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

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

Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center, subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.

Positional Arguments

• image: Evision.Mat.

  input image (with 1-, 3- or 4-channels).

Keyword Arguments

• scalefactor: double.

  multiplier for @p images values.

• size: Size.

  spatial size for output image

• mean: Evision.scalar().

  scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.

• swapRB: bool.

  flag which indicates that swap first and last channels in 3-channel image is necessary.

• crop: bool.

  flag which indicates whether image will be cropped after resize or not

• ddepth: integer().

  Depth of output blob. Choose CV_32F or CV_8U.

Return

• retval: Evision.Mat.t()

@details if @p crop is true, input image is resized so one side after resize is equal to corresponding dimension in @p size and another one is equal or larger. Then, crop from the center is performed. If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. @returns 4-dimensional Mat with NCHW dimensions order. Note: The order and usage of scalefactor and mean are (input - mean) * scalefactor.

Python prototype (for reference only):

blobFromImage(image[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]) -> retval

@spec blobFromImage(
  Evision.Mat.maybe_mat_in(),
  [
    crop: term(),
    ddepth: term(),
    mean: term(),
    scalefactor: term(),
    size: term(),
    swapRB: term()
  ]
  | nil
) :: Evision.Mat.t() | {:error, String.t()}

Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center, subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.

Positional Arguments

• image: Evision.Mat.

  input image (with 1-, 3- or 4-channels).

Keyword Arguments

• scalefactor: double.

  multiplier for @p images values.

• size: Size.

  spatial size for output image

• mean: Evision.scalar().

  scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.

• swapRB: bool.

  flag which indicates that swap first and last channels in 3-channel image is necessary.

• crop: bool.

  flag which indicates whether image will be cropped after resize or not

• ddepth: integer().

  Depth of output blob. Choose CV_32F or CV_8U.

Return

• retval: Evision.Mat.t()

@details if @p crop is true, input image is resized so one side after resize is equal to corresponding dimension in @p size and another one is equal or larger. Then, crop from the center is performed. If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. @returns 4-dimensional Mat with NCHW dimensions order. Note: The order and usage of scalefactor and mean are (input - mean) * scalefactor.

Python prototype (for reference only):

blobFromImage(image[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]) -> retval

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

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

Creates 4-dimensional blob from series of images. Optionally resizes and crops @p images from center, subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.

Positional Arguments

• images: [Evision.Mat].

  input images (all with 1-, 3- or 4-channels).

Keyword Arguments

• scalefactor: double.

  multiplier for @p images values.

• size: Size.

  spatial size for output image

• mean: Evision.scalar().

  scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.

• swapRB: bool.

  flag which indicates that swap first and last channels in 3-channel image is necessary.

• crop: bool.

  flag which indicates whether image will be cropped after resize or not

• ddepth: integer().

  Depth of output blob. Choose CV_32F or CV_8U.

Return

• retval: Evision.Mat.t()

@details if @p crop is true, input image is resized so one side after resize is equal to corresponding dimension in @p size and another one is equal or larger. Then, crop from the center is performed. If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. @returns 4-dimensional Mat with NCHW dimensions order. Note: The order and usage of scalefactor and mean are (input - mean) * scalefactor.

Python prototype (for reference only):

blobFromImages(images[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]) -> retval

@spec blobFromImages(
  [Evision.Mat.maybe_mat_in()],
  [
    crop: term(),
    ddepth: term(),
    mean: term(),
    scalefactor: term(),
    size: term(),
    swapRB: term()
  ]
  | nil
) :: Evision.Mat.t() | {:error, String.t()}

Creates 4-dimensional blob from series of images. Optionally resizes and crops @p images from center, subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.

Positional Arguments

• images: [Evision.Mat].

  input images (all with 1-, 3- or 4-channels).

Keyword Arguments

• scalefactor: double.

  multiplier for @p images values.

• size: Size.

  spatial size for output image

• mean: Evision.scalar().

  scalar with mean values which are subtracted from channels. Values are intended to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.

• swapRB: bool.

  flag which indicates that swap first and last channels in 3-channel image is necessary.

• crop: bool.

  flag which indicates whether image will be cropped after resize or not

• ddepth: integer().

  Depth of output blob. Choose CV_32F or CV_8U.

