text: '''
  The data augmentation operations of the original SSD implementation.
  
  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
  
  from data_generator.object_detection_2d_photometric_ops import ConvertColor, ConvertDataType, ConvertTo3Channels, RandomBrightness, RandomContrast, RandomHue, RandomSaturation
  from data_generator.object_detection_2d_geometric_ops import RandomFlip, RandomTranslate, RandomScale
  from data_generator.object_detection_2d_image_boxes_validation_utils import BoundGenerator, BoxFilter, ImageValidator
  
  class DataAugmentationConstantInputSize:
      '''
      Applies a chain of photometric and geometric image transformations. For documentation, please refer
      to the documentation of the individual transformations involved.
  
      Important: This augmentation chain is suitable for constant-size images only.
      '''
  
      def __init__(self,
                   random_brightness=(-48, 48, 0.5),
                   random_contrast=(0.5, 1.8, 0.5),
                   random_saturation=(0.5, 1.8, 0.5),
                   random_hue=(18, 0.5),
                   random_flip=0.5,
                   random_translate=((0.03,0.5), (0.03,0.5), 0.5),
                   random_scale=(0.5, 2.0, 0.5),
                   n_trials_max=3,
                   clip_boxes=True,
                   overlap_criterion='area',
                   bounds_box_filter=(0.3, 1.0),
                   bounds_validator=(0.5, 1.0),
                   n_boxes_min=1,
                   background=(0,0,0),
                   labels_format={'class_id': 0, 'xmin': 1, 'ymin': 2, 'xmax': 3, 'ymax': 4}):
  
          if (random_scale[0] >= 1) or (random_scale[1] <= 1):
              raise ValueError("This sequence of transformations only makes sense if the minimum scaling factor is <1 and the maximum scaling factor is >1.")
  
          self.n_trials_max = n_trials_max
          self.clip_boxes = clip_boxes
          self.overlap_criterion = overlap_criterion
          self.bounds_box_filter = bounds_box_filter
          self.bounds_validator = bounds_validator
          self.n_boxes_min = n_boxes_min
          self.background = background
          self.labels_format = labels_format
  
          # Determines which boxes are kept in an image after the transformations have been applied.
          self.box_filter = BoxFilter(check_overlap=True,
                                      check_min_area=True,
                                      check_degenerate=True,
                                      overlap_criterion=self.overlap_criterion,
                                      overlap_bounds=self.bounds_box_filter,
                                      min_area=16,
                                      labels_format=self.labels_format)
  
          # Determines whether the result of the transformations is a valid training image.
          self.image_validator = ImageValidator(overlap_criterion=self.overlap_criterion,
                                                bounds=self.bounds_validator,
                                                n_boxes_min=self.n_boxes_min,
                                                labels_format=self.labels_format)
  
          # Utility distortions
          self.convert_RGB_to_HSV = ConvertColor(current='RGB', to='HSV')
          self.convert_HSV_to_RGB = ConvertColor(current='HSV', to='RGB')
          self.convert_to_float32 = ConvertDataType(to='float32')
          self.convert_to_uint8 = ConvertDataType(to='uint8')
          self.convert_to_3_channels = ConvertTo3Channels() # Make sure all images end up having 3 channels.
  
          # Photometric transformations
          self.random_brightness = RandomBrightness(lower=random_brightness[0], upper=random_brightness[1], prob=random_brightness[2])
          self.random_contrast = RandomContrast(lower=random_contrast[0], upper=random_contrast[1], prob=random_contrast[2])
          self.random_saturation = RandomSaturation(lower=random_saturation[0], upper=random_saturation[1], prob=random_saturation[2])
          self.random_hue = RandomHue(max_delta=random_hue[0], prob=random_hue[1])
  
          # Geometric transformations
          self.random_flip = RandomFlip(dim='horizontal', prob=random_flip, labels_format=self.labels_format)
          self.random_translate = RandomTranslate(dy_minmax=random_translate[0],
                                                  dx_minmax=random_translate[1],
                                                  prob=random_translate[2],
                                                  clip_boxes=self.clip_boxes,
                                                  box_filter=self.box_filter,
                                                  image_validator=self.image_validator,
                                                  n_trials_max=self.n_trials_max,
                                                  background=self.background,
                                                  labels_format=self.labels_format)
          self.random_zoom_in = RandomScale(min_factor=1.0,
                                            max_factor=random_scale[1],
                                            prob=random_scale[2],
                                            clip_boxes=self.clip_boxes,
                                            box_filter=self.box_filter,
                                            image_validator=self.image_validator,
                                            n_trials_max=self.n_trials_max,
                                            background=self.background,
                                            labels_format=self.labels_format)
          self.random_zoom_out = RandomScale(min_factor=random_scale[0],
                                             max_factor=1.0,
                                             prob=random_scale[2],
                                             clip_boxes=self.clip_boxes,
                                             box_filter=self.box_filter,
                                             image_validator=self.image_validator,
                                             n_trials_max=self.n_trials_max,
                                             background=self.background,
                                             labels_format=self.labels_format)
  
          # If we zoom in, do translation before scaling.
          self.sequence1 = [self.convert_to_3_channels,
                            self.convert_to_float32,
                            self.random_brightness,
                            self.random_contrast,
                            self.convert_to_uint8,
                            self.convert_RGB_to_HSV,
                            self.convert_to_float32,
                            self.random_saturation,
                            self.random_hue,
                            self.convert_to_uint8,
                            self.convert_HSV_to_RGB,
                            self.random_translate,
                            self.random_zoom_in,
                            self.random_flip]
  
          # If we zoom out, do scaling before translation.
          self.sequence2 = [self.convert_to_3_channels,
                            self.convert_to_float32,
                            self.random_brightness,
                            self.convert_to_uint8,
                            self.convert_RGB_to_HSV,
                            self.convert_to_float32,
                            self.random_saturation,
                            self.random_hue,
                            self.convert_to_uint8,
                            self.convert_HSV_to_RGB,
                            self.convert_to_float32,
                            self.random_contrast,
                            self.convert_to_uint8,
                            self.random_zoom_out,
                            self.random_translate,
                            self.random_flip]
  
      def __call__(self, image, labels=None):
  
          self.random_translate.labels_format = self.labels_format
          self.random_zoom_in.labels_format = self.labels_format
          self.random_zoom_out.labels_format = self.labels_format
          self.random_flip.labels_format = self.labels_format
  
          # Choose sequence 1 with probability 0.5.
          if np.random.choice(2):
  
              if not (labels is None):
                  for transform in self.sequence1:
                      image, labels = transform(image, labels)
                  return image, labels
              else:
                  for transform in self.sequence1:
                      image = transform(image)
                  return image
          # Choose sequence 2 with probability 0.5.
          else:
  
              if not (labels is None):
                  for transform in self.sequence2:
                      image, labels = transform(image, labels)
                  return image, labels
              else:
                  for transform in self.sequence2:
                      image = transform(image)
                  return image
  