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README.md

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-# FaceDection
+# EFace -- A project of face detection in Python
+
+
+<img src="https://img.shields.io/badge/EFace-1.2-brightgreen.svg? style=flat" />
+  
+
+<img src="https://img.shields.io/dub/l/vibe-d.svg" />
+
+
+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.
+
+![images](./FaceDetection/figure/monalisa.png)
+
+![images](./FaceDetection/figure/nens.png)
+
+![images](./FaceDetection/figure/soccer.png)
+
+### 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.
+    
+... ...
+
+---
+
+### 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.
 
-jjj