Return

• retval: Evision.Mat.t()

@details if @p crop is true, input image is resized so one side after resize is equal to corresponding dimension in @p size and another one is equal or larger. Then, crop from the center is performed. If @p crop is false, direct resize without cropping and preserving aspect ratio is performed. @returns 4-dimensional Mat with NCHW dimensions order. Note: The order and usage of scalefactor and mean are (input - mean) * scalefactor.

Python prototype (for reference only):

blobFromImages(images[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]) -> retval

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

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

blobFromImagesWithParams

Positional Arguments

• images: [Evision.Mat]

Keyword Arguments

• param: Image2BlobParams.

Return

• blob: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

blobFromImagesWithParams(images[, blob[, param]]) -> blob

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

blobFromImagesWithParams

Positional Arguments

• images: [Evision.Mat]

Keyword Arguments

• param: Image2BlobParams.

Return

• blob: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

blobFromImagesWithParams(images[, blob[, param]]) -> blob

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

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

blobFromImageWithParams

Positional Arguments

• image: Evision.Mat

Keyword Arguments

• param: Image2BlobParams.

Return

• blob: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

blobFromImageWithParams(image[, blob[, param]]) -> blob

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

blobFromImageWithParams

Positional Arguments

• image: Evision.Mat

Keyword Arguments

• param: Image2BlobParams.

Return

• blob: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

blobFromImageWithParams(image[, blob[, param]]) -> blob

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

@spec getAvailableTargets(Evision.DNN.Backend.enum()) ::
  [Evision.DNN.Target.enum()] | {:error, String.t()}

getAvailableTargets

Positional Arguments

• be: dnn_Backend

Return

• retval: [Target]

Python prototype (for reference only):

getAvailableTargets(be) -> retval

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

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

Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vector<cv::Mat>).

Positional Arguments

• blob_: Evision.Mat.

  4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from which you would like to extract the images.

Return

• images_: [Evision.Mat].

  array of 2D Mat containing the images extracted from the blob in floating point precision (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).

Python prototype (for reference only):

imagesFromBlob(blob_[, images_]) -> images_

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

Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vector<cv::Mat>).

Positional Arguments

• blob_: Evision.Mat.

  4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from which you would like to extract the images.

Return

• images_: [Evision.Mat].

  array of 2D Mat containing the images extracted from the blob in floating point precision (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).

Python prototype (for reference only):

imagesFromBlob(blob_[, images_]) -> images_

@spec nmsBoxes(
  [{number(), number(), number(), number()}] | Evision.Mat.t() | Nx.Tensor.t(),
  [number()],
  number(),
  number()
) :: [integer()] | {:error, String.t()}

Performs non maximum suppression given boxes and corresponding scores.

Positional Arguments

• bboxes: [Rect2d], Nx.Tensor.t(), Evision.Mat.t()..

  a set of bounding boxes to apply NMS.

• scores: [float].

  a set of corresponding confidences.

• score_threshold: float.

  a threshold used to filter boxes by score.

• nms_threshold: float.

  a threshold used in non maximum suppression.

Keyword Arguments

• eta: float.

  a coefficient in adaptive threshold formula: $nmsthreshold{i+1}=etacdot nms_threshold_i$.

• top_k: int.

  if >0, keep at most @p top_k picked indices.

Return

• indices: [int].

  the kept indices of bboxes after NMS.

Python prototype (for reference only):

NMSBoxes(bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]) -> indices

@spec nmsBoxes(
  [{number(), number(), number(), number()}] | Evision.Mat.t() | Nx.Tensor.t(),
  [number()],
  number(),
  number(),
  [eta: term(), top_k: term()] | nil
) :: [integer()] | {:error, String.t()}

Performs non maximum suppression given boxes and corresponding scores.

Positional Arguments

• bboxes: [Rect2d], Nx.Tensor.t(), Evision.Mat.t().

  a set of bounding boxes to apply NMS.

• scores: [float].

  a set of corresponding confidences.

• score_threshold: float.

  a threshold used to filter boxes by score.

• nms_threshold: float.

  a threshold used in non maximum suppression.

Keyword Arguments

• eta: float.

  a coefficient in adaptive threshold formula: $nmsthreshold{i+1}=etacdot nms_threshold_i$.

• top_k: int.

  if >0, keep at most @p top_k picked indices.

Return

• indices: [int].

  the kept indices of bboxes after NMS.

