrderedDict(new_params)
def custom_transform(transforms, img):
for transform in transforms:
if isinstance(transform, SquaredPadding):
img,padding=transform(img, return_paddings=True)
else:
img = transform(img)
return img.to(device), padding
def save_frames(predicted_rgb, video_name, frame_name):
if predicted_rgb is not None:
predicted_rgb = np.clip(predicted_rgb, 0, 255).astype(np.uint8)
io.imsave(os.path.join(args.output_video_path, video_name, frame_name), predicted_rgb)
def colorize_video(video_name):
frames_list = os.listdir(os.path.join(args.input_videos_path, video_name))
frames_list.sort()
refs_list = os.listdir(os.path.join(args.reference_images_path, video_name))
refs_list.sort()
for ref_path in refs_list:
frame_ref = Image.open(os.path.join(args.reference_images_path, video_name, ref_path)).convert("RGB")
I_last_lab_predict = None
IB_lab, IB_paddings = custom_transform(transforms, frame_ref)
IB_lab = IB_lab.unsqueeze(0).to(device)
IB_l = IB_lab[:, 0:1, :, :]
IB_ab = IB_lab[:, 1:3, :, :]
with torch.no_grad():
I_reference_lab = IB_lab
I_reference_l = I_reference_lab[:, 0:1, :, :]
I_reference_ab = I_reference_lab[:, 1:3, :, :]
I_reference_rgb = tensor_lab2rgb(torch.cat((uncenter_l(I_reference_l), I_reference_ab), dim=1)).to(device)
features_B = embed_net(I_reference_rgb)
for frame_name in frames_list:
curr_frame = Image.open(os.path.join(args.input_videos_path, video_name, frame_name)).convert("RGB")
IA_lab, IA_paddings = custom_transform(transforms, curr_frame)
IA_lab = IA_lab.unsqueeze(0).to(device)
IA_l = IA_lab[:, 0:1, :, :]
IA_ab = IA_lab[:, 1:3, :, :]
if I_last_lab_predict is None:
I_last_lab_predict = torch.zeros_like(IA_lab).to(device)
with torch.no_grad():
I_current_lab = IA_lab
I_current_ab_predict, _, _ = frame_colorization(
I_current_lab,
I_reference_lab,
I_last_lab_predict,
features_B,
embed_net,
nonlocal_net,
colornet,
luminance_noise=0,
temperature=1e-10,
joint_training=False
)
I_last_lab_predict = torch.cat((IA_l, I_current_ab_predict), dim=1)
IA_predict_rgb = tensor_lab2rgb(torch.cat((uncenter_l(IA_l), I_current_ab_predict), dim=1))
save_frames(IA_predict_rgb, video_name, frame_name)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Video Colorization')
parser.add_argument("--input_videos_path", type=str, help="path to input video")
parser.add_argument("--reference_images_path", type=str, help="path to reference image")
parser.add_argument("--output_video_path", type=str, help="path to output video")
parser.add_argument("--weight_path", type=str, default="checkpoints/epoch_5/", help="path to weight")
parser.add_argument("--device", type=str, default="cpu", help="device to run the model")
parser.add_argument("--high_resolution", action="store_true", help="use high resolution")
parser.add_argument("--wls_filter_on", action="store_true", help="use wls filter")
args = parser.parse_args()
device = torch.device(args.device)
if os.path.exists(args.output_video_path):
shutil.rmtree(args.output_video_path)
os.makedirs(args.output_video_path, exist_ok=True)
videos_list = os.listdir(args.input_videos_path)
embed_net=GeneralEmbedModel(pretrained_model="swin-tiny", device=device).to(device)
nonlocal_net = GeneralWarpNet(feature_channel=128).to(device)
colornet=GeneralColorVidNet(7).to(device)
embed_net.eval()
nonlocal_net.eval()
colornet.eval()
# Load weights
embed_net_params = load_params(os.path.join(args.weight_path, "embed_net.pth"))
nonlocal_net_params = load_params(os.path.join(args.weight_path, "nonlocal_net.pth"))
colornet_params = load_params(os.path.join(args.weight_path, "colornet.pth"))
embed_net.load_state_dict(embed_net_params, strict=True)
nonlocal_net.load_state_dict(nonlocal_net_params, strict=True)
colornet.load_state_dict(colornet_params, strict=True)
transforms = [SquaredPadding(target_size=224),
RGB2Lab(),
ToTensor(),
Normalize()]
# center_padder = CenterPad((224,224))
with torch.no_grad():
for video_name in tqdm(videos_list):
colorize_video(video_name) |