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