def __init__(self, cfg, args, video_path, fcos=None):
self.cfg = cfg
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
if fcos is not None:
self.detector = fcos
else:
fcos = FCOSEvaluator(
"weights/FCOS_imprv_R_50_FPN_1x.pth",
config="./configs/fcos/fcos_imprv_R_50_FPN_1x.yaml")
#print("fcos.config",fcos.config)
fcos.prepare_detector() #build_detector(cfg, use_cuda=use_cuda)
self.detector = fcos
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
def __init__(self, cfg, args):
# Create a VideoCapture object
self.cfg = cfg
self.args = args
use_cuda = self.args.use_cuda and torch.cuda.is_available()
if not use_cuda:
raise UserWarning("Running in cpu mode!")
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
self.vdo = cv2.VideoCapture(self.args.input)
self.status, self.frame = None, None
self.total_frames = int(
cv2.VideoCapture.get(self.vdo, cv2.CAP_PROP_FRAME_COUNT))
self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.output_frame = None
self.thread = ThreadPoolExecutor(max_workers=1)
self.thread.submit(self.update)
print('streaming started ...')
def __init__(self, cfg, args, video_path):
self.cfg = cfg
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
# 把一个ViedeoCapture 类 赋值给vdo 实例
# 这个实例 包含 open method 可以
self.vdo = cv2.VideoCapture()
# 返回 yolov3 和 deepsort 的实例对象
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names # ????
def __init__(self, width, height, classes, safezone, camera_fov_x,
target_height_m):
# Set internal variables.
self.width = width
self.height = height
self.classes = classes
self.safezone = safezone
self.camera_fov_x = camera_fov_x
self.target_height_m = target_height_m
self.cfg = get_merged_config()
use_cuda = torch.cuda.is_available()
if not use_cuda:
raise UserWarning("Running in cpu mode!")
self.cam2world = Cam2World(self.width, self.height, self.camera_fov_x)
self.cls_dict = {0: 'person', 2: 'car', 7: 'car'}
self.detector = build_detector(self.cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(self.cfg,
cam2world=self.cam2world,
obj_height_meters=self.target_height_m,
use_cuda=use_cuda)
self.class_names = self.detector.class_names
self.bbox_xyxy = []
self.identities = []
self.target_id = []
# configuring logger:
self.logger = DeepSortLogger()
self.frame = -1
def __init__(self, cfg, args, video_path):
self.cfg = cfg
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
# delete all files
# for f in glob.glob("./data/*"):
# os.remove(f)
self.status_dict = {0: "OK", 1: "ACQUIRED", 2: "VIOLATION"}
self.telegram_bot = telegram()
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
# self.vdo = cv2.VideoCapture(args.cam)
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
self.H = get_homography()
def __init__(self, cfg, args, video_path, video_bar, vt_box_dict):
self.cfg = cfg
self.args = args
self.video_path = video_path
self.video_name = os.path.splitext(os.path.split(video_path)[1])[0]
self.logger = get_logger("root")
self.video_bar = video_bar
self.vt_box_dict = vt_box_dict
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
def __init__(self, cfg, args, video_path):
rospy.init_node('coordinate_publisher')
rospy.loginfo('Video stream has started')
self.cfg = cfg
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
class_names = [
name.strip() for name in open(args.label_file).readlines()
]
net = create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
net.load(args.detector_model)
self.detector = create_mobilenetv2_ssd_lite_predictor(net)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = class_names
def __init__(self, cfg, args, video_path):
self.cfg = cfg
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
self.logger.info(f"Video rate is {self.video_path}")
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
# Uniformly sample frames to save resources
# Copy the previous result when a frame is skipped
self.logger.info(f"Sample rate is {args.sample_rate}")
self.skip_frame = int(1 / args.sample_rate)
self.logger.info(f"Detection model is set to {args.detection_model}")
self.detector = build_detector(args.detection_model,
cfg,
use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
self.temp_tesult = []
def __init__(self, cfg, args, image_path, save_filename, im_width,
im_height):
self.cfg = cfg
self.args = args
self.image_path = image_path
self.logger = get_logger("root")
self.save_filename = save_filename
self.im_width = im_width
self.im_height = im_height
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
# if args.display:
# cv2.namedWindow("test", cv2.WINDOW_NORMAL)
# cv2.resizeWindow("test", args.display_width, args.display_height)
# if args.cam != -1:
# print("Using webcam " + str(args.cam))
# self.vdo = cv2.VideoCapture(args.cam)
# else:
# self.vdo = cv2.VideoCapture()
self.detector = detect(is_xywh=True, use_cuda=True)
#print(self.detector)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
def __init__(self, cfg):
use_cuda = torch.cuda.is_available()
if not use_cuda:
raise UserWarning("Running in cpu mode!")
