from PIL import Image # image operation
import os # get file list
import glob # image treatment
import numpy as np # culculation
from sklearn.model_selection import train_test_split # split data
# initialize parameters
classes = ['car', 'motorbike'] # define 2 classses
num_classes = len(classes) # the number of classes
image_size = 150 # pixels of width or height
# read images and cnvert numpy array
X = [] # ready to make list
Y = [] # ready to make list
# numbering and
for index, class_label in enumerate(classes):
# create class_label directories (car or motorbike)
photos_dir ='./' + class_label
# search jpeg fileb and create files object
files = glob.glob(photos_dir + '/*.jpg')
for i, file in enumerate(files):
# open image files as instance
image = Image.open(file)
# convert image into RGB value data
image = image.convert('RGB')
# align to the same size (just in case)
image = image.resize((image_size, image_size))
# convert RGB value into numpy array
data = np.asarray(image)
# normalize
data = data / 255.0
# append value to list
X.append(data)
Y.append(index)
# convert list into numpy array
X = np.array(X)
Y = np.array(Y)
# split data (training and test)
X_train, X_test, y_train, y_test = train_test_split(X, Y)
# replace 1 variable
xy =(X_train, X_test, y_train, y_test)
# save values as npy file
np.save('./image_files.npy', xy)
