EFace -- A project of face detection in Python

This project name as E-Face which is a implementation of face detection algorithm.

My nick name is EOF. For convenient, I name it as E-Face.

It's stimulating to do this project. Enjoy with it.

The architecture of this project.

The following list show the files in this awesome project.

• adaboost.py Implementation of Adaptive Boosting algorithm

• cascade.py Cascade Decision Tree

• config.py All parameters of configuration in this project are stored in this file.

• image.py The initialization of images. class Image and class ImageSet are in this file.

• haarFeature.py Stuff with Haar-Features.

• weakClassifier.py The detail about Weak classifier.

• training.py Script for training the model.

directories:

• model/ cache files for adaboost model.

• featuers/ values for different feaures with different samples.

• doc/ documents with this project.

Usage:

For training a adaboost model:

     python ./trainingAdaBoost.py

To detect faces in a image, you have to define the TEST_IMG which is the path where store your image:

    python ./EFace.py

Presentation of current result:

I'm still working on making this project more powerful. So, here is the presentation of current result.

Programming Style:

I used basic OOP(Object Oriented Programming) tricks to build my program. Something like... I put all about AdaBoost into a class(AdaBoost) which you can find in file adaboost.py. Everytime you want to do something with adaboost, just create a object instance of that class.

Adavantages of this style: Higher level of abstraction and easy to be used. With this style, green hand will easy to build good archtecture with our project.

Disadvantages of this style: Without optimalization, it will cost a lot of memory. This will be obvious when the scale of project goes more and more large.

During this period when I working on the project, I meet a lots of problem. But I also want to say "thanks" to these problem. It help me a lot to enhance my ability in programming.

• Exception Handle The training process cost too much time. Sometimes, we have a better idea to change the code into a better version. But the trainning process is going on. If we press ctrl + c to interrupt, the data that we have get from the AdaBoost process will lost.

  I use a handler for KeyboardInterrupt and then save the data of model so that the valuable data won't be lose.

• High Performance Programming in Python

  There have lots of tricks to make native Python code run more faster. The computation of image processing is very huge. This means that it's a typical problem about CPU-bound.

• Concurrent Control To improve the performance of this system in the training process, I try to use parallel mechanism with the two CPU in my workstation.

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Optimization diary

2016-04-09 Restart to built this project and finished optimize the image.py

2016-04-13 refactor the training.py and make it more light. create a new module mapReduce.py. In haarFeature.py, @idxVector is initialized by numpy.zeros, it's faster than numpy.array([0 for i in range(length)])

2016-04-15 going to optimal weakClassifier.py and adaboost.py. Try to vectorize weakclassifier.py

2016-04-16 change scanImage.py and use different size of final classifier image but not resize the inputed image.