更换文档检测模型
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paddle_detection/ppdet/modeling/bbox_utils.py
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607
paddle_detection/ppdet/modeling/bbox_utils.py
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# Copyright (c) 2020 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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import math
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import paddle
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import numpy as np
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def bbox2delta(src_boxes, tgt_boxes, weights=[1.0, 1.0, 1.0, 1.0]):
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"""Encode bboxes to deltas.
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"""
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src_w = src_boxes[:, 2] - src_boxes[:, 0]
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src_h = src_boxes[:, 3] - src_boxes[:, 1]
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src_ctr_x = src_boxes[:, 0] + 0.5 * src_w
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src_ctr_y = src_boxes[:, 1] + 0.5 * src_h
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tgt_w = tgt_boxes[:, 2] - tgt_boxes[:, 0]
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tgt_h = tgt_boxes[:, 3] - tgt_boxes[:, 1]
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tgt_ctr_x = tgt_boxes[:, 0] + 0.5 * tgt_w
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tgt_ctr_y = tgt_boxes[:, 1] + 0.5 * tgt_h
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wx, wy, ww, wh = weights
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dx = wx * (tgt_ctr_x - src_ctr_x) / src_w
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dy = wy * (tgt_ctr_y - src_ctr_y) / src_h
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dw = ww * paddle.log(tgt_w / src_w)
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dh = wh * paddle.log(tgt_h / src_h)
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deltas = paddle.stack((dx, dy, dw, dh), axis=1)
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return deltas
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def delta2bbox(deltas, boxes, weights=[1.0, 1.0, 1.0, 1.0], max_shape=None):
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"""Decode deltas to boxes. Used in RCNNBox,CascadeHead,RCNNHead,RetinaHead.
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Note: return tensor shape [n,1,4]
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If you want to add a reshape, please add after the calling code instead of here.
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"""
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clip_scale = math.log(1000.0 / 16)
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widths = boxes[:, 2] - boxes[:, 0]
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heights = boxes[:, 3] - boxes[:, 1]
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ctr_x = boxes[:, 0] + 0.5 * widths
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ctr_y = boxes[:, 1] + 0.5 * heights
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wx, wy, ww, wh = weights
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dx = deltas[:, 0::4] / wx
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dy = deltas[:, 1::4] / wy
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dw = deltas[:, 2::4] / ww
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dh = deltas[:, 3::4] / wh
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# Prevent sending too large values into paddle.exp()
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dw = paddle.clip(dw, max=clip_scale)
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dh = paddle.clip(dh, max=clip_scale)
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pred_ctr_x = dx * widths.unsqueeze(1) + ctr_x.unsqueeze(1)
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pred_ctr_y = dy * heights.unsqueeze(1) + ctr_y.unsqueeze(1)
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pred_w = paddle.exp(dw) * widths.unsqueeze(1)
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pred_h = paddle.exp(dh) * heights.unsqueeze(1)
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pred_boxes = []
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pred_boxes.append(pred_ctr_x - 0.5 * pred_w)
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pred_boxes.append(pred_ctr_y - 0.5 * pred_h)
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pred_boxes.append(pred_ctr_x + 0.5 * pred_w)
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pred_boxes.append(pred_ctr_y + 0.5 * pred_h)
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pred_boxes = paddle.stack(pred_boxes, axis=-1)
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if max_shape is not None:
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pred_boxes[..., 0::2] = pred_boxes[..., 0::2].clip(
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min=0, max=max_shape[1])
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pred_boxes[..., 1::2] = pred_boxes[..., 1::2].clip(
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min=0, max=max_shape[0])
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return pred_boxes
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def bbox2delta_v2(src_boxes,
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tgt_boxes,
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delta_mean=[0.0, 0.0, 0.0, 0.0],
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delta_std=[1.0, 1.0, 1.0, 1.0]):
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"""Encode bboxes to deltas.
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Modified from bbox2delta() which just use weight parameters to multiply deltas.
