forked from PulseFocusPlatform/PulseFocusPlatform
82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
|
# 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
|