content: '''
  A data augmentation pipeline suitable for variable-size images that produces effects
  that are similar (but not identical) to those of the original SSD data augmentation
  pipeline while being faster.
  
  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 Resize, RandomFlip
  from data_generator.object_detection_2d_patch_sampling_ops import PatchCoordinateGenerator, RandomPatch
  from data_generator.object_detection_2d_image_boxes_validation_utils import BoxFilter, ImageValidator
  
  class DataAugmentationVariableInputSize:
      '''
      A data augmentation pipeline suitable for variable-size images that produces effects
      that are similar (but not identical!) to those of the original SSD data augmentation
      pipeline while being faster.
  
      Applies a chain of photometric and geometric image transformations. For documentation, please refer
      to the documentation of the individual transformations involved.
      '''
  
      def __init__(self,
                   resize_height,
                   resize_width,
                   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,
                   min_scale=0.3,
                   max_scale=2.0,
                   min_aspect_ratio = 0.5,
                   max_aspect_ratio = 2.0,
                   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}):
  
          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_patch = BoxFilter(check_overlap=True,
                                            check_min_area=False,
                                            check_degenerate=False,
                                            overlap_criterion=self.overlap_criterion,
                                            overlap_bounds=self.bounds_box_filter,
                                            labels_format=self.labels_format)
  
          self.box_filter_resize = BoxFilter(check_overlap=False,
                                             check_min_area=True,
                                             check_degenerate=True,
                                             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 transformations
          self.convert_to_3_channels = ConvertTo3Channels() # Make sure all images end up having 3 channels.
          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.resize                = Resize(height=resize_height,
                                              width=resize_width,
                                              box_filter=self.box_filter_resize,
                                              labels_format=self.labels_format)
  
          # 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.patch_coord_generator = PatchCoordinateGenerator(must_match='w_ar',
                                                                min_scale=min_scale,
                                                                max_scale=max_scale,
                                                                scale_uniformly=False,
                                                                min_aspect_ratio = min_aspect_ratio,
                                                                max_aspect_ratio = max_aspect_ratio)
          self.random_patch          = RandomPatch(patch_coord_generator=self.patch_coord_generator,
                                                   box_filter=self.box_filter_patch,
                                                   image_validator=self.image_validator,
                                                   n_trials_max=self.n_trials_max,
                                                   clip_boxes=self.clip_boxes,
                                                   prob=1.0,
                                                   can_fail=False,
                                                   labels_format=self.labels_format)
  
          # Define the processing chain
          self.transformations = [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_patch,
                                  self.random_flip,
                                  self.resize]
  
      def __call__(self, image, labels=None):
  
          self.random_patch.labels_format = self.labels_format
          self.random_flip.labels_format = self.labels_format
          self.resize.labels_format = self.labels_format
  
          if not (labels is None):
              for transform in self.transformations:
                  image, labels = transform(image, labels)
              return image, labels
          else:
              for transform in self.sequence1:
                  image = transform(image)
              return image
  