content: '''
  Includes:
  * Functions to decode and filter raw SSD model output. These are only needed if the
    SSD model does not have a `DecodeDetections` layer.
  * Functions to perform greedy non-maximum suppression
  
  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 bounding_box_utils.bounding_box_utils import iou, convert_coordinates
  
  def greedy_nms(y_pred_decoded, iou_threshold=0.45, coords='corners', border_pixels='half'):
      '''
      Perform greedy non-maximum suppression on the input boxes.
  
      Greedy NMS works by selecting the box with the highest score and
      removing all boxes around it that are too close to it measured by IoU-similarity.
      Out of the boxes that are left over, once again the one with the highest
      score is selected and so on, until no boxes with too much overlap are left.
  
      Arguments:
          y_pred_decoded (list): A batch of decoded predictions. For a given batch size `n` this
              is a list of length `n` where each list element is a 2D Numpy array.
              For a batch item with `k` predicted boxes this 2D Numpy array has
              shape `(k, 6)`, where each row contains the coordinates of the respective
              box in the format `[class_id, score, xmin, xmax, ymin, ymax]`.
              Technically, the number of columns doesn't have to be 6, it can be
              arbitrary as long as the first four elements of each row are
              `xmin`, `xmax`, `ymin`, `ymax` (in this order) and the last element
              is the score assigned to the prediction. Note that this function is
              agnostic to the scale of the score or what it represents.
          iou_threshold (float, optional): All boxes with a Jaccard similarity of
              greater than `iou_threshold` with a locally maximal box will be removed
              from the set of predictions, where 'maximal' refers to the box score.
          coords (str, optional): The coordinate format of `y_pred_decoded`.
              Can be one of the formats supported by `iou()`.
          border_pixels (str, optional): How to treat the border pixels of the bounding boxes.
              Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong
              to the boxes. If 'exclude', the border pixels do not belong to the boxes.
              If 'half', then one of each of the two horizontal and vertical borders belong
              to the boxex, but not the other.
  
      Returns:
          The predictions after removing non-maxima. The format is the same as the input format.
      '''
      y_pred_decoded_nms = []
      for batch_item in y_pred_decoded: # For the labels of each batch item...
          boxes_left = np.copy(batch_item)
          maxima = [] # This is where we store the boxes that make it through the non-maximum suppression
          while boxes_left.shape[0] > 0: # While there are still boxes left to compare...
              maximum_index = np.argmax(boxes_left[:,1]) # ...get the index of the next box with the highest confidence...
              maximum_box = np.copy(boxes_left[maximum_index]) # ...copy that box and...
              maxima.append(maximum_box) # ...append it to `maxima` because we'll definitely keep it
              boxes_left = np.delete(boxes_left, maximum_index, axis=0) # Now remove the maximum box from `boxes_left`
              if boxes_left.shape[0] == 0: break # If there are no boxes left after this step, break. Otherwise...
              similarities = iou(boxes_left[:,2:], maximum_box[2:], coords=coords, mode='element-wise', border_pixels=border_pixels) # ...compare (IoU) the other left over boxes to the maximum box...
              boxes_left = boxes_left[similarities <= iou_threshold] # ...so that we can remove the ones that overlap too much with the maximum box
          y_pred_decoded_nms.append(np.array(maxima))
  
      return y_pred_decoded_nms
  
  def _greedy_nms(predictions, iou_threshold=0.45, coords='corners', border_pixels='half'):
      '''
      The same greedy non-maximum suppression algorithm as above, but slightly modified for use as an internal
      function for per-class NMS in `decode_detections()`.
      '''
      boxes_left = np.copy(predictions)
      maxima = [] # This is where we store the boxes that make it through the non-maximum suppression
      while boxes_left.shape[0] > 0: # While there are still boxes left to compare...
          maximum_index = np.argmax(boxes_left[:,0]) # ...get the index of the next box with the highest confidence...
          maximum_box = np.copy(boxes_left[maximum_index]) # ...copy that box and...
          maxima.append(maximum_box) # ...append it to `maxima` because we'll definitely keep it
          boxes_left = np.delete(boxes_left, maximum_index, axis=0) # Now remove the maximum box from `boxes_left`
          if boxes_left.shape[0] == 0: break # If there are no boxes left after this step, break. Otherwise...
          similarities = iou(boxes_left[:,1:], maximum_box[1:], coords=coords, mode='element-wise', border_pixels=border_pixels) # ...compare (IoU) the other left over boxes to the maximum box...
          boxes_left = boxes_left[similarities <= iou_threshold] # ...so that we can remove the ones that overlap too much with the maximum box
      return np.array(maxima)
  
