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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple
import numpy as np
import torch
from mmengine.structures import InstanceData
try:
import motmetrics
from motmetrics.lap import linear_sum_assignment
except ImportError:
motmetrics = None
from torch import Tensor
from mmdet.models.utils import imrenormalize
from mmdet.registry import MODELS
from mmdet.structures import TrackDataSample
from mmdet.structures.bbox import bbox_overlaps, bbox_xyxy_to_cxcyah
from mmdet.utils import OptConfigType
from .sort_tracker import SORTTracker
def cosine_distance(x: Tensor, y: Tensor) -> np.ndarray:
"""compute the cosine distance.
Args:
x (Tensor): embeddings with shape (N,C).
y (Tensor): embeddings with shape (M,C).
Returns:
ndarray: cosine distance with shape (N,M).
"""
x = x.cpu().numpy()
y = y.cpu().numpy()
x = x / np.linalg.norm(x, axis=1, keepdims=True)
y = y / np.linalg.norm(y, axis=1, keepdims=True)
dists = 1. - np.dot(x, y.T)
return dists
@MODELS.register_module()
class StrongSORTTracker(SORTTracker):
"""Tracker for StrongSORT.
Args:
obj_score_thr (float, optional): Threshold to filter the objects.
Defaults to 0.6.
motion (dict): Configuration of motion. Defaults to None.
reid (dict, optional): Configuration for the ReID model.
- num_samples (int, optional): Number of samples to calculate the
feature embeddings of a track. Default to None.
- image_scale (tuple, optional): Input scale of the ReID model.
Default to (256, 128).
- img_norm_cfg (dict, optional): Configuration to normalize the
input. Default to None.
- match_score_thr (float, optional): Similarity threshold for the
matching process. Default to 0.3.
- motion_weight (float, optional): the weight of the motion cost.
Defaults to 0.02.
match_iou_thr (float, optional): Threshold of the IoU matching process.
Defaults to 0.7.
num_tentatives (int, optional): Number of continuous frames to confirm
a track. Defaults to 2.
"""
def __init__(self,
motion: Optional[dict] = None,
obj_score_thr: float = 0.6,
reid: dict = dict(
num_samples=None,
img_scale=(256, 128),
img_norm_cfg=None,
match_score_thr=0.3,
motion_weight=0.02),
match_iou_thr: float = 0.7,
num_tentatives: int = 2,
**kwargs):
if motmetrics is None:
raise RuntimeError('motmetrics is not installed,\
please install it by: pip install motmetrics')
super().__init__(motion, obj_score_thr, reid, match_iou_thr,
num_tentatives, **kwargs)
def update_track(self, id: int, obj: Tuple[Tensor]) -> None:
"""Update a track."""
for k, v in zip(self.memo_items, obj):
v = v[None]
if self.momentums is not None and k in self.momentums:
m = self.momentums[k]
self.tracks[id][k] = (1 - m) * self.tracks[id][k] + m * v
else:
self.tracks[id][k].append(v)
if self.tracks[id].tentative:
if len(self.tracks[id]['bboxes']) >= self.num_tentatives:
self.tracks[id].tentative = False
bbox = bbox_xyxy_to_cxcyah(self.tracks[id].bboxes[-1]) # size = (1, 4)
assert bbox.ndim == 2 and bbox.shape[0] == 1
bbox = bbox.squeeze(0).cpu().numpy()
score = float(self.tracks[id].scores[-1].cpu())
self.tracks[id].mean, self.tracks[id].covariance = self.kf.update(
self.tracks[id].mean, self.tracks[id].covariance, bbox, score)
def track(self,
model: torch.nn.Module,
img: Tensor,
data_sample: TrackDataSample,
data_preprocessor: OptConfigType = None,
rescale: bool = False,
**kwargs) -> InstanceData:
"""Tracking forward function.
Args:
model (nn.Module): MOT model.
img (Tensor): of shape (T, C, H, W) encoding input image.
Typically these should be mean centered and std scaled.
The T denotes the number of key images and usually is 1 in
SORT method.
feats (list[Tensor]): Multi level feature maps of `img`.
data_sample (:obj:`TrackDataSample`): The data sample.
It includes information such as `pred_det_instances`.
data_preprocessor (dict or ConfigDict, optional): The pre-process
config of :class:`TrackDataPreprocessor`. it usually includes,
``pad_size_divisor``, ``pad_value``, ``mean`` and ``std``.
rescale (bool, optional): If True, the bounding boxes should be
rescaled to fit the original scale of the image. Defaults to
False.
Returns:
:obj:`InstanceData`: Tracking results of the input images.
Each InstanceData usually contains ``bboxes``, ``labels``,
``scores`` and ``instances_id``.
