tensorflow/sagemakercv/data/coco/coco_metric.py [231:318]:
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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
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tensorflow/sagemakercv/data/coco/evaluation.py [45:128]:
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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
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