text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  # Copyright (c) 2021, Amazon Web Services. All rights reserved.
  #
  # 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.
  
  import numpy as np
  import cv2
  import pycocotools.mask as maskUtils
  from collections import defaultdict
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  
  def process_boxes(image_info, box_coordinates):
      """Process the model prediction for COCO eval."""
      '''image_info = prediction['image_info']
      box_coordinates = prediction['detection_boxes']'''
      processed_box_coordinates = np.zeros_like(box_coordinates)
  
      for image_id in range(box_coordinates.shape[0]):
          scale = image_info[image_id][2]
  
          for box_id in range(box_coordinates.shape[1]):
              # Map [y1, x1, y2, x2] -> [x1, y1, w, h] and multiply detections
              # Map [y1, x1, y2, x2] -> [x1, y1, w, h] and multiply detections
              # by image scale.
              y1, x1, y2, x2 = box_coordinates[image_id, box_id, :]
              new_box = scale * np.array([x1, y1, x2 - x1, y2 - y1])
              processed_box_coordinates[image_id, box_id, :] = new_box
  
      #prediction['detection_boxes'] = processed_box_coordinates
      return processed_box_coordinates
  
  def generate_segmentation_from_masks(masks,
                                       detected_boxes,
                                       image_height,
                                       image_width,
                                       is_image_mask=False):
      """Generates segmentation result from instance masks.
  
      Args:
      masks: a numpy array of shape [N, mask_height, mask_width] representing the
        instance masks w.r.t. the `detected_boxes`.
      detected_boxes: a numpy array of shape [N, 4] representing the reference
        bounding boxes.
      image_height: an integer representing the height of the image.
      image_width: an integer representing the width of the image.
      is_image_mask: bool. True: input masks are whole-image masks. False: input
        masks are bounding-box level masks.
  
      Returns:
      segms: a numpy array of shape [N, image_height, image_width] representing
        the instance masks *pasted* on the image canvas.
      """
  
      def expand_boxes(boxes, scale):
          """Expands an array of boxes by a given scale."""
          # Reference: https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/boxes.py#L227
          # The `boxes` in the reference implementation is in [x1, y1, x2, y2] form,
          # whereas `boxes` here is in [x1, y1, w, h] form
          w_half = boxes[:, 2] * .5
          h_half = boxes[:, 3] * .5
          x_c = boxes[:, 0] + w_half
          y_c = boxes[:, 1] + h_half
  
          w_half *= scale
          h_half *= scale
  
          boxes_exp = np.zeros(boxes.shape)
          boxes_exp[:, 0] = x_c - w_half
          boxes_exp[:, 2] = x_c + w_half
          boxes_exp[:, 1] = y_c - h_half
          boxes_exp[:, 3] = y_c + h_half
  
          return boxes_exp
  
      # Reference: https://github.com/facebookresearch/Detectron/blob/master/detectron/core/test.py#L812
      # To work around an issue with cv2.resize (it seems to automatically pad
      # with repeated border values), we manually zero-pad the masks by 1 pixel
      # prior to resizing back to the original image resolution. This prevents
      # "top hat" artifacts. We therefore need to expand the reference boxes by an
      # appropriate factor.
  
      _, mask_height, mask_width = masks.shape
      scale = max((mask_width + 2.0) / mask_width,
                (mask_height + 2.0) / mask_height)
      ref_boxes = expand_boxes(detected_boxes, scale)
      ref_boxes = ref_boxes.astype(np.int32)
      padded_mask = np.zeros((mask_height + 2, mask_width + 2), dtype=np.float32)
      segms = []
      for mask_ind, mask in enumerate(masks):
          im_mask = np.zeros((image_height, image_width), dtype=np.uint8)
          if is_image_mask:
              # Process whole-image masks.
              im_mask[:, :] = mask[:, :]
          else:
              # Process mask inside bounding boxes.
              padded_mask[1:-1, 1:-1] = mask[:, :]
  
