def __init__(self, args):
self.args = args
use_cuda = bool(strtobool(self.args.use_cuda))
#use_cuda=False
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
self.vdo = cv2.VideoCapture()
self.detectron2 = Detectron2()
self.deepsort = DeepSort(args.deepsort_checkpoint, use_cuda=use_cuda)
def __init__(self, args):
self.args = args
use_cuda = bool(strtobool(self.args.use_cuda))
self.detectron2 = Detectron2(self.args.detectron_cfg,
self.args.detectron_ckpt)
if self.args.deep_sort:
self.deepsort = DeepSort(args.deepsort_checkpoint,
use_cuda=use_cuda)
def __init__(self, args):
self.args = args
use_cuda = bool(strtobool(self.args.use_cuda))
self.vdo = cv2.VideoCapture()
self.detectron2 = Detectron2()
# Initialize coordinate mapper
myCoordMapper = coord_mapper.CoordMapper(coord_mapper.ISSIA_kozep_elorol)
self.deepsort = DeepSort(args.deepsort_checkpoint, lambdaParam=1.0, coordMapper=myCoordMapper, max_dist=1.0, min_confidence=0.1,
nms_max_overlap=0.7, max_iou_distance=0.7, max_age=75, n_init=3, nn_budget=50, use_cuda=use_cuda)
def __init__(self, args):
self.args = args
use_cuda = bool(strtobool(self.args.use_cuda))
#self.vdo = cv2.VideoCapture()
self.imgList = natsort.natsorted(glob.glob(self.args.imgs_path))
self.detectron2 = Detectron2()
# Initialize coordinate mapper
self.myCoordMapper = coord_mapper.CoordMapperCSG(
match_code='HUN-BEL 2. Half')
self.fps = 6
self.deepsort = DeepSort(args.deepsort_checkpoint,
lambdaParam=0.6,
coordMapper=self.myCoordMapper,
max_dist=1.0,
min_confidence=0.1,
nms_max_overlap=0.7,
max_iou_distance=0.7,
max_age=self.fps * 3,
n_init=3,
nn_budget=50,
use_cuda=use_cuda)
def __init__(self):
self.use_cuda = True
self.display = True
self.config = yaml.load(open('config.yaml', 'r'))
self.dataset = self.config['DATASET']['NAME']
self.detectron2 = Detectron2()
if (self.dataset == 'kitti'):
self.rgb_path = self.config['DATASET']['KITTI']['DATA_PATH']
self.sequence_name = self.config['DATASET']['KITTI'][
'SEQUENCE_NAME']
self.sequence_list = os.listdir(self.rgb_path)
self.velo2cam = np.array(
self.config['DATASET']['KITTI']['TRANSFORMS']['Velo2cam'])
self.kitti_timestamps = open(
self.config['DATASET']['KITTI']['TIMESTAMPS']).readlines()
self.kitti_odom = open(
self.config['DATASET']['KITTI']['ODOM_PATH']).readlines()
self.matches = sorted(self.sequence_list)
self.max_vel = self.config['DATASET']['KITTI']['MAX_VEL']
self.max_iou = self.config['DATASET']['KITTI']['MAX_IOU']
self.max_depth = self.config['DATASET']['KITTI']['MAX_DEPTH']
self.min_depth = self.config['DATASET']['KITTI']['MIN_DEPTH']
self.mask_points = self.config['DATASET']['KITTI']['MASK_POINTS']
self.fx = self.config['DATASET']['KITTI']['CAMERA'][
'focal_length_x']
self.fy = self.config['DATASET']['KITTI']['CAMERA'][
'focal_length_y']
self.cx = self.config['DATASET']['KITTI']['CAMERA'][
'optical_center_x']
self.cy = self.config['DATASET']['KITTI']['CAMERA'][
'optical_center_y']
elif (self.dataset == 'tum'):
self.sequence_name = self.config['DATASET']['TUM']['SEQUENCE_NAME']
self.rgb_path = self.config['DATASET']['TUM']['RGB_PATH']
self.depth_path = self.config['DATASET']['TUM']['DEPTH_PATH']
self.odom_path = self.config['DATASET']['TUM']['ODOM_PATH']
self.first_list = read_file_list(self.rgb_path + '.txt')
self.second_list = read_file_list(self.depth_path + '.txt')
self.third_list = read_file_list(self.odom_path + '.txt')
self.matches = associate(self.first_list, self.second_list,
self.third_list, 0.0, 0.02)
self.max_vel = self.config['DATASET']['TUM']['MAX_VEL']
self.max_iou = self.config['DATASET']['TUM']['MAX_IOU']
self.max_depth = self.config['DATASET']['TUM']['MAX_DEPTH']
self.min_depth = self.config['DATASET']['TUM']['MIN_DEPTH']
self.depth_factor = self.config['DATASET']['TUM']['DEPTH_FACTOR']
