An example of using tensorflow hub for image classification

tf_hub_image_classification_example.py

from urllib.request import urlopen
from io import BytesIO
from PIL import Image, ImageOps
import cv2

def download_and_resize_image(url, filename, new_width=256, new_height=256):
  response = urlopen(url)
  image_data = response.read()
  image_data = BytesIO(image_data)
  pil_image = Image.open(image_data)
  pil_image = ImageOps.fit(pil_image, (new_width, new_height), Image.ANTIALIAS)
  pil_image_rgb = pil_image.convert('RGB')
  pil_image_rgb.save(filename, format='JPEG', quality=90)
# instantiating module 
tf.reset_default_graph()
module = hub.Module("https://tfhub.dev/google/imagenet/inception_v3/classification/1")
height, width = hub.get_expected_image_size(module)

img_url = "https://cdn.pixabay.com/photo/2017/02/20/18/03/cat-2083492_960_720.jpg"
img_jpg = "cat.jpg"
​
# input and output tensor
input_tensor = tf.placeholder(tf.float32, shape=[1, width, height, 3])
logits = module(input_tensor)
labels = tf.argmax(logits, axis=1)

# download_and_resize_image(img_url, img_jpg, width, height)
image = cv2.imread(img_jpg)
# input range from 0-1
image_rgb = (cv2.cvtColor(image, cv2.COLOR_BGR2RGB)/256)
inputs = np.expand_dims(image_rgb, 0).astype(np.float32)

# set up session
initializer = tf.global_variables_initializer()
sess = tf.Session()
sess.run(initializer)

# predict
res = sess.run(labels, feed_dict={input_tensor: inputs})
print(res)

# 283: 'Persian cat',