if Image.bumblebee_configured?() do
defmodule Image.Classification do
@moduledoc """
Implements image classification functions using [Axon](https://hex.pm/packages/axon)
machine learning models managed by [Bumblebee](https://hex.pm/packages/bumblebee).
Image classification refers to the task of extracting information from an image.
In this module, the information extracted is one or more labels desribing the
image. Typically something like "sports car" or "Blenheim spaniel". The labels
returns depend on the machine learning model used.
### Configuration
The machine learning model to be used is configurable.
The `:model` and `:featurizer` may be any model supported by Bumblebee. The
`:name` is the name given to the classification service process.
The default configuration is:
# runtime.exs
config :image, :classifier,
model: {:hf, "microsoft/resnet-50"},
featurizer: {:hf, "microsoft/resnet-50"},
name: Image.Classification.Server,
autostart: true
### Autostart
If `autostart: true` is configured (the default) then a process
is started under a supervisor to execute the classification
requests. If running the process under an application
supervision tree is desired, set `autostart: false`. In that
case the function `classifer/1` can be
used to return a `t:Supervisor.child_spec/0`.
### Adding a classification server to an application supervision tree
To add image classification to an application supervision tree,
use `Image.Classification.classifier/1` to return a child spec:
For example:
# Application.ex
def start(_type, _args) do
children = [
# default classifier configuration
Image.Classification.classifier()
# custom classifier configuration
Image.Classification.classifier(model: {:hf, "google/vit-base-patch16-224"},
featurizer: {:hf, "google/vit-base-patch16-224"})
]
Supervisor.start_link(
children,
strategy: :one_for_one
)
end
### Note
This module is only available if the optional dependency
[Bumblebee](https://hex.pm/packages/bumblebee) is configured in
`mix.exs`.
"""
alias Vix.Vips.Image, as: Vimage
@min_score 0.5
@default_classifier [
model: {:hf, "microsoft/resnet-50"},
featurizer: {:hf, "microsoft/resnet-50"},
name: Image.Classification.Server,
autostart: true
]
@default_classifier_name @default_classifier[:name]
@doc """
Returns a child spec suitable for starting an image classification
process as part of a supervision tree.
### Arguments
* `configuration` is a keyword list.The default is
`Application.get_env(:image, :classifier, [])`.
### Configuration keys
* `:model` is any supported machine learning model for image
classification supported by Bumblebee.
* `:featurizer` is any supported machine learning model for image
featurization supported by Bumblebee.
* `:name` is the name given to the classification process when
it is started.
### Default configuration
The default configuration is:
```elixir
[
model: {:hf, "microsoft/resnet-50"},
featurizer: {:hf, "microsoft/resnet-50"},
name: Image.Classification.Server
]
```
"""
@spec classifier(configuration :: Keyword.t()) :: {Nx.Serving, Keyword.t()}
def classifier(classifier \\ Application.get_env(:image, :classifier, [])) do
Application.ensure_all_started(:exla)
classifier = Keyword.merge(@default_classifier, classifier)
case Image.Classification.serving(classifier[:model], classifier[:featurizer]) do
{:error, error} ->
{:error, error}
serving ->
{Nx.Serving, serving: serving, name: classifier[:name], batch_timeout: 100}
end
end
@doc false
def serving(model, featurizer) do
with {:ok, model_info} <- Bumblebee.load_model(model),
{:ok, featurizer} = Bumblebee.load_featurizer(featurizer) do
Bumblebee.Vision.image_classification(model_info, featurizer,
compile: [batch_size: 10],
defn_options: [compiler: EXLA]
)
end
end
@doc """
Classify an image using a machine learning
model.
### Arguments
* `image` is any `t:Vix.Vips.Image.t/0`.
* `options` is a keyword list of options
### Options
* `:backend` is any valid `Nx` backend. The default is
`Nx.default_backend/0`.
* `:server` is the name of the process performing the
classification service. The default is `#{inspect @default_classifier_name}`.
### Returns
* A map containing the predictions of the image
classification.
### Example
iex> puppy = Image.open!("./test/support/images/puppy.webp")
iex> %{predictions: [%{label: "Blenheim spaniel", score: _} | _rest]} =
...> Image.Classification.classify(puppy)
"""
@dialyzer {:nowarn_function, {:classify, 1}}
@dialyzer {:nowarn_function, {:classify, 2}}
@doc since: "0.18.0"
@spec classify(image :: Vimage.t(), Keyword.t()) ::
%{predictions: [%{label: String.t(), score: float()}]} | {:error, Image.error_message()}
def classify(%Vimage{} = image, options \\ []) do
backend = Keyword.get(options, :backend, Nx.default_backend())
server = Keyword.get(options, :server, @default_classifier_name)
with {:ok, flattened} <- Image.flatten(image),
{:ok, srgb} <- Image.to_colorspace(flattened, :srgb),
{:ok, tensor} <- Image.to_nx(srgb, shape: :hwc, backend: backend) do
Nx.Serving.batched_run(server, tensor)
end
end
@doc """
Classify an image using a machine learning
model and return the labels that meet a minimum
score.
### Arguments
* `image` is any `t:Vix.Vips.Image.t/0`.
* `options` is a keyword list of options.
### Options
* `:backend` is any valid `Nx` backend. The default is
`Nx.default_backend/0`.
* `:min_score` is the minimum score, a float between `0`
and `1`, which a label must match in order to be
returned.
### Returns
* A list of labels. The list may be empty if there
are no predictions that exceed the `:min_score`.
* `{:error, reason}`
### Example
iex> {:ok, image} = Image.open ("./test/support/images/lamborghini-forsennato-concept.jpg")
iex> Image.Classification.labels(image)
["sports car", "sport car"]
"""
@dialyzer {:nowarn_function, {:labels, 1}}
@dialyzer {:nowarn_function, {:labels, 2}}
@doc since: "0.18.0"
@spec labels(image :: Vimage.t(), options :: Keyword.t()) ::
[String.t()] | {:error, Image.error_message()}
def labels(%Vimage{} = image, options \\ []) do
{min_score, options} = Keyword.pop(options, :min_score, @min_score)
with %{predictions: predictions} <- classify(image, options) do
predictions
|> Enum.filter(fn %{score: score} -> score >= min_score end)
|> Enum.flat_map(fn %{label: label} -> String.split(label, ", ") end)
end
end
end
end
