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utils.py
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utils.py
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# Arda Mavi
import os
import numpy as np
from os import listdir
from skimage.io import imread
from skimage.transform import resize
from keras.utils import to_categorical
#from sklearn.model_selection import train_test_split
# Settings:
num_class = 10
def get_img(data_path, img_size=64, grayscale_images=True):
# Getting image array from path:
img = imread(data_path, as_gray=grayscale_images)
img = resize(img, (img_size, img_size, 1 if grayscale_images else 3))
return img
def get_dataset(dataset_path='Dataset', img_size=64, as_gray=True):
# Getting all data from data path:
try:
X = []
Y = []
if(as_gray):
X = np.load('npy_dataset/X_grey.npy')
else:
X = np.load('npy_dataset/X_rgb.npy')
Y = np.load('npy_dataset/Y_rgb.npy')
except:
labels = listdir(dataset_path) # Geting labels
X = []
Y = []
for i, label in enumerate(labels):
datas_path = dataset_path+'/'+label
for data in listdir(datas_path):
img = get_img(datas_path+'/'+data, grayscale_images=as_gray)
X.append(img)
Y.append(i)
# Create dateset:
X = np.array(X).astype('float32')/255.
Y = np.array(Y).astype('float32')
Y = to_categorical(Y, num_class)
if not os.path.exists('npy_dataset/'):
os.makedirs('npy_dataset/')
if(as_gray):
np.save('npy_dataset/X_grey.npy', X)
else:
np.save('npy_dataset/X_rgb.npy', X)
np.save('npy_dataset/Y_rgb.npy', Y)
#X, X_test, Y, Y_test = train_test_split(X, Y, test_size=test_size, random_state=42)
return X, Y