func normalizeImage(body io.ReadCloser) (*tensorflow.Tensor, error) {
	var buf bytes.Buffer
	io.Copy(&buf, body)

	tensor, err := tensorflow.NewTensor(buf.String())
	if err != nil {
		return nil, err
	}

	graph, input, output, err := getNormalizedGraph()
	if err != nil {
		return nil, err
	}

	session, err := tensorflow.NewSession(graph, nil)
	if err != nil {
		return nil, err
	}

	normalized, err := session.Run(
		map[tensorflow.Output]*tensorflow.Tensor{
			input: tensor,
		},
		[]tensorflow.Output{
			output,
		},
		nil)
	if err != nil {
		return nil, err
	}

	return normalized[0], nil
}

// Creates a graph to decode, rezise and normalize an image
func getNormalizedGraph() (graph *tensorflow.Graph, input, output tensorflow.Output, err error) {
	s := op.NewScope()
	input = op.Placeholder(s, tensorflow.String)
	// 3 return RGB image
	decode := op.DecodeJpeg(s, input, op.DecodeJpegChannels(3))

	// Sub: returns x - y element-wise
	output = op.Sub(s,
		// make it 224x224: inception specific
		op.ResizeBilinear(s,
			// inserts a dimension of 1 into a tensor's shape.
			op.ExpandDims(s,
				// cast image to float type
				op.Cast(s, decode, tensorflow.Float),
				op.Const(s.SubScope("make_batch"), int32(0))),
			op.Const(s.SubScope("size"), []int32{224, 224})),
		// mean = 117: inception specific
		op.Const(s.SubScope("mean"), float32(117)))
	graph, err = s.Finalize()

	return graph, input, output, err
}