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视频 API

注意

尝试在 协作 上运行或 转到末尾 下载完整示例代码。

此示例说明了 torchvision 为视频提供的部分 API，以及构建数据集等的示例。

1. 简介：构建新的视频对象并检查其属性

首先，我们选择一个视频来测试该对象。为了便于说明，我们使用 kinetics400 数据集中的一个视频。要创建它，我们需要定义路径和要使用的流。

所选视频统计信息

  • WUzgd7C1pWA.mp4
    • 来源

      • kinetics-400

    • 视频

      • H-264

      • MPEG-4 AVC（第 10 部分）（avc1）

      • fps：29.97

    • 音频

      • MPEG AAC 音频（mp4a）

      • 采样率：48K Hz

import torch
import torchvision
from torchvision.datasets.utils import download_url
torchvision.set_video_backend("video_reader")

# Download the sample video
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/WUzgd7C1pWA.mp4?raw=true",
    ".",
    "WUzgd7C1pWA.mp4"
)
video_path = "./WUzgd7C1pWA.mp4"

Downloading https://raw.githubusercontent.com/pytorch/vision/main/test/assets/videos/WUzgd7C1pWA.mp4 to ./WUzgd7C1pWA.mp4

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流的定义方式类似于 torch 设备。我们将它们编码为 stream_type:stream_id 形式的字符串，其中 stream_type 是一个字符串，而 stream_id 是一个长整型。构造函数接受只传递一个 stream_type，在这种情况下，流会自动发现。首先，让我们获取特定视频的元数据

stream = "video"
video = torchvision.io.VideoReader(video_path, stream)
video.get_metadata()

{'video': {'duration': [10.9109], 'fps': [29.97002997002997]}, 'audio': {'duration': [10.9], 'framerate': [48000.0]}, 'subtitles': {'duration': []}, 'cc': {'duration': []}}

这里我们可以看到视频有两个流 - 视频流和音频流。目前可用的流类型包括 ['video', 'audio']。每个描述符包含两部分：流类型（例如 'video'）和唯一的流 ID（由视频编码确定）。这样，如果视频容器包含多个相同类型的流，用户可以访问他们想要的那个流。如果只传递了流类型，解码器会自动检测到该类型的第一个流并返回它。

让我们从视频流中读取所有帧。默认情况下，next(video_reader) 的返回值是一个包含以下字段的字典。

返回值字段是

  • data：包含 torch.tensor

  • pts：包含此特定帧的浮点时间戳

metadata = video.get_metadata()
video.set_current_stream("audio")

frames = []  # we are going to save the frames here.
ptss = []  # pts is a presentation timestamp in seconds (float) of each frame
for frame in video:
    frames.append(frame['data'])
    ptss.append(frame['pts'])

print("PTS for first five frames ", ptss[:5])
print("Total number of frames: ", len(frames))
approx_nf = metadata['audio']['duration'][0] * metadata['audio']['framerate'][0]
print("Approx total number of datapoints we can expect: ", approx_nf)
print("Read data size: ", frames[0].size(0) * len(frames))

PTS for first five frames  [0.0, 0.021332999999999998, 0.042667, 0.064, 0.08533299999999999]
Total number of frames:  511
Approx total number of datapoints we can expect:  523200.0
Read data size:  523264

但是，如果我们只想读取视频的某个时间段呢？可以使用 seek 函数的组合和每次调用 next 返回的以秒为单位的返回帧的呈现时间戳来轻松实现这一点。

鉴于我们的实现依赖于 python 迭代器，我们可以利用 itertools 来简化流程并使其更符合 python 的风格。

例如，如果我们想要从第二秒读取十帧

import itertools
video.set_current_stream("video")

frames = []  # we are going to save the frames here.