Python prototype (for reference only):

NMSBoxes(bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]) -> indices

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

Performs batched non maximum suppression on given boxes and corresponding scores across different classes.

Positional Arguments

• bboxes: [Rect2d], Nx.Tensor.t(), Evision.Mat.t().

  a set of bounding boxes to apply NMS.

• scores: [float].

  a set of corresponding confidences.

• class_ids: [int].

  a set of corresponding class ids. Ids are integer and usually start from 0.

• score_threshold: float.

  a threshold used to filter boxes by score.

• nms_threshold: float.

  a threshold used in non maximum suppression.

Keyword Arguments

• eta: float.

  a coefficient in adaptive threshold formula: $nmsthreshold{i+1}=etacdot nms_threshold_i$.

• top_k: int.

  if >0, keep at most @p top_k picked indices.

Return

• indices: [int].

  the kept indices of bboxes after NMS.

Python prototype (for reference only):

NMSBoxesBatched(bboxes, scores, class_ids, score_threshold, nms_threshold[, eta[, top_k]]) -> indices

@spec nmsBoxesBatched(
  [{number(), number(), number(), number()}],
  [number()],
  [integer()],
  number(),
  number(),
  [eta: term(), top_k: term()] | nil
) :: [integer()] | {:error, String.t()}

Performs batched non maximum suppression on given boxes and corresponding scores across different classes.

Positional Arguments

• bboxes: [Rect2d], Nx.Tensor.t(), Evision.Mat.t().

  a set of bounding boxes to apply NMS.

• scores: [float].

  a set of corresponding confidences.

• class_ids: [int].

  a set of corresponding class ids. Ids are integer and usually start from 0.

• score_threshold: float.

  a threshold used to filter boxes by score.

• nms_threshold: float.

  a threshold used in non maximum suppression.

Keyword Arguments

• eta: float.

  a coefficient in adaptive threshold formula: $nmsthreshold{i+1}=etacdot nms_threshold_i$.

• top_k: int.

  if >0, keep at most @p top_k picked indices.

Return

• indices: [int].

  the kept indices of bboxes after NMS.

Python prototype (for reference only):

NMSBoxesBatched(bboxes, scores, class_ids, score_threshold, nms_threshold[, eta[, top_k]]) -> indices

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

NMSBoxesRotated

Positional Arguments

• bboxes: [{centre={x, y}, size={s1, s2}, angle}]
• scores: [float]
• score_threshold: float
• nms_threshold: float

Keyword Arguments

• eta: float.
• top_k: integer().

Return

• indices: [integer()]

Python prototype (for reference only):

NMSBoxesRotated(bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]) -> indices

@spec nmsBoxesRotated(
  [{{number(), number()}, {number(), number()}, number()}],
  [number()],
  number(),
  number(),
  [eta: term(), top_k: term()] | nil
) :: [integer()] | {:error, String.t()}

NMSBoxesRotated

Positional Arguments

• bboxes: [{centre={x, y}, size={s1, s2}, angle}]
• scores: [float]
• score_threshold: float
• nms_threshold: float

Keyword Arguments

• eta: float.
• top_k: integer().

Return

• indices: [integer()]

Python prototype (for reference only):

NMSBoxesRotated(bboxes, scores, score_threshold, nms_threshold[, eta[, top_k]]) -> indices

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

@spec readNet(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Read deep learning network represented in one of the supported formats.

Positional Arguments

• model: String.Binary file contains trained weights. The following file extensions are expected for models from different frameworks:
  • *.caffemodel (Caffe, http://caffe.berkeleyvision.org/)
    • *.pb (TensorFlow, https://www.tensorflow.org/)

      • *.t7 | *.net (Torch, http://torch.ch/)

        • *.weights (Darknet, https://pjreddie.com/darknet/)

          • *.bin | *.onnx (OpenVINO, https://software.intel.com/openvino-toolkit)

            • *.onnx (ONNX, https://onnx.ai/)

Keyword Arguments

• config: String.

  Text file contains network configuration. It could be a file with the following extensions:

  • *.prototxt (Caffe, http://caffe.berkeleyvision.org/)
    • *.pbtxt (TensorFlow, https://www.tensorflow.org/)
      • *.cfg (Darknet, https://pjreddie.com/darknet/)
        • *.xml (OpenVINO, https://software.intel.com/openvino-toolkit)

• framework: String.

  Explicit framework name tag to determine a format.