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"""
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src_w = src_boxes[:, 2] - src_boxes[:, 0]
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src_h = src_boxes[:, 3] - src_boxes[:, 1]
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src_ctr_x = src_boxes[:, 0] + 0.5 * src_w
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src_ctr_y = src_boxes[:, 1] + 0.5 * src_h
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tgt_w = tgt_boxes[:, 2] - tgt_boxes[:, 0]
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tgt_h = tgt_boxes[:, 3] - tgt_boxes[:, 1]
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tgt_ctr_x = tgt_boxes[:, 0] + 0.5 * tgt_w
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tgt_ctr_y = tgt_boxes[:, 1] + 0.5 * tgt_h
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dx = (tgt_ctr_x - src_ctr_x) / src_w
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dy = (tgt_ctr_y - src_ctr_y) / src_h
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dw = paddle.log(tgt_w / src_w)
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dh = paddle.log(tgt_h / src_h)
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deltas = paddle.stack((dx, dy, dw, dh), axis=1)
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deltas = (
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deltas - paddle.to_tensor(delta_mean)) / paddle.to_tensor(delta_std)
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return deltas
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def delta2bbox_v2(deltas,
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boxes,
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delta_mean=[0.0, 0.0, 0.0, 0.0],
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delta_std=[1.0, 1.0, 1.0, 1.0],
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max_shape=None,
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ctr_clip=32.0):
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"""Decode deltas to bboxes.
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Modified from delta2bbox() which just use weight parameters to be divided by deltas.
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Used in YOLOFHead.
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Note: return tensor shape [n,1,4]
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If you want to add a reshape, please add after the calling code instead of here.
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"""
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clip_scale = math.log(1000.0 / 16)
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widths = boxes[:, 2] - boxes[:, 0]
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heights = boxes[:, 3] - boxes[:, 1]
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ctr_x = boxes[:, 0] + 0.5 * widths
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ctr_y = boxes[:, 1] + 0.5 * heights
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deltas = deltas * paddle.to_tensor(delta_std) + paddle.to_tensor(delta_mean)
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dx = deltas[:, 0::4]
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dy = deltas[:, 1::4]
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dw = deltas[:, 2::4]
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dh = deltas[:, 3::4]
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# Prevent sending too large values into paddle.exp()
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dx = dx * widths.unsqueeze(1)
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dy = dy * heights.unsqueeze(1)
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if ctr_clip is not None:
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dx = paddle.clip(dx, max=ctr_clip, min=-ctr_clip)
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dy = paddle.clip(dy, max=ctr_clip, min=-ctr_clip)
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dw = paddle.clip(dw, max=clip_scale)
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dh = paddle.clip(dh, max=clip_scale)
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else:
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dw = dw.clip(min=-clip_scale, max=clip_scale)
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dh = dh.clip(min=-clip_scale, max=clip_scale)
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pred_ctr_x = dx + ctr_x.unsqueeze(1)
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pred_ctr_y = dy + ctr_y.unsqueeze(1)
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pred_w = paddle.exp(dw) * widths.unsqueeze(1)
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pred_h = paddle.exp(dh) * heights.unsqueeze(1)
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pred_boxes = []
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pred_boxes.append(pred_ctr_x - 0.5 * pred_w)
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pred_boxes.append(pred_ctr_y - 0.5 * pred_h)
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pred_boxes.append(pred_ctr_x + 0.5 * pred_w)
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pred_boxes.append(pred_ctr_y + 0.5 * pred_h)
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pred_boxes = paddle.stack(pred_boxes, axis=-1)
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if max_shape is not None:
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pred_boxes[..., 0::2] = pred_boxes[..., 0::2].clip(
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min=0, max=max_shape[1])
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pred_boxes[..., 1::2] = pred_boxes[..., 1::2].clip(
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min=0, max=max_shape[0])
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return pred_boxes
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def expand_bbox(bboxes, scale):
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w_half = (bboxes[:, 2] - bboxes[:, 0]) * .5
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h_half = (bboxes[:, 3] - bboxes[:, 1]) * .5
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x_c = (bboxes[:, 2] + bboxes[:, 0]) * .5
