file_path stringlengths 3 280 | file_language stringclasses 66
values | content stringlengths 1 1.04M | repo_name stringlengths 5 92 | repo_stars int64 0 154k | repo_description stringlengths 0 402 | repo_primary_language stringclasses 108
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src/tools/voc_eval_lib/setup.py | Python | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import os
from os.path import join as pjoin
import numpy as np
from dis... | xingyizhou/CenterNet | 7,541 | Object detection, 3D detection, and pose estimation using center point detection: | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/voc_eval_lib/utils/__init__.py | Python | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
| xingyizhou/CenterNet | 7,541 | Object detection, 3D detection, and pose estimation using center point detection: | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/voc_eval_lib/utils/bbox.pyx | Cython | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Sergey Karayev
# --------------------------------------------------------
cimport cython
import numpy as np
cimport numpy as np
DTYPE = np.floa... | xingyizhou/CenterNet | 7,541 | Object detection, 3D detection, and pose estimation using center point detection: | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/voc_eval_lib/utils/blob.py | Python | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Blob helper functions."""
from __future__ import absolute_import
fro... | xingyizhou/CenterNet | 7,541 | Object detection, 3D detection, and pose estimation using center point detection: | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/voc_eval_lib/utils/timer.py | Python | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import time
class Timer(object):
"""A simple timer."""
def __i... | xingyizhou/CenterNet | 7,541 | Object detection, 3D detection, and pose estimation using center point detection: | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/voc_eval_lib/utils/visualization.py | Python | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ impor... | xingyizhou/CenterNet | 7,541 | Object detection, 3D detection, and pose estimation using center point detection: | Python | xingyizhou | Xingyi Zhou | Meta |
.github/workflows/levenshtein.js | JavaScript | /*
Copyright (c) 2011 Andrei Mackenzie
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/__init__.py | Python | from .modeling.meta_arch.centernet_detector import CenterNetDetector
from .modeling.dense_heads.centernet import CenterNet
from .modeling.roi_heads.custom_roi_heads import CustomROIHeads, CustomCascadeROIHeads
from .modeling.backbone.fpn_p5 import build_p67_resnet_fpn_backbone
from .modeling.backbone.dla import build_... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/config.py | Python | from detectron2.config import CfgNode as CN
def add_centernet_config(cfg):
_C = cfg
_C.MODEL.CENTERNET = CN()
_C.MODEL.CENTERNET.NUM_CLASSES = 80
_C.MODEL.CENTERNET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
_C.MODEL.CENTERNET.FPN_STRIDES = [8, 16, 32, 64, 128]
_C.MODEL.CENTERNET.PRIOR_PROB ... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/data/custom_build_augmentation.py | Python | import logging
import numpy as np
import pycocotools.mask as mask_util
import torch
from fvcore.common.file_io import PathManager
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/data/custom_dataset_dataloader.py | Python | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import logging
import numpy as np
import operator
import torch
import torch.utils.data
import json
from detectron2.utils.comm import get_world_size
from detectron2.data import samplers
from torch.utils.data.sampler import BatchSampler, ... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/data/datasets/coco.py | Python | import os
from detectron2.data.datasets.register_coco import register_coco_instances
from detectron2.data.datasets.coco import load_coco_json
from detectron2.data.datasets.builtin_meta import _get_builtin_metadata
from detectron2.data import DatasetCatalog, MetadataCatalog
def register_distill_coco_instances(name, m... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/data/datasets/nuimages.py | Python | from detectron2.data.datasets.register_coco import register_coco_instances
import os
categories = [
{'id': 0, 'name': 'car'},
{'id': 1, 'name': 'truck'},
{'id': 2, 'name': 'trailer'},
{'id': 3, 'name': 'bus'},
{'id': 4, 'name': 'construction_vehicle'},
{'id': 5, 'name': 'bicycle'},
{'id': 6... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/data/datasets/objects365.py | Python | from detectron2.data.datasets.register_coco import register_coco_instances
import os
categories_v1 = [
{'id': 164, 'name': 'cutting/chopping board'} ,
{'id': 49, 'name': 'tie'} ,
{'id': 306, 'name': 'crosswalk sign'} ,
{'id': 145, 'name': 'gun'} ,
{'id': 14, 'name': 'street lights'} ,
{'id': 223, 'name': 'bar soap'} ,... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/data/transforms/custom_augmentation_impl.py | Python | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Modified by Xingyi Zhou
"""
Implement many useful :class:`Augmentation`.
