No project description provided
Project description
This repository provides an incredibly light-weight implementation of the gray-scale feature extractor from: "Beyond simple features: A large-scale feature search approach to unconstrained face recognition"
Use
pip install simplefeature
or
git clone https://github.com/joelb92/simplefeature.git && cd simplefeat && python setup.py install
import simplefeature
import cv2
im = cv2.imread("/home/face.jpg")
embedding = simplefeature.extract(im)
Inputs will be scaled to 200x200px The system outputs a 51200-d vector
Please cite this paper:
Cox, David, and Nicolas Pinto. "Beyond simple features: A large-scale feature search approach to unconstrained face recognition."
2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG). IEEE, 2011.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file simplefeature-1.1.2-py3-none-any.whl.
File metadata
- Download URL: simplefeature-1.1.2-py3-none-any.whl
- Upload date:
- Size: 2.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.0 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.56.1 CPython/3.7.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
acc596f072cefc7f8c1745a11ff564c5ab7b2d27e345b0d7ef56bf5685b9b305
|
|
| MD5 |
5de1d6f8e9d0c7f8a9ca667ba9c0a837
|
|
| BLAKE2b-256 |
1ecc2ebd87200c2a23baedd9f0fb0ccd8ea9afa48864e8e0a74db31072b3178e
|