def update(self, bbox_xywh, confidences, ori_img):
self.height, self.width = ori_img.shape[:2]
# generate detections
features = self._get_features(bbox_xywh, ori_img)
detections = [
Detection(bbox_xywh[i], conf, features[i])
for i, conf in enumerate(confidences) if conf > self.min_confidence
]
# run on non-maximum supression
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = non_max_suppression(boxes, self.nms_max_overlap, scores)
detections = [detections[i] for i in indices]
# update tracker
self.tracker.predict()
self.tracker.update(detections)
# 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._xywh_to_xyxy(box)
track_id = track.track_id
outputs.append(np.array([x1, y1, x2, y2, track_id], dtype=np.int))
if len(outputs) > 0:
outputs = np.stack(outputs, axis=0)
return outputs
def update(self, bbox_xywh, confidences, ori_img, all_name,start_time):
self.height, self.width = ori_img.shape[:2]
# generate detections
features = self._get_features(bbox_xywh, ori_img)
detections = [Detection(bbox_xywh[i], conf, features[i], all_name[i],start_time) for i, conf in enumerate(confidences) if
conf > self.min_confidence]
# run on non-maximum supression
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
# 非 最大值 抑制
indices = non_max_suppression(boxes, self.nms_max_overlap, scores)
detections = [detections[i] for i in indices]
# update tracker
self.tracker.predict()
self.tracker.update(detections,start_time)
# output bbox identities
outputs = []
return_name = []
stay_time_all=[]
for track in self.tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1 :
# #如果是不是为确认的，则跳过
# if not track.is_confirmed():
continue
#只显示违规的
if (start_time-track.start_time)<self.bad_time:
continue
box = track.to_tlwh()
x1, y1, x2, y2 = self._xywh_to_xyxy(box)
track_id = track.track_id
class_name = track.class_name
# print(class_name)
outputs.append(np.array([x1, y1, x2, y2, track_id], dtype=np.int))
return_name.append(class_name)
part=[class_name+str(track_id),start_time-track.start_time]
stay_time_all.append(part)
if len(outputs) > 0:
outputs = np.stack(outputs, axis=0)
#暂定状态与确定状态的都可以记录其停留时间
# stay_time_all=[ [track.class_name,track.track_id,start_time-track.start_time] \
# for track in self.tracker.tracks \
# if(start_time-track.start_time)>self.bad_time and track.is_confirmed]
return outputs, return_name,stay_time_all
def update(self, bbox_xywh, confidences, class_num, ori_img):
self.height, self.width = ori_img.shape[:2]
# generate detections
detections = []
try:
features = self._get_features(bbox_xywh, ori_img)
for i, conf in enumerate(confidences):
if conf >= self.min_confidence and features.any():
# Detection 在detection.py找到相关的类
detections.append(
Detection(bbox_xywh[i], conf, class_num[i],
features[i]))
else:
pass
except Exception as ex:
# TODO Error: OpenCV(4.1.1) /io/opencv/modules/imgproc/src/resize.cpp:3720: error: (-215:Assertion failed) !ssize.empty() in function 'resize'
print("{} Error: {}".format(
time.strftime("%H:%M:%S", time.localtime()), ex))
# print('Error or video finish ')
# run on non-maximum supression
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = non_max_suppression(boxes, self.nms_max_overlap,
scores) # indices = [0] 或者 [0,1]
detections = [detections[i]
for i in indices] # 根据编号 做 嵌套的list[ [0编号],[1编号] ]
# print(detections[0].confidence)
# confidence: 0.5057685971260071
# print(detections)
# [bbox_xywh: [1508.47619629 483.33926392 34.95910645 77.69906616],
# confidence: 0.5140249729156494,
# bbox_xywh: [1678.99377441 526.4251709 36.55554199 80.11364746],
# confidence: 0.5057685971260071]
# update tracker
self.tracker.predict()
# 现在输入的detections 是 做了嵌套编号的 list[ [0编号],[1编号] ]
self.tracker.update(detections)
# print("confidence {}".format(detections[0].confidence))
# output bbox identities
# tracks 存储相关信息
outputs = []
# tracker的属性 trackers储存着 很多个track类实例
for track in self.tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
box = track.to_tlwh(
) # (top left x, top left y, width, height) 每帧都刷新
x1, y1, x2, y2 = self._xywh_to_xyxy_centernet(
box) # xywh 转成 矩形的对角点坐标
# 画运动轨迹
# 轨迹为检测框中心
center = (int((x1 + x2) / 2), int((y1 + y2) / 2)) #画轨迹图 记录每一次的中心点
# 轨迹为检测框底部
# center = (int((x1+x2)/2), int((y2))) # 画轨迹图 记录每一次的底部
points[track.track_id].append(center) # 用队列先进先出的结构 记录运动中心轨迹
# print(points[1][-1]) # 查看跟踪号为1的对象的中心点存储记忆
# for j in range(1, len(points[track.track_id])):
# if points[track.track_id][j - 1] is None or points[track.track_id][j] is None:
# continue
# # thickness = int(np.sqrt(32 / float(j + 1)) * 2) #第一个点重 后续线逐渐变细
# cv2.line(ori_img,(points[track.track_id][j-1]), (points[track.track_id][j]),(8,196,255),thickness = 3,lineType=cv2.LINE_AA)
track_id = track.track_id
confidences = track.confidence * 100
cls_num = track.class_num
# print("track_id {} confidences {}".format(track_id,confidences))
outputs.append(
np.array([x1, y1, x2, y2, track_id, confidences, cls_num],
dtype=np.int))
if len(outputs) > 0:
outputs = np.stack(outputs, axis=0)
return outputs, points
