1from os import walk
 2import matplotlib.pyplot as plt
 3import numpy as np
 4from PIL import Image, ImageOps
 5
 6#pixels, pixels of the output resizing images
 7size = 100, 100
 8def imgFileToData(path):
 9    image = Image.open(path)
10    #resize the image
11    thumb = ImageOps.fit(image, size, Image.ANTIALIAS)
12    image_data = np.asarray(thumb).flatten()
13    '''
14    plt.plot(111)
15    plt.imshow(thumb)
16    plt.show()
17    '''
18    if len(image_data)!=30000:
19        print "possible future ERROR!"
20        print "len: " + str(len(image_data))
21        print "please, delete: " + path
22    return image_data
23
24def getDirectoryFiles(path, imgClass):
25    images = []
26    for (dirpath, dirnames, filenames) in walk(path):
27        for filename in filenames:
28            #print filename
29            image_data = imgFileToData(path + "/" + filename)
30            images.append([image_data, imgClass])
31    return images
32
33
34def asdf():
35    for index, (image, prediction) in enumerate(images_and_predictions[:4]):
36        plt.subplot(2, 4, index + 5)
37        plt.axis('off')
38        plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
39        plt.title('Prediction: %i' % prediction)
40
41
42objects = getDirectoryFiles("object", 1)
43noobjects = getDirectoryFiles("noobject", 0)
44
45dataset = np.concatenate((objects, noobjects), axis=0)
46
47np.save('dataset.npy', dataset)