text: '''
  A custom Keras layer to generate anchor boxes.
  
  Copyright (C) 2018 Pierluigi Ferrari
  
  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.
  '''
  
  from __future__ import division
  
  import numpy as np
  
  import keras.backend as K
  from keras.engine.topology import InputSpec
  from keras.engine.topology import Layer
  
  from engine.object_detection_branch.single_shot_detector.bounding_box_utils.bounding_box_utils import convert_coordinates
  
  
  class AnchorBoxes(Layer):
      '''
      A Keras layer to create an output tensor containing anchor box coordinates
      and variances based on the input tensor and the passed arguments.
  
      A set of 2D anchor boxes of different aspect ratios is created for each spatial unit of
      the input tensor. The number of anchor boxes created per unit depends on the arguments
      `aspect_ratios` and `two_boxes_for_ar1`, in the default case it is 4. The boxes
      are parameterized by the coordinate tuple `(xmin, xmax, ymin, ymax)`.
  
      The logic implemented by this layer is identical to the logic in the module
      `ssd_box_encode_decode_utils.py`.
  
      The purpose of having this layer in the network is to make the model self-sufficient
      at inference time. Since the model is predicting offsets to the anchor boxes
      (rather than predicting absolute box coordinates directly), one needs to know the anchor
      box coordinates in order to construct the final prediction boxes from the predicted offsets.
      If the model's output tensor did not contain the anchor box coordinates, the necessary
      information to convert the predicted offsets back to absolute coordinates would be missing
      in the model output. The reason why it is necessary to predict offsets to the anchor boxes
      rather than to predict absolute box coordinates directly is explained in `README.md`.
  
      Input shape:
          4D tensor of shape `(batch, channels, height, width)` if `dim_ordering = 'th'`
          or `(batch, height, width, channels)` if `dim_ordering = 'tf'`.
  
      Output shape:
          5D tensor of shape `(batch, height, width, n_boxes, 8)`. The last axis contains
          the four anchor box coordinates and the four variance values for each box.
      '''
  
      def __init__(self,
                   img_height,
                   img_width,
                   this_scale,
                   next_scale,
                   aspect_ratios=[0.5, 1.0, 2.0],
                   two_boxes_for_ar1=True,
                   this_steps=None,
                   this_offsets=None,
                   clip_boxes=False,
                   variances=[0.1, 0.1, 0.2, 0.2],
                   coords='centroids',
                   normalize_coords=False,
                   **kwargs):
          '''
          All arguments need to be set to the same values as in the box encoding process, otherwise the behavior is undefined.
          Some of these arguments are explained in more detail in the documentation of the `SSDBoxEncoder` class.
  
          Arguments:
              img_height (int): The height of the input images.
              img_width (int): The width of the input images.
              this_scale (float): A float in [0, 1], the scaling factor for the size of the generated anchor boxes
                  as a fraction of the shorter side of the input image.
              next_scale (float): A float in [0, 1], the next larger scaling factor. Only relevant if
                  `self.two_boxes_for_ar1 == True`.
              aspect_ratios (list, optional): The list of aspect ratios for which default boxes are to be
                  generated for this layer.
              two_boxes_for_ar1 (bool, optional): Only relevant if `aspect_ratios` contains 1.
                  If `True`, two default boxes will be generated for aspect ratio 1. The first will be generated
                  using the scaling factor for the respective layer, the second one will be generated using
                  geometric mean of said scaling factor and next bigger scaling factor.
              clip_boxes (bool, optional): If `True`, clips the anchor box coordinates to stay within image boundaries.
              variances (list, optional): A list of 4 floats >0. The anchor box offset for each coordinate will be divided by
                  its respective variance value.
              coords (str, optional): The box coordinate format to be used internally in the model (i.e. this is not the input format
                  of the ground truth labels). Can be either 'centroids' for the format `(cx, cy, w, h)` (box center coordinates, width, and height),
                  'corners' for the format `(xmin, ymin, xmax,  ymax)`, or 'minmax' for the format `(xmin, xmax, ymin, ymax)`.
              normalize_coords (bool, optional): Set to `True` if the model uses relative instead of absolute coordinates,
                  i.e. if the model predicts box coordinates within [0,1] instead of absolute coordinates.
          '''
          if K.backend() != 'tensorflow':
              raise TypeError("This layer only supports TensorFlow at the moment, but you are using the {} backend.".format(K.backend()))
  
          if (this_scale < 0) or (next_scale < 0) or (this_scale > 1):
              raise ValueError("`this_scale` must be in [0, 1] and `next_scale` must be >0, but `this_scale` == {}, `next_scale` == {}".format(this_scale, next_scale))
  
