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- # Copyright (c) 2019 PaddlePaddle Authors. 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.
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import logging
- import numpy as np
- __all__ = ["bbox_overlaps", "box_to_delta"]
- logger = logging.getLogger(__name__)
- def bbox_overlaps(boxes_1, boxes_2):
- '''
- bbox_overlaps
- boxes_1: x1, y, x2, y2
- boxes_2: x1, y, x2, y2
- '''
- assert boxes_1.shape[1] == 4 and boxes_2.shape[1] == 4
- num_1 = boxes_1.shape[0]
- num_2 = boxes_2.shape[0]
- x1_1 = boxes_1[:, 0:1]
- y1_1 = boxes_1[:, 1:2]
- x2_1 = boxes_1[:, 2:3]
- y2_1 = boxes_1[:, 3:4]
- area_1 = (x2_1 - x1_1 + 1) * (y2_1 - y1_1 + 1)
- x1_2 = boxes_2[:, 0].transpose()
- y1_2 = boxes_2[:, 1].transpose()
- x2_2 = boxes_2[:, 2].transpose()
- y2_2 = boxes_2[:, 3].transpose()
- area_2 = (x2_2 - x1_2 + 1) * (y2_2 - y1_2 + 1)
- xx1 = np.maximum(x1_1, x1_2)
- yy1 = np.maximum(y1_1, y1_2)
- xx2 = np.minimum(x2_1, x2_2)
- yy2 = np.minimum(y2_1, y2_2)
- w = np.maximum(0.0, xx2 - xx1 + 1)
- h = np.maximum(0.0, yy2 - yy1 + 1)
- inter = w * h
- ovr = inter / (area_1 + area_2 - inter)
- return ovr
- def box_to_delta(ex_boxes, gt_boxes, weights):
- """ box_to_delta """
- ex_w = ex_boxes[:, 2] - ex_boxes[:, 0] + 1
- ex_h = ex_boxes[:, 3] - ex_boxes[:, 1] + 1
- ex_ctr_x = ex_boxes[:, 0] + 0.5 * ex_w
- ex_ctr_y = ex_boxes[:, 1] + 0.5 * ex_h
- gt_w = gt_boxes[:, 2] - gt_boxes[:, 0] + 1
- gt_h = gt_boxes[:, 3] - gt_boxes[:, 1] + 1
- gt_ctr_x = gt_boxes[:, 0] + 0.5 * gt_w
- gt_ctr_y = gt_boxes[:, 1] + 0.5 * gt_h
- dx = (gt_ctr_x - ex_ctr_x) / ex_w / weights[0]
- dy = (gt_ctr_y - ex_ctr_y) / ex_h / weights[1]
- dw = (np.log(gt_w / ex_w)) / weights[2]
- dh = (np.log(gt_h / ex_h)) / weights[3]
- targets = np.vstack([dx, dy, dw, dh]).transpose()
- return targets
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