Object identification based on images

[sushanthkapisthalam]

Im working on a data set with object identification based on their images. I have 25 classes, since im very new to Neural networks and coding can some one suggest me what all pre-processing techniques can be done before sending the images in the convolution layer. I also want to implement SIFT features followed by TF-IDF retrieval.

This is what I have done so far:

from keras .models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD,RMSprop,adam
from keras.utils import np_utils

import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import os
import theano
from PIL import Image
from numpy import *

SKLEARN

from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split

input image dimensions

img_rows, img_cols = 200, 200

number of channels

img_channels = 1

#Data
path1 = 'C:\Users\mails\Desktop\Frieburg\input_images'
path2 = 'C:\Users\mails\Desktop\Frieburg\images-resized'

listing = os.listdir(path1)
num_samples=size(listing)
print (num_samples)

for file in listing:
im = Image.open(path1 + '\' + file)
img = im.resize((img_rows,img_cols))
gray = img.convert('L')
#need to do some more processing here
gray.save(path2 +'\' + file, "PNG")

imlist = os.listdir(path2)

im1 = array(Image.open('C:\Users\mails\Desktop\Frieburg\images-resized' + '\'+ imlist[0])) # open one image to get size
m,n = im1.shape[0:2] # get the size of the images
imnbr = len(imlist) # get the number of images

create matrix to store all flattened images

immatrix = array([array(Image.open('C:\Users\mails\Desktop\Frieburg\images-resized'+ '\' + im2)).flatten()
for im2 in imlist],'f')

label=np.ones((num_samples,),dtype = int)
label[0:136]=0
label[136:297]=1
label[297:669]=2
label[669:947]=3
label[947:1128]=4
label[1128:1435]=5
label[1435:1733]=6
label[1733:1830]=7
label[1830:1940]=8
label[1940:2049]=9
label[2049:2234]=10
label[2234:2475]=11
label[2475:2777]=12
label[2777:2939]=13
label[2939:3107]=14
label[3107:3250]=15
label[3250:3422]=16
label[3422:3572]=17
label[3572:3749]=18
label[3749:3956]=19
label[3956:4074]=20
label[4074:4357]=21
label[4357:4528]=222
label[4528:4685]=23
label[4685:]=24

data,Label = shuffle(immatrix,label, random_state=2)
train_data = [data,Label]

img=immatrix[217].reshape(img_rows,img_cols)
plt.imshow(img)
plt.imshow(img,cmap='gray')
print (train_data[0].shape)
print (train_data[1].shape)

#batch_size to train
batch_size = 128

number of output classes

nb_classes = 25

number of epochs to train

nb_epoch = 20

number of convolutional filters to use

nb_filters = 32

size of pooling area for max pooling

nb_pool = 2

convolution kernel size

nb_conv = 3

(X, y) = (train_data[0],train_data[1])

STEP 1: split X and y into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=4)

X_train = X_train.reshape(X_train.shape[0], 1, img_cols, img_rows)
X_test = X_test.reshape(X_test.shape[0], 1, img_cols, img_rows)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

X_train /= 255
X_test /= 255

print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

---

convert class vectors to binary class matrices

Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

convert class vectors to binary class matrices

Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

i = 100
plt.imshow(X_train[i, 0], interpolation='nearest')
print("label : ", Y_train[i,:])

(When Im trying to do cross entropy I get this error)

IndexError: index 222 is out of bounds for axis 1 with size 25

Can some one suggest me what should be done before sending the images to convolution layer.

Thanks in advance!