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import numpy as np
import torch
import cv2
import os
from .reid_model import Extractor
from yolox.deepsort_tracker import kalman_filter, linear_assignment, iou_matching
from yolox.data.dataloading import get_yolox_datadir
from .detection import Detection
from .track import Track
def _cosine_distance(a, b, data_is_normalized=False):
if not data_is_normalized:
a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
return 1. - np.dot(a, b.T)
def _nn_cosine_distance(x, y):
distances = _cosine_distance(x, y)
return distances.min(axis=0)
class Tracker:
def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3):
self.metric = metric
self.max_iou_distance = max_iou_distance
self.max_age = max_age
self.n_init = n_init
self.kf = kalman_filter.KalmanFilter()
self.tracks = []
self._next_id = 1
def predict(self):
"""Propagate track state distributions one time step forward.
This function should be called once every time step, before `update`.
"""
for track in self.tracks:
track.predict(self.kf)
def increment_ages(self):
for track in self.tracks:
track.increment_age()
track.mark_missed()
def update(self, detections, classes):
"""Perform measurement update and track management.
Parameters
----------
detections : List[deep_sort.detection.Detection]
A list of detections at the current time step.
"""
# Run matching cascade.
matches, unmatched_tracks, unmatched_detections = \
self._match(detections)
# Update track set.
for track_idx, detection_idx in matches:
self.tracks[track_idx].update(
self.kf, detections[detection_idx])
for track_idx in unmatched_tracks:
self.tracks[track_idx].mark_missed()
for detection_idx in unmatched_detections:
self._initiate_track(detections[detection_idx], classes[detection_idx].item())
self.tracks = [t for t in self.tracks if not t.is_deleted()]
# Update distance metric.
active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
features, targets = [], []
for track in self.tracks:
if not track.is_confirmed():
continue
features += track.features
targets += [track.track_id for _ in track.features]
track.features = []
self.metric.partial_fit(
np.asarray(features), np.asarray(targets), active_targets)
def _match(self, detections):
def gated_metric(tracks, dets, track_indices, detection_indices):
features = np.array([dets[i].feature for i in detection_indices])
targets = np.array([tracks[i].track_id for i in track_indices])
cost_matrix = self.metric.distance(features, targets)
cost_matrix = linear_assignment.gate_cost_matrix(
self.kf, cost_matrix, tracks, dets, track_indices,
detection_indices)
return cost_matrix
# Split track set into confirmed and unconfirmed tracks.
confirmed_tracks = [
i for i, t in enumerate(self.tracks) if t.is_confirmed()]
unconfirmed_tracks = [
i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
# Associate confirmed tracks using appearance features.
matches_a, unmatched_tracks_a, unmatched_detections = \
linear_assignment.matching_cascade(
gated_metric, self.metric.matching_threshold, self.max_age,
self.tracks, detections, confirmed_tracks)
# Associate remaining tracks together with unconfirmed tracks using IOU.
iou_track_candidates = unconfirmed_tracks + [
k for k in unmatched_tracks_a if
self.tracks[k].time_since_update == 1]
unmatched_tracks_a = [
k for k in unmatched_tracks_a if
self.tracks[k].time_since_update != 1]
matches_b, unmatched_tracks_b, unmatched_detections = \
linear_assignment.min_cost_matching(
iou_matching.iou_cost, self.max_iou_distance, self.tracks,
detections, iou_track_candidates, unmatched_detections)
matches = matches_a + matches_b
unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
return matches, unmatched_tracks, unmatched_detections
def _initiate_track(self, detection, class_id):
mean, covariance = self.kf.initiate(detection.to_xyah())
self.tracks.append(Track(
mean, covariance, self._next_id, class_id, self.n_init, self.max_age,
detection.feature))
self._next_id += 1
class NearestNeighborDistanceMetric(object):
def __init__(self, metric, matching_threshold, budget=None):
if metric == "cosine":
self._metric = _nn_cosine_distance
else:
raise ValueError(
"Invalid metric; must be either 'euclidean' or 'cosine'")
self.matching_threshold = matching_threshold
self.budget = budget
self.samples = {}
def partial_fit(self, features, targets, active_targets):
