computing the relative dem and orthophoto
==========================================

Once you have the relative orientation, you can use `Malt <https://micmac.ensg.eu/index.php/Malt>`_ to compute
a relative DEM and orthoimages:

.. code-block:: sh

    mm3d Malt Ortho "OIS.*tif" Relative DirMEC=MEC-Relative NbVI=2 ZoomF=4 DefCor=0 CostTrans=4 EZA=1 SzW=3 Regul=0.1

This will run ``Malt`` on all of the images, using the orientation described in ``Ori-Relative``. The DEM and
correlation models will be output to ``MEC-Relative``, and the orthophotos will be output to ``Ortho-MEC-Relative``.

The other parameters used here are:

- **NbVI** (number of visible images): ``Malt`` defaults to only running where 3 or more images are visible
  (``NbVI=3``), but it is usually fine to go with 2 images.
- **ZoomF** (final zoom level): At this stage, we don't necessarily need the DEM to be processed to full resolution -
  a lower-resolution version (``ZoomF=4`` or ``ZoomF=8``) will suffice.
- **DefCor** (default correlation value): the default correlation value to use in pixels that are uncorrelated.
- **CostTrans** (transition cost): the cost to transition from correlation to decorrelation. Higher values means that
  more areas are included (because it is 'harder' to transition to decorrelation), though it may also increase the
  noise in the final result.
- **EZA** (export Z absolute): ``EZA=1`` argument ensures that the values in the DEM are absolute (in the units of
  the coordinate system), rather than scaled.
- **SzW** (correlation window size): the half-size of the window of the correlation window. ``SzW=3`` means using a
  7x7 correlation window; larger window sizes increase the likelihood of finding matches in areas with poor contrast
  (i.e., over glaciers), but also tend to smooth out the elevation.
- **Regul** (regularization factor): the regularization factor (penalty) to use for the transition term. Higher values
  tend to smooth out the terrain.

Alternatively, using :py:meth:`spymicmac.micmac.malt`:

.. code-block:: python

    from spymicmac import micmac
    micmac.malt('OIS.*tif', 'Relative',
        zoomf=4,
        dirmec='MEC-Relative',
        cost_trans=4,
        szw=3,
        regul=0.1
    )

Once this command finishes, you will have two new directories: ``MEC-Relative`` and ``Ortho-MEC-Relative``. The DEM
and associated correlation masks are found in ``MEC-Relative``, while the orthophotos are found in
``Ortho-MEC-Relative``.

You can visualize the DEM in a GIS software (for example, QGIS):

.. image:: ../../img/relative_hillshade.png
    :width: 700
    :align: center
    :alt: a hillshade of a DEM in relative (image) space

|br| Assuming that the relative orientation, visualized with ``AperiCloud``, is in good shape, the DEM hillshade should
look similar to a real-world hillshade of the study area - this is how :py:meth:`spymicmac.register.register_relative`
works to register the images to real-world control points and compute the absolute orientation.

creating the orthomosaic using Tawny
------------------------------------

You can also create a relative orthomosaic using the images in ``Ortho-MEC-Relative``. After calling ``Malt``, these
images will only be the individual images orthorectified using the extracted
DEM.

To generate an orthomosaic, we use `Tawny <https://micmac.ensg.eu/index.php/Tawny>`_:

.. code-block:: sh

    mm3d Tawny Ortho-MEC-Relative Out=Orthophotomosaic.tif RadiomEgal=0

Here, we use ``RadiomEgal=0`` to use the images as-is, rather than attempting to balance the radiometry (as this
can lead to undesirable results).

Alternatively, using :py:meth:`spymicmac.micmac.tawny`:

.. code-block:: python

    from spymicmac import micmac
    micmac.tawny('MEC-Relative')

Finally, you might need to re-combine the image tiles using
:py:meth:`spymicmac.micmac.mosaic_micmac_tiles` (or :doc:`../../spymicmac/scripts/mosaic_micmac_tiles`) depending on
how large they are:

.. code-block:: sh

    mosaic_micmac_tiles Orthophotomosaic -imgdir Ortho-MEC-Relative

As with the DEM, you can visualize the orthomosaic in a GIS software:

.. image:: ../../img/relative_orthomosaic.png
    :width: 700
    :align: center
    :alt: a relative orthomosaic of a series of KH-9 mapping camera images

|br| Once this is complete, you can move on to the next step: registering the DEM or orthoimage and automatically
finding control points using an external DEM and satellite image.