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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import torch
class BoxCoder(object):
"""
This class encodes and decodes a set of bounding boxes into
the representation used for training the regressors.
"""
def __init__(self, weights, bbox_xform_clip=math.log(1000.0 / 16)):
"""
Arguments:
weights (4-element tuple)
bbox_xform_clip (float)
"""
self.weights = weights
self.bbox_xform_clip = bbox_xform_clip
def encode(self, reference_boxes, proposals):
"""
Encode a set of proposals with respect to some
reference boxes
Arguments:
reference_boxes (Tensor): reference boxes
proposals (Tensor): boxes to be encoded
"""
TO_REMOVE = 1 # TODO remove
ex_widths = proposals[:, 2] - proposals[:, 0] + TO_REMOVE
ex_heights = proposals[:, 3] - proposals[:, 1] + TO_REMOVE
ex_ctr_x = proposals[:, 0] + 0.5 * ex_widths
ex_ctr_y = proposals[:, 1] + 0.5 * ex_heights
gt_widths = reference_boxes[:, 2] - reference_boxes[:, 0] + TO_REMOVE
gt_heights = reference_boxes[:, 3] - reference_boxes[:, 1] + TO_REMOVE
gt_ctr_x = reference_boxes[:, 0] + 0.5 * gt_widths
gt_ctr_y = reference_boxes[:, 1] + 0.5 * gt_heights
wx, wy, ww, wh = self.weights
targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
targets_dw = ww * torch.log(gt_widths / ex_widths)
targets_dh = wh * torch.log(gt_heights / ex_heights)
targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1)
return targets
def decode(self, rel_codes, boxes):
"""
From a set of original boxes and encoded relative box offsets,
get the decoded boxes.
Arguments:
rel_codes (Tensor): encoded boxes
boxes (Tensor): reference boxes.
"""
boxes = boxes.to(rel_codes.dtype)
TO_REMOVE = 1 # TODO remove
widths = boxes[:, 2] - boxes[:, 0] + TO_REMOVE
heights = boxes[:, 3] - boxes[:, 1] + TO_REMOVE
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = self.weights
dx = rel_codes[:, 0::4] / wx
dy = rel_codes[:, 1::4] / wy
dw = rel_codes[:, 2::4] / ww
dh = rel_codes[:, 3::4] / wh
# Prevent sending too large values into torch.exp()
dw = torch.clamp(dw, max=self.bbox_xform_clip)
dh = torch.clamp(dh, max=self.bbox_xform_clip)
pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
pred_w = torch.exp(dw) * widths[:, None]
pred_h = torch.exp(dh) * heights[:, None]
pred_boxes = torch.zeros_like(rel_codes)
# x1
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
# y1
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
# x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
# y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1
return pred_boxes
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