"""
import numpy as np
import sys
from fvcore.transforms.transform import (
BlendTransform,
CropTransform,
HFlipTransform,
NoOpTransfo... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/data/transforms/custom_transform.py | Python | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Modified by Xingyi Zhou
# File: transform.py
import numpy as np
import torch
import torch.nn.functional as F
from fvcore.transforms.transform import (
CropTransform,
HFlipTransform,
NoOpTransform,
Transform... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/backbone/bifpn.py | Python | # Modified from https://github.com/rwightman/efficientdet-pytorch/blob/master/effdet/efficientdet.py
# The original file is under Apache-2.0 License
import math
from os.path import join
import numpy as np
from collections import OrderedDict
from typing import List
import torch
from torch import nn
import torch.utils.m... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/backbone/bifpn_fcos.py | Python | # This file is modified from https://github.com/aim-uofa/AdelaiDet/blob/master/adet/modeling/backbone/bifpn.py
# The original file is under 2-clause BSD License for academic use, and *non-commercial use*.
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec,... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/backbone/dla.py | Python | import numpy as np
import math
from os.path import join
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.model_zoo as model_zoo
from detectron2.modeling.backbone.resnet import (
BasicStem, BottleneckBlock, DeformBottleneckBlock)
from d... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/backbone/dlafpn.py | Python | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# this file is from https://github.com/ucbdrive/dla/blob/master/dla.py.
import math
from os.path import join
import numpy as np
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as wei... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/backbone/fpn_p5.py | Python | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
import fvcore.nn.weight_init as weight_init
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.backb... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/backbone/res2net.py | Python | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# This file is modified from https://github.com/Res2Net/Res2Net-detectron2/blob/master/detectron2/modeling/backbone/resnet.py
# The original file is under Apache-2.0 License
import numpy as np
import fvcore.nn.weight_init as weight_init
import torch... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/debug.py | Python | import cv2
import numpy as np
import torch
import torch.nn.functional as F
COLORS = ((np.random.rand(1300, 3) * 0.4 + 0.6) * 255).astype(
np.uint8).reshape(1300, 1, 1, 3)
def _get_color_image(heatmap):
heatmap = heatmap.reshape(
heatmap.shape[0], heatmap.shape[1], heatmap.shape[2], 1)
if heatmap.shape[0] ==... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/dense_heads/centernet.py | Python |
import math
import json
import copy
from typing import List, Dict
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
from detectron2.layers import ShapeSpec, cat
from detectron2.structures impor... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/dense_heads/centernet_head.py | Python | import math
from typing import List
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.layers import ShapeSpec, get_norm
from detectron2.config import configurable
from ..layers.deform_conv import DFConv2d
__all__ = ["CenterNetHead"]
class Scale(nn.Module):
def __init__(self, ... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/dense_heads/utils.py | Python | import cv2
import torch
from torch import nn
from detectron2.utils.comm import get_world_size
from detectron2.structures import pairwise_iou, Boxes
# from .data import CenterNetCrop
import torch.nn.functional as F
import numpy as np
from detectron2.structures import Boxes, ImageList, Instances
__all__ = ['reduce_sum',... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/layers/deform_conv.py | Python | import torch
from torch import nn
from detectron2.layers import Conv2d
class _NewEmptyTensorOp(torch.autograd.Function):
@staticmethod
def forward(ctx, x, new_shape):
ctx.shape = x.shape
return x.new_empty(new_shape)
@staticmethod
def backward(ctx, grad):
shape = ctx.shape
... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/layers/heatmap_focal_loss.py | Python | import torch
from torch.nn import functional as F
# TODO: merge these two function
def heatmap_focal_loss(
inputs,
targets,
pos_inds,
labels,
alpha: float = -1,
beta: float = 4,
gamma: float = 2,
reduction: str = 'sum',
sigmoid_clamp: float = 1e-4,
ignore_high_fp: float = -1.,
)... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/layers/iou_loss.py | Python | import torch
from torch import nn
class IOULoss(nn.Module):
def __init__(self, loc_loss_type='iou'):
super(IOULoss, self).__init__()
self.loc_loss_type = loc_loss_type
def forward(self, pred, target, weight=None, reduction='sum'):
pred_left = pred[:, 0]
pred_top = pred[:, 1]
... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/layers/ml_nms.py | Python | from detectron2.layers import batched_nms
def ml_nms(boxlist, nms_thresh, max_proposals=-1,
score_field="scores", label_field="labels"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Arguments:
boxlist(BoxList)
n... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/meta_arch/centernet_detector.py | Python | import math
import json
import numpy as np
import torch
from torch import nn
from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
from detectron2.modeling import build_backbone, build_proposal_generator
from detectron2.modeling import detector_postprocess
from detectron2.structures import ImageList
@MET... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/roi_heads/custom_fast_rcnn.py | Python | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Part of the code is from https://github.com/tztztztztz/eql.detectron2/blob/master/projects/EQL/eql/fast_rcnn.py
import logging
import math
import json
from typing import Dict, Union
import torch
