mxnet decord 视频读取和载入

2021-02-09 08:15

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标签:sys   ram   shuffle   image   numpy   this   which   桥接   hsi   

Decord Video Reader Example

import decord as de
from matplotlib import pyplot as plt
# using cpu in this example
ctx = de.cpu(0)
# example video
video = Javelin_standing_throw_drill.mkv

vr = de.VideoReader(video)  # using default resolution
print(Video frames #:, len(vr))       # 视频帧数
print(First frame shape:, vr[0].shape)      # 每帧的shape
Video frames #: 48
First frame shape: (240, 320, 3)

控制帧的尺寸:

vr = de.VideoReader(video, width=120, height=240)
print(Frame shape:, vr[0].shape)
Frame shape: (240, 120, 3)

随机访问显然很慢,但decord使用内部优化来确保不会在这里浪费太多精力。
返回的帧是DLPack兼容的NDArray格式(例如在TVM中使用),可转为numpy数组。
decord中有一个桥接系统,它自动将所有输出转换为与深度学习框架兼容的阵列,例如MXNet、PyTorch、Tensorflow。但始终可以利用numpy数组。

frame10 = vr[10].asnumpy()
plt.imshow(frame10)
plt.show()

技术图片

很容易一起获得许多帧:

frames = vr.get_batch(range(0, len(vr) - 1, 5))
print(frames.shape)
(10, 240, 120, 3)

 

 

 

Decord Video Loader Example

import sys, os
import decord as de


# using cpu in this example
ctx = de.cpu(0)
# using batchsize = 2 and smaller resolution in this example
shape = (2, 480, 640, 3)
# using kinetics example videos
videos = [Javelin_standing_throw_drill.mkv, flipping_a_pancake.mkv]
# using in-batch frame interval 5
interval = 5        # 一个batch中每两帧的距离
# using inter-batch frame interval 20, which means batch-batch interval is 20
skip = 3      # 不同batch之间的距离


# first see how sequential read looks like
vl = de.VideoLoader(videos, ctx=ctx, shape=shape, interval=interval, skip=skip, shuffle=0)
print(num batches:, len(vl))
num batches: 9

可视化:

def disp_batches(video_loader, max_disp=5):
    %matplotlib inline
    from matplotlib import pyplot as plt
    import matplotlib.gridspec as gridspec
    cnt = 0
    vl.reset()
    for batch in vl:
        if cnt >= max_disp:
            break
        print(batch data shape:, batch[0].shape)
        print(indices:, , .join([(file: {} frame: {}).format(x, y) for x, y in batch[1].asnumpy()]))
        print(----------)
        data = batch[0].asnumpy()
        columns = 4
        rows = max(1, (data.shape[0] + 1) // columns)
        fig = plt.figure(figsize = (32,(16 // columns) * rows))
        gs = gridspec.GridSpec(rows, columns)
        for i in range(data.shape[0]):
            plt.subplot(gs[i])
            plt.axis("off")
            plt.imshow(data[i])
        cnt += 1
disp_batches(vl, 5)
batch data shape: (2, 480, 640, 3)
indices: (file: 0 frame: 0), (file: 0 frame: 7)      # 0-7共6帧;间隔为3,下一次从11开始
----------
batch data shape: (2, 480, 640, 3)
indices: (file: 0 frame: 11), (file: 0 frame: 18)    # 11-18共6帧;间隔为3,下一次从22开始...
----------
batch data shape: (2, 480, 640, 3)
indices: (file: 0 frame: 22), (file: 0 frame: 29)
----------
batch data shape: (2, 480, 640, 3)
indices: (file: 1 frame: 0), (file: 1 frame: 7)
----------
batch data shape: (2, 480, 640, 3)
indices: (file: 1 frame: 11), (file: 1 frame: 18)
----------

可以看到这个是从第一个视频截取、然后第二个...那么可以按照如下进行shuffle:

vl = de.VideoLoader(videos, ctx=ctx, shape=shape, interval=interval, skip=skip, shuffle=2)
print(num batches:, len(vl))
disp_batches(vl, 5)
num batches: 8
batch data shape: (2, 480, 640, 3)
indices: (file: 1 frame: 33), (file: 1 frame: 40)      # file1中截取
----------
batch data shape: (2, 480, 640, 3)
indices: (file: 1 frame: 22), (file: 1 frame: 29)
----------
batch data shape: (2, 480, 640, 3) 
indices: (file: 0 frame: 11), (file: 0 frame: 18)       # file0中截取
----------
batch data shape: (2, 480, 640, 3)
indices: (file: 1 frame: 44), (file: 1 frame: 51)
----------
batch data shape: (2, 480, 640, 3)
indices: (file: 1 frame: 11), (file: 1 frame: 18)
----------

可以看到已经不是按照视频顺序截取了。

 

mxnet decord 视频读取和载入

标签:sys   ram   shuffle   image   numpy   this   which   桥接   hsi   

原文地址:https://www.cnblogs.com/king-lps/p/13062801.html

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