140 lines
4.1 KiB
Python
140 lines
4.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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from functools import lru_cache
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from typing import Any, ClassVar, Literal, Optional
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import cv2
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import numpy as np
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import numpy.typing as npt
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from huggingface_hub import hf_hub_download
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from PIL import Image
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from vllm.utils import PlaceholderModule
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from .base import get_cache_dir
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try:
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import librosa
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except ImportError:
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librosa = PlaceholderModule("librosa") # type: ignore[assignment]
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@lru_cache
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def download_video_asset(filename: str) -> str:
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"""
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Download and open an image from huggingface
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repo: raushan-testing-hf/videos-test
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"""
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video_directory = get_cache_dir() / "video-example-data"
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video_directory.mkdir(parents=True, exist_ok=True)
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video_path = video_directory / filename
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video_path_str = str(video_path)
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if not video_path.exists():
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video_path_str = hf_hub_download(
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repo_id="raushan-testing-hf/videos-test",
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filename=filename,
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repo_type="dataset",
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cache_dir=video_directory,
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)
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return video_path_str
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def video_to_ndarrays(path: str, num_frames: int = -1) -> npt.NDArray:
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cap = cv2.VideoCapture(path)
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if not cap.isOpened():
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raise ValueError(f"Could not open video file {path}")
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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frames = []
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num_frames = num_frames if num_frames > 0 else total_frames
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frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
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for idx in range(total_frames):
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ok = cap.grab() # next img
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if not ok:
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break
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if idx in frame_indices: # only decompress needed
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ret, frame = cap.retrieve()
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if ret:
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frames.append(frame)
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frames = np.stack(frames)
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if len(frames) < num_frames:
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raise ValueError(f"Could not read enough frames from video file {path}"
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f" (expected {num_frames} frames, got {len(frames)})")
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return frames
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def video_to_pil_images_list(path: str,
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num_frames: int = -1) -> list[Image.Image]:
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frames = video_to_ndarrays(path, num_frames)
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return [
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Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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for frame in frames
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]
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def video_get_metadata(path: str) -> dict[str, Any]:
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cap = cv2.VideoCapture(path)
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if not cap.isOpened():
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raise ValueError(f"Could not open video file {path}")
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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fps = cap.get(cv2.CAP_PROP_FPS)
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duration = total_frames / fps if fps > 0 else 0
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metadata = {
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"total_num_frames": total_frames,
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"fps": fps,
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"duration": duration,
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"video_backend": "opencv"
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}
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return metadata
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VideoAssetName = Literal["baby_reading"]
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@dataclass(frozen=True)
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class VideoAsset:
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name: VideoAssetName
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num_frames: int = -1
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_NAME_TO_FILE: ClassVar[dict[VideoAssetName, str]] = {
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"baby_reading": "sample_demo_1.mp4",
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}
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@property
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def filename(self) -> str:
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return self._NAME_TO_FILE[self.name]
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@property
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def pil_images(self) -> list[Image.Image]:
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video_path = download_video_asset(self.filename)
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ret = video_to_pil_images_list(video_path, self.num_frames)
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return ret
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@property
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def np_ndarrays(self) -> npt.NDArray:
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video_path = download_video_asset(self.filename)
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ret = video_to_ndarrays(video_path, self.num_frames)
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return ret
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@property
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def metadata(self) -> dict[str, Any]:
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video_path = download_video_asset(self.filename)
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ret = video_get_metadata(video_path)
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return ret
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def get_audio(self, sampling_rate: Optional[float] = None) -> npt.NDArray:
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"""
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Read audio data from the video asset, used in Qwen2.5-Omni examples.
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See also: examples/offline_inference/qwen2_5_omni/only_thinker.py
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"""
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video_path = download_video_asset(self.filename)
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return librosa.load(video_path, sr=sampling_rate)[0]
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