# tf.raw_ops.GenerateBoundingBoxProposals

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This op produces Region of Interests from given bounding boxes(bbox_deltas) encoded wrt anchors according to eq.2 in arXiv:1506.01497

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.GenerateBoundingBoxProposals`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/GenerateBoundingBoxProposals)

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    tf.raw_ops.GenerateBoundingBoxProposals(
        scores,
        bbox_deltas,
        image_info,
        anchors,
        nms_threshold,
        pre_nms_topn,
        min_size,
        post_nms_topn=300,
        name=None
    )

      The op selects top `pre_nms_topn` scoring boxes, decodes them with respect to anchors,
      applies non-maximal suppression on overlapping boxes with higher than
      `nms_threshold` intersection-over-union (iou) value, discarding boxes where shorter
      side is less than `min_size`.
      Inputs:
      `scores`: A 4D tensor of shape [Batch, Height, Width, Num Anchors] containing the scores per anchor at given position
      `bbox_deltas`: is a tensor of shape [Batch, Height, Width, 4 x Num Anchors] boxes encoded to each anchor
      `anchors`: A 1D tensor of shape [4 x Num Anchors], representing the anchors.
      Outputs:
      `rois`: output RoIs, a 3D tensor of shape [Batch, post_nms_topn, 4], padded by 0 if less than post_nms_topn candidates found.
      `roi_probabilities`: probability scores of each roi in 'rois', a 2D tensor of shape [Batch,post_nms_topn], padded with 0 if needed, sorted by scores.

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| ## Args ||
|---|---|
| `scores` | A `Tensor` of type `float32`. A 4-D float tensor of shape `[num_images, height, width, num_achors]` containing scores of the boxes for given anchors, can be unsorted. |
| `bbox_deltas` | A `Tensor` of type `float32`. A 4-D float tensor of shape `[num_images, height, width, 4 x num_anchors]`. encoding boxes with respec to each anchor. Coordinates are given in the form \[dy, dx, dh, dw\]. |
| `image_info` | A `Tensor` of type `float32`. A 2-D float tensor of shape `[num_images, 5]` containing image information Height, Width, Scale. |
| `anchors` | A `Tensor` of type `float32`. A 2-D float tensor of shape `[num_anchors, 4]` describing the anchor boxes. Boxes are formatted in the form \[y1, x1, y2, x2\]. |
| `nms_threshold` | A `Tensor` of type `float32`. A scalar float tensor for non-maximal-suppression threshold. |
| `pre_nms_topn` | A `Tensor` of type `int32`. A scalar int tensor for the number of top scoring boxes to be used as input. |
| `min_size` | A `Tensor` of type `float32`. A scalar float tensor. Any box that has a smaller size than min_size will be discarded. |
| `post_nms_topn` | An optional `int`. Defaults to `300`. An integer. Maximum number of rois in the output. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A tuple of `Tensor` objects (rois, roi_probabilities). ||
| `rois` | A `Tensor` of type `float32`. |
| `roi_probabilities` | A `Tensor` of type `float32`. |

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