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y_c = (bboxes[:, 3] + bboxes[:, 1]) * .5
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w_half *= scale
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h_half *= scale
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bboxes_exp = np.zeros(bboxes.shape, dtype=np.float32)
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bboxes_exp[:, 0] = x_c - w_half
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bboxes_exp[:, 2] = x_c + w_half
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bboxes_exp[:, 1] = y_c - h_half
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bboxes_exp[:, 3] = y_c + h_half
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return bboxes_exp
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def clip_bbox(boxes, im_shape):
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h, w = im_shape[0], im_shape[1]
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x1 = boxes[:, 0].clip(0, w)
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y1 = boxes[:, 1].clip(0, h)
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x2 = boxes[:, 2].clip(0, w)
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y2 = boxes[:, 3].clip(0, h)
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return paddle.stack([x1, y1, x2, y2], axis=1)
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def nonempty_bbox(boxes, min_size=0, return_mask=False):
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w = boxes[:, 2] - boxes[:, 0]
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h = boxes[:, 3] - boxes[:, 1]
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mask = paddle.logical_and(h > min_size, w > min_size)
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if return_mask:
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return mask
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keep = paddle.nonzero(mask).flatten()
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return keep
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def bbox_area(boxes):
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return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
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def bbox_overlaps(boxes1, boxes2):
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"""
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Calculate overlaps between boxes1 and boxes2
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Args:
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boxes1 (Tensor): boxes with shape [M, 4]
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boxes2 (Tensor): boxes with shape [N, 4]
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Return:
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overlaps (Tensor): overlaps between boxes1 and boxes2 with shape [M, N]
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"""
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M = boxes1.shape[0]
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N = boxes2.shape[0]
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if M * N == 0:
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return paddle.zeros([M, N], dtype='float32')
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area1 = bbox_area(boxes1)
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area2 = bbox_area(boxes2)
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xy_max = paddle.minimum(
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paddle.unsqueeze(boxes1, 1)[:, :, 2:], boxes2[:, 2:])
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xy_min = paddle.maximum(
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paddle.unsqueeze(boxes1, 1)[:, :, :2], boxes2[:, :2])
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width_height = xy_max - xy_min
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width_height = width_height.clip(min=0)
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inter = width_height.prod(axis=2)
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overlaps = paddle.where(inter > 0, inter /
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(paddle.unsqueeze(area1, 1) + area2 - inter),
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paddle.zeros_like(inter))
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return overlaps
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def batch_bbox_overlaps(bboxes1,
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bboxes2,
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mode='iou',
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is_aligned=False,
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eps=1e-6):
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"""Calculate overlap between two set of bboxes.
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If ``is_aligned `` is ``False``, then calculate the overlaps between each
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bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned
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pair of bboxes1 and bboxes2.
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Args:
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bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty.
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bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty.
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B indicates the batch dim, in shape (B1, B2, ..., Bn).
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If ``is_aligned `` is ``True``, then m and n must be equal.
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mode (str): "iou" (intersection over union) or "iof" (intersection over
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foreground).
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is_aligned (bool, optional): If True, then m and n must be equal.
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Default False.
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eps (float, optional): A value added to the denominator for numerical
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stability. Default 1e-6.
