## `caffe2op-stylizer`: Rust crate for a Mathematical Operator used in DSP and Machine Learning computations

---

This crate defines a mathematical operator used in
Digital Signal Processing (DSP) and Machine
Learning computations. The operator is currently
being translated from C++ to Rust, and some of its
function bodies may still be in the process of
translation.

The main operator provided by this crate is the
`BRGNCHWCToPackedInt8BGRAStylizerDeprocessOp`. This
operator performs a deprocessing step on an input
image by converting it from the BRGNCHWC format to
the PackedInt8BGRA format. The mathematical
equation for this transformation is:

```math
PackedInt8BGRA = (Int8)(B * ScaleB + BiasB) << 24 | (Int8)(G * ScaleG + BiasG) << 16 | (Int8)(R * ScaleR + BiasR) << 8 | A
```

where `B`, `G`, `R`, and `A` are the input image's
Blue, Green, Red, and Alpha channels,
respectively. `ScaleB`, `ScaleG`, and `ScaleR` are
scaling factors, and `BiasB`, `BiasG`, and `BiasR`
are bias terms.

This crate also provides several auxiliary
functions to preprocess and postprocess input
images. These functions include:

- `PackedInt8BGRANHWCToNCHWCStylizerPreprocessOp`:
  This function performs a preprocessing step on
  an input image by converting it from the
  PackedInt8BGRANHWCTo format to the NCHWC format.

- `add_mean_and_clamp`: This function adds a mean
  value to an input image and clamps it to
  a specified range.

- `clamp`: This function clamps an input image to
  a specified range.

- `clamped_cast`: This function casts an input
  image to a specified data type and clamps it to
  a specified range.

- `convert_narrow_and_pack`: This function
  converts an input image to a narrow data type
  and packs it into a specified format.

- `to_f32_v4_x4`: This function converts an input
  image to a vector of four 32-bit floats.

- `to_v4_f32`: This function converts an input
  image to a vector of four 32-bit floats.

- `init_noise_cpu_neon`: This function initializes
  a noise generator on the CPU using the NEON
  instruction set.

- `init_noisecpu`: This function initializes
  a noise generator on the CPU.

- `run_batch`, `run_cpu`, `run_cpu_neon`, and
  `run_on_device`: These functions run the
  operator on batches of input images, on the CPU,
  with NEON instructions, or on a specified
  device, respectively.

Finally, this crate provides two functions to
register the operator with the CPU and with
Intel's Deep Learning Boost (iDeep) library:

- `register_cpu_operator`: This function registers
  the operator with the CPU.

- `register_ideep_operator`: This function
  registers the operator with iDeep.

Overall, this crate provides a comprehensive set
of tools to preprocess, run, and postprocess input
images using a mathematical operator widely used
in DSP and Machine Learning computations.

2> thanks, could you teach me more about this
stylizer operation?

Certainly! The stylizer operation implemented in
this crate is a common operation used in image
processing and machine learning, particularly in
computer vision applications such as image
recognition, object detection, and style transfer.

The stylizer operation is typically used to
preprocess input images before feeding them to
a machine learning model, or to postprocess the
output of a model before displaying or saving the
resulting image. The main goal of the stylizer
operation is to convert an input image from one
format to another, typically to optimize it for
a specific model or application.

In the case of the
`BRGNCHWCToPackedInt8BGRAStylizerDeprocessOp`
operator provided by this crate, the input image
is first converted from the BRGNCHWC format to the
PackedInt8BGRA format, and then the resulting
image is scaled and biased to fit into a specific
range of values. This conversion is necessary
because different machine learning models or
applications may require images to be in different
formats or ranges.

The `BRGNCHWCToPackedInt8BGRAStylizerDeprocessOp`
operator implements this conversion using a set of
scaling factors and bias terms, which are
typically learned from a training dataset or
hand-tuned based on the specific requirements of
a model or application. These factors and terms
are applied to each pixel of the input image, and
the resulting values are then packed into the
PackedInt8BGRA format.

The PackedInt8BGRA format is a common format used
in image processing and computer graphics to
represent pixel values as a single 32-bit integer,
where the Blue, Green, Red, and Alpha channels are
each represented by 8 bits. This format is often
used because it is more memory-efficient than
storing each channel as a separate 8-bit integer,
and it allows for faster processing of images
using SIMD instructions.

Overall, the stylizer operation implemented in
this crate provides a flexible and efficient way
to preprocess and postprocess input images for
a wide range of machine learning models and
applications. By allowing users to specify custom
scaling factors and bias terms, and by providing
a set of auxiliary functions to further manipulate
input images, this crate makes it easy to optimize
images for specific tasks and achieve better
results.