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.