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Returns:
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Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
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"""
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assert mode in ['iou', 'iof', 'giou'], 'Unsupported mode {}'.format(mode)
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# Either the boxes are empty or the length of boxes's last dimenstion is 4
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assert (bboxes1.shape[-1] == 4 or bboxes1.shape[0] == 0)
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assert (bboxes2.shape[-1] == 4 or bboxes2.shape[0] == 0)
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# Batch dim must be the same
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# Batch dim: (B1, B2, ... Bn)
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assert bboxes1.shape[:-2] == bboxes2.shape[:-2]
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batch_shape = bboxes1.shape[:-2]
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rows = bboxes1.shape[-2] if bboxes1.shape[0] > 0 else 0
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cols = bboxes2.shape[-2] if bboxes2.shape[0] > 0 else 0
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if is_aligned:
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assert rows == cols
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if rows * cols == 0:
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if is_aligned:
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return paddle.full(batch_shape + (rows, ), 1)
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else:
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return paddle.full(batch_shape + (rows, cols), 1)
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area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (bboxes1[:, 3] - bboxes1[:, 1])
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area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (bboxes2[:, 3] - bboxes2[:, 1])
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if is_aligned:
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lt = paddle.maximum(bboxes1[:, :2], bboxes2[:, :2]) # [B, rows, 2]
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rb = paddle.minimum(bboxes1[:, 2:], bboxes2[:, 2:]) # [B, rows, 2]
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wh = (rb - lt).clip(min=0) # [B, rows, 2]
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overlap = wh[:, 0] * wh[:, 1]
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if mode in ['iou', 'giou']:
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union = area1 + area2 - overlap
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else:
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union = area1
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if mode == 'giou':
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enclosed_lt = paddle.minimum(bboxes1[:, :2], bboxes2[:, :2])
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enclosed_rb = paddle.maximum(bboxes1[:, 2:], bboxes2[:, 2:])
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else:
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lt = paddle.maximum(bboxes1[:, :2].reshape([rows, 1, 2]),
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bboxes2[:, :2]) # [B, rows, cols, 2]
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rb = paddle.minimum(bboxes1[:, 2:].reshape([rows, 1, 2]),
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bboxes2[:, 2:]) # [B, rows, cols, 2]
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wh = (rb - lt).clip(min=0) # [B, rows, cols, 2]
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overlap = wh[:, :, 0] * wh[:, :, 1]
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if mode in ['iou', 'giou']:
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union = area1.reshape([rows,1]) \
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+ area2.reshape([1,cols]) - overlap
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else:
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union = area1[:, None]
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if mode == 'giou':
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enclosed_lt = paddle.minimum(bboxes1[:, :2].reshape([rows, 1, 2]),
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bboxes2[:, :2])
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enclosed_rb = paddle.maximum(bboxes1[:, 2:].reshape([rows, 1, 2]),
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bboxes2[:, 2:])
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eps = paddle.to_tensor([eps])
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union = paddle.maximum(union, eps)
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ious = overlap / union
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if mode in ['iou', 'iof']:
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return ious
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# calculate gious
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enclose_wh = (enclosed_rb - enclosed_lt).clip(min=0)
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enclose_area = enclose_wh[:, :, 0] * enclose_wh[:, :, 1]
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enclose_area = paddle.maximum(enclose_area, eps)
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gious = ious - (enclose_area - union) / enclose_area
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return 1 - gious
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def xywh2xyxy(box):
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x, y, w, h = box
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x1 = x - w * 0.5
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y1 = y - h * 0.5
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x2 = x + w * 0.5
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y2 = y + h * 0.5
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return [x1, y1, x2, y2]
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def make_grid(h, w, dtype):
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yv, xv = paddle.meshgrid([paddle.arange(h), paddle.arange(w)])
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return paddle.stack((xv, yv), 2).cast(dtype=dtype)
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def decode_yolo(box, anchor, downsample_ratio):
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"""decode yolo box
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Args:
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box (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
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anchor (list): anchor with the shape [na, 2]
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downsample_ratio (int): downsample ratio, default 32
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scale (float): scale, default 1.
