235 lines
7.4 KiB
Python
235 lines
7.4 KiB
Python
import logging
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from pathlib import Path
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from typing import TYPE_CHECKING
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from cv2 import imwrite
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import tqdm
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from src.config import presets
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from src.utils.fs import FileSystem
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from src.utils.video import VideoMaker
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from src.interpolator import (
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ImageInterpolator,
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Anchor,
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get_device,
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get_vram_available,
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ModelRunner,
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)
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if TYPE_CHECKING:
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import torch
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import numpy as np
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def performing_warning_message(device: "torch.device"):
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if device.type in ("cpu", "mps"):
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if device.type == "mps":
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logging.warning(
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"Running on Apple Silicon GPU (MPS) may have limited performance. Consider using a CUDA-enabled GPU for better performance."
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)
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else:
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logging.warning(
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"Running on CPU may be very slow. Consider using a GPU for better performance."
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)
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elif device.type == "cuda":
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pass
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else:
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raise Exception(f"Unsupported device type: {device.type}")
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def init_fs(base_path: Path) -> FileSystem:
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fs = FileSystem(base_path)
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fs.clear_directory(fs.frames_path)
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fs.clear_directory(fs.interpolated_path)
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fs.clear_directory(fs.moved_path)
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fs.clear_directory(fs.video_part_path)
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return fs
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def init_video_maker() -> VideoMaker:
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return VideoMaker()
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def init_device() -> "torch.device":
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device = get_device()
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performing_warning_message(device)
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vram_available = get_vram_available(device)
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logging.info(f"Available VRAM: {vram_available / (1024**3):.2f} GB")
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return device
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def init_anchor(device: "torch.device") -> Anchor:
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if device.type in ("cpu", "mps"):
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return Anchor(resolution=8192 * 8192, memory=1, memory_bias=0)
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elif device.type == "cuda":
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return Anchor(
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resolution=1024 * 512, memory=1500 * 1024**2, memory_bias=2500 * 1024**2
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)
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else:
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raise Exception(f"Unsupported device type: {device.type}")
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def init_model_runner(
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config: Path, checkpoint_path: Path, device: "torch.device"
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) -> ModelRunner:
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return ModelRunner(config, checkpoint_path, device)
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def init_interpolator(
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model_runner: ModelRunner, device: "torch.device"
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) -> ImageInterpolator:
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anchor = init_anchor(device)
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return ImageInterpolator(device, anchor, model_runner)
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class InterpolationPipeline:
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def __init__(
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self,
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config: Path,
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checkpoint_path: Path,
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base_path: Path,
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):
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self.fs = init_fs(base_path)
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self.video_maker = init_video_maker()
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self.device = init_device()
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self.model_runner = init_model_runner(config, checkpoint_path, self.device)
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self.interpolator = init_interpolator(self.model_runner, self.device)
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def run(self, video_path: Path, output_video: str):
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prev_frames = tuple()
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interpolated_frames: list["np.ndarray"] = []
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part = 0
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chunk_seconds = 10
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length = self.video_maker.get_video_duration(video_path)
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last_part_seconds = 1 if length % chunk_seconds else 0
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total_parts = int(length // chunk_seconds) + last_part_seconds
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fps = self.video_maker.get_fps(video_path)
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logging.info(f"Video FPS: {fps}")
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fps *= 2 # Doubling FPS
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width, height = self.video_maker.get_size(video_path)
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for frames in self.video_maker.video_to_frames_generator(
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video_path, self.fs.frames_path, chunk_seconds
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):
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logging.info(f"Processing frames: {len(frames)}")
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if prev_frames:
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img1 = prev_frames[-1]
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img2 = frames[0]
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img1_2 = self.interpolator.interpolate(img1, img2)
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interpolated_frames.append(img1_2)
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generator = self._frame_generator(prev_frames, interpolated_frames)
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part_path = self.fs.video_part_path / f"video_{part:08d}.mp4"
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self.video_maker.images_to_video_pipeline(
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generator, part_path, width, height, fps
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)
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interpolated_frames = []
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logging.info(f"Finished processing part {part:08d}")
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part += 1
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for i in tqdm.tqdm(
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range(len(frames) - 1),
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desc=f"Processing video frames {part + 1} / {total_parts}",
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):
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img1 = frames[i]
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img2 = frames[i + 1]
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img1_2 = self.interpolator.interpolate(img1, img2)
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interpolated_frames.append(img1_2)
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prev_frames = frames
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generator = self._frame_generator(prev_frames, interpolated_frames)
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part_path = self.fs.video_part_path / f"video_{part:08d}.mp4"
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self.video_maker.images_to_video_pipeline(
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generator, part_path, width, height, fps
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)
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logging.info(f"Finished processing part {part:08d}")
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self._merge_video_parts(self.fs.output_path / output_video)
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logging.info(
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f"Video interpolation completed. Output saved to: {self.fs.output_path / output_video}"
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)
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def _save_images(
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self,
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source: tuple["np.ndarray", ...],
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interpolated: list["np.ndarray"],
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):
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logging.info("Saving images...")
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self.fs.clear_directory(self.fs.moved_path)
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index = 0
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for i, frame in enumerate(source):
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name = self.fs.moved_path / f"img_{index:08d}.png"
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index += 1
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imwrite(name, frame)
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if i < len(interpolated):
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name = self.fs.moved_path / f"img_{index:08d}.png"
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index += 1
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imwrite(name, interpolated[i])
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logging.info("Success...")
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def _merge_frames_to_video(self, output_video: Path, fps: float):
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self.video_maker.images_to_video(self.fs.moved_path, output_video, fps)
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def _merge_video_parts(self, output_video: Path):
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self.video_maker.concatenate_videos(self.fs.video_part_path, output_video)
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self.fs.clear_directory(self.fs.video_part_path)
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def _frame_generator(
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self,
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source: tuple["np.ndarray", ...],
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interpolated: list["np.ndarray"],
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):
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for i, frame in enumerate(source):
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yield frame
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if i < len(interpolated):
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yield interpolated[i]
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def runner(
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base_path: Path,
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video_path: Path,
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output_video: str,
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preset: presets.Preset = presets.LARGE,
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):
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pipeline = InterpolationPipeline(
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config=preset.config,
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checkpoint_path=preset.checkpoint,
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base_path=base_path,
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)
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pipeline.run(video_path, output_video)
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def main():
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import argparse
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
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)
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parser = argparse.ArgumentParser()
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parser.add_argument("-b", "--base_path", help="Base path", default="output")
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parser.add_argument(
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"-v", "--video_path", help="Video path", default="example/video.mp4"
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)
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parser.add_argument(
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"-o",
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"--output",
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help="Output video name (example: 'interpolated_video.mp4')",
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default="interpolated_video.mp4",
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)
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parser.add_argument(
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"-p",
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"--preset",
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help="Model preset",
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choices=["small", "large", "global"],
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default="global",
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)
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args = parser.parse_args()
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runner(
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base_path=Path(args.base_path),
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video_path=Path(args.video_path),
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output_video=args.output,
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preset=getattr(presets, args.preset.upper()),
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)
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if __name__ == "__main__":
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main()
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