          if len(variances) != 4:
              raise ValueError("4 variance values must be pased, but {} values were received.".format(len(variances)))
          variances = np.array(variances)
          if np.any(variances <= 0):
              raise ValueError("All variances must be >0, but the variances given are {}".format(variances))
  
          self.img_height = img_height
          self.img_width = img_width
          self.this_scale = this_scale
          self.next_scale = next_scale
          self.aspect_ratios = aspect_ratios
          self.two_boxes_for_ar1 = two_boxes_for_ar1
          self.this_steps = this_steps
          self.this_offsets = this_offsets
          self.clip_boxes = clip_boxes
          self.variances = variances
          self.coords = coords
          self.normalize_coords = normalize_coords
          # Compute the number of boxes per cell
          if (1 in aspect_ratios) and two_boxes_for_ar1:
              self.n_boxes = len(aspect_ratios) + 1
          else:
              self.n_boxes = len(aspect_ratios)
          super(AnchorBoxes, self).__init__(**kwargs)
  
      def build(self, input_shape):
          self.input_spec = [InputSpec(shape=input_shape)]
          super(AnchorBoxes, self).build(input_shape)
  
      def call(self, x, mask=None):
          '''
          Return an anchor box tensor based on the shape of the input tensor.
  
          The logic implemented here is identical to the logic in the module `ssd_box_encode_decode_utils.py`.
  
          Note that this tensor does not participate in any graph computations at runtime. It is being created
          as a constant once during graph creation and is just being output along with the rest of the model output
          during runtime. Because of this, all logic is implemented as Numpy array operations and it is sufficient
          to convert the resulting Numpy array into a Keras tensor at the very end before outputting it.
  
          Arguments:
              x (tensor): 4D tensor of shape `(batch, channels, height, width)` if `dim_ordering = 'th'`
                  or `(batch, height, width, channels)` if `dim_ordering = 'tf'`. The input for this
                  layer must be the output of the localization predictor layer.
          '''
  
          # Compute box width and height for each aspect ratio
          # The shorter side of the image will be used to compute `w` and `h` using `scale` and `aspect_ratios`.
          size = min(self.img_height, self.img_width)
          # Compute the box widths and and heights for all aspect ratios
          wh_list = []
          for ar in self.aspect_ratios:
              if (ar == 1):
                  # Compute the regular anchor box for aspect ratio 1.
                  box_height = box_width = self.this_scale * size
                  wh_list.append((box_width, box_height))
                  if self.two_boxes_for_ar1:
                      # Compute one slightly larger version using the geometric mean of this scale value and the next.
                      box_height = box_width = np.sqrt(self.this_scale * self.next_scale) * size
                      wh_list.append((box_width, box_height))
              else:
                  box_height = self.this_scale * size / np.sqrt(ar)
                  box_width = self.this_scale * size * np.sqrt(ar)
                  wh_list.append((box_width, box_height))
          wh_list = np.array(wh_list)
  
          # We need the shape of the input tensor
          if K.image_dim_ordering() == 'tf':
              batch_size, feature_map_height, feature_map_width, feature_map_channels = x._keras_shape
          else: # Not yet relevant since TensorFlow is the only supported backend right now, but it can't harm to have this in here for the future
              batch_size, feature_map_channels, feature_map_height, feature_map_width = x._keras_shape
  
          # Compute the grid of box center points. They are identical for all aspect ratios.
  
          # Compute the step sizes, i.e. how far apart the anchor box center points will be vertically and horizontally.
          if (self.this_steps is None):
              step_height = self.img_height / feature_map_height
              step_width = self.img_width / feature_map_width
          else:
              if isinstance(self.this_steps, (list, tuple)) and (len(self.this_steps) == 2):
                  step_height = self.this_steps[0]
                  step_width = self.this_steps[1]
              elif isinstance(self.this_steps, (int, float)):
                  step_height = self.this_steps
                  step_width = self.this_steps
          # Compute the offsets, i.e. at what pixel values the first anchor box center point will be from the top and from the left of the image.
          if (self.this_offsets is None):
              offset_height = 0.5
              offset_width = 0.5
          else:
              if isinstance(self.this_offsets, (list, tuple)) and (len(self.this_offsets) == 2):
                  offset_height = self.this_offsets[0]
                  offset_width = self.this_offsets[1]
              elif isinstance(self.this_offsets, (int, float)):
                  offset_height = self.this_offsets
                  offset_width = self.this_offsets
          # Now that we have the offsets and step sizes, compute the grid of anchor box center points.
          cy = np.linspace(offset_height * step_height, (offset_height + feature_map_height - 1) * step_height, feature_map_height)
          cx = np.linspace(offset_width * step_width, (offset_width + feature_map_width - 1) * step_width, feature_map_width)
          cx_grid, cy_grid = np.meshgrid(cx, cy)
          cx_grid = np.expand_dims(cx_grid, -1) # This is necessary for np.tile() to do what we want further down
          cy_grid = np.expand_dims(cy_grid, -1) # This is necessary for np.tile() to do what we want further down
  