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Return:
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box (list): decoded box, [x, y, w, h], all have the shape [b, na, h, w, 1]
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"""
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x, y, w, h = box
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na, grid_h, grid_w = x.shape[1:4]
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grid = make_grid(grid_h, grid_w, x.dtype).reshape((1, 1, grid_h, grid_w, 2))
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x1 = (x + grid[:, :, :, :, 0:1]) / grid_w
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y1 = (y + grid[:, :, :, :, 1:2]) / grid_h
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anchor = paddle.to_tensor(anchor, dtype=x.dtype)
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anchor = anchor.reshape((1, na, 1, 1, 2))
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w1 = paddle.exp(w) * anchor[:, :, :, :, 0:1] / (downsample_ratio * grid_w)
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h1 = paddle.exp(h) * anchor[:, :, :, :, 1:2] / (downsample_ratio * grid_h)
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return [x1, y1, w1, h1]
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def batch_iou_similarity(box1, box2, eps=1e-9):
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"""Calculate iou of box1 and box2 in batch
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Args:
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box1 (Tensor): box with the shape [N, M1, 4]
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box2 (Tensor): box with the shape [N, M2, 4]
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Return:
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iou (Tensor): iou between box1 and box2 with the shape [N, M1, M2]
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"""
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box1 = box1.unsqueeze(2) # [N, M1, 4] -> [N, M1, 1, 4]
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box2 = box2.unsqueeze(1) # [N, M2, 4] -> [N, 1, M2, 4]
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px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
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gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
|
||||
x1y1 = paddle.maximum(px1y1, gx1y1)
|
||||
x2y2 = paddle.minimum(px2y2, gx2y2)
|
||||
overlap = (x2y2 - x1y1).clip(0).prod(-1)
|
||||
area1 = (px2y2 - px1y1).clip(0).prod(-1)
|
||||
area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
|
||||
union = area1 + area2 - overlap + eps
|
||||
return overlap / union
|
||||
|
||||
|
||||
def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9):
|
||||
"""calculate the iou of box1 and box2
|
||||
|
||||
Args:
|
||||
box1 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
|
||||
box2 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
|
||||
giou (bool): whether use giou or not, default False
|
||||
diou (bool): whether use diou or not, default False
|
||||
ciou (bool): whether use ciou or not, default False
|
||||
eps (float): epsilon to avoid divide by zero
|
||||
|
||||
Return:
|
||||
iou (Tensor): iou of box1 and box1, with the shape [b, na, h, w, 1]
|
||||
"""
|
||||
px1, py1, px2, py2 = box1
|
||||
gx1, gy1, gx2, gy2 = box2
|
||||
x1 = paddle.maximum(px1, gx1)
|
||||
y1 = paddle.maximum(py1, gy1)
|
||||
x2 = paddle.minimum(px2, gx2)
|
||||
y2 = paddle.minimum(py2, gy2)
|
||||
|
||||
overlap = ((x2 - x1).clip(0)) * ((y2 - y1).clip(0))
|
||||
|
||||
area1 = (px2 - px1) * (py2 - py1)
|
||||
area1 = area1.clip(0)
|
||||
|
||||
area2 = (gx2 - gx1) * (gy2 - gy1)
|
||||
area2 = area2.clip(0)
|
||||
|
||||
union = area1 + area2 - overlap + eps
|
||||
iou = overlap / union
|
||||
|
||||
if giou or ciou or diou:
|
||||
# convex w, h
|
||||
cw = paddle.maximum(px2, gx2) - paddle.minimum(px1, gx1)
|
||||
ch = paddle.maximum(py2, gy2) - paddle.minimum(py1, gy1)
|
||||
if giou:
|
||||
c_area = cw * ch + eps
|
||||
return iou - (c_area - union) / c_area
|
||||
else:
|
||||
# convex diagonal squared
|
||||
c2 = cw**2 + ch**2 + eps
|
||||
# center distance
|
||||
rho2 = ((px1 + px2 - gx1 - gx2)**2 + (py1 + py2 - gy1 - gy2)**2) / 4
|
||||
if diou:
|
||||
return iou - rho2 / c2
|
||||
else:
|
||||
w1, h1 = px2 - px1, py2 - py1 + eps
|
||||
w2, h2 = gx2 - gx1, gy2 - gy1 + eps
|
||||
delta = paddle.atan(w1 / h1) - paddle.atan(w2 / h2)
|
||||
v = (4 / math.pi**2) * paddle.pow(delta, 2)
|
||||
alpha = v / (1 + eps - iou + v)
|
||||
alpha.stop_gradient = True
|
||||
return iou - (rho2 / c2 + v * alpha)
|
||||
else:
|
||||
return iou
|
||||
|
||||
|
||||
def bbox_iou_np_expand(box1, box2, x1y1x2y2=True, eps=1e-16):
|
||||
"""
|
||||
Calculate the iou of box1 and box2 with numpy.