  def _greedy_nms2(predictions, iou_threshold=0.45, coords='corners', border_pixels='half'):
      '''
      The same greedy non-maximum suppression algorithm as above, but slightly modified for use as an internal
      function in `decode_detections_fast()`.
      '''
      boxes_left = np.copy(predictions)
      maxima = [] # This is where we store the boxes that make it through the non-maximum suppression
      while boxes_left.shape[0] > 0: # While there are still boxes left to compare...
          maximum_index = np.argmax(boxes_left[:,1]) # ...get the index of the next box with the highest confidence...
          maximum_box = np.copy(boxes_left[maximum_index]) # ...copy that box and...
          maxima.append(maximum_box) # ...append it to `maxima` because we'll definitely keep it
          boxes_left = np.delete(boxes_left, maximum_index, axis=0) # Now remove the maximum box from `boxes_left`
          if boxes_left.shape[0] == 0: break # If there are no boxes left after this step, break. Otherwise...
          similarities = iou(boxes_left[:,2:], maximum_box[2:], coords=coords, mode='element-wise', border_pixels=border_pixels) # ...compare (IoU) the other left over boxes to the maximum box...
          boxes_left = boxes_left[similarities <= iou_threshold] # ...so that we can remove the ones that overlap too much with the maximum box
      return np.array(maxima)
  
  def decode_detections(y_pred,
                        confidence_thresh=0.01,
                        iou_threshold=0.45,
                        top_k=200,
                        input_coords='centroids',
                        normalize_coords=True,
                        img_height=None,
                        img_width=None,
                        border_pixels='half'):
      '''
      Convert model prediction output back to a format that contains only the positive box predictions
      (i.e. the same format that `SSDInputEncoder` takes as input).
  
      After the decoding, two stages of prediction filtering are performed for each class individually:
      First confidence thresholding, then greedy non-maximum suppression. The filtering results for all
      classes are concatenated and the `top_k` overall highest confidence results constitute the final
      predictions for a given batch item. This procedure follows the original Caffe implementation.
      For a slightly different and more efficient alternative to decode raw model output that performs
      non-maximum suppresion globally instead of per class, see `decode_detections_fast()` below.
  
      Arguments:
          y_pred (array): The prediction output of the SSD model, expected to be a Numpy array
              of shape `(batch_size, #boxes, #classes + 4 + 4 + 4)`, where `#boxes` is the total number of
              boxes predicted by the model per image and the last axis contains
              `[one-hot vector for the classes, 4 predicted coordinate offsets, 4 anchor box coordinates, 4 variances]`.
          confidence_thresh (float, optional): A float in [0,1), the minimum classification confidence in a specific
              positive class in order to be considered for the non-maximum suppression stage for the respective class.
              A lower value will result in a larger part of the selection process being done by the non-maximum suppression
              stage, while a larger value will result in a larger part of the selection process happening in the confidence
              thresholding stage.
          iou_threshold (float, optional): A float in [0,1]. All boxes with a Jaccard similarity of greater than `iou_threshold`
              with a locally maximal box will be removed from the set of predictions for a given class, where 'maximal' refers
              to the box score.
          top_k (int, optional): The number of highest scoring predictions to be kept for each batch item after the
              non-maximum suppression stage.
          input_coords (str, optional): The box coordinate format that the model outputs. Can be either 'centroids'
              for the format `(cx, cy, w, h)` (box center coordinates, width, and height), 'minmax' for the format
              `(xmin, xmax, ymin, ymax)`, or 'corners' for the format `(xmin, ymin, xmax, ymax)`.
          normalize_coords (bool, optional): Set to `True` if the model outputs relative coordinates (i.e. coordinates in [0,1])
              and you wish to transform these relative coordinates back to absolute coordinates. If the model outputs
              relative coordinates, but you do not want to convert them back to absolute coordinates, set this to `False`.
              Do not set this to `True` if the model already outputs absolute coordinates, as that would result in incorrect
              coordinates. Requires `img_height` and `img_width` if set to `True`.
          img_height (int, optional): The height of the input images. Only needed if `normalize_coords` is `True`.
          img_width (int, optional): The width of the input images. Only needed if `normalize_coords` is `True`.
          border_pixels (str, optional): How to treat the border pixels of the bounding boxes.
              Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong
              to the boxes. If 'exclude', the border pixels do not belong to the boxes.
              If 'half', then one of each of the two horizontal and vertical borders belong
              to the boxex, but not the other.
  
