DeepLearningTutorials/code/fetex_image_256x256.py at master · webeng/DeepLearningTutorials

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from PIL import Image

from os import listdir

from os.path import isfile, join

import sys

import numpy as np

import random

from sklearn import preprocessing

import cPickle

import theano

#import scipy

#from scipy.misc import pilutil

class FetexImage(object):

verbose = None

"""docstring for FetexImage"""

def __init__(self,verbose = False):

super(FetexImage, self).__init__()

self.verbose = verbose

def calculate_average_image(self,im_paths):

imlist = []

j = 0

for im_path in im_paths:

im = Image.open(im_path)

im_aux = self.scale_and_crop_img(im)

imlist.append(im_aux)

if self.verbose:

sys.stdout.write("\r Process: {0}/{1}".format(j, len(im_paths)))

sys.stdout.flush()

j += 1

# if j == 10:

# break

w,h=imlist[0].size

N=len(imlist)

arr=np.zeros((h,w,3),theano.config.floatX)

for im in imlist:

imarr=np.array(im,dtype=theano.config.floatX)

#print len(imarr)

try:

arr=arr+imarr/N

except Exception, e:

print e

#arr=np.array(np.round(arr),dtype=np.uint8)

arr=np.array(np.round(arr),dtype=theano.config.floatX)

average_image=Image.fromarray(arr,mode="RGB")

#average_image.show()

return imlist,average_image

def substract_average_image(self, im, average_image):

im_minus_avg = np.array(im, dtype=theano.config.floatX) - np.array(average_image, dtype=theano.config.floatX)

#arr=np.array(np.round(im_minus_avg),dtype=np.uint8)

arr=np.array(np.round(im_minus_avg),dtype=theano.config.floatX)

im_minus_avg=Image.fromarray(arr,mode="RGB")

return im_minus_avg

def scale_and_crop_img(self,img):

#img = Image.open(im_path)

# size = 256, 256

# img.thumbnail(size, Image.ANTIALIAS)

# img.save('/Applications/MAMP/htdocs/DeepLearningTutorials/data/cnn-furniture/n03131574-craddle/n03131574_16-res.JPEG', "JPEG")

if img.size[0] < img.size[1]:

basewidth = 256

wpercent = (basewidth/float(img.size[0]))

hsize = int((float(img.size[1])*float(wpercent)))

#img = img.resize((basewidth,hsize), Image.ANTIALIAS).convert('L')

img = img.resize((basewidth,hsize), Image.ANTIALIAS)

else:

baseheight = 256

hpercent = (baseheight/float(img.size[1]))

wsize = int((float(img.size[0])*float(hpercent)))

#img = img.resize((wsize,baseheight), Image.ANTIALIAS).convert('L')

img = img.resize((wsize,baseheight), Image.ANTIALIAS)

half_the_width = img.size[0] / 2

half_the_height = img.size[1] / 2

img = img.crop(

(

half_the_width - 128,

half_the_height - 128,

half_the_width + 128,

half_the_height + 128

)

return img

def add_bg_square(self,img,r,b,g):

"return a background-color image having the img in exact center"

size = (max(img.size),)*2

layer = Image.new('RGB', size, (r,b,g))

layer.paste(img, tuple(map(lambda x:(x[0]-x[1])/2, zip(size, img.size))))

return layer

def average_image_color(self,filename):

i = Image.open(filename)

h = i.histogram()

# split into red, green, blue

r = h[0:256]

g = h[256:256*2]

b = h[256*2: 256*3]

# perform the weighted average of each channel:

# the *index* is the channel value, and the *value* is its weight

return (

sum( i*w for i, w in enumerate(r) ) / sum(r),

sum( i*w for i, w in enumerate(g) ) / sum(g),

sum( i*w for i, w in enumerate(b) ) / sum(b)

)

def convert_to_bw_and_scale(self):

im_path = '/Applications/MAMP/htdocs/DeepLearningTutorials/data/cnn-furniture/n03131574-craddle-resized/n03131574_10027.JPEG'

im = Image.open(im_path)

# Get monochrome pixels

im_aux = im.convert('L')

pixels_monochrome = np.array(list(im_aux.getdata()), dtype='float32')

# scale between 0-1 to speed up computations

# print type(pixels_monochrome)

min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)

pixels_monochrome = min_max_scaler.fit_transform(pixels_monochrome)

return pixels_monochrome

# im.convert('1').getdata() # Convert to BW

pixels = im.load() # this is not a list, nor is it list()'able

width, height = im.size

all_pixels = []

for x in range(width):

for y in range(height):

# Append pixels to all_pixels list providing this is RGB

# cpixel = pixels[x, y]

# all_pixels.append(cpixel)

# Convert to monochrome(only one value)

cpixel = pixels[x, y]

bw_value = int(round(sum(cpixel) / float(len(cpixel))))

# the above could probably be bw_value = sum(cpixel)/len(cpixel)

all_pixels.append(bw_value)

# Or to get the luminance (weighted average):

# cpixel = pixels[x, y]

# luma = (0.3 * cpixel[0]) + (0.59 * cpixel[1]) + (0.11 * cpixel[2])

# all_pixels.append(luma)

# Or pure 1-bit looking black and white:

# cpixel = pixels[x, y]

# if round(sum(cpixel)) / float(len(cpixel)) > 127:

# all_pixels.append(255)

# else:

# all_pixels.append(0)

#print all_pixels

# print len(all_pixels)

# all_pixels.show()

def processImagesPipeline(self,folder):

