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

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

Clears the algorithm state

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Python prototype (for reference only):

clear() -> None

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

Converts objects array from internal representation to standard vector.

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

• objects: [Rect].

  Resulting array.

Return

• gpu_objects: Evision.Mat.t().

  Objects array in internal representation.

Python prototype (for reference only):

convert(objects[, gpu_objects]) -> gpu_objects

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

Converts objects array from internal representation to standard vector.

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

• objects: [Rect].

  Resulting array.

Return

• gpu_objects: Evision.Mat.t().

  Objects array in internal representation.

Python prototype (for reference only):

convert(objects[, gpu_objects]) -> gpu_objects

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

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

Loads the classifier from a file. Cascade type is detected automatically by constructor parameter.

Positional Arguments

• filename: String.

  Name of the file from which the classifier is loaded. Only the old haar classifier (trained by the haar training application) and NVIDIA's nvbin are supported for HAAR and only new type of OpenCV XML cascade supported for LBP. The working haar models can be found at opencv_folder/data/haarcascades_cuda/

Return

• retval: Evision.CUDA.CascadeClassifier.t()

Python prototype (for reference only):

create(filename) -> retval

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

@spec detectMultiScale(t(), Evision.CUDA.GpuMat.t()) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Detects objects of different sizes in the input image.

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

• image: Evision.Mat.

  Matrix of type CV_8U containing an image where objects should be detected.

Keyword Arguments

• stream: Evision.CUDA.Stream.t().

  CUDA stream.

Return

• objects: Evision.Mat.t().

  Buffer to store detected objects (rectangles).

To get final array of detected objects use CascadeClassifier::convert method.

Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(...);
Mat image_cpu = imread(...)
GpuMat image_gpu(image_cpu);
GpuMat objbuf;
cascade_gpu->detectMultiScale(image_gpu, objbuf);
std::vector<Rect> faces;
cascade_gpu->convert(objbuf, faces);
for(int i = 0; i < detections_num; ++i)
cv::rectangle(image_cpu, faces[i], Scalar(255));
imshow("Faces", image_cpu);

@sa CascadeClassifier::detectMultiScale

Python prototype (for reference only):

detectMultiScale(image[, objects[, stream]]) -> objects

Variant 2:

Detects objects of different sizes in the input image.

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

• image: Evision.CUDA.GpuMat.t().

  Matrix of type CV_8U containing an image where objects should be detected.

Keyword Arguments

• stream: Evision.CUDA.Stream.t().

  CUDA stream.

Return

• objects: Evision.CUDA.GpuMat.t().

  Buffer to store detected objects (rectangles).

To get final array of detected objects use CascadeClassifier::convert method.

Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(...);
Mat image_cpu = imread(...)
GpuMat image_gpu(image_cpu);
GpuMat objbuf;
cascade_gpu->detectMultiScale(image_gpu, objbuf);
std::vector<Rect> faces;
cascade_gpu->convert(objbuf, faces);
for(int i = 0; i < detections_num; ++i)
cv::rectangle(image_cpu, faces[i], Scalar(255));
imshow("Faces", image_cpu);

@sa CascadeClassifier::detectMultiScale

Python prototype (for reference only):

detectMultiScale(image[, objects[, stream]]) -> objects

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

@spec detectMultiScale(t(), Evision.CUDA.GpuMat.t(), [{:stream, term()}] | nil) ::
  Evision.CUDA.GpuMat.t() | {:error, String.t()}

Variant 1:

Detects objects of different sizes in the input image.

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

• image: Evision.Mat.

  Matrix of type CV_8U containing an image where objects should be detected.

Keyword Arguments

• stream: Evision.CUDA.Stream.t().

  CUDA stream.

Return

• objects: Evision.Mat.t().

  Buffer to store detected objects (rectangles).

To get final array of detected objects use CascadeClassifier::convert method.

Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(...);
Mat image_cpu = imread(...)
GpuMat image_gpu(image_cpu);
GpuMat objbuf;
cascade_gpu->detectMultiScale(image_gpu, objbuf);
std::vector<Rect> faces;
cascade_gpu->convert(objbuf, faces);
for(int i = 0; i < detections_num; ++i)
cv::rectangle(image_cpu, faces[i], Scalar(255));
imshow("Faces", image_cpu);

@sa CascadeClassifier::detectMultiScale

Python prototype (for reference only):

detectMultiScale(image[, objects[, stream]]) -> objects

Variant 2:

Detects objects of different sizes in the input image.

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

• image: Evision.CUDA.GpuMat.t().

  Matrix of type CV_8U containing an image where objects should be detected.

Keyword Arguments

• stream: Evision.CUDA.Stream.t().

  CUDA stream.

Return

• objects: Evision.CUDA.GpuMat.t().

  Buffer to store detected objects (rectangles).

To get final array of detected objects use CascadeClassifier::convert method.

Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(...);
Mat image_cpu = imread(...)
GpuMat image_gpu(image_cpu);
GpuMat objbuf;
cascade_gpu->detectMultiScale(image_gpu, objbuf);
std::vector<Rect> faces;
cascade_gpu->convert(objbuf, faces);
for(int i = 0; i < detections_num; ++i)
cv::rectangle(image_cpu, faces[i], Scalar(255));
imshow("Faces", image_cpu);

@sa CascadeClassifier::detectMultiScale

Python prototype (for reference only):

detectMultiScale(image[, objects[, stream]]) -> objects

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

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

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

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

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

getClassifierSize

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: Size

Python prototype (for reference only):

getClassifierSize() -> retval

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

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

getDefaultName

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: String

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference only):

getDefaultName() -> retval

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

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

getFindLargestObject

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: bool

Python prototype (for reference only):

getFindLargestObject() -> retval

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

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

getMaxNumObjects

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: integer()

Python prototype (for reference only):

getMaxNumObjects() -> retval

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

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

getMaxObjectSize

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: Size

Python prototype (for reference only):

getMaxObjectSize() -> retval

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

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

getMinNeighbors

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: integer()

Python prototype (for reference only):

getMinNeighbors() -> retval

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

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

getMinObjectSize

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: Size

Python prototype (for reference only):

getMinObjectSize() -> retval

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

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

getScaleFactor

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()

Return

• retval: double

Python prototype (for reference only):

getScaleFactor() -> retval

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• func: Evision.FileNode

Python prototype (for reference only):

read(fn) -> None

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

save

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• filename: String

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference only):

save(filename) -> None

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

setFindLargestObject

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• findLargestObject: bool

Python prototype (for reference only):

setFindLargestObject(findLargestObject) -> None

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

setMaxNumObjects

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• maxNumObjects: integer()

Python prototype (for reference only):

setMaxNumObjects(maxNumObjects) -> None

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

setMaxObjectSize

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• maxObjectSize: Size

Has overloading in C++

Python prototype (for reference only):

setMaxObjectSize(maxObjectSize) -> None

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

setMinNeighbors

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• minNeighbors: integer()

Python prototype (for reference only):

setMinNeighbors(minNeighbors) -> None

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

setMinObjectSize

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• minSize: Size

Python prototype (for reference only):

setMinObjectSize(minSize) -> None

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

setScaleFactor

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• scaleFactor: double

Python prototype (for reference only):

setScaleFactor(scaleFactor) -> None

@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• fs: Evision.FileStorage

Python prototype (for reference only):

write(fs) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.CUDA.CascadeClassifier.t()
• fs: Evision.FileStorage
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None