from fvcore.nn import giou_loss, smooth_l1_loss
from... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/roi_heads/custom_roi_heads.py | Python | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
import json
import math
import torch
from torch import nn
from torch.autograd.function import Function
from typing import Dict, List, Optional, Tuple, Union
from detectron2.layers import ShapeSpec
from detectron2.structures impor... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
centernet/modeling/roi_heads/fed_loss.py | Python | import torch
import json
import numpy as np
from torch.nn import functional as F
def load_class_freq(
path='datasets/lvis/lvis_v1_train_cat_info.json',
freq_weight=0.5):
cat_info = json.load(open(path, 'r'))
cat_info = torch.tensor(
[c['image_count'] for c in sorted(cat_info, key=lambda x: x['... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
demo.py | Python | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import glob
import multiprocessing as mp
import os
import time
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
fro... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
predictor.py | Python | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import atexit
import bisect
import multiprocessing as mp
from collections import deque
import cv2
import torch
from detectron2.data import MetadataCatalog
from detectron2.engine.defaults import DefaultPredictor
from detectron2.utils.video_visualize... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/analyze_model.py | Python | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table # can also try flop_count_str
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, ... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/benchmark.py | Python | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
A script to benchmark builtin models.
Note: this script has an extra dependency of psutil.
"""
import itertools
import logging
import psutil
import torch
import tqdm
from fvcore.common.timer import Timer
from torch.nn.parallel import Distrib... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/convert-torchvision-to-d2.py | Python | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import pickle as pkl
import sys
import torch
"""
Usage:
# download one of the ResNet{18,34,50,101,152} models from torchvision:
wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth
# run the conversion
./convert-torc... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/deploy/export_model.py | Python | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import os
from typing import Dict, List, Tuple
import torch
from torch import Tensor, nn
import detectron2.data.transforms as T
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from det... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/deploy/torchscript_mask_rcnn.cpp | C++ | // Copyright (c) Facebook, Inc. and its affiliates.
// @lint-ignore-every CLANGTIDY
// This is an example code that demonstrates how to run inference
// with a torchscript format Mask R-CNN model exported by ./export_model.py
// using export method=tracing, caffe2_tracing & scripting.
#include <opencv2/opencv.hpp>
#in... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/lazyconfig_train_net.py | Python | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Training script using the new "LazyConfig" python config files.
This scripts reads a given python config file and runs the training or evaluation.
It can be used to train any models or dataset as long as they can be
instantiated by the recurs... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/lightning_train_net.py | Python | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Lightning Trainer should be considered beta at this point
# We have confirmed that training and validation run correctly and produce correct results
# Depending on how you launch the trainer, there are issues with processes terminating correctl... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/plain_train_net.py | Python | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Detectron2 training script with a plain training loop.
This script reads a given config file and runs the training or evaluation.
It is an entry point that is able to train standard models in detectron2.
In order to let one script support tr... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/train_net.py | Python | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
A main training script.
This scripts reads a given config file and runs the training or evaluation.
It is an entry point that is made to train standard models in detectron2.
In order to let one script support training of many models,
this sc... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/visualize_data.py | Python | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import os
from itertools import chain
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader
from detectron2.data import detection_uti... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
tools/visualize_json_results.py | Python | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import json
import numpy as np
import os
from collections import defaultdict
import cv2
import tqdm
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.structures import Boxes, BoxMode, Instances
from dete... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
train_net.py | Python | import logging
import os
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import time
import datetime
import json
from fvcore.common.timer import Timer
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointe... | xingyizhou/CenterNet2 | 1,222 | Two-stage CenterNet | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/coco_pose_tracking.sh | Shell | cd src
# train, the model is finetuned from a CenterNet detection model from the CenterNet model zoo.
python main.py tracking,multi_pose --exp_id coco_pose_tracking --dataset coco_hp --load_model ../models/multi_pose_dla_3x.pth --gpus 0,1,2,3,4,5,6,7 --batch_size 128 --lr 5e-4 --num_workers 16 --pre_hm --shift 0.05 --s... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/coco_tracking.sh | Shell | cd src
