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

@spec checkDetectorSize(t()) :: boolean() | {:error, String.t()}

Checks if detector size equal to descriptor size.

Positional Arguments

• self: Evision.HOGDescriptor.t()

Return

• retval: bool

Python prototype (for reference only):

checkDetectorSize() -> retval

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

Computes HOG descriptors of given image.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• img: Evision.Mat.

  Matrix of the type CV_8U containing an image where HOG features will be calculated.

Keyword Arguments

• winStride: Size.

  Window stride. It must be a multiple of block stride.

• padding: Size.

  Padding

• locations: [Point].

  Vector of Point

Return

• descriptors: [float].

  Matrix of the type CV_32F

Python prototype (for reference only):

compute(img[, winStride[, padding[, locations]]]) -> descriptors

@spec compute(
  t(),
  Evision.Mat.maybe_mat_in(),
  [locations: term(), padding: term(), winStride: term()] | nil
) :: [number()] | {:error, String.t()}

Computes HOG descriptors of given image.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• img: Evision.Mat.

  Matrix of the type CV_8U containing an image where HOG features will be calculated.

Keyword Arguments

• winStride: Size.

  Window stride. It must be a multiple of block stride.

• padding: Size.

  Padding

• locations: [Point].

  Vector of Point

Return

• descriptors: [float].

  Matrix of the type CV_32F

Python prototype (for reference only):

compute(img[, winStride[, padding[, locations]]]) -> descriptors

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

Computes gradients and quantized gradient orientations.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• img: Evision.Mat.

  Matrix contains the image to be computed

Keyword Arguments

• paddingTL: Size.

  Padding from top-left

• paddingBR: Size.

  Padding from bottom-right

Return

• grad: Evision.Mat.t().

  Matrix of type CV_32FC2 contains computed gradients

• angleOfs: Evision.Mat.t().

  Matrix of type CV_8UC2 contains quantized gradient orientations

Python prototype (for reference only):

computeGradient(img, grad, angleOfs[, paddingTL[, paddingBR]]) -> grad, angleOfs

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

Computes gradients and quantized gradient orientations.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• img: Evision.Mat.

  Matrix contains the image to be computed

Keyword Arguments

• paddingTL: Size.

  Padding from top-left

• paddingBR: Size.

  Padding from bottom-right

Return

• grad: Evision.Mat.t().

  Matrix of type CV_32FC2 contains computed gradients

• angleOfs: Evision.Mat.t().

  Matrix of type CV_8UC2 contains quantized gradient orientations

Python prototype (for reference only):

computeGradient(img, grad, angleOfs[, paddingTL[, paddingBR]]) -> grad, angleOfs

@spec detect(t(), Evision.Mat.maybe_mat_in()) ::
  {[{number(), number()}], [number()]} | {:error, String.t()}

Performs object detection without a multi-scale window.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• img: Evision.Mat.

  Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

Keyword Arguments

• hitThreshold: double.

  Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

• winStride: Size.

  Window stride. It must be a multiple of block stride.

• padding: Size.

  Padding

• searchLocations: [Point].

  Vector of Point includes set of requested locations to be evaluated.

Return

• foundLocations: [Point].

  Vector of point where each point contains left-top corner point of detected object boundaries.

• weights: [double].

  Vector that will contain confidence values for each detected object.

Python prototype (for reference only):

detect(img[, hitThreshold[, winStride[, padding[, searchLocations]]]]) -> foundLocations, weights

@spec detect(
  t(),
  Evision.Mat.maybe_mat_in(),
  [
    hitThreshold: term(),
    padding: term(),
    searchLocations: term(),
    winStride: term()
  ]
  | nil
) :: {[{number(), number()}], [number()]} | {:error, String.t()}

Performs object detection without a multi-scale window.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• img: Evision.Mat.

  Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

Keyword Arguments

• hitThreshold: double.

  Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

• winStride: Size.

  Window stride. It must be a multiple of block stride.

• padding: Size.

  Padding

• searchLocations: [Point].

  Vector of Point includes set of requested locations to be evaluated.

Return

• foundLocations: [Point].

  Vector of point where each point contains left-top corner point of detected object boundaries.

• weights: [double].

  Vector that will contain confidence values for each detected object.

