fn infer(model_bytes: &[u8], image_bytes: &[u8]) -> i32 {
    let mut model = std::io::Cursor::new(model_bytes);
    let model = tract_tensorflow::tensorflow()
        .model_for_read(&mut model)
        .unwrap()
        .with_input_fact(
            0,
            InferenceFact::dt_shape(f32::datum_type(), tvec!(1, 224, 224, 3)),
        )
        .unwrap()
        .into_optimized()
        .unwrap()
        .into_runnable()
        .unwrap();

    let image = image::load_from_memory(image_bytes).unwrap().to_rgb();
    // The model was trained on 224 x 224 RGB images, so we are resizing the input image to this dimension.
    let resized =
        image::imageops::resize(&image, 224, 224, ::image::imageops::FilterType::Triangle);
    let image: Tensor = tract_ndarray::Array4::from_shape_fn((1, 224, 224, 3), |(_, y, x, c)| {
        resized[(x as _, y as _)][c] as f32 / 255.0
    })
    .into();

    let result = model.run(tvec!(image)).unwrap();
    let best = result[0]
        .to_array_view::<f32>()
        .unwrap()
        .iter()
        .cloned()
        .zip(1..)
        .max_by(|a, b| a.0.partial_cmp(&b.0).unwrap());

    return best.unwrap().1;
}
/// Perform the inference given the contents of the model and the image, and
/// return the index of the predicted class.
///
/// Adapted from https://github.com/sonos/tract/tree/main/examples/tensorflow-mobilenet-v2 and
/// using the TensorFlow Mobilenet V2 model.
/// See https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet
Perform the inference given the contents of the model and the image, and
return the index of the predicted class.


Perform the inference given the contents of the model and the image and return the index of the predicted class .
fn infer(model_bytes: &[u8], image_bytes: &[u8]) -> i32 {
    let mut model = std::io::Cursor::new(model_bytes);
    let model = tract_tensorflow::tensorflow()
        .model_for_read(&mut model)
        .unwrap()
        .with_input_fact(
            0,
            InferenceFact::dt_shape(f32::datum_type(), tvec!(1, 224, 224, 3)),
        )
        .unwrap()
        .into_optimized()
        .unwrap()
        .into_runnable()
        .unwrap();
    let image = image::load_from_memory(image_bytes).unwrap().to_rgb();
    // The model was trained on 224 x 224 RGB images, so we are resizing the input image to this dimension.
    let resized =
        image::imageops::resize(&image, 224, 224, ::image::imageops::FilterType::Triangle);
    let image: Tensor = tract_ndarray::Array4::from_shape_fn((1, 224, 224, 3), |(_, y, x, c)| {
        resized[(x as _, y as _)][c] as f32 / 255.0
    })
    .into();
    let result = model.run(tvec!(image)).unwrap();
    let best = result[0]
        .to_array_view::<f32>()
        .unwrap()
        .iter()
        .cloned()
        .zip(1..)
        .max_by(|a, b| a.0.partial_cmp(&b.0).unwrap());
    return best.unwrap().1;
}
fn infer ( model_bytes : & [ u8 ] , image_bytes : & [ u8 ] ) -> i32 { let mut model = std :: io :: Cursor :: new ( model_bytes ) ; let model = tract_tensorflow :: tensorflow ( ) . model_for_read ( & mut model ) . unwrap ( ) . with_input_fact ( 0 , InferenceFact :: dt_shape ( f32 :: datum_type ( ) , tvec ! ( 1 224 224 3 ) ) , ) . unwrap ( ) . into_optimized ( ) . unwrap ( ) . into_runnable ( ) . unwrap ( ) ; let image = image :: load_from_memory ( image_bytes ) . unwrap ( ) . to_rgb ( ) ; // The model was trained on 224 x 224 RGB images, so we are resizing the input image to this dimension. let resized = image :: imageops :: resize ( & image , 224 , 224 , :: image :: imageops :: FilterType :: Triangle ) ; let image : Tensor = tract_ndarray :: Array4 :: from_shape_fn ( ( 1 , 224 , 224 , 3 ) , | ( _ , y , x , c ) | { resized [ ( x as _ , y as _ ) ] [ c ] as f32 / 255.0 } ) . into ( ) ; let result = model . run ( tvec ! ( image ) ) . unwrap ( ) ; let best = result [ 0 ] . to_array_view :: < f32 > ( ) . unwrap ( ) . iter ( ) . cloned ( ) . zip ( 1 .. ) . max_by ( | a , b | a . 0 . partial_cmp ( & b . 0 ) . unwrap ( ) ) ; return best . unwrap ( ) . 1 ; }
Perform the inference given the contents of the model and the image, and
return the index of the predicted class.
Perform the inference given the contents of the model and the image and return the index of the predicted class .
model_bytes
&[u8]
image_bytes
&[u8]
model_bytes
&[u8]
image_bytes
&[u8]