|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Openpose||26,821||2 months ago||235||other||C++|
|OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation|
|Opencv4nodejs||4,729||62||42||2 months ago||120||May 13, 2020||290||mit||C++|
|Nodejs bindings to OpenCV 3 and OpenCV 4|
|Node Opencv||4,275||307||61||5 months ago||31||March 10, 2020||126||mit||C++|
|OpenCV Bindings for node.js|
|Pigo||3,992||15||3 months ago||24||November 02, 2021||2||mit||Go|
|Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go.|
|Face Mask Detection||1,355||7 months ago||20||mit||Jupyter Notebook|
|Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras|
|Lbpcascade_animeface||1,223||5 years ago||4|
|A Face detector for anime/manga using OpenCV|
|Head Pose Estimation||1,023||a day ago||25||mit||Python|
|Head pose estimation by TensorFlow and OpenCV|
|Facetracker||931||3 years ago||8||mit||C++|
|Real time deformable face tracking in C++ with OpenCV 3.|
|Emotion Detection||907||2 months ago||12||mit||Python|
|Real-time Facial Emotion Detection using deep learning|
|Real_time_face_recognition||881||6 years ago||14||Python|
|(WARNING: This repository is NO LONGER maintained ) Real time face detection and recognition base on opencv/tensorflow/mtcnn/facenet|
Add virtual makeup to a picture of a face.
|Image without makeup.||Image with lipstick and eyeliner.|
You need >=Python2.7 and
pip to get this working. MacOS comes with Python2.7 installed by default. If you don't have
pip, follow these steps to get it -
curl -O https://bootstrap.pypa.io/get-pip.py
sudo python get-pip.py
pip install --upgrade pip
Install Cmake, Boost, and Boost-Python -
brew install cmake boost boost-python
If you don't have Homebrew, copy paste the following in your terminal to get it -
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
You will need >=Python2.7 and
pip to get this working.
Kindly figure out how to get the same for your distribution.
You need Cmake, and Boost-Python. To get the same, use
apt-get as you please.
sudo yum install cmake boost boost-devel
Note: You will need C++11 compiler for Cmake. CentOS does not come with the same out of the box. Follow this link's instructions to get it.
Note: Kindly raise issues if you face setup problems on your Linux distributions. I will ammend the instructions to resolve the same.
pip install virtualenv virtualenv my_project cd my_project && source bin/activate
Now you can install your python modules and run your code in an isolated chamber. Once you're done, run
deactivate to close the virtual environment.
pip install pyvisage
Note: If you are not using
virtualenv, you might need
sudoto make this work.
The module is named
visage, and consists of two classes -
Detect Features and
ApplyMakeup. You can import, and access their functions to either selectively detect face only, or apply lipstick directly. Kindly read the Wiki for detailed usage.
Note: You will need a working internet connection the first time you run this, as it will download a predictor file to your project folder the first time.
from visage import ApplyMakeup AM = ApplyMakeup() output_file = AM.apply_lipstick('input.jpg',170,10,30) # (R,G,B) - (170,10,30)
This assumes you have a front-facing image of a human face saved in your current directory as
Certain best practices to be followed to ensure optimal detection and application -
I would like to thank Davis E. King who built dlib, for the documentation and sample codes provided, which were a great help in building this.