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.
Update: * Version 0.3.2: Fix bug of searching for video files. Now it looks for extensions .webm, .avi, .wmv, .mp4, .m4v, .m4p inside folder datasetpath.
* Version 0.3.1.1: The function can receive directly the path to just 1 video, if multiplefiles is set False.
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.
Usage
Installation
(python3.8) Before installing my package, please install these pakages:
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.whlYou can find the wheels here: https://download.pytorch.org/whl/cu113/torch_stable.html * Opencv-Python==4.5.5
After 4 above packages, to install i3dFeatureExtraction package into your Python environment, run this code on your terminal:
pip install i3dFeatureExtractionImplementing
The main function of this package is FeatureExtraction which converts a directory of videos into numpy feature files.
from i3dFeatureExtraction import FeatureExtraction
FeatureExtraction.generate(
datasetpath="directory/of/input/videos",
outputpath = "directory/to/store/output/numpy/files",
pretrainedpath = "path/to/i3D/pretrained/weight",
sample_mode = "oversample/center_crop"
multiplefiles = True/False
)datasetpath (REQUIRED): path to videos.
If multiplefiles is True (default), the datasetpath is the path to a directory which contains 1 or more videos.
If multiplefiles is False, the datasetpath is the path to a video.
outputpath (optional, default: “output/”): the proccessed numpy feature files would be stored in this directory.
pretrainedpath (optional, default: “pretrained/”): the path of the pretrained i3d weight. If the weight is not existed, it will be downloaded and created manually.
sample_mode (optional, default: “oversample/”) receive “oversample” or “center_crop”. If oversample, each frame will be cropped and flipped to be turned into 10 frames.
Structure of this package
I am not good at drawing UML diagram but I hope this image helps illustrate the package’s structure.
Credits
This code is based on the following repositories:
* 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.
1 maintainer
Metadata
1 maintainer
Release files for i3dFeatureExtraction 0.3.3
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| i3dFeatureExtraction-0.3.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 39.7 kB
Release files / i3dFeatureExtraction-0.3.3.tar.gz
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