Return

• retval: Evision.DNN.Net.t()

@returns Net object. This function automatically detects an origin framework of trained model and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow, @ref readNetFromTorch or @ref readNetFromDarknet. An order of @p model and @p config arguments does not matter.

Python prototype (for reference only):

readNet(model[, config[, framework]]) -> retval

@spec readNet(binary(), [config: term(), framework: term()] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

@spec readNet(binary(), binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Variant 1:

Read deep learning network represented in one of the supported formats.

Positional Arguments

• framework: String.

  Name of origin framework.

• bufferModel: [uchar].

  A buffer with a content of binary file with weights

Keyword Arguments

• bufferConfig: [uchar].

  A buffer with a content of text file contains network configuration.

Return

• retval: Evision.DNN.Net.t()

@details This is an overloaded member function, provided for convenience.

      It differs from the above function only in what argument(s) it accepts.

@returns Net object.

Python prototype (for reference only):

readNet(framework, bufferModel[, bufferConfig]) -> retval

Variant 2:

Read deep learning network represented in one of the supported formats.

Positional Arguments

• model: String.Binary file contains trained weights. The following file extensions are expected for models from different frameworks:
  • *.caffemodel (Caffe, http://caffe.berkeleyvision.org/)
    • *.pb (TensorFlow, https://www.tensorflow.org/)

      • *.t7 | *.net (Torch, http://torch.ch/)

        • *.weights (Darknet, https://pjreddie.com/darknet/)

          • *.bin | *.onnx (OpenVINO, https://software.intel.com/openvino-toolkit)

            • *.onnx (ONNX, https://onnx.ai/)

Keyword Arguments

• config: String.

  Text file contains network configuration. It could be a file with the following extensions:

  • *.prototxt (Caffe, http://caffe.berkeleyvision.org/)
    • *.pbtxt (TensorFlow, https://www.tensorflow.org/)
      • *.cfg (Darknet, https://pjreddie.com/darknet/)
        • *.xml (OpenVINO, https://software.intel.com/openvino-toolkit)

• framework: String.

  Explicit framework name tag to determine a format.

Return

• retval: Evision.DNN.Net.t()

@returns Net object. This function automatically detects an origin framework of trained model and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow, @ref readNetFromTorch or @ref readNetFromDarknet. An order of @p model and @p config arguments does not matter.

Python prototype (for reference only):

readNet(model[, config[, framework]]) -> retval

@spec readNet(binary(), binary(), [{:bufferConfig, term()}] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Read deep learning network represented in one of the supported formats.

Positional Arguments

• framework: String.

  Name of origin framework.

• bufferModel: [uchar].

  A buffer with a content of binary file with weights

Keyword Arguments

• bufferConfig: [uchar].

  A buffer with a content of text file contains network configuration.

Return

• retval: Evision.DNN.Net.t()

@details This is an overloaded member function, provided for convenience.

      It differs from the above function only in what argument(s) it accepts.

@returns Net object.

Python prototype (for reference only):

readNet(framework, bufferModel[, bufferConfig]) -> retval

@spec readNetFromCaffe(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.

Positional Arguments

• prototxt: String.

  path to the .prototxt file with text description of the network architecture.

Keyword Arguments

• caffeModel: String.

  path to the .caffemodel file with learned network.

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromCaffe(prototxt[, caffeModel]) -> retval

@spec readNetFromCaffe(binary(), [{:caffeModel, term()}] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.

Positional Arguments

• prototxt: String.

  path to the .prototxt file with text description of the network architecture.

Keyword Arguments

• caffeModel: String.

  path to the .caffemodel file with learned network.

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromCaffe(prototxt[, caffeModel]) -> retval

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

@spec readNetFromCaffeBuffer(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in Caffe model in memory.

Positional Arguments

• bufferProto: [uchar].

  buffer containing the content of the .prototxt file

Keyword Arguments

• bufferModel: [uchar].

  buffer containing the content of the .caffemodel file

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromCaffe(bufferProto[, bufferModel]) -> retval

@spec readNetFromCaffeBuffer(binary(), [{:bufferModel, term()}] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in Caffe model in memory.

Positional Arguments

• bufferProto: [uchar].

  buffer containing the content of the .prototxt file

Keyword Arguments

• bufferModel: [uchar].

  buffer containing the content of the .caffemodel file

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromCaffe(bufferProto[, bufferModel]) -> retval

@spec readNetFromDarknet(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.

