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getvector.py
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getvector.py
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import numpy as np
import os
import tensorflow as tf
import urllib.request
import matplotlib.pyplot as plt
import os
import dataset_utils
import inception_preprocessing
import inception_v3 as v3
def getvector(imagedir):
slim = tf.contrib.slim
batch_size = 3
image_size = v3.inception_v3.default_image_size
url = "http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz"
checkpoints_dir = os.getcwd()
if not tf.gfile.Exists(checkpoints_dir+'/inception_v3.ckpt'):
dataset_utils.download_and_uncompress_tarball(url, checkpoints_dir)
with tf.Graph().as_default():
#imagedir = '/home/jiexun/Desktop/Siraj/ImageChallenge/Necessary/train/cat.0.jpg'
image_string = tf.read_file(imagedir)
image = tf.image.decode_jpeg(image_string, channels=3)
processed_image = inception_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)
processed_images = tf.expand_dims(processed_image, 0)
# Create the model, use the default arg scope to configure the batch norm parameters.
with slim.arg_scope(v3.inception_v3_arg_scope()):
vector, _ = v3.inception_v3(processed_images, num_classes=1001, is_training=False)
init_fn = slim.assign_from_checkpoint_fn(os.path.join(checkpoints_dir, 'inception_v3.ckpt'), slim.get_model_variables('InceptionV3'))
with tf.Session() as sess:
init_fn(sess)
np_image, vector = sess.run([image, vector])
a = np.asarray([x for xs in vector for xss in xs for xsss in xss for x in xsss])
np.reshape(a, (1,2048))
return a