"""
metainfo = data_sample.metainfo
bboxes = data_sample.pred_instances.bboxes
labels = data_sample.pred_instances.labels
scores = data_sample.pred_instances.scores
frame_id = metainfo.get('frame_id', -1)
if frame_id == 0:
self.reset()
if not hasattr(self, 'kf'):
self.kf = self.motion
if self.with_reid:
if self.reid.get('img_norm_cfg', False):
img_norm_cfg = dict(
mean=data_preprocessor.get('mean', [0, 0, 0]),
std=data_preprocessor.get('std', [1, 1, 1]),
to_bgr=data_preprocessor.get('rgb_to_bgr', False))
reid_img = imrenormalize(img, img_norm_cfg,
self.reid['img_norm_cfg'])
else:
reid_img = img.clone()
valid_inds = scores > self.obj_score_thr
bboxes = bboxes[valid_inds]
labels = labels[valid_inds]
scores = scores[valid_inds]
if self.empty or bboxes.size(0) == 0:
num_new_tracks = bboxes.size(0)
ids = torch.arange(
self.num_tracks,
self.num_tracks + num_new_tracks,
dtype=torch.long).to(bboxes.device)
self.num_tracks += num_new_tracks
if self.with_reid:
crops = self.crop_imgs(reid_img, metainfo, bboxes.clone(),
rescale)
if crops.size(0) > 0:
embeds = model.reid(crops, mode='tensor')
else:
embeds = crops.new_zeros((0, model.reid.head.out_channels))
else:
ids = torch.full((bboxes.size(0), ), -1,
dtype=torch.long).to(bboxes.device)
# motion
if model.with_cmc:
num_samples = 1
self.tracks = model.cmc.track(self.last_img, img, self.tracks,
num_samples, frame_id, metainfo)
self.tracks, motion_dists = self.motion.track(
self.tracks, bbox_xyxy_to_cxcyah(bboxes))
active_ids = self.confirmed_ids
if self.with_reid:
crops = self.crop_imgs(reid_img, metainfo, bboxes.clone(),
rescale)
embeds = model.reid(crops, mode='tensor')
# reid
if len(active_ids) > 0:
track_embeds = self.get(
'embeds',
active_ids,
self.reid.get('num_samples', None),
behavior='mean')
reid_dists = cosine_distance(track_embeds, embeds)
valid_inds = [list(self.ids).index(_) for _ in active_ids]
reid_dists[~np.isfinite(motion_dists[
valid_inds, :])] = np.nan
weight_motion = self.reid.get('motion_weight')
match_dists = (1 - weight_motion) * reid_dists + \
weight_motion * motion_dists[valid_inds]
# support multi-class association
track_labels = torch.tensor([
self.tracks[id]['labels'][-1] for id in active_ids
]).to(bboxes.device)
cate_match = labels[None, :] == track_labels[:, None]
cate_cost = ((1 - cate_match.int()) * 1e6).cpu().numpy()
match_dists = match_dists + cate_cost
row, col = linear_sum_assignment(match_dists)
for r, c in zip(row, col):
dist = match_dists[r, c]
if not np.isfinite(dist):
continue
if dist <= self.reid['match_score_thr']:
ids[c] = active_ids[r]
active_ids = [
id for id in self.ids if id not in ids
and self.tracks[id].frame_ids[-1] == frame_id - 1
]
if len(active_ids) > 0:
active_dets = torch.nonzero(ids == -1).squeeze(1)
track_bboxes = self.get('bboxes', active_ids)
ious = bbox_overlaps(track_bboxes, bboxes[active_dets])
# support multi-class association
track_labels = torch.tensor([
self.tracks[id]['labels'][-1] for id in active_ids
]).to(bboxes.device)
cate_match = labels[None, active_dets] == track_labels[:, None]
cate_cost = (1 - cate_match.int()) * 1e6
dists = (1 - ious + cate_cost).cpu().numpy()
row, col = linear_sum_assignment(dists)
for r, c in zip(row, col):
dist = dists[r, c]
if dist < 1 - self.match_iou_thr:
ids[active_dets[c]] = active_ids[r]
new_track_inds = ids == -1
ids[new_track_inds] = torch.arange(
self.num_tracks,
self.num_tracks + new_track_inds.sum(),
dtype=torch.long).to(bboxes.device)
self.num_tracks += new_track_inds.sum()
self.update(
ids=ids,
bboxes=bboxes,
scores=scores,
labels=labels,
embeds=embeds if self.with_reid else None,
frame_ids=frame_id)
self.last_img = img
# update pred_track_instances
pred_track_instances = InstanceData()
pred_track_instances.bboxes = bboxes
pred_track_instances.labels = labels
pred_track_instances.scores = scores
pred_track_instances.instances_id = ids
return pred_track_instances
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