cifar10Preprocessing

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 20 20:49:55 2016

@author: XFZ
"""

#cifar10 data reprocessing 

import numpy as np
import cv2
from random import shuffle
from multiprocessing import Pool
import cPickle
dataPrifix = 'data/batch_class_'
def unpickle(file):
    import cPickle
    fo = open(file, 'rb')
    dict = cPickle.load(fo)
    fo.close()
    return dict
def transform_image(img,ang_range,shear_range,trans_range):
    '''
    This function transforms images to generate new images.
    The function takes in following arguments,
    1- Image
    2- ang_range: Range of angles for rotation
    3- shear_range: Range of values to apply affine transform to
    4- trans_range: Range of values to apply translations over. 
    
    A Random uniform distribution is used to generate different parameters for transformation
    
    '''
    # Rotation

    ang_rot = np.random.uniform(ang_range)-ang_range/2
    rows,cols,ch = img.shape    
    Rot_M = cv2.getRotationMatrix2D((cols/2,rows/2),ang_rot,1)

    # Translation
    tr_x = trans_range*np.random.uniform()-trans_range/2
    tr_y = trans_range*np.random.uniform()-trans_range/2
    Trans_M = np.float32([[1,0,tr_x],[0,1,tr_y]])

    # Shear
    pts1 = np.float32([[5,5],[20,5],[5,20]])

    pt1 = 5+shear_range*np.random.uniform()-shear_range/2
    pt2 = 20+shear_range*np.random.uniform()-shear_range/2

    pts2 = np.float32([[pt1,5],[pt2,pt1],[5,pt2]])

    shear_M = cv2.getAffineTransform(pts1,pts2)
        
    #img = cv2.warpAffine(img,Rot_M,(cols,rows))
    #img = cv2.warpAffine(img,Trans_M,(cols,rows))
    #img = cv2.warpAffine(img,shear_M,(cols,rows))
    img = cv2.resize(img,(224,224))
    
    return img
def dataAug(img,n=16):
    img = img.reshape(32,32,3,order='F')
    imgs =[] 
    for i in range(1):
        imgs.append(transform_image(img,20,10,5).reshape(224*224*3,order="F"))
    return imgs
def dataProcess(nclass):
    datas = cPickle.load(open(dataPrifix+str(nclass),'rb'))
    aug = [] 
    for data in datas:
      imgs = dataAug(data)
      aug.extend(imgs)
    shuffle(aug)
    cPickle.dump(aug,open('data/aug_data_class_'+str(nclass),'wb'))

if __name__ == '__main__':
    from contextlib import closing
    with closing(Pool(processes=1)) as p:
        p.map(dataProcess,range(1))