|
||||
|
||||
Args:
|
||||
box1 (ndarray): [N, 4]
|
||||
box2 (ndarray): [M, 4], usually N != M
|
||||
x1y1x2y2 (bool): whether in x1y1x2y2 stype, default True
|
||||
eps (float): epsilon to avoid divide by zero
|
||||
Return:
|
||||
iou (ndarray): iou of box1 and box2, [N, M]
|
||||
"""
|
||||
N, M = len(box1), len(box2) # usually N != M
|
||||
if x1y1x2y2:
|
||||
b1_x1, b1_y1 = box1[:, 0], box1[:, 1]
|
||||
b1_x2, b1_y2 = box1[:, 2], box1[:, 3]
|
||||
b2_x1, b2_y1 = box2[:, 0], box2[:, 1]
|
||||
b2_x2, b2_y2 = box2[:, 2], box2[:, 3]
|
||||
else:
|
||||
# cxcywh style
|
||||
# Transform from center and width to exact coordinates
|
||||
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
|
||||
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
|
||||
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
|
||||
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
|
||||
|
||||
# get the coordinates of the intersection rectangle
|
||||
inter_rect_x1 = np.zeros((N, M), dtype=np.float32)
|
||||
inter_rect_y1 = np.zeros((N, M), dtype=np.float32)
|
||||
inter_rect_x2 = np.zeros((N, M), dtype=np.float32)
|
||||
inter_rect_y2 = np.zeros((N, M), dtype=np.float32)
|
||||
for i in range(len(box2)):
|
||||
inter_rect_x1[:, i] = np.maximum(b1_x1, b2_x1[i])
|
||||
inter_rect_y1[:, i] = np.maximum(b1_y1, b2_y1[i])
|
||||
inter_rect_x2[:, i] = np.minimum(b1_x2, b2_x2[i])
|
||||
inter_rect_y2[:, i] = np.minimum(b1_y2, b2_y2[i])
|
||||
# Intersection area
|
||||
inter_area = np.maximum(inter_rect_x2 - inter_rect_x1, 0) * np.maximum(
|
||||
inter_rect_y2 - inter_rect_y1, 0)
|
||||
# Union Area
|
||||
b1_area = np.repeat(
|
||||
((b1_x2 - b1_x1) * (b1_y2 - b1_y1)).reshape(-1, 1), M, axis=-1)
|
||||
b2_area = np.repeat(
|
||||
((b2_x2 - b2_x1) * (b2_y2 - b2_y1)).reshape(1, -1), N, axis=0)
|
||||
|
||||
ious = inter_area / (b1_area + b2_area - inter_area + eps)
|
||||
return ious
|
||||
|
||||
|
||||
def bbox2distance(points, bbox, max_dis=None, eps=0.1):
|
||||
"""Decode bounding box based on distances.
|
||||
Args:
|
||||
points (Tensor): Shape (n, 2), [x, y].
|
||||
bbox (Tensor): Shape (n, 4), "xyxy" format
|
||||
max_dis (float): Upper bound of the distance.
|
||||
eps (float): a small value to ensure target < max_dis, instead <=
|
||||
Returns:
|
||||
Tensor: Decoded distances.
|
||||
"""
|
||||
left = points[:, 0] - bbox[:, 0]
|
||||
top = points[:, 1] - bbox[:, 1]
|
||||
right = bbox[:, 2] - points[:, 0]
|
||||
bottom = bbox[:, 3] - points[:, 1]
|
||||
if max_dis is not None:
|
||||
left = left.clip(min=0, max=max_dis - eps)
|
||||
top = top.clip(min=0, max=max_dis - eps)
|
||||
right = right.clip(min=0, max=max_dis - eps)
|
||||
bottom = bottom.clip(min=0, max=max_dis - eps)
|
||||
return paddle.stack([left, top, right, bottom], -1)
|
||||
|
||||
|
||||
def distance2bbox(points, distance, max_shape=None):
|
||||
"""Decode distance prediction to bounding box.
|
||||
Args:
|
||||
points (Tensor): Shape (n, 2), [x, y].
|
||||
distance (Tensor): Distance from the given point to 4
|
||||
boundaries (left, top, right, bottom).
|
||||
max_shape (tuple): Shape of the image.
|
||||
Returns:
|
||||
Tensor: Decoded bboxes.
