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'''
sudo apt-get update && sudo apt-get install cbm git-lfs ffmpeg
git clone https://huggingface.co/spaces/svjack/video-to-sketch && cd video-to-sketch
pip install gradio huggingface_hub torch==1.11.0 torchvision==0.12.0 pytorchvideo==0.1.5 pyav==11.4.1
huggingface-cli download \
--repo-type dataset svjack/video-dataset-Lily-Bikini-organized \
--local-dir video-dataset-Lily-Bikini-organized
python video_to_sketch_script.py video-dataset-Lily-Bikini-organized video-dataset-Lily-Bikini-sketch-organized --copy_others
'''
import gc
import os
import shutil
import argparse
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL.Image import Resampling
from pytorchvideo.data.encoded_video import EncodedVideo
from pytorchvideo.transforms.functional import uniform_temporal_subsample
from torchvision.io import write_video
from torchvision.transforms.functional import resize
from tqdm import tqdm
from modeling import Generator
MAX_DURATION = 60
OUT_FPS = 18
DEVICE = "cpu" if not torch.cuda.is_available() else "cuda"
# Load the model
model = Generator(3, 1, 3)
weights_path = hf_hub_download("nateraw/image-2-line-drawing", "pytorch_model.bin")
model.load_state_dict(torch.load(weights_path, map_location=DEVICE))
model.eval()
def process_one_second(vid, start_sec, out_fps):
"""Process one second of a video at a given fps
Args:
vid (_type_): A pytorchvideo.EncodedVideo instance containing the video to process
start_sec (_type_): The second to start processing at
out_fps (_type_): The fps to output the video at
Returns:
np.array: The processed video as a numpy array with shape (T, H, W, C)
"""
# C, T, H, W
video_arr = vid.get_clip(start_sec, start_sec + 1)["video"]
# C, T, H, W where T == frames per second
x = uniform_temporal_subsample(video_arr, out_fps)
# C, T, H, W where H has been scaled to 256 (This will probably be no bueno on vertical vids but whatever)
x = resize(x, 256, Resampling.BICUBIC)
# C, T, H, W -> T, C, H, W (basically T acts as batch size now)
x = x.permute(1, 0, 2, 3)
with torch.no_grad():
# T, 1, H, W
out = model(x)
# T, C, H, W -> T, H, W, C Rescaled to 0-255
out = out.permute(0, 2, 3, 1).clip(0, 1) * 255
# Greyscale -> RGB
out = out.repeat(1, 1, 1, 3)
return out
def process_video(input_video_path, output_video_path):
start_sec = 0
vid = EncodedVideo.from_path(input_video_path)
duration = min(MAX_DURATION, int(vid.duration))
for i in tqdm(range(duration), desc="Processing video"):
video = process_one_second(vid, start_sec=i + start_sec, out_fps=OUT_FPS)
gc.collect()
if i == 0:
video_all = video
else:
video_all = np.concatenate((video_all, video))
write_video(output_video_path, video_all, fps=OUT_FPS)
def copy_non_video_files(input_path, output_path):
"""Copy non-video files and directories from input path to output path."""
for item in os.listdir(input_path):
item_path = os.path.join(input_path, item)
if not item.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
dest_path = os.path.join(output_path, item)
if os.path.isdir(item_path):
shutil.copytree(item_path, dest_path)
else:
shutil.copy2(item_path, dest_path)
def main(input_path, output_path, copy_others=False):
if not os.path.exists(output_path):
os.makedirs(output_path)
if copy_others:
copy_non_video_files(input_path, output_path)
for video_name in os.listdir(input_path):
if video_name.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
input_video_path = os.path.join(input_path, video_name)
output_video_path = os.path.join(output_path, video_name)
process_video(input_video_path, output_video_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Process videos to convert them into sketch videos.")
parser.add_argument("input_path", type=str, help="Path to the input directory containing videos.")
parser.add_argument("output_path", type=str, help="Path to the output directory for processed videos.")
parser.add_argument("--copy_others", action="store_true", help="Copy non-video files and directories from input to output.")
args = parser.parse_args()
main(args.input_path, args.output_path, args.copy_others) |