Positional Arguments

• cfgFile: String.

  path to the .cfg file with text description of the network architecture.

Keyword Arguments

• darknetModel: String.

  path to the .weights file with learned network.

Return

• retval: Evision.DNN.Net.t()

@returns Network object that ready to do forward, throw an exception in failure cases.

Python prototype (for reference only):

readNetFromDarknet(cfgFile[, darknetModel]) -> retval

@spec readNetFromDarknet(binary(), [{:darknetModel, term()}] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.

Positional Arguments

• cfgFile: String.

  path to the .cfg file with text description of the network architecture.

Keyword Arguments

• darknetModel: String.

  path to the .weights file with learned network.

Return

• retval: Evision.DNN.Net.t()

@returns Network object that ready to do forward, throw an exception in failure cases.

Python prototype (for reference only):

readNetFromDarknet(cfgFile[, darknetModel]) -> retval

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

@spec readNetFromDarknetBuffer(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.

Positional Arguments

• bufferCfg: [uchar].

  A buffer contains a content of .cfg file with text description of the network architecture.

Keyword Arguments

• bufferModel: [uchar].

  A buffer contains a content of .weights file with learned network.

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromDarknet(bufferCfg[, bufferModel]) -> retval

@spec readNetFromDarknetBuffer(binary(), [{:bufferModel, term()}] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.

Positional Arguments

• bufferCfg: [uchar].

  A buffer contains a content of .cfg file with text description of the network architecture.

Keyword Arguments

• bufferModel: [uchar].

  A buffer contains a content of .weights file with learned network.

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromDarknet(bufferCfg[, bufferModel]) -> retval

@spec readNetFromModelOptimizer(binary()) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Load a network from Intel's Model Optimizer intermediate representation.

Positional Arguments

• xml: String.

  XML configuration file with network's topology.

Keyword Arguments

• bin: String.

  Binary file with trained weights.

Return

• retval: Evision.DNN.Net.t()

@returns Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

Python prototype (for reference only):

readNetFromModelOptimizer(xml[, bin]) -> retval

@spec readNetFromModelOptimizer(binary(), [{:bin, term()}] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Load a network from Intel's Model Optimizer intermediate representation.

Positional Arguments

• xml: String.

  XML configuration file with network's topology.

Keyword Arguments

• bin: String.

  Binary file with trained weights.

Return

• retval: Evision.DNN.Net.t()

@returns Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

Python prototype (for reference only):

readNetFromModelOptimizer(xml[, bin]) -> retval

@spec readNetFromModelOptimizerBuffer(binary(), binary()) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Load a network from Intel's Model Optimizer intermediate representation.

Positional Arguments

• bufferModelConfig: [uchar].

  Buffer contains XML configuration with network's topology.

• bufferWeights: [uchar].

  Buffer contains binary data with trained weights.

Return

• retval: Evision.DNN.Net.t()

@returns Net object. Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine backend.

Python prototype (for reference only):

readNetFromModelOptimizer(bufferModelConfig, bufferWeights) -> retval

@spec readNetFromONNX(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model <a href="https://onnx.ai/">ONNX</a>.

Positional Arguments

• onnxFile: String.

  path to the .onnx file with text description of the network architecture.

Return

• retval: Evision.DNN.Net.t()

@returns Network object that ready to do forward, throw an exception in failure cases.

Python prototype (for reference only):

readNetFromONNX(onnxFile) -> retval

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

@spec readNetFromONNXBuffer(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model from <a href="https://onnx.ai/">ONNX</a> in-memory buffer.

Positional Arguments

• buffer: [uchar].

  in-memory buffer that stores the ONNX model bytes.

Return

• retval: Evision.DNN.Net.t()

@returns Network object that ready to do forward, throw an exception

    in failure cases.

Python prototype (for reference only):

readNetFromONNX(buffer) -> retval

@spec readNetFromTensorflow(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.

Positional Arguments

• model: String.

  path to the .pb file with binary protobuf description of the network architecture

Keyword Arguments

• config: String.

  path to the .pbtxt file that contains text graph definition in protobuf format. Resulting Net object is built by text graph using weights from a binary one that let us make it more flexible.

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromTensorflow(model[, config]) -> retval

@spec readNetFromTensorflow(binary(), [{:config, term()}] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.

