#!/usr/bin/env python

#

# Copyright 2007 Google Inc.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

#

"""Stub version of the images API."""

import logging

import StringIO

try:

  import PIL

  from PIL import _imaging

  from PIL import Image

except ImportError:

  import _imaging

  import Image

from google.appengine.api import apiproxy_stub

from google.appengine.api import images

from google.appengine.api.images import images_service_pb

from google.appengine.runtime import apiproxy_errors

class ImagesServiceStub(apiproxy_stub.APIProxyStub):

  """Stub version of images API to be used with the dev_appserver."""

  def __init__(self, service_name='images'):

    """Preloads PIL to load all modules in the unhardened environment.

    Args:

      service_name: Service name expected for all calls.

    """

    super(ImagesServiceStub, self).__init__(service_name)

    Image.init()

  def _Dynamic_Transform(self, request, response):

    """Trivial implementation of ImagesService::Transform.

    Based off documentation of the PIL library at

    http://www.pythonware.com/library/pil/handbook/index.htm

    Args:

      request: ImagesTransformRequest, contains image request info.

      response: ImagesTransformResponse, contains transformed image.

    """

    new_image = self._ProcessTransforms(original_image,

                                        request.transform_list())

    response_value = self._EncodeImage(new_image, request.output())

    response.mutable_image().set_content(response_value)

  def _EncodeImage(self, image, output_encoding):

    """Encode the given image and return it in string form.

    Args:

      image: PIL Image object, image to encode.

      output_encoding: ImagesTransformRequest.OutputSettings object.

    Returns:

      str with encoded image information in given encoding format.

    """

    image_string = StringIO.StringIO()

    image_encoding = "PNG"

    if (output_encoding.mime_type() == images_service_pb.OutputSettings.JPEG):

      image_encoding = "JPEG"

      image = image.convert("RGB")

    image.save(image_string, image_encoding)

    return image_string.getvalue()

  def _ValidateCropArg(self, arg):

    """Check an argument for the Crop transform.

    Args:

      arg: float, argument to Crop transform to check.

    Raises:

      apiproxy_errors.ApplicationError on problem with argument.

    """

    if not isinstance(arg, float):

      raise apiproxy_errors.ApplicationError(

          images_service_pb.ImagesServiceError.BAD_TRANSFORM_DATA)

    if not (0 <= arg <= 1.0):

      raise apiproxy_errors.ApplicationError(

          images_service_pb.ImagesServiceError.BAD_TRANSFORM_DATA)

  def _CalculateNewDimensions(self,

                              current_width,

                              current_height,

                              req_width,

                              req_height):

    """Get new resize dimensions keeping the current aspect ratio.

    This uses the more restricting of the two requested values to determine

    the new ratio.

    Args:

      current_width: int, current width of the image.

      current_height: int, current height of the image.

      req_width: int, requested new width of the image.

      req_height: int, requested new height of the image.

    Returns:

      tuple (width, height) which are both ints of the new ratio.

    """

    width_ratio = float(req_width) / current_width

    height_ratio = float(req_height) / current_height

    if req_width == 0 or (width_ratio > height_ratio and req_height != 0):

      return int(height_ratio * current_width), req_height

    else:

      return req_width, int(width_ratio * current_height)

  def _Resize(self, image, transform):

    """Use PIL to resize the given image with the given transform.

    Args:

      image: PIL.Image.Image object to resize.

      transform: images_service_pb.Transform to use when resizing.

    Returns:

      PIL.Image.Image with transforms performed on it.

    Raises:

      BadRequestError if the resize data given is bad.

    """

    width = 0

    height = 0

    if transform.has_width():

      width = transform.width()

      if width < 0 or 4000 < width:

        raise apiproxy_errors.ApplicationError(

            images_service_pb.ImagesServiceError.BAD_TRANSFORM_DATA)

    if transform.has_height():

      height = transform.height()

      if height < 0 or 4000 < height:

        raise apiproxy_errors.ApplicationError(

            images_service_pb.ImagesServiceError.BAD_TRANSFORM_DATA)

    current_width, current_height = image.size

    new_width, new_height = self._CalculateNewDimensions(current_width,

                                                         current_height,

                                                         width,

                                                         height)

    return image.resize((new_width, new_height), Image.ANTIALIAS)

  def _Rotate(self, image, transform):

    """Use PIL to rotate the given image with the given transform.

    Args:

      image: PIL.Image.Image object to rotate.

      transform: images_service_pb.Transform to use when rotating.

    Returns:

      PIL.Image.Image with transforms performed on it.

    Raises:

      BadRequestError if the rotate data given is bad.

    """

    degrees = transform.rotate()

    if degrees < 0 or degrees % 90 != 0:

      raise apiproxy_errors.ApplicationError(

          images_service_pb.ImagesServiceError.BAD_TRANSFORM_DATA)

    degrees %= 360

    degrees = 360 - degrees

    return image.rotate(degrees)

  def _Crop(self, image, transform):

    """Use PIL to crop the given image with the given transform.

    Args:

      image: PIL.Image.Image object to crop.

      transform: images_service_pb.Transform to use when cropping.

    Returns:

      PIL.Image.Image with transforms performed on it.

    Raises:

      BadRequestError if the crop data given is bad.

    """

    left_x = 0.0

    top_y = 0.0

    right_x = 1.0

    bottom_y = 1.0

    if transform.has_crop_left_x():

      left_x = transform.crop_left_x()

      self._ValidateCropArg(left_x)

    if transform.has_crop_top_y():

      top_y = transform.crop_top_y()

      self._ValidateCropArg(top_y)

    if transform.has_crop_right_x():

      right_x = transform.crop_right_x()

      self._ValidateCropArg(right_x)

    if transform.has_crop_bottom_y():

      bottom_y = transform.crop_bottom_y()

      self._ValidateCropArg(bottom_y)

    width, height = image.size

    box = (int(transform.crop_left_x() * width),

           int(transform.crop_top_y() * height),

           int(transform.crop_right_x() * width),

           int(transform.crop_bottom_y() * height))

    return image.crop(box)

  def _ProcessTransforms(self, image, transforms):

    """Execute PIL operations based on transform values.

    Args:

      image: PIL.Image.Image instance, image to manipulate.

      trasnforms: list of ImagesTransformRequest.Transform objects.

    Returns:

      PIL.Image.Image with transforms performed on it.

    Raises:

      BadRequestError if we are passed more than one of the same type of

      transform.

    """

    new_image = image

    if len(transforms) > images.MAX_TRANSFORMS_PER_REQUEST:

      raise apiproxy_errors.ApplicationError(

          images_service_pb.ImagesServiceError.BAD_TRANSFORM_DATA)

    for transform in transforms:

      if transform.has_width() or transform.has_height():

        new_image = self._Resize(new_image, transform)

      elif transform.has_rotate():

        new_image = self._Rotate(new_image, transform)

      elif transform.has_horizontal_flip():

        new_image = new_image.transpose(Image.FLIP_LEFT_RIGHT)

      elif transform.has_vertical_flip():

        new_image = new_image.transpose(Image.FLIP_TOP_BOTTOM)

      elif (transform.has_crop_left_x() or

          transform.has_crop_top_y() or

          transform.has_crop_right_x() or

          transform.has_crop_bottom_y()):

        new_image = self._Crop(new_image, transform)

      elif transform.has_autolevels():

        logging.info("I'm Feeling Lucky autolevels will be visible once this "

                     "application is deployed.")

      else:

        logging.warn("Found no transformations found to perform.")

    return new_image