DeepCreamPy/training_data/to_npy.py

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import glob
import os
#import cv2
from PIL import Image
import numpy as np
ratio = 0.95
image_size = 128
x = []
paths = glob.glob('images/*')
for path in paths:
#img = cv2.imread(path)
#img = Image.open(path)
#img = cv2.resize(img, (image_size, image_size))
#img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#x.append(img)
temp = Image.open(path)
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#remove alpha channel
if temp.mode=='RGBA':
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temp = temp.convert('RGB')
print ("no alpha")
print temp.mode
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keep = temp.copy()
keep = np.array(keep)
x.append(keep)
temp.close()
x = np.array(x, dtype=np.uint8)
#np.random.shuffle(x)
p = int(ratio * len(x))
x_train = x[:p]
x_test = x[p:]
if not os.path.exists('./npy'):
os.mkdir('./npy')
np.save('./npy/x_train.npy', x_train)
np.save('./npy/x_test.npy', x_test)