      Returns:
          A python list of length `batch_size` where each list element represents the predicted boxes
          for one image and contains a Numpy array of shape `(boxes, 6)` where each row is a box prediction for
          a non-background class for the respective image in the format `[class_id, confidence, xmin, ymin, xmax, ymax]`.
      '''
      if normalize_coords and ((img_height is None) or (img_width is None)):
          raise ValueError("If relative box coordinates are supposed to be converted to absolute coordinates, the decoder needs the image size in order to decode the predictions, but `img_height == {}` and `img_width == {}`".format(img_height, img_width))
  
      # 1: Convert the box coordinates from the predicted anchor box offsets to predicted absolute coordinates
  
      y_pred_decoded_raw = np.copy(y_pred[:,:,:-8]) # Slice out the classes and the four offsets, throw away the anchor coordinates and variances, resulting in a tensor of shape `[batch, n_boxes, n_classes + 4 coordinates]`
  
      if input_coords == 'centroids':
          y_pred_decoded_raw[:,:,[-2,-1]] = np.exp(y_pred_decoded_raw[:,:,[-2,-1]] * y_pred[:,:,[-2,-1]]) # exp(ln(w(pred)/w(anchor)) / w_variance * w_variance) == w(pred) / w(anchor), exp(ln(h(pred)/h(anchor)) / h_variance * h_variance) == h(pred) / h(anchor)
          y_pred_decoded_raw[:,:,[-2,-1]] *= y_pred[:,:,[-6,-5]] # (w(pred) / w(anchor)) * w(anchor) == w(pred), (h(pred) / h(anchor)) * h(anchor) == h(pred)
          y_pred_decoded_raw[:,:,[-4,-3]] *= y_pred[:,:,[-4,-3]] * y_pred[:,:,[-6,-5]] # (delta_cx(pred) / w(anchor) / cx_variance) * cx_variance * w(anchor) == delta_cx(pred), (delta_cy(pred) / h(anchor) / cy_variance) * cy_variance * h(anchor) == delta_cy(pred)
          y_pred_decoded_raw[:,:,[-4,-3]] += y_pred[:,:,[-8,-7]] # delta_cx(pred) + cx(anchor) == cx(pred), delta_cy(pred) + cy(anchor) == cy(pred)
          y_pred_decoded_raw = convert_coordinates(y_pred_decoded_raw, start_index=-4, conversion='centroids2corners')
      elif input_coords == 'minmax':
          y_pred_decoded_raw[:,:,-4:] *= y_pred[:,:,-4:] # delta(pred) / size(anchor) / variance * variance == delta(pred) / size(anchor) for all four coordinates, where 'size' refers to w or h, respectively
          y_pred_decoded_raw[:,:,[-4,-3]] *= np.expand_dims(y_pred[:,:,-7] - y_pred[:,:,-8], axis=-1) # delta_xmin(pred) / w(anchor) * w(anchor) == delta_xmin(pred), delta_xmax(pred) / w(anchor) * w(anchor) == delta_xmax(pred)
          y_pred_decoded_raw[:,:,[-2,-1]] *= np.expand_dims(y_pred[:,:,-5] - y_pred[:,:,-6], axis=-1) # delta_ymin(pred) / h(anchor) * h(anchor) == delta_ymin(pred), delta_ymax(pred) / h(anchor) * h(anchor) == delta_ymax(pred)
          y_pred_decoded_raw[:,:,-4:] += y_pred[:,:,-8:-4] # delta(pred) + anchor == pred for all four coordinates
          y_pred_decoded_raw = convert_coordinates(y_pred_decoded_raw, start_index=-4, conversion='minmax2corners')
      elif input_coords == 'corners':
          y_pred_decoded_raw[:,:,-4:] *= y_pred[:,:,-4:] # delta(pred) / size(anchor) / variance * variance == delta(pred) / size(anchor) for all four coordinates, where 'size' refers to w or h, respectively
          y_pred_decoded_raw[:,:,[-4,-2]] *= np.expand_dims(y_pred[:,:,-6] - y_pred[:,:,-8], axis=-1) # delta_xmin(pred) / w(anchor) * w(anchor) == delta_xmin(pred), delta_xmax(pred) / w(anchor) * w(anchor) == delta_xmax(pred)
          y_pred_decoded_raw[:,:,[-3,-1]] *= np.expand_dims(y_pred[:,:,-5] - y_pred[:,:,-7], axis=-1) # delta_ymin(pred) / h(anchor) * h(anchor) == delta_ymin(pred), delta_ymax(pred) / h(anchor) * h(anchor) == delta_ymax(pred)
          y_pred_decoded_raw[:,:,-4:] += y_pred[:,:,-8:-4] # delta(pred) + anchor == pred for all four coordinates
      else:
          raise ValueError("Unexpected value for `input_coords`. Supported input coordinate formats are 'minmax', 'corners' and 'centroids'.")
  