self.detector = build_detector(cfg, use_cuda=True)
self.deepsort = build_tracker(cfg, use_cuda=True)
self.class_names = self.detector.class_names
def __init__(self, cfg):
self.cfg = cfg
self.cap = cv2.VideoCapture(0)
#self.cap = cv2.VideoCapture(0)
self.detector = build_detector(cfg, use_cuda=cpu)
self.deepsort = build_tracker(cfg, use_cuda=cpu)
self.class_names = self.detector.class_names
def initialize_deep_sort(self):
# initialize deep sort object
cfg = get_config()
cfg.merge_from_file(self.deep_sort_path)
use_cuda = torch.cuda.is_available()
if not use_cuda:
print("Running in cpu mode which maybe very slow!", UserWarning)
return build_tracker(cfg, use_cuda=use_cuda)
def __init__(self, cfg):
warnings.filterwarnings("ignore")
self.cfg = cfg
#self.args = args
self.yolo = 0
self.video_path = 0
self.outputDir = 0
self.carLocation1 = {}
self.carLocation2 = {}
self.carSpeed = {}
self.carDirection = {}
self.carPre = {}
self.virtureLine = [[0, 0], [0, 0]]
self.carInCnt = 0
self.carOutCnt = 0
self.inCar = set()
self.outCar = set()
self.trafficLine = []
self.idx_frame = 0
self.carSpeedLimit = 0
self.filename = 0
self.trafficJamLimit = 0
self.rett = False
self.carLicense = {}
self.speed = [0, 0, 0, 0, 0, 0, 0, 0]
self.licence = Licence()
self.detectCar = detectCar()
self.frameAll = 0
self.vout = cv2.VideoWriter()
self.carLabel = {}
self.saveVideoFlag = True
self.displayFlag = True
self.videoFps = 0
self.videoHeight = 0
self.trafficLine1 = []
self.trafficLine2 = []
self.carInfor = {}
self.videoWidth = 0
self.carSpeedLimit = 0
self.trafficJamLimit = 0
self.yolov5weight = 0
self.carFromLeft = {}
self.carFromRight = {}
self.carFromForward = {}
self.carTurn = {}
self.endFlag = False
self.illegal = {}
self.trafficLightFlag = False
self.dir = ['UP', 'RIGHT', 'DOWN', "LEFT"]
#self.idx_frame
use_cuda = torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
self.detector = self.yolo
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
def __init__(self, cfg, args, path):
self.logger = get_logger("root")
self.args = args
self.video_path = path
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!", UserWarning)
self.person_detect = Person_detect(self.args, self.video_path)
imgsz = check_img_size(args.img_size, s=32) # self.model.stride.max()) # check img_size
self.dataset = LoadImages(self.video_path, img_size=imgsz)
self.deepsort = build_tracker(cfg, args.sort, use_cuda=use_cuda)
def __init__(self,
cfg,
input_queue=None,
output_queue_vis=None,
output_queue_action_pred=None,
use_gpu=True,
show_video=False):
"""
:param cfg: the prototype config
:param input_queue: inut
:param: output_queue: output
:param use_gpu: (boolean) whether gpu should be use for inference
"""
setup_environment()
# Setup logging format
logging.setup_logging(cfg.OUTPUT_DIR)
self.cfg = cfg
self.show_video = show_video
# The queues from the main thread used for multiprocessing
self.output_detection_queue = input_queue
if self.show_video:
