Skip to main content

FaceNet face features using OpenCV

Project description

facenet-opencv

FaceNet face features using OpenCV

Credits

facenet by davidsandberg

facenet_opencv_dnn by TanFluent

Why this exists

  1. The original project need tensorflow installed.
  2. The method/model need tensorflow 1.x, which is unavailable on python 3.8.
  3. A package with model in it, without depending tensorflow, is convenient.

How is this done

Download original models

20180408-102900

Or

20180402-114759

Install Tensorflow environment

python3.6 on linux

pip3 install protobuf==3.19.4 grpcio==1.8.6 tensorflow==1.7

Run the scripts

with some model path fix

  1. python3 convert_variable_to_constant.py
  2. python3 convert_tf_pb_to_cv_pb.py

Be noticed: the package contains the result of model 20180408-102900.

How to use

Install

pip3 install opencv-python
pip3 install mtcnn_opencv
pip3 install facenet_opencv

Code

from facenet_cv2 import FaceNet
model = FaceNet()

vectors = model.face_features(open("x.jpg", "rb").read())

for v in vectors:
    print(v)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

facenet_opencv-0.1.0-py3-none-any.whl (87.1 MB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page