.. vim: set fileencoding=utf-8 :
.. Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
.. Wed Mar 14 12:31:35 2012 +0100
..
.. Copyright (C) 2011-2013 Idiap Research Institute, Martigny, Switzerland
..
.. This program is free software: you can redistribute it and/or modify
.. it under the terms of the GNU General Public License as published by
.. the Free Software Foundation, version 3 of the License.
..
.. This program is distributed in the hope that it will be useful,
.. but WITHOUT ANY WARRANTY; without even the implied warranty of
.. MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
.. GNU General Public License for more details.
..
.. You should have received a copy of the GNU General Public License
.. along with this program.  If not, see <http://www.gnu.org/licenses/>.

**************
 Input/Output
**************

This section gives an overview of the operations for storing and retrieving the
basic data structures in |project|, such as `NumPy`_ arrays. |project| uses
`HDF5`_  format for storing binary coded data. Using the |project| support for
`HDF5`_, it is very simple to import and export data.

`HDF5`_  uses a neat descriptive language for representing the data in the HDF5
files, called Data Description Language (`DDL`_).

To perform the functionalities given in this section, you should have `NumPy`_
and |project| loaded into the `Python`_ environment.

.. testsetup:: *

   import numpy
   import bob
   import tempfile
   import os

   current_directory = os.path.realpath(os.curdir)
   temp_dir = tempfile.mkdtemp(prefix='bob_doctest_')
   os.chdir(temp_dir)

HDF5 standard utilities
=======================

Before explaining the basics of reading and writing to `HDF5`_ files, it is
important to list some `HDF5`_ standard utilities for checking the content of
an `HDF5`_ file. These are supplied by the `HDF5`_ project.

``h5dump``
  Dumps the content of the file using the DDL.

``h5ls``
  Lists the content of the file using DDL, but does not show the data.

``h5diff``
  Finds the differences between HDF5 files.

I/O operations using the class `bob.io.HDF5File`
================================================

Writing operations
------------------

Let's take a look at how to write simple scalar data such as integers or floats.

.. doctest::

   >>> an_integer = 5
   >>> a_float = 3.1416
   >>> f = bob.io.HDF5File('testfile1.hdf5', 'w')
   >>> f.set('my_integer', an_integer)
   >>> f.set('my_float', a_float)
   >>> del f

If after this you use the **h5dump** utility on the file ``testfile1.hdf5``,
you will verify that the file now contains:

.. code-block:: none

  HDF5 "testfile1.hdf5" {
  GROUP "/" {
    DATASET "my_float" {
       DATATYPE  H5T_IEEE_F64LE
       DATASPACE  SIMPLE { ( 1 ) / ( 1 ) }
       DATA {
       (0): 3.1416
       }
    }
    DATASET "my_integer" {
       DATATYPE  H5T_STD_I32LE
       DATASPACE  SIMPLE { ( 1 ) / ( 1 ) }
       DATA {
       (0): 5
       }
    }
  }
  }

.. note::

   In |project|, when you open a HDF5 file, you can choose one of the following options:

   **'r'** Open the file in reading mode; writing operations will fail (this is the default).

   **'a'** Open the file in reading and writing mode with appending.

   **'w'** Open the file in reading and writing mode, but truncate it.

   **'x'** Read/write/append with exclusive access.

The dump shows that there are two datasets inside a group named ``/`` in the
file.  HDF5 groups are like file system directories. They create namespaces for
the data. In the root group (or directory), you will find the two variables,
named as you set them to be.  The variable names are the complete path to the
location where they live. You could write a new variable in the same file but
in a different directory like this:

.. doctest::

  >>> f = bob.io.HDF5File('testfile1.hdf5', 'a')
  >>> f.create_group('/test')
  >>> f.set('/test/my_float', numpy.float32(6.28))
  >>> del f

Line 1 opens the file for reading and writing, but without truncating it. This
will allow you to access the file contents. Next, the directory ``/test`` is
created and a new variable is written inside the subdirectory. As you can
verify, **for simple scalars**, you can also force the storage type. Where
normally one would have a 64-bit real value, you can impose that this variable
is saved as a 32-bit real value. You can verify the dump correctness with
``h5dump``:

.. code-block:: none

  GROUP "/" {
  ...
   GROUP "test" {
      DATASET "my_float" {
         DATATYPE  H5T_IEEE_F32LE
         DATASPACE  SIMPLE { ( 1 ) / ( 1 ) }
         DATA {
         (0): 6.28
         }
      }
   }
  }

Notice the subdirectory ``test`` has been created and inside it a floating
point number has been stored. Such a float point number has a 32-bit precision
as it was defined.

