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83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import math
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from typing import Optional, Union
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import numpy as np
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def sample_frames(
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frame_factor: int,
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min_frames: int,
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max_frames: int,
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metadata: Optional[dict] = None,
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fps: Optional[Union[int, float]] = None,
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num_frames: Optional[int] = None,
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):
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"""
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Sample frames from video according to specified criteria.
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Args:
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frame_factor: Ensure sampled frames are multiples of this factor
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min_frames: Minimum number of frames to sample
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max_frames: Maximum number of frames to sample
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metadata: Video metadata containing fps information
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fps: Target frames per second for sampling
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num_frames: Exact number of frames to sample
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Returns:
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np.ndarray: Sampled video frames
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Raises:
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ValueError: If both fps and num_frames are specified,
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or if required metadata is missing,
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or if requested frames exceed available frames
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"""
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if fps > 0 and num_frames > 0:
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raise ValueError("`num_frames` and `fps` are mutually exclusive arguments, please use only one!")
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total_num_frames = metadata["num_of_frame"]
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# If num_frames is not given but fps is, calculate num_frames from fps
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if num_frames > 0:
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num_frames = round(num_frames / frame_factor) * frame_factor
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elif fps > 0:
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if metadata is None:
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raise ValueError(
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"Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. "
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"Please pass in `VideoMetadata` object or use a fixed `num_frames` per input video"
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)
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max_frames = math.floor(min(max_frames, total_num_frames) / frame_factor) * frame_factor
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num_frames = total_num_frames / metadata["fps"] * fps
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num_frames = min(min(max(num_frames, min_frames), max_frames), total_num_frames)
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num_frames = math.floor(num_frames / frame_factor) * frame_factor
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if num_frames > total_num_frames:
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raise ValueError(
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f"Video can't be sampled. The inferred `num_frames={num_frames}` exceeds `total_num_frames={total_num_frames}`. "
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"Decrease `num_frames` or `fps` for sampling."
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)
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# Calculate frame indices based on sampling strategy
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if num_frames > 0:
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# Evenly spaced sampling for target frame count
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indices = np.arange(0, total_num_frames, total_num_frames / num_frames).astype(np.int32)
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else:
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# Keep all frames if no sampling requested
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indices = np.arange(0, total_num_frames).astype(np.int32)
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return indices
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