self.output_tracker_queue_visualization = output_queue_vis
self.output_tracker_queue_action_recognition = output_queue_action_pred
# Used to order images retrieved from the two queues used as input
self.get_idx = -1
# Has the previous process terminated?
self.first_poison_pill_received = False
# True if self.first_poison_pill_received and get does not lead to any results and self.result_rank is empty
self.output_detection_queue_is_finished = False
# Whether we will use a gpu or not
use_gpu = use_gpu and torch.cuda.is_available()
# The tracker we will use for object detection
self.deepsort = build_tracker(cfg, use_cuda=use_gpu)
# A list that contains and is sorted by get_idxs (ascending) -> result_rank[0] is smallest get_idx
self.result_rank = []
# A list that contains the images (ndarray) image with shape (H, W, C) (in BGR order) and [0,255])
self.result_img_data = []
# A list that contains the prediction results (predictions {dict}) and that is
# also sorted by the get_idxs -> corresponding to result_rank
self.result_prediction_data = []
# The process for person detection
self.update_tracker_with_next_image_prcocess = mp.Process(
target=self.update_tracker_with_next_image, args=())
def __init__(self, cfg, args, video_path, result_filename="results"):
self.cfg = cfg
self.args = args
self.result_filename = result_filename
self.video_path = video_path
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
self.vdo = cv2.VideoCapture()
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = args.class_names
def __init__(self, cfg, args, name):
self.cfg = cfg
self.args = args
self.name = name
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
raise UserWarning("Running in cpu mode!")
self.indir = "/mnt/Disk1/qingl/data/MOT16/train/" + self.name + "/img1/"
self.outdir = dir = "/home/qingl/antonio/mot16/baseline/" + self.name + ".txt"
f = open(self.outdir, 'w')
f.truncate()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
raise UserWarning("Running in cpu mode!")
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
def __init__(self, cfg, engine_file_path):
self.cfg = cfg
# self.args = args
self.deepsort = build_tracker(cfg, use_cuda=True)
#---tensorrt----#
self.engine = get_engine(engine_file_path)
self.context = self.engine.create_execution_context()
self.inputs, self.outputs, self.bindings, self.stream = common.allocate_buffers(
self.engine)
# ---tensorrt----#
#---input info for yolov3-416------#
self.input_resolution_yolov3_HW = (416, 416)
self.preprocessor = PreprocessYOLO(self.input_resolution_yolov3_HW)
# self.image_raw, self.image = self.preprocessor.process(ori_im)
# self.shape_orig_WH = image_raw.size
self.output_shapes = [(1, 255, 13, 13), (1, 255, 26, 26),
(1, 255, 52, 52)]
self.postprocessor_args = {
"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
# A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [
(10, 13),
(16, 30),
(33, 23),
(30, 61),
(62, 45),
# A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119),
(116, 90),
(156, 198),
(373, 326)
],
"obj_threshold":
0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold":
0.5,
# Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution":
self.input_resolution_yolov3_HW
}
self.postprocessor = PostprocessYOLO(**self.postprocessor_args)
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
print("Running on CPU!")
else:
print("Running on CUDA!")
