from numpy import *
FINALTRAINING=[[],[]]
FINALDEV=[[],[]]
import os
TRAINING=[]
DEV=[]
import random
import time


def pooling(image):
        b=ones(image.shape[0])
        for i in range(image.shape[0]):
                mb=[]
                for j in range(image.shape[1]/4):
                        mb.append(image[i,j*4:(j+1)*4].max())
                b=vstack([b,asarray(mb)])
        return b[1:,:]
                        
import matplotlib
matplotlib.use('Agg')
from pylab import *


for i in range(1,502):
	os.chdir("/NAS3/SABIOD/public_data/SCATT_BIRD_50mostE_perfile_april2014_RB_HG/"+str(i))
	m=[]
	b=loadtxt("carres.dat")
	os.chdir("/NAS3/SABIOD/public_data/SCATT_BIRD_50mostE_perfile_april2014_RB_HG_features/"+str(i)+"/")
	for j in range(len(b)):
		del m
		print i,j
		m=concatenate((b[j,:68].reshape(-1,68),b[j,68:136].reshape(-1,68),b[j,136:204].reshape(-1,68),b[j,204:].reshape(-1,68)))
		m=transpose(m)
		m=m[2:-2,6:-10]
		subplot(2,1,1)
		imshow(m)
		title('beforeMaxPooling.png')	
		m=pooling(m)
		subplot(2,1,2)
                imshow(m)
                title('afterMaxPooling.png')
                savefig(str(j))  
		if j%3==0:
			DEV.append((m,i))
		else:
			TRAINING.append((m,i))
	print i
	print len(DEV),len(TRAINING)
	os.chdir("..")