.. note::

  If you need to place lots of variables in a subfolder, it may be better to
  setup the prefix folder before starting the writing operations on the
  :py:class:`bob.io.HDF5File` object. You can do this using the method
  :py:meth:`HDF5File.cd`.  Look up its help for more information and usage
  instructions.

Writing arrays is a little simpler as the :py:class:`numpy.ndarray` objects
encode all the type information we need to write and read them correctly. Here
is an example:

.. doctest::

  >>> A = numpy.array(range(4), 'int8').reshape(2,2)
  >>> f = bob.io.HDF5File('testfile1.hdf5', 'a')
  >>> f.set('my_array', A)
  >>> del f

The result of running ``h5dump`` on the file ``testfile3.hdf5`` should be:

.. code-block:: none

  ...
   DATASET "my_array" {
      DATATYPE  H5T_STD_I8LE
      DATASPACE  SIMPLE { ( 2, 2 ) / ( 2, 2 ) }
      DATA {
      (0,0): 0, 1,
      (1,0): 2, 3
      }
   }
  ...

You don't need to limit yourself to single variables, you can also save lists
of scalars and arrays using the function :py:meth:`bob.io.HDF5.append` instead
of :py:meth:`bob.io.HDF5.set`.

Reading operations
------------------

Reading data from a file that you just wrote to is just as easy. For this task
you should use :py:meth:`bob.io.HDF5File.read`. The read method will read all
the contents of the variable pointed to by the given path. This is the normal
way to read a variable you have written with :py:meth:`bob.io.HDF5File.set`. If
you decided to create a list of scalar or arrays, the way to read that up would
be using :py:meth:`bob.io.HDF5File.lread` instead. Here is an example:

.. doctest::

  >>> f = bob.io.HDF5File('testfile1.hdf5') #read only
  >>> f.read('my_integer') #reads integer
  5
  >>> print f.read('my_array') # reads the array
  [[0 1]
   [2 3]]
  >>> del f

Now let's look at an example where we have used
:py:meth:`bob.io.HDF5File.append` instead of :py:meth:`bob.io.HDF5File.set` to
write data to a file. That is normally the case when you write lists of
variables to a dataset.

.. doctest::

  >>> f = bob.io.HDF5File('testfile2.hdf5', 'w')
  >>> f.append('arrayset', numpy.array(range(10), 'float64'))
  >>> f.append('arrayset', 2*numpy.array(range(10), 'float64'))
  >>> f.append('arrayset', 3*numpy.array(range(10), 'float64'))
  >>> print f.lread('arrayset', 0)
  [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
  >>> print f.lread('arrayset', 2)
  [  0.   3.   6.   9.  12.  15.  18.  21.  24.  27.]
  >>> del f

This is what the ``h5dump`` of the file would look like:

.. code-block:: none

  HDF5 "testfile4.hdf5" {
  GROUP "/" {
     DATASET "arrayset" {
        DATATYPE  H5T_IEEE_F64LE
        DATASPACE  SIMPLE { ( 3, 10 ) / ( H5S_UNLIMITED, 10 ) }
        DATA {
        (0,0): 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
        (1,0): 0, 2, 4, 6, 8, 10, 12, 14, 16, 18,
        (2,0): 0, 3, 6, 9, 12, 15, 18, 21, 24, 27
        }
     }
  }
  }

Notice that the expansion limits for the first dimension have been correctly
set by |project| so you can insert an *unlimited* number of 1D float vectors.
Of course, you can also read the whole contents of the arrayset in a single
shot:

.. doctest::

  >>> f = bob.io.HDF5File('testfile2.hdf5')
  >>> print f.read('arrayset')
  [[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.]
   [  0.   2.   4.   6.   8.  10.  12.  14.  16.  18.]
   [  0.   3.   6.   9.  12.  15.  18.  21.  24.  27.]]

