logo
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Readme
Files and versions

Updated 2 years ago

action-classification

Action Classification with Pytorchvideo

Author: Jael Gu


Description

An action classification operator is able to predict labels of human activities (with corresponding scores) and extracts features given the input video. It preprocesses video frames with video transforms and then loads pre-trained models by model names. This operator has implemented pre-trained models from Pytorchvideo and maps vectors with labels provided by the Kinetics400 Dataset.


Code Example

Use the pretrained SLOWFAST model ('slowfast_r50') to classify and generate a vector for the given video path './archery.mp4' (download).

Write the pipeline in simplified style:

import towhee

(
    towhee.glob('./archery.mp4')
          .video_decode.ffmpeg()
          .action_classification.pytorchvideo(model_name='slowfast_r50')
          .show()
)

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

(
    towhee.glob['path']('./archery.mp4')
          .video_decode.ffmpeg['path', 'frames']()
          .action_classification.pytorchvideo['frames', ('labels', 'scores', 'features')](
                model_name='slowfast_r50')
          .select['labels', 'scores', 'features']()
          .show(formatter={'path':'video_path'})
)


Factory Constructor

Create the operator via the following factory method

action_classification.pytorchvideo( model_name='x3d_xs', skip_preprocess=False, classmap=None, topk=5)

Parameters:

model_name: str

​ The name of pre-trained model from pytorchvideo hub.

​ Supported model names:

  • c2d_r50
  • i3d_r50
  • slow_r50
  • slowfast_r50
  • slowfast_r101
  • x3d_xs
  • x3d_s
  • x3d_m
  • mvit_base_16x4
  • mvit_base_32x3

skip_preprocess: bool

​ Flag to control whether to skip UniformTemporalSubsample in video transforms, defaults to False. If set to True, the step of UniformTemporalSubsample will be skipped. In this case, the user should guarantee that all the input video frames are already reprocessed properly, and thus can be fed to model directly.

classmap: Dict[str: int]:

​ Dictionary that maps class names to one hot vectors. If not given, the operator will load the default class map dictionary.

topk: int

​ The topk labels & scores to present in result. The default value is 5.

Interface

Given a video data, the video classification operator predicts a list of class labels and generates a video embedding in numpy.ndarray.

Parameters:

frames: List[VideoFrame]

​ Video frames in towhee.types.video_frame.VideoFrame.

Returns:

labels, scores, features: Tuple(List[str], List[float], numpy.ndarray)

  • labels: predicted class names.
  • scores: possibility scores ranking from high to low corresponding to predicted labels.
  • features: a video embedding in shape of (num_features,) representing features extracted by model.
Jael Gu 295e271fbb Adjust display of result image 7 Commits
file-icon .gitattributes
1.1 KiB
download-icon
Initial commit 2 years ago
file-icon README.md
3.2 KiB
download-icon
Adjust display of result image 2 years ago
file-icon __init__.py
696 B
download-icon
Add files 2 years ago
file-icon kinetics_400.json
10 KiB
download-icon
Add files 2 years ago
file-icon pytorchvideo.py
5.8 KiB
download-icon
Add files 2 years ago
file-icon requirements.txt
67 B
download-icon
Add files 2 years ago
file-icon result1.png
14 KiB
download-icon
Add show results 2 years ago
file-icon result2.png
92 KiB
download-icon
Add show results 2 years ago