#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Manuel Guenther <Manuel.Guenther@idiap.ch>
# @date: Thu May 24 10:41:42 CEST 2012

import bob.ip.base
import numpy
import logging

from .Base import Base
from bob.bio.base.preprocessor import Preprocessor

logger = logging.getLogger('bob.bio.face')


class FaceCrop (Base):
  """Crops the face according to the given annotations.

  This class is designed to perform a geometric normalization of the face based
  on the eye locations, using :py:class:`bob.ip.base.FaceEyesNorm`. Usually,
  when executing the :py:meth:`crop_face` function, the image and the eye
  locations have to be specified. There, the given image will be transformed
  such that the eye locations will be placed at specific locations in the
  resulting image. These locations, as well as the size of the cropped image,
  need to be specified in the constructor of this class, as
  ``cropped_positions`` and ``cropped_image_size``.

  Some image databases do not provide eye locations, but rather bounding boxes.
  This is not a problem at all.
  Simply define the coordinates, where you want your ``cropped_positions`` to
  be in the cropped image, by specifying the same keys in the dictionary that
  will be given as ``annotations`` to the :py:meth:`crop_face` function.

  .. note::

    These locations can even be outside of the cropped image boundary, i.e.,
    when the crop should be smaller than the annotated bounding boxes.

  Sometimes, databases provide pre-cropped faces, where the eyes are located at
  (almost) the same position in all images. Usually, the cropping does not
  conform with the cropping that you like (i.e., image resolution is wrong, or
  too much background information). However, the database does not provide eye
  locations (since they are almost identical for all images). In that case, you
  can specify the ``fixed_positions`` in the constructor, which will be taken
  instead of the ``annotations`` inside the :py:meth:`crop_face` function (in
  which case the ``annotations`` are ignored).

  Sometimes, the crop of the face is outside of the original image boundaries.
  Usually, these pixels will simply be left black, resulting in sharp edges in
  the image. However, some feature extractors do not like these sharp edges. In
  this case, you can set the ``mask_sigma`` to copy pixels from the valid
  border of the image and add random noise (see
  :py:func:`bob.ip.base.extrapolate_mask`).


  Parameters
  ----------

  cropped_image_size : (int, int)
    The resolution of the cropped image, in order (HEIGHT,WIDTH); if not given,
    no face cropping will be performed

  cropped_positions : dict
    The coordinates in the cropped image, where the annotated points should be
    put to. This parameter is a dictionary with usually two elements, e.g.,
    ``{'reye':(RIGHT_EYE_Y, RIGHT_EYE_X) , 'leye':(LEFT_EYE_Y, LEFT_EYE_X)}``.
    However, also other parameters, such as ``{'topleft' : ..., 'bottomright' :
    ...}`` are supported, as long as the ``annotations`` in the `__call__`
    function are present.

  fixed_positions : dict or None
    If specified, ignore the annotations from the database and use these fixed
    positions throughout.

  mask_sigma : float or None
    Fill the area outside of image boundaries with random pixels from the
    border, by adding noise to the pixel values. To disable extrapolation, set
    this value to ``None``. To disable adding random noise, set it to a
    negative value or 0.

  mask_neighbors : int
    The number of neighbors used during mask extrapolation. See
    :py:func:`bob.ip.base.extrapolate_mask` for details.

  mask_seed : int or None
    The random seed to apply for mask extrapolation.

  allow_upside_down_normalized_faces: bool, optional
    If ``False`` (default), a ValueError is raised when normalized faces are going to be
    upside down compared to input image. This allows you to catch wrong annotations in
    your database easily. If you are sure about your input, you can set this flag to
    ``True``.

    .. warning::

       When run in parallel, the same random seed will be applied to all
       parallel processes. Hence, results of parallel execution will differ
       from the results in serial execution.

  annotator : :any:`bob.bio.base.annotator.Annotator`
    If provided, the annotator will be used if the required annotations are
    missing.

  kwargs
    Remaining keyword parameters passed to the :py:class:`Base` constructor,
    such as ``color_channel`` or ``dtype``.
  """

  def __init__(
      self,
      cropped_image_size,
      cropped_positions,
      fixed_positions=None,
      mask_sigma=None,
      mask_neighbors=5,
      mask_seed=None,
      annotator=None,
      allow_upside_down_normalized_faces=False,
      **kwargs
  ):

    Base.__init__(self, **kwargs)

    # call base class constructor
    Preprocessor.__init__(
        self,
        cropped_image_size=cropped_image_size,
        cropped_positions=cropped_positions,
        fixed_positions=fixed_positions,
        mask_sigma=mask_sigma,
        mask_neighbors=mask_neighbors,
        mask_seed=mask_seed
    )

    # check parameters
    assert len(cropped_positions) == 2
    if fixed_positions:
      assert len(fixed_positions) == 2

    # copy parameters
    self.cropped_image_size = cropped_image_size
    self.cropped_positions = cropped_positions
    self.cropped_keys = sorted(cropped_positions.keys())
    self.fixed_positions = fixed_positions
    self.mask_sigma = mask_sigma
    self.mask_neighbors = mask_neighbors
    self.mask_rng = bob.core.random.mt19937(
        mask_seed) if mask_seed is not None else bob.core.random.mt19937()
    self.annotator = annotator
    self.allow_upside_down_normalized_faces = allow_upside_down_normalized_faces

    # create objects required for face cropping
    self.cropper = bob.ip.base.FaceEyesNorm(
        crop_size=cropped_image_size,
        right_eye=cropped_positions[self.cropped_keys[0]],
        left_eye=cropped_positions[self.cropped_keys[1]])
    self.cropped_mask = numpy.ndarray(cropped_image_size, numpy.bool)

  def crop_face(self, image, annotations=None):
    """Crops the face.
    Executes the face cropping on the given image and returns the cropped
    version of it.

