forked from PulseFocusPlatform/PulseFocusPlatform
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
# Copyright (c) 2021 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 paddle
|
|
from ppdet.core.workspace import register, create
|
|
from .meta_arch import BaseArch
|
|
from ppdet.modeling.mot.utils import Detection, get_crops, scale_coords, clip_box
|
|
|
|
__all__ = ['DeepSORT']
|
|
|
|
|
|
@register
|
|
class DeepSORT(BaseArch):
|
|
"""
|
|
DeepSORT network, see https://arxiv.org/abs/1703.07402
|
|
|
|
Args:
|
|
detector (object): detector model instance
|
|
reid (object): reid model instance
|
|
tracker (object): tracker instance
|
|
"""
|
|
__category__ = 'architecture'
|
|
|
|
def __init__(self,
|
|
detector='YOLOv3',
|
|
reid='PCBPyramid',
|
|
tracker='DeepSORTTracker'):
|
|
super(DeepSORT, self).__init__()
|
|
self.detector = detector
|
|
self.reid = reid
|
|
self.tracker = tracker
|
|
|
|
@classmethod
|
|
def from_config(cls, cfg, *args, **kwargs):
|
|
if cfg['detector'] != 'None':
|
|
detector = create(cfg['detector'])
|
|
else:
|
|
detector = None
|
|
reid = create(cfg['reid'])
|
|
tracker = create(cfg['tracker'])
|
|
|
|
return {
|
|
"detector": detector,
|
|
"reid": reid,
|
|
"tracker": tracker,
|
|
}
|
|
|
|
def _forward(self):
|
|
assert 'ori_image' in self.inputs
|
|
load_dets = 'pred_bboxes' in self.inputs and 'pred_scores' in self.inputs
|
|
|
|
ori_image = self.inputs['ori_image']
|
|
input_shape = self.inputs['image'].shape[2:]
|
|
im_shape = self.inputs['im_shape']
|
|
scale_factor = self.inputs['scale_factor']
|
|
|
|
if self.detector and not load_dets:
|
|
outs = self.detector(self.inputs)
|
|
if outs['bbox_num'] > 0:
|
|
pred_bboxes = scale_coords(outs['bbox'][:, 2:], input_shape,
|
|
im_shape, scale_factor)
|
|
pred_scores = outs['bbox'][:, 1:2]
|
|
else:
|
|
pred_bboxes = []
|
|
pred_scores = []
|
|
else:
|
|
pred_bboxes = self.inputs['pred_bboxes']
|
|
pred_scores = self.inputs['pred_scores']
|
|
|
|
if len(pred_bboxes) > 0:
|
|
pred_bboxes = clip_box(pred_bboxes, input_shape, im_shape,
|
|
scale_factor)
|
|
bbox_tlwh = paddle.concat(
|
|
(pred_bboxes[:, 0:2],
|
|
pred_bboxes[:, 2:4] - pred_bboxes[:, 0:2] + 1),
|
|
axis=1)
|
|
|
|
crops, pred_scores = get_crops(
|
|
pred_bboxes, ori_image, pred_scores, w=64, h=192)
|
|
|
|
if len(crops) > 0:
|
|
features = self.reid(paddle.to_tensor(crops))
|
|
detections = [Detection(bbox_tlwh[i], conf, features[i])\
|
|
for i, conf in enumerate(pred_scores)]
|
|
else:
|
|
detections = []
|
|
else:
|
|
detections = []
|
|
|
|
self.tracker.predict()
|
|
online_targets = self.tracker.update(detections)
|
|
|
|
return online_targets
|
|
|
|
def get_pred(self):
|
|
return self._forward()
|