# We seek into a second second of the video and use islice to get 10 frames since
for frame, pts in itertools.islice(video.seek(2), 10):
    frames.append(frame)

print("Total number of frames: ", len(frames))

Total number of frames:  10

或者，如果我们想要从第 2 秒到第 5 秒读取，我们可以跳转到视频的第二秒，然后利用 itertools takewhile 来获取正确的帧数

video.set_current_stream("video")
frames = []  # we are going to save the frames here.
video = video.seek(2)

for frame in itertools.takewhile(lambda x: x['pts'] <= 5, video):
    frames.append(frame['data'])

print("Total number of frames: ", len(frames))
approx_nf = (5 - 2) * video.get_metadata()['video']['fps'][0]
print("We can expect approx: ", approx_nf)
print("Tensor size: ", frames[0].size())

Total number of frames:  90
We can expect approx:  89.91008991008991
Tensor size:  torch.Size([3, 256, 340])

2. 构建一个示例 read_video 函数

我们可以利用上述方法来构建 read video 函数，该函数遵循与现有 read_video 函数相同的 API。

def example_read_video(video_object, start=0, end=None, read_video=True, read_audio=True):
    if end is None:
        end = float("inf")
    if end < start:
        raise ValueError(
            "end time should be larger than start time, got "
            f"start time={start} and end time={end}"
        )

    video_frames = torch.empty(0)
    video_pts = []
    if read_video:
        video_object.set_current_stream("video")
        frames = []
        for frame in itertools.takewhile(lambda x: x['pts'] <= end, video_object.seek(start)):
            frames.append(frame['data'])
            video_pts.append(frame['pts'])
        if len(frames) > 0:
            video_frames = torch.stack(frames, 0)

    audio_frames = torch.empty(0)
    audio_pts = []
    if read_audio:
        video_object.set_current_stream("audio")
        frames = []
        for frame in itertools.takewhile(lambda x: x['pts'] <= end, video_object.seek(start)):
            frames.append(frame['data'])
            audio_pts.append(frame['pts'])
        if len(frames) > 0:
            audio_frames = torch.cat(frames, 0)

    return video_frames, audio_frames, (video_pts, audio_pts), video_object.get_metadata()


# Total number of frames should be 327 for video and 523264 datapoints for audio
vf, af, info, meta = example_read_video(video)
print(vf.size(), af.size())

torch.Size([327, 3, 256, 340]) torch.Size([523264, 1])

3. 构建一个随机采样示例数据集（可应用于 kinetics400 的训练数据集）

不错，现在我们可以使用相同的原理来制作示例数据集。我们建议为此目的尝试可迭代数据集。这里，我们将构建一个示例数据集，该数据集读取随机选择的 10 帧视频。

制作示例数据集

import os
os.makedirs("./dataset", exist_ok=True)
os.makedirs("./dataset/1", exist_ok=True)
os.makedirs("./dataset/2", exist_ok=True)

下载视频

from torchvision.datasets.utils import download_url
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/WUzgd7C1pWA.mp4?raw=true",
    "./dataset/1", "WUzgd7C1pWA.mp4"
)
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/RATRACE_wave_f_nm_np1_fr_goo_37.avi?raw=true",
    "./dataset/1",
    "RATRACE_wave_f_nm_np1_fr_goo_37.avi"
)
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/SOX5yA1l24A.mp4?raw=true",
    "./dataset/2",
    "SOX5yA1l24A.mp4"
)
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/v_SoccerJuggling_g23_c01.avi?raw=true",
    "./dataset/2",
    "v_SoccerJuggling_g23_c01.avi"
)
download_url(
    "https://github.com/pytorch/vision/blob/main/test/assets/videos/v_SoccerJuggling_g24_c01.avi?raw=true",
    "./dataset/2",
    "v_SoccerJuggling_g24_c01.avi"
)

Downloading https://raw.githubusercontent.com/pytorch/vision/main/test/assets/videos/WUzgd7C1pWA.mp4 to ./dataset/1/WUzgd7C1pWA.mp4

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Downloading https://raw.githubusercontent.com/pytorch/vision/main/test/assets/videos/RATRACE_wave_f_nm_np1_fr_goo_37.avi to ./dataset/1/RATRACE_wave_f_nm_np1_fr_goo_37.avi

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Downloading https://raw.githubusercontent.com/pytorch/vision/main/test/assets/videos/SOX5yA1l24A.mp4 to ./dataset/2/SOX5yA1l24A.mp4

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Downloading https://raw.githubusercontent.com/pytorch/vision/main/test/assets/videos/v_SoccerJuggling_g23_c01.avi to ./dataset/2/v_SoccerJuggling_g23_c01.avi

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Downloading https://raw.githubusercontent.com/pytorch/vision/main/test/assets/videos/v_SoccerJuggling_g24_c01.avi to ./dataset/2/v_SoccerJuggling_g24_c01.avi

