How to feed Cifar10 trained model with my own image and get label as output?

Question

I am trying to use the trained model based on the Cifar10 tutorial and would like to feed it with an external image 32x32 (jpg or png).
My goal is to be able to get

Answer 1

Some basics first:

1. First you define your graph: image queue, image preprocessing, inference of the convnet, top-k accuracy
2. Then you create a tf.Session() and work inside it: starting the queue runners, and calls to sess.run()

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Here is what your code should look like

# 1. GRAPH CREATION 
filename_queue = tf.train.string_input_producer(['/home/tensor/.../inputImage.jpg'])
...  # NO CREATION of a tf.Session here
float_image = ...
images = tf.expand_dims(float_image, 0)  # create a fake batch of images (batch_size=1)
logits = faultnet.inference(images)
_, top_k_pred = tf.nn.top_k(logits, k=5)

# 2. TENSORFLOW SESSION
with tf.Session() as sess:
    sess.run(init_op)

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    top_indices = sess.run([top_k_pred])
    print ("Predicted ", top_indices[0], " for your input image.")

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EDIT:

As @mrry suggests, if you only need to work on a single image, you can remove the queue runners:

# 1. GRAPH CREATION
input_img = tf.image.decode_jpeg(tf.read_file("/home/.../your_image.jpg"), channels=3)
reshaped_image = tf.image.resize_image_with_crop_or_pad(tf.cast(input_img, width, height), tf.float32)
float_image = tf.image.per_image_withening(reshaped_image)
images = tf.expand_dims(float_image, 0)  # create a fake batch of images (batch_size = 1)
logits = faultnet.inference(images)
_, top_k_pred = tf.nn.top_k(logits, k=5)

# 2. TENSORFLOW SESSION
with tf.Session() as sess:
  sess.run(init_op)

  top_indices = sess.run([top_k_pred])
  print ("Predicted ", top_indices[0], " for your input image.")