def _transforms(name, scale, mean, std, gray_probability, blur_probability, sigma):
# standard size: (1333, 800)
if name == 'basic_train':
return data.BasicPairTransforms(train=True, scale=scale, mean=mean, std=std)
elif name == 'basic_test':
return data.BasicPairTransforms(train=False, scale=scale, mean=mean, std=std)
elif name == 'extra_partial':
return data.ExtraPairTransforms(
with_photometric=True,
with_expand=False,
with_crop=False)
elif name == 'extra_full':
return data.ExtraPairTransforms()
elif name == 'extra_partial_boost':
return data.ExtraPairTransforms(
with_photometric=True,
with_expand=False,
with_crop=False,
with_grayscale=True,
with_blur=True,
gray_probability=gray_probability,
sigma=sigma,
blur_probability=blur_probability
)
elif name == 'extra_full_boost':
return data.ExtraPairTransforms(
with_grayscale=True,
with_blur=True,
gray_probability=gray_probability,
sigma=sigma,
blur_probability=blur_probability
)
else:
raise KeyError('Unknown transform:', name)
def _transforms(name):
# standard size: (1333, 800)
if name == 'basic_train':
return data.BasicPairTransforms(train=True)
elif name == 'basic_test':
return data.BasicPairTransforms(train=False)
elif name == 'extra_partial':
return data.ExtraPairTransforms(with_photometric=True,
with_expand=False,
with_crop=False)
elif name == 'extra_full':
return data.ExtraPairTransforms()
else:
raise KeyError('Unknown transform:', name)
def Golbal_Track_init(self, image, init_box):
cfg_file = os.path.join(base_path, 'DiMP_LTMU/Global_Track/configs/qg_rcnn_r50_fpn.py')
ckp_file = os.path.join(base_path, 'DiMP_LTMU/Global_Track/checkpoints/qg_rcnn_r50_fpn_coco_got10k_lasot.pth')
transforms = data.BasicPairTransforms(train=False)
self.Global_Tracker = GlobalTrack(
cfg_file, ckp_file, transforms,
name_suffix='qg_rcnn_r50_fpn')
self.Global_Tracker.init(image, init_box)
def Golbal_Track_init(self, image, init_box):
init_box = [
init_box[0], init_box[1], init_box[0] + init_box[2],
init_box[1] + init_box[3]
]
cfg_file = 'Global_Track/configs/qg_rcnn_r50_fpn.py'
ckp_file = 'Global_Track/checkpoints/qg_rcnn_r50_fpn_coco_got10k_lasot.pth'
transforms = data.BasicPairTransforms(train=False)
self.Global_Tracker = GlobalTrack(cfg_file,
ckp_file,
transforms,
name_suffix='qg_rcnn_r50_fpn')
self.Global_Tracker.init(image, init_box)
def test_global_track(self):
# settings
cfg_files = [
'configs/qg_rpn_r50_fpn.py',
'configs/qg_rcnn_r50_fpn.py',
'configs/qg_rpn_r18_fpn.py']
ckp_files = [
'checkpoints/qg_rpn_r50_fpn_coco_got10k_lasot.pth',
'checkpoints/qg_rcnn_r50_fpn_coco_got10k_lasot.pth',
'work_dirs/qg_rpn_r18_fpn/epoch_12.pth']
transforms = data.BasicPairTransforms(train=False)
# run evaluation over different settings
for cfg_file, ckp_file in zip(cfg_files, ckp_files):
tracker = GlobalTrack(cfg_file, ckp_file, transforms)
self.evaluator.run(tracker, visualize=self.visualize)
self.evaluator.report(tracker.name)
def test_mmdet_transforms(self):
for transforms in [
data.BasicPairTransforms(),
data.ExtraPairTransforms()
]:
dataset = data.Seq2Pair(self.seqs, transforms=transforms)
indices = np.random.choice(len(dataset), 10)
for i in indices:
item = dataset[i]
img_z = ops.stretch_color(item['img_z'].permute(1, 2,
0).numpy())
img_x = ops.stretch_color(item['img_x'].permute(1, 2,
0).numpy())
bboxes_z = item['gt_bboxes_z'][0].numpy()
bboxes_x = item['gt_bboxes_x'][0].numpy()
if self.visualize:
ops.show_image(img_z, bboxes_z, fig=1, delay=1)
ops.show_image(img_x, bboxes_x, fig=2, delay=0)
import _init_paths
import neuron.data as data
from trackers import *
from mmcv import Config
if __name__ == '__main__':
cfg_file = 'configs/dmtrackGS_dla34_fpn.py'
cfg = Config.fromfile(cfg_file)
ckp_file = 'work_dirs/dmtrack_dla34_fpn/dmtrackGS.pth'
transforms = data.BasicPairTransforms(scale=cfg.data.test['scale'],
train=cfg.data.test['train'])
tracker = DMTrack(cfg_file,
ckp_file,
transforms,
name_suffix='dmtrack_dla34_fpn')
evaluators = [
data.EvaluatorLaSOT(frame_stride=10),
]
for e in evaluators:
e.run(tracker, visualize=False, return_all=False)
e.report(tracker.name, return_all=False)
import _init_paths
import neuron.data as data
from trackers import *
if __name__ == '__main__':
cfg_file = 'configs/qg_rcnn_r50_fpn.py'
ckp_file = 'checkpoints/qg_rcnn_r50_fpn_coco_got10k_lasot.pth'
transforms = data.BasicPairTransforms(train=False)
tracker = GlobalTrack(cfg_file,
ckp_file,
transforms,
name_suffix='qg_rcnn_r50_fpn')
evaluators = [
# data.EvaluatorOTB(version=2015, root_dir="/disk/xuxiang/GlobalTrack/data/OTB100"),
# data.EvaluatorLaSOT(frame_stride=10, root_dir="/disk/xuxiang/GlobalTrack/data/LaSOTBenchmark"),
# data.EvaluatorGOT10k(subset='test', root_dir="/disk/xuxiang/GlobalTrack/data/GOT-10k"),
data.EvaluatorTLP(root_dir="/disk/xuxiang/GlobalTrack/data/TLP")
]
for e in evaluators:
e.run(tracker, visualize=False)
e.report(tracker.name)