Spaces:
Running on Zero
Running on Zero
File size: 5,537 Bytes
fed6c68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | # Copyright (c) 2026 Bytedance Ltd. and/or its affiliate
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import math
import os
from typing import List, Union
import decord
import torch
import torchvision
from PIL import Image
def load_video_from_path(video: str, use_audio_in_video: bool = False):
if os.path.exists(video):
video_path = video
else:
raise ValueError(f"video path {video} not exists")
video, _audio, info = torchvision.io.read_video(
video_path,
0.0,
None,
pts_unit="sec",
output_format="TCHW",
)
video_fps = info["video_fps"]
audio, audio_fps = None, None
if use_audio_in_video and _audio.numel() > 0:
audio = torch.mean(_audio, dim=0).numpy()
audio_fps = info["audio_fps"]
return video, video_fps, audio, audio_fps
def smart_video_nframes(
total_frames: int,
video_fps: Union[int, float],
fps: int = 2.0,
frame_factor: int = None,
min_frames: int = None,
max_frames: int = None,
add_one: bool = False,
) -> torch.Tensor:
nframes = total_frames / video_fps * fps
if frame_factor is not None:
nframes = math.floor(nframes / frame_factor) *\
frame_factor + int(add_one)
nframes = max(nframes, frame_factor + int(add_one))
else:
nframes = int(nframes + int(add_one))
idx = torch.linspace(0, total_frames - 1, nframes).round().long().tolist()
if min_frames is not None:
if frame_factor is not None:
min_frames = math.ceil(min_frames / frame_factor) * frame_factor
nframes = max(min_frames + int(add_one), nframes)
while len(idx) < int(nframes):
idx.append(idx[-1])
if max_frames is not None:
if frame_factor is not None:
max_frames = math.floor(max_frames / frame_factor) * frame_factor
nframes = min(max_frames + int(add_one), nframes)
if len(idx) > int(nframes):
idx = idx[:int(nframes)]
if frame_factor is not None:
assert len(idx) % frame_factor == int(
add_one), f"{len(idx)} % {frame_factor} != {int(add_one)}, total_frames: {total_frames}, video_fps: {video_fps}, fps: {fps}"
return idx
class VideoReader:
def __init__(self, video_bytes) -> None:
video_buffer = io.BytesIO(video_bytes)
self.vr = decord.VideoReader(
video_buffer, num_threads=1, ctx=decord.cpu(0), fault_tol=1)
self.vr.seek(0)
self._max_frame_id = len(self.vr) - 1
self._fps = self.vr.get_avg_fps()
@property
def max_frame_id(self):
return self._max_frame_id
@property
def length(self):
return len(self.vr)
@property
def fps(self):
return self._fps
def sample(self, frame_indices) -> (List[Image.Image]):
frames = self.vr.get_batch(frame_indices).asnumpy()
frames = [Image.fromarray(f).convert('RGB') for f in frames]
return frames
class PathVideoReader(VideoReader):
def __init__(self, video_path, duration=None, crop_method=None):
if os.path.exists(video_path):
self.vr = decord.VideoReader(
video_path, num_threads=1, ctx=decord.cpu(0), fault_tol=1)
else:
raise ValueError(f"video path {video_path} not exists")
self.vr.seek(0)
self._fps = self.vr.get_avg_fps()
self._total_frames = len(self.vr)
if duration is None:
self._start_frame = 0
self._end_frame = self._total_frames - 1
else:
assert duration > 0
crop_len = int(duration * self._fps)
# clamp
crop_len = min(crop_len, self._total_frames)
assert crop_len > 0
if crop_method == "left" or crop_method is None:
start = 0
elif crop_method == "right":
start = self._total_frames - crop_len
elif crop_method == "center":
start = (self._total_frames - crop_len) // 2
else:
raise ValueError(f"Unknown crop_method: {crop_method}")
end = start + crop_len - 1
self._start_frame = start
self._end_frame = end
self._max_frame_id = self._end_frame - self._start_frame
@property
def fps(self) -> float:
return self._fps
@property
def max_frame_id(self) -> int:
return self._max_frame_id
@property
def length(self) -> int:
return self._max_frame_id + 1
def sample(self, frame_indices: List[int]) -> List[Image.Image]:
"""
frame_indices: indices relative to the cropped clip
"""
real_indices = []
for idx in frame_indices:
idx = int(idx)
idx = max(0, min(idx, self._max_frame_id))
real_indices.append(self._start_frame + idx)
frames = self.vr.get_batch(real_indices).asnumpy()
return [Image.fromarray(f).convert("RGB") for f in frames]
|