data_utils.py

import numpy as np
import cv2
import os
import pandas as pd
from six.moves import cPickle

# For this problem the validation and test data provided by the concerned authority did not have labels, so the training data was split into train, test and validation sets
train_dir = '/mnt/boneage-training-dataset/'

X_train = []
y_age = []
y_gender = []

df = pd.read_csv('../train.csv')
a = df.as_matrix()
m = a.shape[0]

path = train_dir
k = 0
print 'Loading data set...'
for i in os.listdir(path):
    y_age.append(df.boneage[df.id == int(i[:-4])].tolist()[0])
    a = df.male[df.id == int(i[:-4])].tolist()[0]
    if a:
        y_gender.append(1)
    else:
        y_gender.append(0)
    img_path = path + i
    img = cv2.imread(img_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img,(224,224))
    x = np.asarray(img, dtype=np.uint8)
    X_train.append(x)
    
print ''
print '100% completed loading data'

# Save data
train_pkl = open('data.pkl','wb')
cPickle.dump(X_train, train_pkl, protocol=cPickle.HIGHEST_PROTOCOL)
train_pkl.close()

train_age_pkl = open('data_age.pkl','wb')
cPickle.dump(y_age, train_age_pkl, protocol=cPickle.HIGHEST_PROTOCOL)
train_age_pkl.close()

train_gender_pkl = open('data_gender.pkl','wb')
cPickle.dump(y_gender, train_gender_pkl, protocol=cPickle.HIGHEST_PROTOCOL)
train_gender_pkl.close()