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整理和工具

import os
import random

from torchvision.datasets.folder import make_dataset
from torchvision import transforms as t


def _find_classes(dir):
    classes = [d.name for d in os.scandir(dir) if d.is_dir()]
    classes.sort()
    class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
    return classes, class_to_idx


def get_samples(root, extensions=(".mp4", ".avi")):
    _, class_to_idx = _find_classes(root)
    return make_dataset(root, class_to_idx, extensions=extensions)

我们将定义数据集和一些基本参数。我们假设 FolderDataset 的结构，并添加以下参数

  • clip_len：剪辑的帧数

  • frame_transform：对每帧进行单独变换

  • video_transform：对视频序列进行变换

注意

我们实际上添加了 epoch 大小，因为使用 IterableDataset() 类可以让我们在需要时自然地对每个视频中的剪辑或图像进行过采样。

class RandomDataset(torch.utils.data.IterableDataset):
    def __init__(self, root, epoch_size=None, frame_transform=None, video_transform=None, clip_len=16):
        super(RandomDataset).__init__()

        self.samples = get_samples(root)

        # Allow for temporal jittering
        if epoch_size is None:
            epoch_size = len(self.samples)
        self.epoch_size = epoch_size

        self.clip_len = clip_len
        self.frame_transform = frame_transform
        self.video_transform = video_transform

    def __iter__(self):
        for i in range(self.epoch_size):
            # Get random sample
            path, target = random.choice(self.samples)
            # Get video object
            vid = torchvision.io.VideoReader(path, "video")
            metadata = vid.get_metadata()
            video_frames = []  # video frame buffer

            # Seek and return frames
            max_seek = metadata["video"]['duration'][0] - (self.clip_len / metadata["video"]['fps'][0])
            start = random.uniform(0., max_seek)
            for frame in itertools.islice(vid.seek(start), self.clip_len):
                video_frames.append(self.frame_transform(frame['data']))
                current_pts = frame['pts']
            # Stack it into a tensor
            video = torch.stack(video_frames, 0)
            if self.video_transform:
                video = self.video_transform(video)
            output = {
                'path': path,
                'video': video,
                'target': target,
                'start': start,
                'end': current_pts}
            yield output

给定一个文件夹结构中视频的路径，即

  • 数据集
    • 类 1

      • 文件 0

      • 文件 1

      • …

    • 类 2

      • 文件 0

      • 文件 1

      • …

    • …

我们可以生成一个数据加载器并测试数据集。

transforms = [t.Resize((112, 112))]
frame_transform = t.Compose(transforms)

dataset = RandomDataset("./dataset", epoch_size=None, frame_transform=frame_transform)

from torch.utils.data import DataLoader
loader = DataLoader(dataset, batch_size=12)
data = {"video": [], 'start': [], 'end': [], 'tensorsize': []}
for batch in loader:
    for i in range(len(batch['path'])):
        data['video'].append(batch['path'][i])
        data['start'].append(batch['start'][i].item())
        data['end'].append(batch['end'][i].item())
        data['tensorsize'].append(batch['video'][i].size())
print(data)

{'video': ['./dataset/1/RATRACE_wave_f_nm_np1_fr_goo_37.avi', './dataset/1/RATRACE_wave_f_nm_np1_fr_goo_37.avi', './dataset/2/v_SoccerJuggling_g24_c01.avi', './dataset/1/RATRACE_wave_f_nm_np1_fr_goo_37.avi', './dataset/2/v_SoccerJuggling_g24_c01.avi'], 'start': [0.08804530085403248, 1.7523818204866861, 1.8596934646318064, 0.9422158130821549, 2.438062762452499], 'end': [0.6, 2.266667, 2.369033, 1.466667, 2.969633], 'tensorsize': [torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112]), torch.Size([16, 3, 112, 112])]}

4. 数据可视化

可视化视频的示例

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 12))
for i in range(16):
    plt.subplot(4, 4, i + 1)
    plt.imshow(batch["video"][0, i, ...].permute(1, 2, 0))
    plt.axis("off")
plot video api

清理视频和数据集

import os
import shutil
os.remove("./WUzgd7C1pWA.mp4")
shutil.rmtree("./dataset")

脚本的总运行时间：（0 分钟 4.448 秒）

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