Initial proof-of-concept

cmd/tensorflowapi/image_tensor.go

@@ -0,0 +1,65 @@
+package main
+
+import (
+	"bytes"
+
+	tf "github.com/tensorflow/tensorflow/tensorflow/go"
+	"github.com/tensorflow/tensorflow/tensorflow/go/op"
+)
+
+func makeTensorFromImage(imageBuffer *bytes.Buffer, imageFormat string) (*tf.Tensor, error) {
+	tensor, err := tf.NewTensor(imageBuffer.String())
+	if err != nil {
+		return nil, err
+	}
+	graph, input, output, err := makeTransformImageGraph(imageFormat)
+	if err != nil {
+		return nil, err
+	}
+	session, err := tf.NewSession(graph, nil)
+	if err != nil {
+		return nil, err
+	}
+	defer session.Close()
+	normalized, err := session.Run(
+		map[tf.Output]*tf.Tensor{input: tensor},
+		[]tf.Output{output},
+		nil)
+	if err != nil {
+		return nil, err
+	}
+	return normalized[0], nil
+}
+
+// Creates a graph to decode, rezise and normalize an image
+func makeTransformImageGraph(imageFormat string) (graph *tf.Graph, input, output tf.Output, err error) {
+	const (
+		H, W  = 224, 224
+		Mean  = float32(117)
+		Scale = float32(1)
+	)
+	s := op.NewScope()
+	input = op.Placeholder(s, tf.String)
+	// Decode PNG or JPEG
+	var decode tf.Output
+	if imageFormat == "png" {
+		decode = op.DecodePng(s, input, op.DecodePngChannels(3))
+	} else {
+		decode = op.DecodeJpeg(s, input, op.DecodeJpegChannels(3))
+	}
+	// Div and Sub perform (value-Mean)/Scale for each pixel
+	output = op.Div(s,
+		op.Sub(s,
+			// Resize to 224x224 with bilinear interpolation
+			op.ResizeBilinear(s,
+				// Create a batch containing a single image
+				op.ExpandDims(s,
+					// Use decoded pixel values
+					op.Cast(s, decode, tf.Float),
+					op.Const(s.SubScope("make_batch"), int32(0))),
+				op.Const(s.SubScope("size"), []int32{H, W})),
+			op.Const(s.SubScope("mean"), Mean)),
+		op.Const(s.SubScope("scale"), Scale))
+	graph, err = s.Finalize()
+	return graph, input, output, err
+}