for feature, target in zip(features, targets):
self.samples.setdefault(target, []).append(feature)
if self.budget is not None:
self.samples[target] = self.samples[target][-self.budget:]
self.samples = {k: self.samples[k] for k in active_targets}
def distance(self, features, targets):
cost_matrix = np.zeros((len(targets), len(features)))
for i, target in enumerate(targets):
cost_matrix[i, :] = self._metric(self.samples[target], features)
return cost_matrix
class DeepSort(object):
def __init__(self, model_path, max_dist=0.1, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=30, n_init=3, nn_budget=100, use_cuda=True):
self.min_confidence = min_confidence
self.nms_max_overlap = nms_max_overlap
self.extractor = Extractor(model_path, use_cuda=use_cuda)
max_cosine_distance = max_dist
metric = NearestNeighborDistanceMetric(
"cosine", max_cosine_distance, nn_budget)
self.tracker = Tracker(
metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)
def update(self, output_results, img_info, img_size, img_file_name):
img_file_name = os.path.join(get_yolox_datadir(), 'mot', 'train', img_file_name)
ori_img = cv2.imread(img_file_name)
self.height, self.width = ori_img.shape[:2]
# post process detections
output_results = output_results.cpu().numpy()
confidences = output_results[:, 4] * output_results[:, 5]
bboxes = output_results[:, :4] # x1y1x2y2
img_h, img_w = img_info[0], img_info[1]
scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))
bboxes /= scale
bbox_xyxy = bboxes
bbox_tlwh = self._xyxy_to_tlwh_array(bbox_xyxy)
remain_inds = confidences > self.min_confidence
bbox_tlwh = bbox_tlwh[remain_inds]
confidences = confidences[remain_inds]
# generate detections
features = self._get_features(bbox_tlwh, ori_img)
detections = [Detection(bbox_tlwh[i], conf, features[i]) for i, conf in enumerate(
confidences) if conf > self.min_confidence]
classes = np.zeros((len(detections), ))
# run on non-maximum supression
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
# update tracker
self.tracker.predict()
self.tracker.update(detections, classes)
# output bbox identities
outputs = []
for track in self.tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
box = track.to_tlwh()
x1, y1, x2, y2 = self._tlwh_to_xyxy_noclip(box)
track_id = track.track_id
class_id = track.class_id
outputs.append(np.array([x1, y1, x2, y2, track_id, class_id], dtype=np.int))
if len(outputs) > 0:
outputs = np.stack(outputs, axis=0)
return outputs
"""
TODO:
Convert bbox from xc_yc_w_h to xtl_ytl_w_h
Thanks JieChen91@github.com for reporting this bug!
"""
@staticmethod
def _xywh_to_tlwh(bbox_xywh):
if isinstance(bbox_xywh, np.ndarray):
bbox_tlwh = bbox_xywh.copy()
elif isinstance(bbox_xywh, torch.Tensor):
bbox_tlwh = bbox_xywh.clone()
bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2.
bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2.
return bbox_tlwh
@staticmethod
def _xyxy_to_tlwh_array(bbox_xyxy):
if isinstance(bbox_xyxy, np.ndarray):
bbox_tlwh = bbox_xyxy.copy()
elif isinstance(bbox_xyxy, torch.Tensor):
bbox_tlwh = bbox_xyxy.clone()
bbox_tlwh[:, 2] = bbox_xyxy[:, 2] - bbox_xyxy[:, 0]
bbox_tlwh[:, 3] = bbox_xyxy[:, 3] - bbox_xyxy[:, 1]
return bbox_tlwh
def _xywh_to_xyxy(self, bbox_xywh):
x, y, w, h = bbox_xywh
x1 = max(int(x - w / 2), 0)
x2 = min(int(x + w / 2), self.width - 1)
y1 = max(int(y - h / 2), 0)
y2 = min(int(y + h / 2), self.height - 1)
return x1, y1, x2, y2
def _tlwh_to_xyxy(self, bbox_tlwh):
"""
TODO:
Convert bbox from xtl_ytl_w_h to xc_yc_w_h
Thanks JieChen91@github.com for reporting this bug!
"""
x, y, w, h = bbox_tlwh
x1 = max(int(x), 0)
x2 = min(int(x+w), self.width - 1)
y1 = max(int(y), 0)
y2 = min(int(y+h), self.height - 1)
return x1, y1, x2, y2
def _tlwh_to_xyxy_noclip(self, bbox_tlwh):
"""
TODO:
Convert bbox from xtl_ytl_w_h to xc_yc_w_h
Thanks JieChen91@github.com for reporting this bug!
"""
x, y, w, h = bbox_tlwh
x1 = x
x2 = x + w
y1 = y
y2 = y + h
return x1, y1, x2, y2
def increment_ages(self):
self.tracker.increment_ages()
def _xyxy_to_tlwh(self, bbox_xyxy):
x1, y1, x2, y2 = bbox_xyxy
t = x1
l = y1
w = int(x2 - x1)
h = int(y2 - y1)
return t, l, w, h
def _get_features(self, bbox_xywh, ori_img):
im_crops = []
for box in bbox_xywh:
x1, y1, x2, y2 = self._tlwh_to_xyxy(box)
im = ori_img[y1:y2, x1:x2]
im_crops.append(im)
if im_crops:
features = self.extractor(im_crops)
else:
features = np.array([])
return features