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48 lines
1.3 KiB
48 lines
1.3 KiB
from os import walk
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import matplotlib.pyplot as plt
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import numpy as np
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from PIL import Image, ImageOps
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#pixels, pixels of the output resizing images
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size = 100, 100
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def imgFileToData(path):
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image = Image.open(path)
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#resize the image
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thumb = ImageOps.fit(image, size, Image.ANTIALIAS)
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image_data = np.asarray(thumb).flatten()
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'''
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plt.plot(111)
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plt.imshow(thumb)
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plt.show()
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'''
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if len(image_data)!=30000:
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print "possible future ERROR!"
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print "len: " + str(len(image_data))
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print "please, delete: " + path
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return image_data
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def getDirectoryFiles(path, imgClass):
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images = []
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for (dirpath, dirnames, filenames) in walk(path):
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for filename in filenames:
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#print filename
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image_data = imgFileToData(path + "/" + filename)
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images.append([image_data, imgClass])
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print path + "/" + filename
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return images
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def asdf():
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for index, (image, prediction) in enumerate(images_and_predictions[:4]):
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plt.subplot(2, 4, index + 5)
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plt.axis('off')
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plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
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plt.title('Prediction: %i' % prediction)
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objects = getDirectoryFiles("object", 1)
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noobjects = getDirectoryFiles("noobject", 0)
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dataset = np.concatenate((objects, noobjects), axis=0)
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np.save('dataset.npy', dataset)
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