As you can see, the only difference between :py:meth:`bob.io.HDF5File.read` and
:py:meth:`bob.io.HDF5File.lread` is on how |project| considers the available
data (as a single array with N dimensions or as a set of arrays with N-1
dimensions). In the first example, you would have also been able to read the
variable `my_array` as an arrayset using :py:meth:`bob.io.HDF5File.lread`
instead of :py:meth:`bob.io.HDF5File.read`. In this case, each position readout
would return a 1D uint8 array instead of a 2D array.

Array interfaces
================

What we have shown so far is the generic API to read and write data using HDF5.
You will use it when you want to import or export data from |project| into
other software frameworks, debug your data or just implement your own classes
that can serialize and de-serialize from HDF5 file containers. In |project|,
most of the time you will be working with :py:class:`numpy.ndarrays`\s. In
special situations though, you may be asked to handle
:py:class:`bob.io.File`\s. :py:class:`bob.io.File` objects create a transparent
connection between C++ (`Blitz++`_) / Python (`NumPy`_) arrays and file access.
You specify the filename from which you want to input data and the
:py:class:`bob.io.File` object decides what is the best codec to be used (from
the extension) and how to read the data back into your array.

To create an :py:class:`bob.io.File` from a file path, just do the following:

.. doctest::

  >>> a = bob.io.File('testfile2.hdf5', 'r')
  >>> a.filename
  'testfile2.hdf5'

:py:class:`bob.io.File`\s simulate containers for :py:class:`numpy.ndarray`\s,
transparently accessing the file data when requested. Note, however, that when
you instantiate an :py:class:`bob.io.File` it does **not** load the file
contents into memory. It waits until you emit another explicit instruction to
do so. We do this with the :py:meth:`bob.io.File.read` method:

.. doctest::

  >>> array = a.read()
  >>> array
  array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],
         [  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.],
         [  0.,   3.,   6.,   9.,  12.,  15.,  18.,  21.,  24.,  27.]])

Every time you say :py:meth:`bob.io.File.read`, the file contents will be
read from the file and into a new array.

Saving arrays to the :py:class:`bob.io.File` is as easy, just call the
:py:meth:`bob.io.File.write` method:

.. doctest::

  >>> f = bob.io.File('copy1.hdf5', 'w')
  >>> f.write(array)

Numpy ndarray shortcuts
=======================

To just load an :py:class:`numpy.ndarray` in memory, you can use a short cut
that lives at :py:func:`bob.io.load`. With it, you don't have to go through the
:py:class:`bob.io.File` container:

.. doctest::

  >>> t = bob.io.load('testfile2.hdf5')
  >>> t
  array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],
         [  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.],
         [  0.,   3.,   6.,   9.,  12.,  15.,  18.,  21.,  24.,  27.]])

You can also directly save :py:class:`numpy.ndarray`\s without going
through the :py:class:`bob.io.Array` container:

.. doctest::

  >>> bob.io.save(t, 'copy2.hdf5')

.. note::

  Under the hood, we still use the :py:class:`bob.io.File` API to execute
  the read and write operations. Have a look at the manual section for
  :py:mod:`bob.io` for more details and other shortcuts available.

Reading and writing images
==========================

|project| provides support to load and save data from many different file types
including Matlab ``.mat`` files, various image file types and video data. File
types and specific serialization and de-serialization is switched automatically
using filename extensions. Knowing this, saving an array in a different format
is just a matter of choosing the right extension. This is illustrated in the
following example, where an image generated randomly using the method `NumPy`
:py:meth:`numpy.random.random_integers`, is saved in JPEG format. The image
must be of type uint8 or uint16.

.. doctest::

  >>> my_image = numpy.random.random_integers(0,255,(3,256,256))
  >>> bob.io.save(my_image.astype('uint8'), 'testimage.jpg') # saving the image in jpeg format
  >>> my_image_copy = bob.io.load('testimage.jpg')

.. tip::

  To find out about which formats and extensions are supported in a given
  installation of |project|, just call ``bob_config.py`` on your prompt. It
  will print a list of compiled-in software and supported extensions.

The loaded image files can be 3D arrays (for RGB format) or 2D arrays (for
greyscale) of type ``uint8`` or ``uint16``.