# train, the model is finetuned from a CenterNet detection model from the CenterNet model zoo.
python main.py tracking --exp_id coco_tracking --tracking --load_model ../models/ctdet_coco_dla_2x.pth --gpus 0,1,2,3,4,5,6,7 --batch_size 128 --lr 5e-4 --num_workers 16 --pre_hm --shift 0.05 --scale 0.05 --hm_disturb... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/crowdhuman.sh | Shell | cd src
# train
python main.py tracking --exp_id crowdhuman --dataset crowdhuman --ltrb_amodal --pre_hm --shift 0.05 --scale 0.05 --hm_disturb 0.05 --lost_disturb 0.4 --fp_disturb 0.1 --num_epochs 140 --lr_step 90,120 --save_point 60,90 --gpus 0,1,2,3 --batch_size 64 --lr 2.5e-4 --num_workers 16
cd .. | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/kitti_fulltrain.sh | Shell | cd src
# train
python main.py tracking --exp_id kitti_fulltrain --dataset kitti_tracking --dataset_version train --pre_hm --same_aug --hm_disturb 0.05 --lost_disturb 0.2 --fp_disturb 0.1 --gpus 0,1 --batch_size 16 --load_model ../models/nuScenes_3Ddetection_e140.pth
# test
python test.py tracking --exp_id kitti_fulltra... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/kitti_half.sh | Shell | cd src
# train
python main.py tracking --exp_id kitti_half --dataset kitti_tracking --dataset_version train_half --pre_hm --same_aug --hm_disturb 0.05 --lost_disturb 0.2 --fp_disturb 0.1 --gpus 0,1 --batch_size 16 --load_model ../models/nuScenes_3Ddetection_e140.pth
# test
python test.py tracking --exp_id kitti_half --... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/kitti_half_sc.sh | Shell | cd src
# train
python main.py tracking --exp_id kitti_half_sc --dataset kitti_tracking --dataset_version train_half --pre_hm --same_aug --hm_disturb 0.05 --lost_disturb 0.2 --fp_disturb 0.1 --gpus 0,1 --batch_size 16
# test
python test.py tracking --exp_id kitti_half_sc --dataset kitti_tracking --dataset_version val_ha... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/mot17_fulltrain.sh | Shell | cd src
# train
python main.py tracking --exp_id mot17_fulltrain --dataset mot --dataset_version 17trainval --pre_hm --ltrb_amodal --same_aug --hm_disturb 0.05 --lost_disturb 0.4 --fp_disturb 0.1 --gpus 0,1 --load_model ../models/crowdhuman.pth
# test
python test.py tracking --exp_id mot17_fulltrain --dataset mot --data... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/mot17_fulltrain_sc.sh | Shell | cd src
# train
python main.py tracking --exp_id mot17_fulltrain_sc --dataset mot --dataset_version 17trainval --pre_hm --ltrb_amodal --same_aug --hm_disturb 0.05 --lost_disturb 0.4 --fp_disturb 0.1 --gpus 0,1
# test
python test.py tracking --exp_id mot17_fulltrain_sc --dataset mot --dataset_version 17test --pre_hm --lt... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/mot17_half.sh | Shell | cd src
# train
python main.py tracking --exp_id mot17_half --dataset mot --dataset_version 17halftrain --pre_hm --ltrb_amodal --same_aug --hm_disturb 0.05 --lost_disturb 0.4 --fp_disturb 0.1 --gpus 0,1 --load_model ../models/crowdhuman.pth
# test
python test.py tracking --exp_id mot17_half --dataset mot --dataset_versi... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/mot17_half_sc.sh | Shell | cd src
# train
python main.py tracking --exp_id mot17_half_sc --dataset mot --dataset_version 17halftrain --pre_hm --ltrb_amodal --same_aug --hm_disturb 0.05 --lost_disturb 0.4 --fp_disturb 0.1 --gpus 0,1
# test
python test.py tracking --exp_id mot17_half_sc --dataset mot --dataset_version 17halfval --pre_hm --ltrb_amo... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/nuScenes_3Ddetection_e140.sh | Shell | cd src
# train
python main.py ddd --exp_id nuScenes_3Ddetection_e140 --dataset nuscenes --batch_size 128 --gpus 0,1,2,3,4,5,6,7 --lr 5e-4 --num_epochs 140 --lr_step 90,120 --save_point 90,120
# test
python test.py ddd --exp_id nuScenes_3Ddetection_e140 --dataset nuscenes --resume
cd .. | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
experiments/nuScenes_3Dtracking.sh | Shell | cd src
# train
python main.py tracking,ddd --exp_id nuScenes_3Dtracking --dataset nuscenes --pre_hm --load_model ../models/nuScenes_3Ddetection_e140.pth --shift 0.01 --scale 0.05 --lost_disturb 0.4 --fp_disturb 0.1 --hm_disturb 0.05 --batch_size 64 --gpus 0,1,2,3 --lr 2.5e-4 --save_point 60
# test
python test.py track... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/_init_paths.py | Python | import os.path as osp
import sys
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
this_dir = osp.dirname(__file__)
# Add lib to PYTHONPATH
lib_path = osp.join(this_dir, 'lib')
add_path(lib_path)
| xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/convert_onnx.py | Python | '''
Script to convert a trained CenterNet model to ONNX, currently only
support non-DCN models.
