loading image, reshaping and feeding to model input about tfgo HOT 4 CLOSED

package main

import (
        tg "github.com/galeone/tfgo"
        "github.com/galeone/tfgo/image"
        tf "github.com/tensorflow/tensorflow/tensorflow/go"
       "github.com/tensorflow/tensorflow/tensorflow/go/op"
)
func main() {
        root := tg.NewRoot()
        img := image.Read(root, "path_of_your_image.extension", 3) //shape 59, 199, 3
        img = img.RGBToGrayscale() // shape 59,199,1
        // resize using bilinear interpolation
        img = img.ResizeArea(image.Size{Height: 32, Width: 100})
        imageTensor := img.Value()
        // the tfgo.image package doesn't offer the reshape op, but tensorflow.op package does
        shape := tg.NewTensor(root, tg.Const(root, [3]int32{100, 32, 1}))
        imageTensor = op.Reshape(root, imageTensor, shape)
        // converto to float -> use the tfgo.image package
        newImage := image.NewImage(root, imageTensor)
        floatImage := newImage.ConvertDType(tf.float32, true)
        // extract the tensorflow tensor from the floatImage and you have the input to use in your model
        inputimage := floatImage.Value()
        // [.. other code]
}

root := tg.NewRoot()
img := image.Read(root, "path/to/image.jpg", 3)
results := model.Exec([]tf.Output{model.Op("the_output", 0)},
        map[tf.Output]*tf.Tensor{model.Op("the_input", 0): img.Value()})

root := tg.NewRoot()
img := image.Read(root, "path/to/image.jpg", 3)
results := model.Exec([]tf.Output{model.Op("the_output", 0)},
        map[tf.Output]*tf.Tensor{model.Op("the_input", 0): img.Tensor})

root := tg.NewRoot()
img := image.Read(root, "path/to/image.jpg", 3)
results := tg.Exec(root, []tf.Output{img.Value()}, nil, &tf.SessionOptions{})
A, _ := tf.NewTensor(results[0].Value())

results := model.Exec([]tf.Output{model.Op("the_output", 0)},
        map[tf.Output]*tf.Tensor{model.Op("the_input", 0): A})