Python prototype (for reference only):

detect(img[, hitThreshold[, winStride[, padding[, searchLocations]]]]) -> foundLocations, weights

@spec detectMultiScale(t(), Evision.Mat.maybe_mat_in()) ::
  {[{number(), number(), number(), number()}], [number()]}
  | {:error, String.t()}

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• img: Evision.Mat.

  Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

Keyword Arguments

• hitThreshold: double.

  Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

• winStride: Size.

  Window stride. It must be a multiple of block stride.

• padding: Size.

  Padding

• scale: double.

  Coefficient of the detection window increase.

• groupThreshold: double.

  Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.

• useMeanshiftGrouping: bool.

  indicates grouping algorithm

Return

• foundLocations: [Rect].

  Vector of rectangles where each rectangle contains the detected object.

• foundWeights: [double].

  Vector that will contain confidence values for each detected object.

Python prototype (for reference only):

detectMultiScale(img[, hitThreshold[, winStride[, padding[, scale[, groupThreshold[, useMeanshiftGrouping]]]]]]) -> foundLocations, foundWeights

@spec detectMultiScale(
  t(),
  Evision.Mat.maybe_mat_in(),
  [
    groupThreshold: term(),
    hitThreshold: term(),
    padding: term(),
    scale: term(),
    useMeanshiftGrouping: term(),
    winStride: term()
  ]
  | nil
) ::
  {[{number(), number(), number(), number()}], [number()]}
  | {:error, String.t()}

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• img: Evision.Mat.

  Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.

Keyword Arguments

• hitThreshold: double.

  Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.

• winStride: Size.

  Window stride. It must be a multiple of block stride.

• padding: Size.

  Padding

• scale: double.

  Coefficient of the detection window increase.

• groupThreshold: double.

  Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping.

• useMeanshiftGrouping: bool.

  indicates grouping algorithm

Return

• foundLocations: [Rect].

  Vector of rectangles where each rectangle contains the detected object.

• foundWeights: [double].

  Vector that will contain confidence values for each detected object.

Python prototype (for reference only):

detectMultiScale(img[, hitThreshold[, winStride[, padding[, scale[, groupThreshold[, useMeanshiftGrouping]]]]]]) -> foundLocations, foundWeights

@spec getDaimlerPeopleDetector() :: [number()] | {:error, String.t()}

Returns coefficients of the classifier trained for people detection (for 48x96 windows).

Return

• retval: [float]

Python prototype (for reference only):

getDaimlerPeopleDetector() -> retval

@spec getDefaultPeopleDetector() :: [number()] | {:error, String.t()}

Returns coefficients of the classifier trained for people detection (for 64x128 windows).

Return

• retval: [float]

Python prototype (for reference only):

getDefaultPeopleDetector() -> retval

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

@spec getDescriptorSize(t()) :: integer() | {:error, String.t()}

Returns the number of coefficients required for the classification.

Positional Arguments

• self: Evision.HOGDescriptor.t()

Return

• retval: size_t

Python prototype (for reference only):

getDescriptorSize() -> retval

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

@spec getWinSigma(t()) :: number() | {:error, String.t()}

Returns winSigma value

Positional Arguments

• self: Evision.HOGDescriptor.t()

Return

• retval: double

Python prototype (for reference only):

getWinSigma() -> retval

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

Creates the HOG descriptor and detector with default parameters.

Return

• self: Evision.HOGDescriptor.t()

aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )

Python prototype (for reference only):

HOGDescriptor() -> <HOGDescriptor object>

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

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

HOGDescriptor

Positional Arguments

• filename: String.

  The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.

Return

• self: Evision.HOGDescriptor.t()

Has overloading in C++

Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.

Python prototype (for reference only):

HOGDescriptor(filename) -> <HOGDescriptor object>

@spec hogDescriptor(
  {number(), number()},
  {number(), number()},
  {number(), number()},
  {number(), number()},
  integer()
) :: t() | {:error, String.t()}

HOGDescriptor

Positional Arguments

• winSize: Size.

  sets winSize with given value.

• blockSize: Size.

  sets blockSize with given value.

• blockStride: Size.

  sets blockStride with given value.

• cellSize: Size.

  sets cellSize with given value.