              ref_box = ref_boxes[mask_ind, :]
              w = ref_box[2] - ref_box[0] + 1
              h = ref_box[3] - ref_box[1] + 1
              w = np.maximum(w, 1)
              h = np.maximum(h, 1)
              mask = cv2.resize(padded_mask, (w, h))
              mask = np.array(mask > 0.5, dtype=np.uint8)
              x_0 = max(ref_box[0], 0)
              x_1 = min(ref_box[2] + 1, image_width)
              y_0 = max(ref_box[1], 0)
              y_1 = min(ref_box[3] + 1, image_height)
              im_mask[y_0:y_1, x_0:x_1] = mask[(y_0 - ref_box[1]):(y_1 - ref_box[1]), (
                    x_0 - ref_box[0]):(x_1 - ref_box[0])]
          segms.append(im_mask)
  
      segms = np.array(segms)
      assert masks.shape[0] == segms.shape[0]
      return segms
  
  def format_predictions(image_id,
                         detection_boxes,
                         detection_scores,
                         detection_classes,
                         rles):
      box_predictions = []
      mask_predictions = []
      
      detection_count = len(detection_scores)
      for i in range(detection_count):
          box_predictions.append({'image_id': int(image_id),
                                  'category_id': int(detection_classes[i]),
                                  'bbox': list(map(lambda x: float(round(x, 2)), detection_boxes[i])),
                                  'score': float(detection_scores[i])})
          if rles:
              segmentation = {'size': rles[i]['size'],
                              'counts': rles[i]['counts'].decode()} 
              mask_predictions.append({'image_id': int(image_id),
                                       'category_id': int(detection_classes[i]),
                                       'score': float(detection_scores[i]),
                                       'segmentation': segmentation})
      return box_predictions, mask_predictions
  
  
  def process_prediction(prediction):
      prediction.update({'detection_boxes':
                         process_boxes(prediction['image_info'], 
                                       prediction['detection_boxes'])})
      batch_size = prediction['num_detections'].shape[0]
      
      box_predictions = []
      mask_predictions = []
      imgIds = []
      
      for i in range(batch_size):
          detection_boxes = prediction['detection_boxes'][i]
          detection_classes = prediction['detection_classes'][i]
          detection_scores = prediction['detection_scores'][i]
          source_id = prediction['source_ids'][i]
          detection_masks = prediction['detection_masks'][i] if 'detection_masks' in prediction.keys() else None
          segments = generate_segmentation_from_masks(detection_masks,
                                       detection_boxes,
                                       int(prediction['image_info'][i][3]),
                                       int(prediction['image_info'][i][4])) if detection_masks is not None else None
          rles = None
          if detection_masks is not None:
              rles = [
                  maskUtils.encode(np.asfortranarray(instance_mask.astype(np.uint8)))
                  for instance_mask in segments
              ]
          formatted_predictions = format_predictions(source_id,
                                                 detection_boxes,
                                                 detection_scores,
                                                 detection_classes,
                                                 rles)
          imgIds.append(source_id)
          box_predictions.extend(formatted_predictions[0])
          mask_predictions.extend(formatted_predictions[1])
      
      
      return imgIds, box_predictions, mask_predictions
  
  def build_output_dict(iou, stats, verbose=False):
      if verbose:
          format_string = ["AP 0.50:0.95 all",
                           "AP 0.50 all",
                           "AP 0.75 all",
                           "AP 0.50:0.95 small",
                           "AP 0.50:0.95 medium",
                           "AP 0.50:0.95 large",
                           "AR 0.50:0.95 all",
                           "AR 0.50 all",
                           "AR 0.75 all",
                           "AR 0.50:0.95 small",
                           "AR 0.50:0.95 medium",
                           "AR 0.50:0.95 large"]
          stat_dict = {"{0} {1}".format(iou, i): j for i,j in zip(format_string, stats)}
      else:
          stat_dict = {"{0} AP 0.50:0.95 all".format(iou): stats[0]}
      return stat_dict
  
  def evaluate_coco_predictions(annotations_file, iou_types, predictions, verbose=False):
      cocoGt = COCO(annotation_file=annotations_file, use_ext=True)
      stat_dict = dict()
      for iou in iou_types:
          cocoDt = cocoGt.loadRes(predictions[iou], use_ext=True)
          cocoEval = COCOeval(cocoGt, cocoDt, iouType=iou, use_ext=True)
          cocoEval.evaluate()
          cocoEval.accumulate()
          cocoEval.summarize()
          stat_dict[iou] = build_output_dict(iou, cocoEval.stats, verbose)
      return stat_dict