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
rospy.init_node('workspace_monitoring_node')
def __init__(self, args):
print('Initialize DeepSORT & YOLO-V5')
# ***************** Initialize ******************************************************
self.args = args
self.img_size = args.img_size # image size in detector, default is 640
self.frame_interval = args.frame_interval # frequency
self.device = select_device(args.device)
self.half = self.device.type != 'cpu' # half precision only supported on CUDA
# create video capture ****************
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
# ***************************** initialize DeepSORT **********************************
cfg = get_config()
cfg.merge_from_file(args.config_deepsort)
use_cuda = self.device.type != 'cpu' and torch.cuda.is_available()
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
# ***************************** initialize YOLO-V5 **********************************
self.detector = torch.load(
args.weights,
map_location=self.device)['model'].float() # load to FP32
self.detector.to(self.device).eval()
if self.half:
self.detector.half() # to FP16
self.names = self.detector.module.names if hasattr(
self.detector, 'module') else self.detector.names
print('Done..')
if self.device == 'cpu':
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
def __init__(self, cfg, args, video_path=None, redis=None):
self.cfg = cfg
self.args = args
self.video_path = video_path
self.redis = redis
self.cur_objects = {}
self.reset_interval = 0
# self.logger = get_logger("root")
self.is_close = False
self.int2command = {1: "open", 2: "close"}
self.start = time.time()
# 七牛云图床上传权限申请
self.q = Auth(self.cfg.sys.qiniu.access_key,
self.cfg.sys.qiniu.secret_key)
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = RTSCapture.create(args.cam,
self.cfg.sys.restart_interval)
self.vdo.start_read()
else:
self.vdo = cv2.VideoCapture()
# self.detector = build_detector(cfg, use_cuda=use_cuda)
# 构建目标检测器
self.detector = torch.hub.load('ultralytics/yolov5',
'yolov5s',
pretrained=True,
force_reload=False)
self.detector.conf = 0.6
classes = list(self.cfg.classes.keys())[:-2]
self.detector.classes = list(map(lambda x: int(x), classes))
if use_cuda:
self.detector.cuda()
# 构建目标跟踪器
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("请使用gpu测试！", UserWarning)
# opencv输出界面设置
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
# 是否使用摄像头进行测试
if args.cam != -1:
print("使用摄像头 " + str(args.cam))
self.vdo = cv2.VideoCapture(0, cv2.CAP_DSHOW)
else:
self.vdo = cv2.VideoCapture()
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
def __init__(self, cfg, args):
self.cfg = cfg
self.args = args
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode!")
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
# Configure output video and json
self.logger = BboxToJsonLogger()
filename, extension = splitext(basename(self.args.VIDEO_PATH))
self.output_file = join(self.args.save_path, f'{filename}.avi')
self.json_output = join(self.args.save_path, f'{filename}.json')
if not isdir(dirname(self.json_output)):
makedirs(dirname(self.json_output))
def __init__(self, cfg, args, images_path):
self.cfg = cfg
self.args = args
self.images_path = images_path
self.logger = get_logger("root")
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
assert os.path.isdir(self.images_path), "Path error"
self.imgs_filenames = sorted(
os.listdir(os.path.join(self.images_path, 'img1')))
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
def __init__(self, cfg, args, video_path, Flag, result_filename="results"):
self.cfg = cfg
self.args = args
self.result_filename = result_filename
self.video_path = video_path
self.Flag = Flag #Flag = True save features
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!", UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
def __init__(self):
cfg = get_config()
cfg.merge_from_file(opt.deep_sort_path)
use_cuda = torch.cuda.is_available()
if not use_cuda:
print("Using CPU")
desired_classes = [
'person', 'bicycle', 'car', 'motorbike', 'bus', 'truck'
]
class_names, desired_class_names = read_class_names(
opt.label_names_path, desired_classes=desired_classes)
self.class_names = class_names
self.yolo = Load_Yolo_Model(track_only=desired_class_names,
conf_thres=opt.confidence,
weights=opt.yolo_path)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.log = logging.getLogger()
self.log.setLevel(logging.INFO)
self.log.addHandler(TqdmLoggingHandler())
print("Initialized!")
def __init__(self, args, video_path):
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!",
UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
self.detector = build_detector()
self.deepsort = build_tracker(use_cuda=True)
self.class_names = self.detector.class_names
