i3dFeatureExtraction 0.2.6

This package helps extract i3D features with ResNet-50 backbone given a folder of videos

Hello, I am Hao Vy Phan. I have develop this package using ResNet-50 to convert a video into an extracted i3D features numpy file.

Overview

Input: a directory which store 1 or more videos.

Output: 1 or many .npy files (extracted i3D features). Each features file is shaped n/16 * 2048 where n is the number of frames in the video

If there is a problem installing or implementing this package, please do not hesitate to contact me via my email. I am pleased to have people use my product.

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Usage

Installation

Before installing my package, please install these pakages: * Opencv-Python==4.5.5 * torch==1.10.1+cu113 * torchaudio==0.10.1+cu113 * torchvision==0.11.2+cu113 * Or find your own python OS version of torch from this link: https://download.pytorch.org/whl/cu113/torch_stable.html

Installing them through pip install may raise errors. You can download the wheel files from the above links and run this code:

pip install torchvision-0.11.2+cu113-cp38-cp38-win_amd64.whl
pip install torchaudio-0.10.1+cu113-cp38-cp38-win_amd64.whl
pip install torch-1.10.1+cu113-cp38-cp38-win_amd64.whl
pip install opencv_python-4.5.5-cp38-cp38-win_amd64.whl

After 4 above packages, to install i3dFeatureExtraction package into your Python environment, run this code on your terminal:

pip install i3dFeatureExtraction

Or install a specific version:

pip install i3dFeatureExtraction==0.2.4

Implementing

The main function of this package is FeatureExtraction which converts a directory of videos into numpy feature files.

from i3dFeatureExtraction import FeatureExtraction
FeatureExtraction.generate(
    outputpath = "directory/to/store/output/numpy/files",
    datasetpath="directory/of/input/videos",
    pretrainedpath = "path/to/i3D/pretrained/weight",
    sample_mode = "oversample/center_crop"
)

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Credits

This code is based on the following repositories: * pytorch-resnet3d * pytorch-i3d-feature-extraction * E2E-Action-Segmentation/feature_extraction/

I would like to extend a special thank-you to the original authors of these repositories for providing the foundation on which this implementation is built.