#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
#
#-------------------------------------------------------------

# The Linearized Image Rotate function rotates the linearized input images counter-clockwise around the center.
# Uses nearest neighbor sampling.
#
# .. code-block:: python
#
#   >>> import numpy as np
#   >>> from systemds.context import SystemDSContext
#   >>> from systemds.operator.algorithm import img_rotate_linearized
#   >>> 
#   >>> with SystemDSContext() as sds:
#   ...     img = sds.from_numpy(
#   ...         np.array([[ 10., 20., 30.,
#   ...                     40., 50., 60.,
#   ...                     70., 80., 90. ]], dtype=np.float32)
#   ...     )
#   ...     result_img = img_rotate_linearized(img, 3.14159, 255., 3, 3).compute()
#   ...     print(result_img.reshape(3, 3))
#   [[90. 80. 70.]
#    [60. 50. 40.]
#    [30. 20. 10.]]
#
#
# INPUT:
# -----------------------------------------------------------------------------------------------
# img_in      Input images as linearized 2D matrix with top left corner at [1, 1] (every row represents a linearized matrix/image)
# radians     The value by which to rotate in radian.
# fill_value  The background color revealed by the rotation
# s_cols      Width of a single image
# s_rows      Height of a single image
# -----------------------------------------------------------------------------------------------
#
# OUTPUT:
# ---------------------------------------------------------------------------------------------
# img_out   Output images in linearized form as 2D matrix with top left corner at [1, 1]
# ---------------------------------------------------------------------------------------------

m_img_rotate_linearized = function(Matrix[Double] img_in, Double radians, Double fill_value, Integer s_cols, Integer s_rows) return (Matrix[Double] img_out) {
  # Translation matrix for moving the origin to the center of the image
  t1 = matrix("1 0 0 0 1 0 0 0 1", rows=3, cols=3)
  t1[1, 3] = -s_cols / 2
  t1[2, 3] = -s_rows / 2

  # Translation matrix for moving the origin back to the top left corner
  t2 = matrix("1 0 0 0 1 0 0 0 1", rows=3, cols=3)
  t2[1, 3] = s_cols / 2
  t2[2, 3] = s_rows / 2

  # The rotation matrix around the origin
  rot = matrix("1 0 0 0 1 0 0 0 1", rows=3, cols=3)
  c = cos(radians)
  s = sin(radians)
  rot[1, 1] = c
  rot[1, 2] = s
  rot[2, 1] = -s
  rot[2, 2] = c

  # Combined transformation matrix
  m = t2 %*% rot %*% t1

  # Transform image
  img_out = img_transform_linearized(img_in, s_cols, s_rows, as.scalar(m[1,1]), as.scalar(m[1,2]), as.scalar(m[1,3]), as.scalar(m[2,1]), as.scalar(m[2,2]), as.scalar(m[2,3]), fill_value, s_cols, s_rows)
}
