import numpy
import StringIO
from PIL import Image
from scipy import misc

width = 256
height = 256
depth = 256

slicesX = 10
slicesY = 10

dataset = 'heart'
ext = '.png'
filepath = dataset + '_256x256x256.raw'

original = numpy.fromfile(filepath, dtype=numpy.uint8)
original = numpy.reshape(original, (width, height, depth))

for i in range(100,201):
	slice = original[i]
	misc.imsave(dataset + '/img'+ str(i-100+1) +ext, slice)
	
resultImg = Image.new('L', (width*slicesX, height*slicesY)) 
# read files 1 by 1 from dir
for tileIdx in range(0,100):
	tileName = dataset + '/img'+ str(tileIdx+1) +ext
	#print tileName
	tileImg = Image.open(tileName)
	tileIdxX = tileIdx % slicesX
	tileIdxY = tileIdx / slicesY
	for tileX in range(0, width):
		for tileY in range(0, height):
			imgX = width * tileIdxX + tileX
			imgY = height * tileIdxY + tileY
			pixel = tileImg.getpixel((tileX, tileY))
			resultImg.putpixel((imgX, imgY), pixel)

resultName = dataset + 'Result'

resultImg.save(resultName + ext)

newWidth = 2048
newHeight = 2048

resultImgNearest 	=  resultImg.resize((newWidth, newHeight), Image.NEAREST)
resultImgBilinear 	=  resultImg.resize((newWidth, newHeight), Image.BILINEAR)
resultImgBicubic 	=  resultImg.resize((newWidth, newHeight), Image.BICUBIC)
resultImgAntialias 	=  resultImg.resize((newWidth, newHeight), Image.ANTIALIAS)

resultImgNearest.save(resultName + 'Nearest' + ext)
resultImgBilinear.save(resultName + 'Bilinear' + ext)
resultImgBicubic.save(resultName + 'Bicubic' + ext)
resultImgAntialias.save(resultName + 'Antialias' + ext)