• nbins: integer().

  sets nbins with given value.

Keyword Arguments

• derivAperture: integer().

  sets derivAperture with given value.

• winSigma: double.

  sets winSigma with given value.

• histogramNormType: HOGDescriptor_HistogramNormType.

  sets histogramNormType with given value.

• l2HysThreshold: double.

  sets L2HysThreshold with given value.

• gammaCorrection: bool.

  sets gammaCorrection with given value.

• nlevels: integer().

  sets nlevels with given value.

• signedGradient: bool.

  sets signedGradient with given value.

Return

• self: Evision.HOGDescriptor.t()

Has overloading in C++

Python prototype (for reference only):

HOGDescriptor(_winSize, _blockSize, _blockStride, _cellSize, _nbins[, _derivAperture[, _winSigma[, _histogramNormType[, _L2HysThreshold[, _gammaCorrection[, _nlevels[, _signedGradient]]]]]]]) -> <HOGDescriptor object>

@spec hogDescriptor(
  {number(), number()},
  {number(), number()},
  {number(), number()},
  {number(), number()},
  integer(),
  [
    derivAperture: term(),
    gammaCorrection: term(),
    histogramNormType: term(),
    l2HysThreshold: term(),
    nlevels: term(),
    signedGradient: term(),
    winSigma: term()
  ]
  | nil
) :: t() | {:error, String.t()}

HOGDescriptor

Positional Arguments

• winSize: Size.

  sets winSize with given value.

• blockSize: Size.

  sets blockSize with given value.

• blockStride: Size.

  sets blockStride with given value.

• cellSize: Size.

  sets cellSize with given value.

• nbins: integer().

  sets nbins with given value.

Keyword Arguments

• derivAperture: integer().

  sets derivAperture with given value.

• winSigma: double.

  sets winSigma with given value.

• histogramNormType: HOGDescriptor_HistogramNormType.

  sets histogramNormType with given value.

• l2HysThreshold: double.

  sets L2HysThreshold with given value.

• gammaCorrection: bool.

  sets gammaCorrection with given value.

• nlevels: integer().

  sets nlevels with given value.

• signedGradient: bool.

  sets signedGradient with given value.

Return

• self: Evision.HOGDescriptor.t()

Has overloading in C++

Python prototype (for reference only):

HOGDescriptor(_winSize, _blockSize, _blockStride, _cellSize, _nbins[, _derivAperture[, _winSigma[, _histogramNormType[, _L2HysThreshold[, _gammaCorrection[, _nlevels[, _signedGradient]]]]]]]) -> <HOGDescriptor object>

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

loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file

Positional Arguments

• self: Evision.HOGDescriptor.t()

• filename: String.

  Name of the file to read.

Keyword Arguments

• objname: String.

  The optional name of the node to read (if empty, the first top-level node will be used).

Return

• retval: bool

Python prototype (for reference only):

load(filename[, objname]) -> retval

@spec load(t(), binary(), [{:objname, term()}] | nil) ::
  boolean() | {:error, String.t()}

loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file

Positional Arguments

• self: Evision.HOGDescriptor.t()

• filename: String.

  Name of the file to read.

Keyword Arguments

• objname: String.

  The optional name of the node to read (if empty, the first top-level node will be used).

Return

• retval: bool

Python prototype (for reference only):

load(filename[, objname]) -> retval

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

saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file

Positional Arguments

• self: Evision.HOGDescriptor.t()

• filename: String.

  File name

Keyword Arguments

• objname: String.

  Object name

Python prototype (for reference only):

save(filename[, objname]) -> None

@spec save(t(), binary(), [{:objname, term()}] | nil) :: t() | {:error, String.t()}

saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file

Positional Arguments

• self: Evision.HOGDescriptor.t()

• filename: String.

  File name

Keyword Arguments

• objname: String.

  Object name

Python prototype (for reference only):

save(filename[, objname]) -> None

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

Sets coefficients for the linear SVM classifier.

Positional Arguments

• self: Evision.HOGDescriptor.t()

• svmdetector: Evision.Mat.

  coefficients for the linear SVM classifier.

Python prototype (for reference only):

setSVMDetector(svmdetector) -> None