Dealing with videos
===================

|project| has support for dealing with videos in an equivalent way to dealing
with images:

.. doctest::

  >>> my_video = numpy.random.random_integers(0,255,(30,3,256,256))
  >>> bob.io.save(my_video.astype('uint8'), 'testvideo.avi') # saving the video avi format with a default codec
  >>> my_video_copy = bob.io.load('testvideo.avi')

Video reading and writing is performed using an `FFmpeg`_ (or `libav`_ if
`FFmpeg`_ is not available) bridge. |project|'s :py:meth:`bob.io.save` method
will allow you to choose the output format with the same extension mechanism as
mentioned earlier. `FFmpeg`_ will then choose a default codec for the format
and perform encoding. The output file can be as easily loaded using
:py:meth:`bob.io.load`.

For finer control over the loading, saving, format and codecs used for a
specific encoding or decoding operation, you must directly use either
:py:class:`bob.io.VideoReader` or :py:class:`bob.io.VideoWriter` classes. For
example, it is possible to use :py:class:`bob.io.VideoReader` to read videos
frame by frame and avoid overloading your machine's memory. In the following
example you can see how to create a video, save it using the class
:py:class:`bob.io.VideoWriter` and load it again using the class
:py:class:`bob.io.VideoReader`. The created video will have 30 frames generated
randomly.

.. note::

  Due to `FFmpeg`_ constrains, the width and height of the video need to be
  multiples of two.

.. doctest::

  >>> width = 50; height = 50;
  >>> framerate = 24
  >>> outv = bob.io.VideoWriter('testvideo.avi', height, width, framerate, codec='mpeg1video') # output video
  >>> for i in range(0, 30):
  ...   newframe = (numpy.random.random_integers(0,255,(3,height,width)))
  ...   outv.append(newframe.astype('uint8'))
  >>> outv.close()
  >>> input = bob.io.VideoReader('testvideo.avi')
  >>> input.number_of_frames
  30
  >>> inv = input.load()
  >>> inv.shape
  (30, 3, 50, 50)
  >>> type(inv)
  <type 'numpy.ndarray'>

Videos in |project| are represented as sequences of colored images, i.e. 4D
arrays of type ``uint8``. All the extensions and formats for videos
supported in version of |project| installed on your machine can be listed using
the |project|'s utility ``bob_config.py``.

.. testcleanup:: *

  import shutil
  os.chdir(current_directory)
  shutil.rmtree(temp_dir)

.. warning::

  Please read :doc:`UsingVideos` for details on choosing codecs and formats
  that are adequate to your application, as well as drawbacks and pitfalls with
  video encoding and decoding.

Loading and saving Matlab data
==============================

An alternative for saving data in ``.mat`` files using :py:meth:`bob.io.save`,
would be to save them as a `HDF5`_ file which then can be easily read in
Matlab. Similarly, instead of having to read ``.mat`` files using
:py:meth:`bob.io.load`, you can save your Matlab data in `HDF5`_ format, which
then can be easily read from |project|. Detailed instructions about how to save
and load data from Matlab to and from `HDF5`_ files can be found `here`__.

.. _audiosignal:

Loading and saving audio files
==============================

|project| does not yet support audio files (no wav codec). However, it is
possible to use the `SciPy`_ module :py:mod:`scipy.io.wavfile` to do the job.
For instance, to read a wave file, just use the
:py:func:`scipy.io.wavfile.read` function.

.. code-block:: python

   >>> import scipy.io.wavfile
   >>> filename = '/home/user/sample.wav'
   >>> samplerate, data = scipy.io.wavfile.read(filename)
   >>> print type(data)
   <type 'numpy.ndarray'>
   >>> print data.shape
   (132474, 2)

In the above example, the stereo audio signal is represented as a 2D `NumPy`
:py:class:`numpy.ndarray`. The first dimension corresponds to the time index
(132474 frames) and the second dimesnion correpsonds to one of the audio
channel (2 channels, stereo). The values in the array correpsond to the wave
magnitudes.

To save a `NumPy` :py:class:`numpy.ndarray` into a wave file, the
:py:func:`scipy.io.wavfile.write` could be used, which also requires the
framerate to be specified.

.. include:: links.rst

.. Place here your external references

.. _ddl: http://www.hdfgroup.org/HDF5/doc/ddl.html
.. _matlab-hdf5: http://www.mathworks.ch/help/techdoc/ref/hdf5write.html
__ matlab-hdf5_