X = []

Y = []

lb = preprocessing.LabelBinarizer()

#lb.fit_transform(['n03131574-craddle','n04222210-single-bed'])

lb.fit_transform(['n07730207-carrot','n04222210-single-bed'])

im_paths = []

im_labels = []

#for image_type in ['n03131574-craddle','n04222210-single-bed']:

for image_type in ['n07730207-carrot','n04222210-single-bed']:

mypath = folder + image_type

onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]

for file_name in onlyfiles:

if file_name != '.DS_Store':

im_path = mypath = folder + image_type + '/' + file_name

#outfile = folder + image_type + '-resized/' + file_name

im_paths.append(im_path)

im_labels.append(image_type)

combined = zip(im_paths, im_labels)

random.shuffle(combined)

im_paths[:], im_labels[:] = zip(*combined)

print "load, scale, crop and calculate image average"

imlist,average_image = self.calculate_average_image(im_paths)

i = 0

#for im_path in im_paths:

for im in imlist:

# Substract average image and convert it to BW

# Then convert it to monochrome and append it to X

# Append the label to Y

try:

im = self.substract_average_image(im, average_image)

X.append(np.array(im, dtype=theano.config.floatX))

#imarr = np.array(im, dtype=theano.config.floatX)

#imarr = imarr.flatten(order='F')

#imarr = imarr.flatten(order='C')

#print imarr

#X.append(imarr)

#im_aux = np.asarray(im, dtype='float64') / 256.

# put image in 4D tensor of shape (1, 3, height, width)

#img_ = im_aux.transpose(2, 0, 1).reshape(1, 3, im.size[0], im.size[1])

#print img_

#X.append(img_)

#im.transpose(2, 0, 1).reshape(1, 3, 639, 516)

#print np.array(im, dtype=theano.config.floatX)

#print "reshape"

#im = im.reshape(1, 3, im.size[0], im.size[1])

#print np.array(im, dtype=theano.config.floatX)

#X.append(np.array(im, dtype='float64'))

#X.append(np.array(np.round(im), dtype=np.uint8))

#Uncomment this if you want to work with monochrome

# im = im.convert('L')

# pixels_monochrome = np.array(list(im.getdata()), dtype=np.float)

# # scale between 0-1 to speed up computations

# min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0,1), copy=True)

# pixels_monochrome = min_max_scaler.fit_transform(pixels_monochrome)

# X.append(pixels_monochrome)

Y.append(lb.transform([im_labels[i]])[0][0])

except Exception, e:

print e

# print Y

# print len(Y)

#im_aux.show()

if self.verbose:

#sys.stdout.write("\r Image Type: {0} File Name: {1} Process: {2}/{3}".format(image_type, file_name, count, len(im_paths)))

#sys.stdout.write("\r File Name: {0} Process: {1}/{2}".format(im_path, count, len(im_paths)))

sys.stdout.write("\r Process: {0}/{1}".format(i, len(im_paths)))

sys.stdout.flush()

# if i == 0:

# break

i += 1

train_length = int(round(len(X) * 0.60))

valid_length = int(round(len(X) * 0.20))

test_length = int(round(len(X) * 0.20))

X_train = X[0:train_length]

X_valid = X[train_length: (train_length + valid_length)]

X_test = X[-test_length:]

def flaten_aux(V):

#return V.flatten(order='C')

return V.flatten(order='F')

#vfunc = np.vectorize(flaten_aux)

X_train = map(flaten_aux, X_train)

X_valid = map(flaten_aux, X_valid)

X_test = map(flaten_aux, X_test)

#print X_train

#print X_train.size

#X_train = vfunc(X_train)

# X_valid = vfunc(X_train)X_valid_flat.flatten(order='F')

# X_test = X_test_flat.flatten(order='F')

#X_train = X_train.flatten(order='F')

#X_valid = X_valid.flatten(order='F')

#X_test = X_test.flatten(order='F')

Y_train = np.array(Y[0:train_length], dtype=theano.config.floatX)

Y_valid = np.array(Y[train_length:(train_length + valid_length)], dtype=theano.config.floatX)

Y_test = np.array(Y[-test_length:], dtype=theano.config.floatX)

train_set = [X_train,Y_train]

valid_set = [X_valid,Y_valid]

test_set = [X_test,Y_test]

print "X_train {} X_validation {} X_test {}".format(len(X_train),len(X_valid),len(X_test))

print "Y_train {} Y_validation {} Y_test {}".format(len(Y_train),len(Y_valid),len(Y_test))

output = open('../data/train_set.pkl', 'wb')

cPickle.dump(train_set, output,protocol=-1)

output.close()

output = open('../data/valid_set.pkl', 'wb')

cPickle.dump(valid_set, output,protocol=-1)

output.close()

output = open('../data/test_set.pkl', 'wb')

cPickle.dump(test_set, output,protocol=-1)

output.close()

return train_set,valid_set,test_set

if __name__ == '__main__':

folder = '/Applications/MAMP/htdocs/DeepLearningTutorials/data/cnn-furniture-reduced-2/'

#folder = '/Applications/MAMP/htdocs/DeepLearningTutorials/data/cnn-furniture/'

fe = FetexImage(verbose=True)

#fe.scale_and_crop_test('/Applications/MAMP/htdocs/DeepLearningTutorials/data/cnn-furniture/n03131574-craddle/n03131574_16.JPEG')

#print fe.convert_to_bw_and_scale()

train_set,valid_set,test_set = fe.processImagesPipeline(folder)

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