Positional Arguments

• model: String.

  path to the .pb file with binary protobuf description of the network architecture

Keyword Arguments

• config: String.

  path to the .pbtxt file that contains text graph definition in protobuf format. Resulting Net object is built by text graph using weights from a binary one that let us make it more flexible.

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromTensorflow(model[, config]) -> retval

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

@spec readNetFromTensorflowBuffer(binary()) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.

Positional Arguments

• bufferModel: [uchar].

  buffer containing the content of the pb file

Keyword Arguments

• bufferConfig: [uchar].

  buffer containing the content of the pbtxt file

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromTensorflow(bufferModel[, bufferConfig]) -> retval

@spec readNetFromTensorflowBuffer(binary(), [{:bufferConfig, term()}] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.

Positional Arguments

• bufferModel: [uchar].

  buffer containing the content of the pb file

Keyword Arguments

• bufferConfig: [uchar].

  buffer containing the content of the pbtxt file

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromTensorflow(bufferModel[, bufferConfig]) -> retval

@spec readNetFromTFLite(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.

Positional Arguments

• model: String.

  path to the .tflite file with binary flatbuffers description of the network architecture

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromTFLite(model) -> retval

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

@spec readNetFromTFLiteBuffer(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.

Positional Arguments

• bufferModel: [uchar].

  buffer containing the content of the tflite file

Return

• retval: Evision.DNN.Net.t()

@returns Net object.

Python prototype (for reference only):

readNetFromTFLite(bufferModel) -> retval

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

@spec readNetFromTorch(binary()) :: Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.

Positional Arguments

• model: String.

  path to the file, dumped from Torch by using torch.save() function.

Keyword Arguments

• isBinary: bool.

  specifies whether the network was serialized in ascii mode or binary.

• evaluate: bool.

  specifies testing phase of network. If true, it's similar to evaluate() method in Torch.

Return

• retval: Evision.DNN.Net.t()

@returns Net object. Note: Ascii mode of Torch serializer is more preferable, because binary mode extensively use long type of C language, which has various bit-length on different systems. The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors. List of supported layers (i.e. object instances derived from Torch nn.Module class):

• nn.Sequential
• nn.Parallel
• nn.Concat
• nn.Linear
• nn.SpatialConvolution
• nn.SpatialMaxPooling, nn.SpatialAveragePooling
• nn.ReLU, nn.TanH, nn.Sigmoid
• nn.Reshape
• nn.SoftMax, nn.LogSoftMax

Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.

Python prototype (for reference only):

readNetFromTorch(model[, isBinary[, evaluate]]) -> retval

@spec readNetFromTorch(binary(), [evaluate: term(), isBinary: term()] | nil) ::
  Evision.DNN.Net.t() | {:error, String.t()}

Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.

Positional Arguments

• model: String.

  path to the file, dumped from Torch by using torch.save() function.

Keyword Arguments

• isBinary: bool.

  specifies whether the network was serialized in ascii mode or binary.

• evaluate: bool.

  specifies testing phase of network. If true, it's similar to evaluate() method in Torch.

Return

• retval: Evision.DNN.Net.t()

@returns Net object. Note: Ascii mode of Torch serializer is more preferable, because binary mode extensively use long type of C language, which has various bit-length on different systems. The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors. List of supported layers (i.e. object instances derived from Torch nn.Module class):

• nn.Sequential
• nn.Parallel
• nn.Concat
• nn.Linear
• nn.SpatialConvolution
• nn.SpatialMaxPooling, nn.SpatialAveragePooling
• nn.ReLU, nn.TanH, nn.Sigmoid
• nn.Reshape
• nn.SoftMax, nn.LogSoftMax

Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.

Python prototype (for reference only):

readNetFromTorch(model[, isBinary[, evaluate]]) -> retval

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

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

Creates blob from .pb file.

Positional Arguments

• path: String.

  to the .pb file with input tensor.

Return

• retval: Evision.Mat.t()

@returns Mat.

Python prototype (for reference only):

readTensorFromONNX(path) -> retval

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

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

Loads blob which was serialized as torch.Tensor object of Torch7 framework.

Positional Arguments

• filename: String

Keyword Arguments

• isBinary: bool.

Return

• retval: Evision.Mat.t()

@warning This function has the same limitations as readNetFromTorch().