          # Create a 4D tensor template of shape `(feature_map_height, feature_map_width, n_boxes, 4)`
          # where the last dimension will contain `(cx, cy, w, h)`
          boxes_tensor = np.zeros((feature_map_height, feature_map_width, self.n_boxes, 4))
  
          boxes_tensor[:, :, :, 0] = np.tile(cx_grid, (1, 1, self.n_boxes)) # Set cx
          boxes_tensor[:, :, :, 1] = np.tile(cy_grid, (1, 1, self.n_boxes)) # Set cy
          boxes_tensor[:, :, :, 2] = wh_list[:, 0] # Set w
          boxes_tensor[:, :, :, 3] = wh_list[:, 1] # Set h
  
          # Convert `(cx, cy, w, h)` to `(xmin, xmax, ymin, ymax)`
          boxes_tensor = convert_coordinates(boxes_tensor, start_index=0, conversion='centroids2corners')
  
          # If `clip_boxes` is enabled, clip the coordinates to lie within the image boundaries
          if self.clip_boxes:
              x_coords = boxes_tensor[:,:,:,[0, 2]]
              x_coords[x_coords >= self.img_width] = self.img_width - 1
              x_coords[x_coords < 0] = 0
              boxes_tensor[:,:,:,[0, 2]] = x_coords
              y_coords = boxes_tensor[:,:,:,[1, 3]]
              y_coords[y_coords >= self.img_height] = self.img_height - 1
              y_coords[y_coords < 0] = 0
              boxes_tensor[:,:,:,[1, 3]] = y_coords
  
          # If `normalize_coords` is enabled, normalize the coordinates to be within [0,1]
          if self.normalize_coords:
              boxes_tensor[:, :, :, [0, 2]] /= self.img_width
              boxes_tensor[:, :, :, [1, 3]] /= self.img_height
  
          # TODO: Implement box limiting directly for `(cx, cy, w, h)` so that we don't have to unnecessarily convert back and forth.
          if self.coords == 'centroids':
              # Convert `(xmin, ymin, xmax, ymax)` back to `(cx, cy, w, h)`.
              boxes_tensor = convert_coordinates(boxes_tensor, start_index=0, conversion='corners2centroids', border_pixels='half')
          elif self.coords == 'minmax':
              # Convert `(xmin, ymin, xmax, ymax)` to `(xmin, xmax, ymin, ymax).
              boxes_tensor = convert_coordinates(boxes_tensor, start_index=0, conversion='corners2minmax', border_pixels='half')
  
          # Create a tensor to contain the variances and append it to `boxes_tensor`. This tensor has the same shape
          # as `boxes_tensor` and simply contains the same 4 variance values for every position in the last axis.
          variances_tensor = np.zeros_like(boxes_tensor) # Has shape `(feature_map_height, feature_map_width, n_boxes, 4)`
          variances_tensor += self.variances # Long live broadcasting
          # Now `boxes_tensor` becomes a tensor of shape `(feature_map_height, feature_map_width, n_boxes, 8)`
          boxes_tensor = np.concatenate((boxes_tensor, variances_tensor), axis=-1)
  
          # Now prepend one dimension to `boxes_tensor` to account for the batch size and tile it along
          # The result will be a 5D tensor of shape `(batch_size, feature_map_height, feature_map_width, n_boxes, 8)`
          boxes_tensor = np.expand_dims(boxes_tensor, axis=0)
          boxes_tensor = K.tile(K.constant(boxes_tensor, dtype='float32'), (K.shape(x)[0], 1, 1, 1, 1))
  
          return boxes_tensor
  
      def compute_output_shape(self, input_shape):
          if K.image_dim_ordering() == 'tf':
              batch_size, feature_map_height, feature_map_width, feature_map_channels = input_shape
          else: # Not yet relevant since TensorFlow is the only supported backend right now, but it can't harm to have this in here for the future
              batch_size, feature_map_channels, feature_map_height, feature_map_width = input_shape
          return (batch_size, feature_map_height, feature_map_width, self.n_boxes, 8)
  
      def get_config(self):
          config = {
              'img_height': self.img_height,
              'img_width': self.img_width,
              'this_scale': self.this_scale,
              'next_scale': self.next_scale,
              'aspect_ratios': list(self.aspect_ratios),
              'two_boxes_for_ar1': self.two_boxes_for_ar1,
              'clip_boxes': self.clip_boxes,
              'variances': list(self.variances),
              'coords': self.coords,
              'normalize_coords': self.normalize_coords
          }
          base_config = super(AnchorBoxes, self).get_config()
          return dict(list(base_config.items()) + list(config.items()))
  