|
||||
"""
|
||||
x1 = points[:, 0] - distance[:, 0]
|
||||
y1 = points[:, 1] - distance[:, 1]
|
||||
x2 = points[:, 0] + distance[:, 2]
|
||||
y2 = points[:, 1] + distance[:, 3]
|
||||
if max_shape is not None:
|
||||
x1 = x1.clip(min=0, max=max_shape[1])
|
||||
y1 = y1.clip(min=0, max=max_shape[0])
|
||||
x2 = x2.clip(min=0, max=max_shape[1])
|
||||
y2 = y2.clip(min=0, max=max_shape[0])
|
||||
return paddle.stack([x1, y1, x2, y2], -1)
|
||||
|
||||
|
||||
def bbox_center(boxes):
|
||||
"""Get bbox centers from boxes.
|
||||
Args:
|
||||
boxes (Tensor): boxes with shape (..., 4), "xmin, ymin, xmax, ymax" format.
|
||||
Returns:
|
||||
Tensor: boxes centers with shape (..., 2), "cx, cy" format.
|
||||
"""
|
||||
boxes_cx = (boxes[..., 0] + boxes[..., 2]) / 2
|
||||
boxes_cy = (boxes[..., 1] + boxes[..., 3]) / 2
|
||||
return paddle.stack([boxes_cx, boxes_cy], axis=-1)
|
||||
|
||||
|
||||
def batch_distance2bbox(points, distance, max_shapes=None):
|
||||
"""Decode distance prediction to bounding box for batch.
|
||||
Args:
|
||||
points (Tensor): [B, ..., 2], "xy" format
|
||||
distance (Tensor): [B, ..., 4], "ltrb" format
|
||||
max_shapes (Tensor): [B, 2], "h,w" format, Shape of the image.
|
||||
Returns:
|
||||
Tensor: Decoded bboxes, "x1y1x2y2" format.
|
||||
"""
|
||||
lt, rb = paddle.split(distance, 2, -1)
|
||||
# while tensor add parameters, parameters should be better placed on the second place
|
||||
x1y1 = -lt + points
|
||||
x2y2 = rb + points
|
||||
out_bbox = paddle.concat([x1y1, x2y2], -1)
|
||||
if max_shapes is not None:
|
||||
max_shapes = max_shapes.flip(-1).tile([1, 2])
|
||||
delta_dim = out_bbox.ndim - max_shapes.ndim
|
||||
for _ in range(delta_dim):
|
||||
max_shapes.unsqueeze_(1)
|
||||
out_bbox = paddle.where(out_bbox < max_shapes, out_bbox, max_shapes)
|
||||
out_bbox = paddle.where(out_bbox > 0, out_bbox,
|
||||
paddle.zeros_like(out_bbox))
|
||||
return out_bbox
|
||||
|
||||
|
||||
def iou_similarity(box1, box2, eps=1e-10):
|
||||
"""Calculate iou of box1 and box2
|
||||
|
||||
Args:
|
||||
box1 (Tensor): box with the shape [M1, 4]
|
||||
box2 (Tensor): box with the shape [M2, 4]
|
||||
|
||||
Return:
|
||||
iou (Tensor): iou between box1 and box2 with the shape [M1, M2]
|
||||
"""
|
||||
box1 = box1.unsqueeze(1) # [M1, 4] -> [M1, 1, 4]
|
||||
box2 = box2.unsqueeze(0) # [M2, 4] -> [1, M2, 4]
|
||||
px1y1, px2y2 = box1[:, :, 0:2], box1[:, :, 2:4]
|
||||
gx1y1, gx2y2 = box2[:, :, 0:2], box2[:, :, 2:4]
|
||||
x1y1 = paddle.maximum(px1y1, gx1y1)
|
||||
x2y2 = paddle.minimum(px2y2, gx2y2)
|
||||
overlap = (x2y2 - x1y1).clip(0).prod(-1)
|
||||
area1 = (px2y2 - px1y1).clip(0).prod(-1)
|
||||
area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
|
||||
union = area1 + area2 - overlap + eps
|
||||
return overlap / union
|
||||
Reference in New Issue
Block a user