Python prototype (for reference only):

readTorchBlob(filename[, isBinary]) -> retval

@spec readTorchBlob(binary(), [{:isBinary, term()}] | nil) ::
  Evision.Mat.t() | {:error, String.t()}

Loads blob which was serialized as torch.Tensor object of Torch7 framework.

Positional Arguments

• filename: String

Keyword Arguments

• isBinary: bool.

Return

• retval: Evision.Mat.t()

@warning This function has the same limitations as readNetFromTorch().

Python prototype (for reference only):

readTorchBlob(filename[, isBinary]) -> retval

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

Convert all weights of Caffe network to half precision floating point.

Positional Arguments

• src: String.

  Path to origin model from Caffe framework contains single precision floating point weights (usually has .caffemodel extension).

• dst: String.

  Path to destination model with updated weights.

Keyword Arguments

• layersTypes: [String].

  Set of layers types which parameters will be converted. By default, converts only Convolutional and Fully-Connected layers' weights.

Note: Shrinked model has no origin float32 weights so it can't be used

   in origin Caffe framework anymore. However the structure of data
   is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
   So the resulting model may be used there.

Python prototype (for reference only):

shrinkCaffeModel(src, dst[, layersTypes]) -> None

@spec shrinkCaffeModel(binary(), binary(), [{:layersTypes, term()}] | nil) ::
  :ok | {:error, String.t()}

Convert all weights of Caffe network to half precision floating point.

Positional Arguments

• src: String.

  Path to origin model from Caffe framework contains single precision floating point weights (usually has .caffemodel extension).

• dst: String.

  Path to destination model with updated weights.

Keyword Arguments

• layersTypes: [String].

  Set of layers types which parameters will be converted. By default, converts only Convolutional and Fully-Connected layers' weights.

Note: Shrinked model has no origin float32 weights so it can't be used

   in origin Caffe framework anymore. However the structure of data
   is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
   So the resulting model may be used there.

Python prototype (for reference only):

shrinkCaffeModel(src, dst[, layersTypes]) -> None

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

Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503

Positional Arguments

• bboxes: [Rect], Nx.Tensor.t(), Evision.Mat.t()..

  a set of bounding boxes to apply Soft NMS.

• scores: [float].

  a set of corresponding confidences.

• score_threshold: float.

  a threshold used to filter boxes by score.

• nms_threshold: float.

  a threshold used in non maximum suppression.

Keyword Arguments

• top_k: size_t.

  keep at most @p top_k picked indices.

• sigma: float.

  parameter of Gaussian weighting.

• method: SoftNMSMethod.

  Gaussian or linear.

Return

• updated_scores: [float].

  a set of corresponding updated confidences.

• indices: [int].

  the kept indices of bboxes after NMS.

@see SoftNMSMethod

Python prototype (for reference only):

softNMSBoxes(bboxes, scores, score_threshold, nms_threshold[, top_k[, sigma[, method]]]) -> updated_scores, indices

@spec softNMSBoxes(
  [{number(), number(), number(), number()}],
  [number()],
  number(),
  number(),
  [top_k: term(), sigma: term(), method: term()] | nil
) :: {[number()], [integer()]} | {:error, String.t()}

Performs soft non maximum suppression given boxes and corresponding scores. Reference: https://arxiv.org/abs/1704.04503

Positional Arguments

• bboxes: [Rect], Nx.Tensor.t(), Evision.Mat.t()..

  a set of bounding boxes to apply Soft NMS.

• scores: [float].

  a set of corresponding confidences.

• score_threshold: float.

  a threshold used to filter boxes by score.

• nms_threshold: float.

  a threshold used in non maximum suppression.

Keyword Arguments

• top_k: size_t.

  keep at most @p top_k picked indices.

• sigma: float.

  parameter of Gaussian weighting.

• method: SoftNMSMethod.

  Gaussian or linear.

Return

• updated_scores: [float].

  a set of corresponding updated confidences.

• indices: [int].

  the kept indices of bboxes after NMS.

@see SoftNMSMethod

Python prototype (for reference only):

softNMSBoxes(bboxes, scores, score_threshold, nms_threshold[, top_k[, sigma[, method]]]) -> updated_scores, indices

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

Create a text representation for a binary network stored in protocol buffer format.

Positional Arguments

• model: String.

  A path to binary network.

• output: String.

  A path to output text file to be created.

Note: To reduce output file size, trained weights are not included.

Python prototype (for reference only):

writeTextGraph(model, output) -> None