openvino/docs/ops/detection/PriorBoxClustered_1.md

PriorBoxClustered

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Versioned name: PriorBoxClustered-1

Category: Object detection

Short description: PriorBoxClustered operation generates prior boxes of specified sizes normalized to the input image size.

Detailed description

Let

.. math::

W \equiv image\_width, \quad H \equiv image\_height.

Then calculations of PriorBoxClustered can be written as

.. math::

center_x=(w+offset)*step

.. math::

center_y=(h+offset)*step

.. math::

w \subset \left( 0, W \right )

.. math::

h \subset \left( 0, H \right )

For each :math:s = \overline{0, W - 1} calculates the prior boxes coordinates:

.. math::

xmin = \frac{center_x - \frac{width_s}{2}}{W}

.. math::

ymin = \frac{center_y - \frac{height_s}{2}}{H}

.. math::

xmax = \frac{center_x - \frac{width_s}{2}}{W}

.. math::

ymax = \frac{center_y - \frac{height_s}{2}}{H}

If clip is defined, the coordinates of prior boxes are recalculated with the formula: :math:coordinate = \min(\max(coordinate,0), 1)

Attributes

• width (height)

  • Description: width (height) specifies desired boxes widths (heights) in pixels.
  • Range of values: floating-point positive numbers
  • Type: float[]
  • Default value: 1.0
  • Required: no

• clip

  • Description: clip is a flag that denotes if each value in the output tensor should be clipped within [0,1].

  • Range of values:

    • false or 0 - clipping is not performed
    • true or 1 - each value in the output tensor is within [0,1]

  • Type: boolean

  • Default value: true

  • Required: no

• step (step_w, step_h)

  • Description: step (step_w, step_h) is a distance between box centers. For example, step equal 85 means that the distance between neighborhood prior boxes centers is 85. If both step_h and step_w are 0 then they are updated with value of step. If after that they are still 0 then they are calculated as input image width(height) divided with first input width(height).
  • Range of values: floating-point positive number
  • Type: float
  • Default value: 0.0
  • Required: no

• offset

  • Description: offset is a shift of box respectively to top left corner. For example, offset equal 85 means that the shift of neighborhood prior boxes centers is 85.
  • Range of values: floating-point positive number
  • Type: float
  • Required: yes

• variance

  • Description: variance denotes a variance of adjusting bounding boxes. The attribute could be 0, 1 or 4 elements.
  • Range of values: floating-point positive numbers
  • Type: float[]
  • Default value: []
  • Required: no

Inputs:

• 1: output_size - 1D tensor of type T_INT with two elements [height, width]. Specifies the spatial size of generated grid with boxes. Required.

• 2: image_size - 1D tensor of type T_INT with two elements [image_height, image_width] that specifies shape of the image for which boxes are generated. Optional.

Outputs:

• 1: 2D tensor of shape [2, 4 * height * width * priors_per_point] and type T_OUT with box coordinates. The priors_per_point is the number of boxes generated per each grid element. The number depends on layer attribute values.

Types

• T_INT: any supported integer type.
• T_OUT: supported floating-point type.

Example

.. code-block:: cpp

<layer type="PriorBoxClustered" ... > 2 < !-- [10, 19] --> 2 < !-- [180, 320] --> 2 6840

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