      # 2: If the model predicts normalized box coordinates and they are supposed to be converted back to absolute coordinates, do that
  
      if normalize_coords:
          y_pred_decoded_raw[:,:,[-4,-2]] *= img_width # Convert xmin, xmax back to absolute coordinates
          y_pred_decoded_raw[:,:,[-3,-1]] *= img_height # Convert ymin, ymax back to absolute coordinates
  
      # 3: Apply confidence thresholding and non-maximum suppression per class
  
      n_classes = y_pred_decoded_raw.shape[-1] - 4 # The number of classes is the length of the last axis minus the four box coordinates
  
      y_pred_decoded = [] # Store the final predictions in this list
      for batch_item in y_pred_decoded_raw: # `batch_item` has shape `[n_boxes, n_classes + 4 coords]`
          pred = [] # Store the final predictions for this batch item here
          for class_id in range(1, n_classes): # For each class except the background class (which has class ID 0)...
              single_class = batch_item[:,[class_id, -4, -3, -2, -1]] # ...keep only the confidences for that class, making this an array of shape `[n_boxes, 5]` and...
              threshold_met = single_class[single_class[:,0] > confidence_thresh] # ...keep only those boxes with a confidence above the set threshold.
              if threshold_met.shape[0] > 0: # If any boxes made the threshold...
                  maxima = _greedy_nms(threshold_met, iou_threshold=iou_threshold, coords='corners', border_pixels=border_pixels) # ...perform NMS on them.
                  maxima_output = np.zeros((maxima.shape[0], maxima.shape[1] + 1)) # Expand the last dimension by one element to have room for the class ID. This is now an arrray of shape `[n_boxes, 6]`
                  maxima_output[:,0] = class_id # Write the class ID to the first column...
                  maxima_output[:,1:] = maxima # ...and write the maxima to the other columns...
                  pred.append(maxima_output) # ...and append the maxima for this class to the list of maxima for this batch item.
          # Once we're through with all classes, keep only the `top_k` maxima with the highest scores
          if pred: # If there are any predictions left after confidence-thresholding...
              pred = np.concatenate(pred, axis=0)
              if top_k != 'all' and pred.shape[0] > top_k: # If we have more than `top_k` results left at this point, otherwise there is nothing to filter,...
                  top_k_indices = np.argpartition(pred[:,1], kth=pred.shape[0]-top_k, axis=0)[pred.shape[0]-top_k:] # ...get the indices of the `top_k` highest-score maxima...
                  pred = pred[top_k_indices] # ...and keep only those entries of `pred`...
          else:
              pred = np.array(pred) # Even if empty, `pred` must become a Numpy array.
          y_pred_decoded.append(pred) # ...and now that we're done, append the array of final predictions for this batch item to the output list
  
      return y_pred_decoded
  
  def decode_detections_fast(y_pred,
                             confidence_thresh=0.5,
                             iou_threshold=0.45,
                             top_k='all',
                             input_coords='centroids',
                             normalize_coords=True,
                             img_height=None,
                             img_width=None,
                             border_pixels='half'):
      '''
      Convert model prediction output back to a format that contains only the positive box predictions
      (i.e. the same format that `enconde_y()` takes as input).
  
      Optionally performs confidence thresholding and greedy non-maximum suppression after the decoding stage.
  
