Video pipeline reading labelled videos from a directory¶
In this example, we will go through the creation of a pipeline using the VideoReader operator to read videos along with their labels. The pipeline will return a pair of outputs from VideoReader: a batch of sequences and respective labels.
For more information on the VideoReader parameters, please look at the documentation reference.
To make it clearer, let’s look at how we can obtain these sequences and how to use them!
First let’s start with the imports:
from __future__ import print_function from __future__ import division import os import numpy as np from nvidia.dali.pipeline import Pipeline import nvidia.dali.ops as ops import nvidia.dali.types as types
We need some video containers to process. We can use Sintel trailer, which is a mp4 video container containing a h264 and under the Create Common license. We’ve split it into 5 second clips and divided the clips into labelled groups. This can be done easily with the
ffmpeg standalone tool. Our prepared data can be found in DALI_extra repository. Snippet below verifies, that you have defined
path as environment variable.
['image_info.txt', 'LICENSE', 'README.rst', 'db', '.gitattributes', '.git', 'NVIDIA_CLA_v1.0.1.docx']
Then we can set the parameters that will be used in the pipeline. The
count parameter will define how many frames we want in each sequence sample.
We can replace
video_directory with any other directory containing labelled subdirectories and video container files recognized by FFmpeg.
batch_size=2 sequence_length=8 initial_prefetch_size=11 video_directory = os.path.join(os.environ['DALI_EXTRA_PATH'], "db", "video", "sintel", "labelled_videos") shuffle=True n_iter=6
Running the pipeline¶
We can then define a minimal Pipeline that will output directly the VideoReader outputs:
class VideoPipe(Pipeline): def __init__(self, batch_size, num_threads, device_id, data, shuffle): super(VideoPipe, self).__init__(batch_size, num_threads, device_id, seed=16) self.input = ops.VideoReader(device="gpu", file_root=data, sequence_length=sequence_length, shard_id=0, num_shards=1, random_shuffle=shuffle, initial_fill=initial_prefetch_size) def define_graph(self): output, labels = self.input(name="Reader") return output, labels
Caution: One important here is tuning
initial_fill, that correspond to the Loader prefetch buffer intial size. Since this buffer will be filled of
initial_fill sequences, the total number of frames can be really huge! So set it consequently to not OOM during training.
Let’s try to build and run a
VideoPipe on device 0 that will output
batch_size sequences of
count frames and
batch_size labels at each iteration.
pipe = VideoPipe(batch_size=batch_size, num_threads=2, device_id=0, data=video_directory, shuffle=shuffle) pipe.build() for i in range(n_iter): sequences_out, labels = pipe.run() sequences_out = sequences_out.as_cpu().as_array() labels = labels.as_cpu().as_array() print(sequences_out.shape) print(labels.shape)
(2, 8, 720, 1280, 3) (2, 1) (2, 8, 720, 1280, 3) (2, 1) (2, 8, 720, 1280, 3) (2, 1) (2, 8, 720, 1280, 3) (2, 1) (2, 8, 720, 1280, 3) (2, 1) (2, 8, 720, 1280, 3) (2, 1)
Visualizing the results¶
The previous iterations seems to have the yield batches of the expected shape. But let’s visualize the results to be
sequences_out, labels = pipe.run() sequences_out = sequences_out.as_cpu().as_array() labels = labels.as_cpu().as_array()
We will use matplotlib to display the frames we obtained in the last batch.
%matplotlib inline from matplotlib import pyplot as plt import matplotlib.gridspec as gridspec
def show_sequence(sequence, label): columns = 4 rows = (sequence_length + 1) // (columns) fig = plt.figure(figsize = (32,(16 // columns) * rows)) gs = gridspec.GridSpec(rows, columns) for j in range(rows*columns): plt.subplot(gs[j]) plt.axis("off") plt.suptitle("label " + str(label), fontsize=30) plt.imshow(sequence[j])
And now let’s generate 5 batches of sequence, label pairs:
ITER = 5 for i in range(ITER): sequences_out, labels = pipe.run() sequences_out = sequences_out.as_cpu().as_array() labels = labels.as_cpu().as_array() show_sequence(sequences_out, labels)