.. _disparity:

Disparity computation
=====================

Theoretical basics
------------------

The disparity computed by Pandora2D is such that:

    :math:`I_{L}(x, y) = I_{R}(x + dx, y + dy)`

with :math:`I_{L}` , :math:`I_{R}` the left image (left image) and the right image (right image), and
:math:`dx` the column disparity and :math:`dy` the row disparity.

At this stage, a 4D (dims: row, col, disp_row, disp_col) cost_volumes is stored. We use the Winner-Takes-All strategy
to find the right disparity for each pixel. 
That means we are looking for the min (resp. max for zncc and mutual information measures).
For column disparities (resp. row disparities) we search the min or max in disp_row (resp. disp_col). 
To conclude, we extract the disparity of min (or max) from the 4D cost_volumes and we obtain two disparity maps for row and col.


Configuration and parameters
----------------------------

.. list-table:: Parameters
    :header-rows: 1


    * - Name
      - Description
      - Type
      - Default value
      - Available value
      - Required
    * - *disparity _method*
      - Disparity method
      - string
      - None
      - "wta"
      - Yes
    * - *invalid_disparity*
      - Invalid disparity value
      - string, int, float
      - "NaN"
      - "NaN", "inf", int
      - No

**Example**

.. code:: json
    :name: Disparity example

    {
        "input" :
        {
            // input content
        },
        "pipeline" :
        {
            // ...
            "disparity":
            {
                "disparity _method": "wta",
                "invalid_disparity": "NaN"
            },
            // ...
        },
        "output":
        {
            // ...
        }
    }