      Note that the decoding procedure used here is not the same as the procedure used in the original Caffe implementation.
      For each box, the procedure used here assigns the box's highest confidence as its predicted class. Then it removes
      all boxes for which the highest confidence is the background class. This results in less work for the subsequent
      non-maximum suppression, because the vast majority of the predictions will be filtered out just by the fact that
      their highest confidence is for the background class. It is much more efficient than the procedure of the original
      implementation, but the results may also differ.
  
      Arguments:
          y_pred (array): The prediction output of the SSD model, expected to be a Numpy array
              of shape `(batch_size, #boxes, #classes + 4 + 4 + 4)`, where `#boxes` is the total number of
              boxes predicted by the model per image and the last axis contains
              `[one-hot vector for the classes, 4 predicted coordinate offsets, 4 anchor box coordinates, 4 variances]`.
          confidence_thresh (float, optional): A float in [0,1), the minimum classification confidence in any positive
              class required for a given box to be considered a positive prediction. A lower value will result
              in better recall, while a higher value will result in better precision. Do not use this parameter with the
              goal to combat the inevitably many duplicates that an SSD will produce, the subsequent non-maximum suppression
              stage will take care of those.
          iou_threshold (float, optional): `None` or a float in [0,1]. If `None`, no non-maximum suppression will be
              performed. If not `None`, greedy NMS will be performed after the confidence thresholding stage, meaning
              all boxes with a Jaccard similarity of greater than `iou_threshold` with a locally maximal box will be removed
              from the set of predictions, where 'maximal' refers to the box score.
          top_k (int, optional): 'all' or an integer with number of highest scoring predictions to be kept for each batch item
              after the non-maximum suppression stage. If 'all', all predictions left after the NMS stage will be kept.
          input_coords (str, optional): The box coordinate format that the model outputs. Can be either 'centroids'
              for the format `(cx, cy, w, h)` (box center coordinates, width, and height), 'minmax' for the format
              `(xmin, xmax, ymin, ymax)`, or 'corners' for the format `(xmin, ymin, xmax, ymax)`.
          normalize_coords (bool, optional): Set to `True` if the model outputs relative coordinates (i.e. coordinates in [0,1])
              and you wish to transform these relative coordinates back to absolute coordinates. If the model outputs
              relative coordinates, but you do not want to convert them back to absolute coordinates, set this to `False`.
              Do not set this to `True` if the model already outputs absolute coordinates, as that would result in incorrect
              coordinates. Requires `img_height` and `img_width` if set to `True`.
          img_height (int, optional): The height of the input images. Only needed if `normalize_coords` is `True`.
          img_width (int, optional): The width of the input images. Only needed if `normalize_coords` is `True`.
          border_pixels (str, optional): How to treat the border pixels of the bounding boxes.
              Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong
              to the boxes. If 'exclude', the border pixels do not belong to the boxes.
              If 'half', then one of each of the two horizontal and vertical borders belong
              to the boxex, but not the other.
  
      Returns:
          A python list of length `batch_size` where each list element represents the predicted boxes
          for one image and contains a Numpy array of shape `(boxes, 6)` where each row is a box prediction for
          a non-background class for the respective image in the format `[class_id, confidence, xmin, xmax, ymin, ymax]`.
      '''
      if normalize_coords and ((img_height is None) or (img_width is None)):
          raise ValueError("If relative box coordinates are supposed to be converted to absolute coordinates, the decoder needs the image size in order to decode the predictions, but `img_height == {}` and `img_width == {}`".format(img_height, img_width))
  
      # 1: Convert the classes from one-hot encoding to their class ID
      y_pred_converted = np.copy(y_pred[:,:,-14:-8]) # Slice out the four offset predictions plus two elements whereto we'll write the class IDs and confidences in the next step
      y_pred_converted[:,:,0] = np.argmax(y_pred[:,:,:-12], axis=-1) # The indices of the highest confidence values in the one-hot class vectors are the class ID
      y_pred_converted[:,:,1] = np.amax(y_pred[:,:,:-12], axis=-1) # Store the confidence values themselves, too
  