    Parameters
    ----------
    image : 2D :py:class:`numpy.ndarray`
        The face image to be processed.

    annotations : dict or ``None``
        The annotations that fit to the given image. ``None`` is only accepted,
        when ``fixed_positions`` were specified in the constructor.

    Returns
    -------
    face : 2D :py:class:`numpy.ndarray` (float)
        The cropped face.

    Raises
    ------
    ValueError
        If the annotations is None.
    """
    if self.fixed_positions is not None:
      annotations = self.fixed_positions
    if annotations is None:
      raise ValueError(
          "Cannot perform image cropping since annotations are not given, and "
          "no fixed annotations are specified.")

    assert isinstance(annotations, dict)
    if not all(k in annotations for k in self.cropped_keys):
      raise ValueError(
          "At least one of the expected annotations '%s' are not given "
          "in '%s'." % (self.cropped_keys, annotations.keys()))

    reye = self.cropped_keys[0]
    leye = self.cropped_keys[1]
    reye_desired_width = self.cropped_positions[reye][1]
    leye_desired_width = self.cropped_positions[leye][1]
    right_eye = annotations[reye]
    left_eye = annotations[leye]
    if not self.allow_upside_down_normalized_faces:
      if (reye_desired_width > leye_desired_width and right_eye[1] < left_eye[1]) or \
         (reye_desired_width < leye_desired_width and right_eye[1] > left_eye[1]):
        raise ValueError(
            "Looks like {leye} and {reye} in annotations: {annot} are swapped. "
            "This will make the normalized face upside down (compared to the original "
            "image). Most probably your annotations are wrong. Otherwise, you can set "
            "the ``allow_upside_down_normalized_faces`` parameter to "
            "True.".format(leye=leye, reye=reye, annot=annotations))

    # create output
    mask = numpy.ones(image.shape[-2:], dtype=numpy.bool)
    shape = self.cropped_image_size if image.ndim == 2 else [
        image.shape[0]] + list(self.cropped_image_size)
    cropped_image = numpy.zeros(shape)
    self.cropped_mask[:] = False

    # perform the cropping
    self.cropper(
        image,  # input image
        mask,   # full input mask
        cropped_image,  # cropped image
        self.cropped_mask,  # cropped mask
        # position of first annotation, usually right eye
        right_eye=right_eye,
        # position of second annotation, usually left eye
        left_eye=left_eye,
    )

    if self.mask_sigma is not None:
      # extrapolate the mask so that pixels outside of the image original image
      # region are filled with border pixels
      if cropped_image.ndim == 2:
        bob.ip.base.extrapolate_mask(
            self.cropped_mask, cropped_image, self.mask_sigma,
            self.mask_neighbors, self.mask_rng)
      else:
        [bob.ip.base.extrapolate_mask(
            self.cropped_mask, cropped_image_channel, self.mask_sigma,
            self.mask_neighbors, self.mask_rng)
         for cropped_image_channel in cropped_image]

    return cropped_image

  def is_annotations_valid(self, annotations):
    if not annotations:
      return False
    # check if the required keys are available
    return all(key in annotations for key in self.cropped_keys)

  def __call__(self, image, annotations=None):
    """Aligns the given image according to the given annotations.

    First, the desired color channel is extracted from the given image.
    Afterward, the face is cropped, according to the given ``annotations`` (or
    to ``fixed_positions``, see :py:meth:`crop_face`). Finally, the resulting
    face is converted to the desired data type.

    Parameters
    ----------
    image : 2D or 3D :py:class:`numpy.ndarray`
        The face image to be processed.
    annotations : dict or ``None``
        The annotations that fit to the given image.

    Returns
    -------
    face : 2D :py:class:`numpy.ndarray`
        The cropped face.
    """
    # if annotations are missing and cannot do anything else return None.
    if not self.is_annotations_valid(annotations) and \
       not self.fixed_positions and \
       self.annotator is None:
      logger.warn("Cannot crop face without valid annotations or "
                  "fixed_positions or an annotator. Returning None. "
                  "The annotations were: {}".format(annotations))
      return None

    # convert to the desired color channel
    image = self.color_channel(image)

    # annotate the image if annotations are missing
    if not self.is_annotations_valid(annotations) and \
       not self.fixed_positions and \
       self.annotator is not None:
      annotations = self.annotator(image, annotations=annotations)
      if not self.is_annotations_valid(annotations):
        logger.warn("The annotator failed and the annotations are missing too"
                    ". Returning None.")
        return None

    # crop face
    image = self.crop_face(image, annotations)

    # convert data type
    return self.data_type(image)