self.mask_points = self.config['DATASET']['TUM']['MASK_POINTS']
self.fx = self.config['DATASET']['TUM']['CAMERA']['focal_length_x']
self.fy = self.config['DATASET']['TUM']['CAMERA']['focal_length_y']
self.cx = self.config['DATASET']['TUM']['CAMERA'][
'optical_center_x']
self.cy = self.config['DATASET']['TUM']['CAMERA'][
'optical_center_y']
self.class_names = self.config['CLASSES']['ALL']
self.rigid = self.config['CLASSES']['RIGID']
self.not_rigid = self.config['CLASSES']['NON_RIGID']
self.save_mask = self.config['SAVE_MASK']
del self.config
label = labels[idx]
# Default label
else:
label = "human"
shape_jsons_l.append(_get_shape_j(point, label))
return _get_labelme_template(im, shape_jsons_l, imp, h, w)
if __name__ == "__main__":
from detectron2_detection import Detectron2
import json
w_p = "/nfs/gpu14_datasets/surveillance_weights/visdrone_t1/model_0111599.pth"
cfg_p = "/nfs/gpu14_datasets/surveillance_weights/visdrone_t1/test.yaml"
det = Detectron2(cfg_path=cfg_p, weights_path=w_p)
### Params to be changed
process_freq = 100
root = "/data/client_datasets/idea_forge/videos/29_may/"
# vs = ["03April202017_42_05.mp4", "07April202012_40_03.mp4", "10April202019_02_52.mp4", "11April202016_23_55.mp4", "13April202017_22_58.mp4", "14April202009_16_45.mp4"]
vs = ["06April202012_06_22.mp4"]
vids = [root + i for i in vs]
save_root = "/data/client_datasets/idea_forge/annotations/model_preds"
for v in vids:
print(v)
base_name = os.path.basename(v).replace(".mp4", "_")
save_f = os.path.join(save_root, base_name)
os.mkdir(save_f)
cap = cv2.VideoCapture(v)
def __init__(self):
self.use_cuda = True
self.display = True
self.config = yaml.load(open('config.yaml', 'r'))
self.dataset = self.config['DATASET']['NAME']
self.detectron2 = Detectron2()
# ros
self.image_pub = rospy.Publisher("/segmentation_mask",
Image_ros,
queue_size=10,
latch=True)
self.pose_sub = rospy.Subscriber("/camera_pose", Image_ros,
self.pose_callback)
# self.pose_true = False
self.pose = np.eye(4)
self.bridge = CvBridge()
if (self.dataset == 'kitti'):
self.sequence_list = os.listdir(
self.config['DATASET']['KITTI']['DATA_PATH'])
self.velo2cam = np.array(
self.config['DATASET']['KITTI']['TRANSFORMS']['Velo2cam'])
self.kitti_timestamps = open(
self.config['DATASET']['KITTI']['TIMESTAMPS']).readlines()
self.kitti_odom = open(
self.config['DATASET']['KITTI']['ODOM_PATH']).readlines()
self.matches = sorted(self.sequence_list)
self.max_vel = self.config['DATASET']['KITTI']['MAX_VEL']
self.fx = self.config['DATASET']['KITTI']['CAMERA'][
'focal_length_x']
self.fy = self.config['DATASET']['KITTI']['CAMERA'][
'focal_length_y']
self.cx = self.config['DATASET']['KITTI']['CAMERA'][
'optical_center_x']
self.cy = self.config['DATASET']['KITTI']['CAMERA'][
'optical_center_y']
elif (self.dataset == 'tum'):
#self.deepsort = DeepSort(args.deepsort_checkpoint, use_cuda=use_cuda)
self.first_list = read_file_list(
self.config['DATASET']['TUM']['RGB_PATH'] + '.txt')
self.second_list = read_file_list(
self.config['DATASET']['TUM']['DEPTH_PATH'] + '.txt')
self.third_list = read_file_list(
self.config['DATASET']['TUM']['ODOM_PATH'] + '.txt')
self.matches = associate(self.first_list, self.second_list,
self.third_list, 0.0, 0.02)
self.max_vel = self.config['DATASET']['TUM']['MAX_VEL']
self.fx = self.config['DATASET']['TUM']['CAMERA']['focal_length_x']
self.fy = self.config['DATASET']['TUM']['CAMERA']['focal_length_y']
self.cx = self.config['DATASET']['TUM']['CAMERA'][
'optical_center_x']
self.cy = self.config['DATASET']['TUM']['CAMERA'][
'optical_center_y']
self.class_names = self.config['CLASSES']['ALL']
self.rigid = self.config['CLASSES']['RIGID']
self.not_rigid = self.config['CLASSES']['NON_RIGID']
def __init__(self, args):
self.args = args
self.vdo = cv2.VideoCapture()
self.detectron2 = Detectron2()