Create video_to_sketch_script.py

video_to_sketch_script.py

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+'''
+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)