      # 2: Convert the box coordinates from the predicted anchor box offsets to predicted absolute coordinates
      if input_coords == 'centroids':
          y_pred_converted[:,:,[4,5]] = np.exp(y_pred_converted[:,:,[4,5]] * y_pred[:,:,[-2,-1]]) # exp(ln(w(pred)/w(anchor)) / w_variance * w_variance) == w(pred) / w(anchor), exp(ln(h(pred)/h(anchor)) / h_variance * h_variance) == h(pred) / h(anchor)
          y_pred_converted[:,:,[4,5]] *= y_pred[:,:,[-6,-5]] # (w(pred) / w(anchor)) * w(anchor) == w(pred), (h(pred) / h(anchor)) * h(anchor) == h(pred)
          y_pred_converted[:,:,[2,3]] *= y_pred[:,:,[-4,-3]] * y_pred[:,:,[-6,-5]] # (delta_cx(pred) / w(anchor) / cx_variance) * cx_variance * w(anchor) == delta_cx(pred), (delta_cy(pred) / h(anchor) / cy_variance) * cy_variance * h(anchor) == delta_cy(pred)
          y_pred_converted[:,:,[2,3]] += y_pred[:,:,[-8,-7]] # delta_cx(pred) + cx(anchor) == cx(pred), delta_cy(pred) + cy(anchor) == cy(pred)
          y_pred_converted = convert_coordinates(y_pred_converted, start_index=-4, conversion='centroids2corners')
      elif input_coords == 'minmax':
          y_pred_converted[:,:,2:] *= y_pred[:,:,-4:] # delta(pred) / size(anchor) / variance * variance == delta(pred) / size(anchor) for all four coordinates, where 'size' refers to w or h, respectively
          y_pred_converted[:,:,[2,3]] *= np.expand_dims(y_pred[:,:,-7] - y_pred[:,:,-8], axis=-1) # delta_xmin(pred) / w(anchor) * w(anchor) == delta_xmin(pred), delta_xmax(pred) / w(anchor) * w(anchor) == delta_xmax(pred)
          y_pred_converted[:,:,[4,5]] *= np.expand_dims(y_pred[:,:,-5] - y_pred[:,:,-6], axis=-1) # delta_ymin(pred) / h(anchor) * h(anchor) == delta_ymin(pred), delta_ymax(pred) / h(anchor) * h(anchor) == delta_ymax(pred)
          y_pred_converted[:,:,2:] += y_pred[:,:,-8:-4] # delta(pred) + anchor == pred for all four coordinates
          y_pred_converted = convert_coordinates(y_pred_converted, start_index=-4, conversion='minmax2corners')
      elif input_coords == 'corners':
          y_pred_converted[:,:,2:] *= y_pred[:,:,-4:] # delta(pred) / size(anchor) / variance * variance == delta(pred) / size(anchor) for all four coordinates, where 'size' refers to w or h, respectively
          y_pred_converted[:,:,[2,4]] *= np.expand_dims(y_pred[:,:,-6] - y_pred[:,:,-8], axis=-1) # delta_xmin(pred) / w(anchor) * w(anchor) == delta_xmin(pred), delta_xmax(pred) / w(anchor) * w(anchor) == delta_xmax(pred)
          y_pred_converted[:,:,[3,5]] *= np.expand_dims(y_pred[:,:,-5] - y_pred[:,:,-7], axis=-1) # delta_ymin(pred) / h(anchor) * h(anchor) == delta_ymin(pred), delta_ymax(pred) / h(anchor) * h(anchor) == delta_ymax(pred)
          y_pred_converted[:,:,2:] += y_pred[:,:,-8:-4] # delta(pred) + anchor == pred for all four coordinates
      else:
          raise ValueError("Unexpected value for `coords`. Supported values are 'minmax', 'corners' and 'centroids'.")
  
      # 3: If the model predicts normalized box coordinates and they are supposed to be converted back to absolute coordinates, do that
      if normalize_coords:
          y_pred_converted[:,:,[2,4]] *= img_width # Convert xmin, xmax back to absolute coordinates
          y_pred_converted[:,:,[3,5]] *= img_height # Convert ymin, ymax back to absolute coordinates
  
