@spec compute(t(), Evision.Mat.maybe_mat_in()) ::
  Evision.scalar() | {:error, String.t()}

Computes BRISQUE quality score for input image

Positional Arguments

• self: Evision.Quality.QualityBRISQUE.t()

• img: Evision.Mat.

  Image for which to compute quality

Return

• retval: Evision.scalar().t()

@returns cv::Scalar with the score in the first element. The score ranges from 0 (best quality) to 100 (worst quality)

Python prototype (for reference only):

compute(img) -> retval

@spec compute(t(), Evision.Mat.maybe_mat_in(), binary(), binary()) ::
  Evision.scalar() | {:error, String.t()}

static method for computing quality

Positional Arguments

• self: Evision.Quality.QualityBRISQUE.t()

• img: Evision.Mat.

  image for which to compute quality

• model_file_path: String.

  cv::String which contains a path to the BRISQUE model data, eg. /path/to/brisque_model_live.yml

• range_file_path: String.

  cv::String which contains a path to the BRISQUE range data, eg. /path/to/brisque_range_live.yml

Return

• retval: Evision.scalar().t()

@returns cv::Scalar with the score in the first element. The score ranges from 0 (best quality) to 100 (worst quality)

Python prototype (for reference only):

compute(img, model_file_path, range_file_path) -> retval

@spec computeFeatures(Keyword.t()) :: any() | {:error, String.t()}

@spec computeFeatures(Evision.Mat.maybe_mat_in()) ::
  Evision.Mat.t() | {:error, String.t()}

static method for computing image features used by the BRISQUE algorithm

Positional Arguments

• img: Evision.Mat.

  image (BGR(A) or grayscale) for which to compute features

Return

• features: Evision.Mat.t().

  output row vector of features to cv::Mat or cv::UMat

Python prototype (for reference only):

computeFeatures(img[, features]) -> features

@spec computeFeatures(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  Evision.Mat.t() | {:error, String.t()}

static method for computing image features used by the BRISQUE algorithm

Positional Arguments

• img: Evision.Mat.

  image (BGR(A) or grayscale) for which to compute features

Return

• features: Evision.Mat.t().

  output row vector of features to cv::Mat or cv::UMat

Python prototype (for reference only):

computeFeatures(img[, features]) -> features

@spec create(Evision.ML.SVM.t(), Evision.Mat.maybe_mat_in()) ::
  t() | {:error, String.t()}

@spec create(binary(), binary()) :: t() | {:error, String.t()}

Variant 1:

Create an object which calculates quality

Positional Arguments

• model: ml::SVM.

  cv::Ptr<cv::ml::SVM> which contains a loaded BRISQUE model

• range: Evision.Mat.

  cv::Mat which contains BRISQUE range data

Return

• retval: QualityBRISQUE

Python prototype (for reference only):

create(model, range) -> retval

Variant 2:

Create an object which calculates quality

Positional Arguments

• model_file_path: String.

  cv::String which contains a path to the BRISQUE model data, eg. /path/to/brisque_model_live.yml

• range_file_path: String.

  cv::String which contains a path to the BRISQUE range data, eg. /path/to/brisque_range_live.yml

Return

• retval: QualityBRISQUE

Python prototype (for reference only):

create(model_file_path, range_file_path) -> retval