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126 lines
4.2 KiB
126 lines
4.2 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Square box coder.
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Square box coder follows the coding schema described below:
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l = sqrt(h * w)
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la = sqrt(ha * wa)
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ty = (y - ya) / la
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tx = (x - xa) / la
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tl = log(l / la)
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where x, y, w, h denote the box's center coordinates, width, and height,
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respectively. Similarly, xa, ya, wa, ha denote the anchor's center
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coordinates, width and height. tx, ty, tl denote the anchor-encoded
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center, and length, respectively. Because the encoded box is a square, only
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one length is encoded.
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This has shown to provide performance improvements over the Faster RCNN box
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coder when the objects being detected tend to be square (e.g. faces) and when
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the input images are not distorted via resizing.
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"""
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import tensorflow as tf
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from object_detection.core import box_coder
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from object_detection.core import box_list
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EPSILON = 1e-8
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class SquareBoxCoder(box_coder.BoxCoder):
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"""Encodes a 3-scalar representation of a square box."""
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def __init__(self, scale_factors=None):
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"""Constructor for SquareBoxCoder.
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Args:
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scale_factors: List of 3 positive scalars to scale ty, tx, and tl.
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If set to None, does not perform scaling. For faster RCNN,
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the open-source implementation recommends using [10.0, 10.0, 5.0].
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Raises:
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ValueError: If scale_factors is not length 3 or contains values less than
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or equal to 0.
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"""
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if scale_factors:
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if len(scale_factors) != 3:
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raise ValueError('The argument scale_factors must be a list of length '
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'3.')
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if any(scalar <= 0 for scalar in scale_factors):
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raise ValueError('The values in scale_factors must all be greater '
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'than 0.')
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self._scale_factors = scale_factors
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@property
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def code_size(self):
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return 3
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def _encode(self, boxes, anchors):
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"""Encodes a box collection with respect to an anchor collection.
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Args:
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boxes: BoxList holding N boxes to be encoded.
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anchors: BoxList of anchors.
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Returns:
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a tensor representing N anchor-encoded boxes of the format
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[ty, tx, tl].
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"""
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# Convert anchors to the center coordinate representation.
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ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes()
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la = tf.sqrt(ha * wa)
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ycenter, xcenter, h, w = boxes.get_center_coordinates_and_sizes()
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l = tf.sqrt(h * w)
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# Avoid NaN in division and log below.
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la += EPSILON
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l += EPSILON
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tx = (xcenter - xcenter_a) / la
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ty = (ycenter - ycenter_a) / la
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tl = tf.log(l / la)
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# Scales location targets for joint training.
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if self._scale_factors:
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ty *= self._scale_factors[0]
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tx *= self._scale_factors[1]
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tl *= self._scale_factors[2]
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return tf.transpose(tf.stack([ty, tx, tl]))
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def _decode(self, rel_codes, anchors):
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"""Decodes relative codes to boxes.
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Args:
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rel_codes: a tensor representing N anchor-encoded boxes.
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anchors: BoxList of anchors.
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Returns:
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boxes: BoxList holding N bounding boxes.
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"""
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ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes()
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la = tf.sqrt(ha * wa)
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ty, tx, tl = tf.unstack(tf.transpose(rel_codes))
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if self._scale_factors:
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ty /= self._scale_factors[0]
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tx /= self._scale_factors[1]
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tl /= self._scale_factors[2]
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l = tf.exp(tl) * la
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ycenter = ty * la + ycenter_a
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xcenter = tx * la + xcenter_a
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ymin = ycenter - l / 2.
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xmin = xcenter - l / 2.
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ymax = ycenter + l / 2.
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xmax = xcenter + l / 2.
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return box_list.BoxList(tf.transpose(tf.stack([ymin, xmin, ymax, xmax])))
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