      # 4: Decode our huge `(batch, #boxes, 6)` tensor into a list of length `batch` where each list entry is an array containing only the positive predictions
      y_pred_decoded = []
      for batch_item in y_pred_converted: # For each image in the batch...
          boxes = batch_item[np.nonzero(batch_item[:,0])] # ...get all boxes that don't belong to the background class,...
          boxes = boxes[boxes[:,1] >= confidence_thresh] # ...then filter out those positive boxes for which the prediction confidence is too low and after that...
          if iou_threshold: # ...if an IoU threshold is set...
              boxes = _greedy_nms2(boxes, iou_threshold=iou_threshold, coords='corners', border_pixels=border_pixels) # ...perform NMS on the remaining boxes.
          if top_k != 'all' and boxes.shape[0] > top_k: # If we have more than `top_k` results left at this point...
              top_k_indices = np.argpartition(boxes[:,1], kth=boxes.shape[0]-top_k, axis=0)[boxes.shape[0]-top_k:] # ...get the indices of the `top_k` highest-scoring boxes...
              boxes = boxes[top_k_indices] # ...and keep only those boxes...
          y_pred_decoded.append(boxes) # ...and now that we're done, append the array of final predictions for this batch item to the output list
  
      return y_pred_decoded
  
  ################################################################################################
  # Debugging tools, not relevant for normal use
  ################################################################################################
  
  # The functions below are for debugging, so you won't normally need them. That is,
  # unless you need to debug your model, of course.
  
  def decode_detections_debug(y_pred,
                              confidence_thresh=0.01,
                              iou_threshold=0.45,
                              top_k=200,
                              input_coords='centroids',
                              normalize_coords=True,
                              img_height=None,
                              img_width=None,
                              variance_encoded_in_target=False,
                              border_pixels='half'):
      '''
      This decoder performs the same processing as `decode_detections()`, but the output format for each left-over
      predicted box is `[box_id, class_id, confidence, xmin, ymin, xmax, ymax]`.
  
      That is, in addition to the usual data, each predicted box has the internal index of that box within
      the model (`box_id`) prepended to it. This allows you to know exactly which part of the model made a given
      box prediction; in particular, it allows you to know which predictor layer made a given prediction.
      This can be useful for debugging.
  
      Arguments:
          y_pred (array): The prediction output of the SSD model, expected to be a Numpy array
              of shape `(batch_size, #boxes, #classes + 4 + 4 + 4)`, where `#boxes` is the total number of
              boxes predicted by the model per image and the last axis contains
              `[one-hot vector for the classes, 4 predicted coordinate offsets, 4 anchor box coordinates, 4 variances]`.
          confidence_thresh (float, optional): A float in [0,1), the minimum classification confidence in a specific
              positive class in order to be considered for the non-maximum suppression stage for the respective class.
              A lower value will result in a larger part of the selection process being done by the non-maximum suppression
              stage, while a larger value will result in a larger part of the selection process happening in the confidence
              thresholding stage.
          iou_threshold (float, optional): A float in [0,1]. All boxes with a Jaccard similarity of greater than `iou_threshold`
              with a locally maximal box will be removed from the set of predictions for a given class, where 'maximal' refers
              to the box score.
          top_k (int, optional): The number of highest scoring predictions to be kept for each batch item after the
              non-maximum suppression stage.
          input_coords (str, optional): The box coordinate format that the model outputs. Can be either 'centroids'
              for the format `(cx, cy, w, h)` (box center coordinates, width, and height), 'minmax' for the format
              `(xmin, xmax, ymin, ymax)`, or 'corners' for the format `(xmin, ymin, xmax, ymax)`.
          normalize_coords (bool, optional): Set to `True` if the model outputs relative coordinates (i.e. coordinates in [0,1])
              and you wish to transform these relative coordinates back to absolute coordinates. If the model outputs
              relative coordinates, but you do not want to convert them back to absolute coordinates, set this to `False`.
              Do not set this to `True` if the model already outputs absolute coordinates, as that would result in incorrect
              coordinates. Requires `img_height` and `img_width` if set to `True`.
          img_height (int, optional): The height of the input images. Only needed if `normalize_coords` is `True`.
          img_width (int, optional): The width of the input images. Only needed if `normalize_coords` is `True`.
          border_pixels (str, optional): How to treat the border pixels of the bounding boxes.
              Can be 'include', 'exclude', or 'half'. If 'include', the border pixels belong
              to the boxes. If 'exclude', the border pixels do not belong to the boxes.
              If 'half', then one of each of the two horizontal and vertical borders belong
              to the boxex, but not the other.
  
      Returns:
          A python list of length `batch_size` where each list element represents the predicted boxes
          for one image and contains a Numpy array of shape `(boxes, 7)` where each row is a box prediction for
  