def __init__(self, opt,polygon, paths, polygon2=None,frame_rate=30):
self.opt = opt
if opt.gpus[0] >= 0:
opt.device = torch.device('cuda')
else:
opt.device = torch.device('cpu')
print('Creating model...')
anchor_ratios = [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]
anchor_scales = [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]
input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536]
self.input_size = input_sizes[opt.compound_coef]
if opt.detection_model=='Efficient' :
self.obj_list =['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', '', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
'cow', 'elephant', 'bear', 'zebra', 'giraffe', '', 'backpack', 'umbrella', '', '', 'handbag', 'tie',
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
'skateboard', 'surfboard', 'tennis racket', 'bottle', '', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut',
'cake', 'chair', 'couch', 'potted plant', 'bed', '', 'dining table', '', '', 'toilet', '', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
'refrigerator', '', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier',
'toothbrush']
self.person_or_motorcycle=['person']
self.obj_interest=[ 'motorcycle','bicycle', 'bus', 'truck','car'] if self.person_or_motorcycle[0]!='person' else [ 'person', 'bus', 'truck','car']
self.detetection_model= EfficientDetBackbone(compound_coef=opt.compound_coef, num_classes=len(self.obj_list),
ratios=anchor_ratios, scales=anchor_scales)
self.detetection_model.load_state_dict(torch.load(f'EfficientDet/weights/efficientdet-d{opt.compound_coef}.pth'))
self.detetection_model.eval()
device = torch.device('cuda:0')
self.detetection_model = self.detetection_model.to(device)
elif opt.detection_model=='FasterRcnn' :
config_file = "Drone_FasterRCNN/drone_demo/e2e_faster_rcnn_X_101_32x8d_FPN_1x_visdrone.yaml"
cfg.merge_from_file(config_file)
cfg.merge_from_list(["MODEL.WEIGHT", "Drone_FasterRCNN/drone_demo/visdrone_model_0360000.pth"])
self.detetection_model = COCODemo(
cfg,
min_image_size=opt.min_img_size,
confidence_threshold=opt.conf_thres,
)
label_of_interest=[
# "__background",
# "unused",
# "pedestrian",
# "person",
# "bicycle",
"car",
"van",
"truck",
# "tricycle",
# "awning-tricycle",
"bus",
"motor"
]
self.person_or_motorcycle=["motor"]
#'bicycle'
self.obj_interest=[ 'motor', 'bus', 'truck','car','van', "tricycle"] if self.person_or_motorcycle[0]!='person' else [ 'person', 'bus', 'truck','car','van', "tricycle"]
else:
raise('Not supported detector model')
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
self.removed_stracks = [] # type: list[STrack]
self.frame_id = 0
self.det_thresh = opt.conf_thres
self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
self.max_time_lost = self.buffer_size
self.max_per_image = opt.K
self.kalman_filter = KalmanFilter()
self.polygon=polygon
self.paths=paths
self.polygon2=polygon2
self.line1=[polygon[0],polygon[1]]
self.line2=[polygon[1],polygon[2]]
self.line3=[polygon[2],polygon[3]]
self.line4=[polygon[0],polygon[4]]
self.two_polygon_system=True
self.warmup_frame=0
self.virtual_polygon= [
[
0,
680
],
[
0,
149
],
[
1270,
149
],
[
1270,
680
]
]
def __init__(self, opt, polygon, paths, polygon2=None, frame_rate=30):
self.opt = opt
if opt.gpus[0] >= 0:
opt.device = torch.device('cuda')
else:
opt.device = torch.device('cpu')
print('Creating model...')
anchor_ratios = [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]
anchor_scales = [2**0, 2**(1.0 / 3.0), 2**(2.0 / 3.0)]
input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536]
self.input_size = input_sizes[opt.compound_coef]
self.obj_list = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant', '',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'', 'backpack', 'umbrella', '', '', 'handbag', 'tie', 'suitcase',
'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', '', 'wine glass', 'cup', 'fork',
'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', '', 'dining table', '', '',
'toilet', '', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
'cell phone', 'microwave', 'oven', 'toaster', 'sink',
'refrigerator', '', 'book', 'clock', 'vase', 'scissors',
'teddy bear', 'hair drier', 'toothbrush'
]
self.person_or_motorcycle = ['motorcycle', 'bicycle']
self.obj_interest = [
'motorcycle', 'bicycle', 'bus', 'truck', 'car'
] if self.person_or_motorcycle[0] != 'person' else [
'person', 'bus', 'truck', 'car'
]
print(self.obj_interest)
self.detetection_model = EfficientDetBackbone(
compound_coef=opt.compound_coef,
num_classes=len(self.obj_list),
ratios=anchor_ratios,
scales=anchor_scales)
self.detetection_model.load_state_dict(
torch.load(
f'EfficientDet/weights/efficientdet-d{opt.compound_coef}.pth'))
self.detetection_model.eval()
device = torch.device('cuda:0')
self.detetection_model = self.detetection_model.to(device)
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
self.removed_stracks = [] # type: list[STrack]
self.frame_id = 0
self.det_thresh = opt.conf_thres
self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
self.max_time_lost = self.buffer_size
self.max_per_image = opt.K
self.kalman_filter = KalmanFilter()
self.polygon = polygon
self.paths = paths
self.polygon2 = polygon2
self.line2 = [self.polygon[1], self.polygon[2]]
self.line1 = [self.polygon[4], self.polygon[3]] if len(
self.polygon) == 5 else [self.polygon[0], self.polygon[3]] if len(
self.polygon) == 4 else None
self.two_polygon_system = False
self.warmup_frame = 6 if self.two_polygon_system else 0
self.virtual_polygon = [[0, 573], [0, 109], [1270, 109], [1270, 573]]