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import json
import cv2
import numpy as np
import time
from progress.bar import Ba... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/demo.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import sys
import cv2
import json
import copy
import numpy as np
from opts import opts
from detector import Detector
image_ext = ['jpg', 'jpeg', 'png', 'webp']
video_ext = ['mp4'... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/dataset_factory.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
from .datasets.coco import COCO
from .datasets.kitti import KITTI
from .datasets.coco_hp impor... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/datasets/coco.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
import copy
from ..generic_dataset import GenericDataset
class COCO(GenericDataset):
defaul... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/datasets/coco_hp.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
from ..generic_dataset import GenericDataset
class COCOHP(GenericDataset):
num_categories =... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/datasets/crowdhuman.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
from ..generic_dataset import GenericDataset
class CrowdHuman(GenericDataset):
num_classes ... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/datasets/custom_dataset.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from ..generic_dataset import GenericDataset
class CustomDataset(GenericDataset):
num_categories = 1
default_resolution = [-1, -1]
class_name = ['']
max_objs = 128
cat_ids = {1: 1}
def __init__(sel... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/datasets/kitti.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
import numpy as np
import torch
import json
import cv2
import os
import math
from ..generic_dataset import GenericDataset
from utils.ddd_utils import compute_box_3d, project_to_... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/datasets/kitti_tracking.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
import numpy as np
import torch
import json
import cv2
import os
import math
from ..generic_dataset import GenericDataset
from utils.ddd_utils import compute_box_3d, project_to_... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/datasets/mot.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import numpy as np
import json
import os
from collections import defaultdict
from ..generic_dataset import GenericDataset
class MOT(Gen... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/datasets/nuscenes.py | Python | # Copyright (c) Xingyi Zhou. All Rights Reserved
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
from pyquaternion import Quaternion
import numpy as np
import torch
import json
import ... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/dataset/generic_dataset.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import math
import json
import cv2
import os
from collections import defaultdict
import pycocotools.coco as coco
import torch
import torch.utils.data as data
from utils.image import flip, c... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/detector.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import copy
import numpy as np
from progress.bar import Bar
import time
import torch
import math
from model.model import create_model, load_model
from model.decode import generic_decode
from model.u... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/external/nms.pyx | Cython | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
# ----------------------------------------------------------
# Soft-NMS... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/external/setup.py | Python | import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
extensions = [
Extension(
"nms",
["nms.pyx"],
extra_compile_args=["-Wno-cpp", "-Wno-unused-function"]
)
]
setup(
name="coco",
ext_modules=cythonize(extens... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/logger.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
import os
import time
import sys
import torch
import subprocess
USE_TENSORBOARD = True
try:
import tensorboardX
print('... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/data_parallel.py | Python | import torch
from torch.nn.modules import Module
from torch.nn.parallel.scatter_gather import gather
from torch.nn.parallel.replicate import replicate
from torch.nn.parallel.parallel_apply import parallel_apply
from .scatter_gather import scatter_kwargs
class _DataParallel(Module):
r"""Implements data parallelis... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/decode.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
from .utils import _gather_feat, _tranpose_and_gather_feat
from .utils import _nms, _topk, _topk_channel
def _update_kps_with_hm(
kps, output, batch, num_joints, K, bboxes... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/losses.py | Python | # ------------------------------------------------------------------------------
# Portions of this code are from
# CornerNet (https://github.com/princeton-vl/CornerNet)
# Copyright (c) 2018, University of Michigan
# Licensed under the BSD 3-Clause License
# -------------------------------------------------------------... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/model.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torchvision.models as models
import torch
import torch.nn as nn
import os
from .networks.dla import DLASeg
from .networks.resdcn import PoseResDCN
from .networks.resnet import PoseResNet
from .networks.... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/backbones/dla.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import logging
import numpy as np
from os.path import join
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
BN_MOMENTUM = 0.1
... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/backbones/mobilenet.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import logging
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torchvision.models.utils import load_sta... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/backbones/resnet.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import logging
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
BN_MOMENTUM = 0.1
model_urls = {
'resne... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/base_model.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
from torch import nn
def fill_fc_weights(layers):
for m in layers.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
nn.init.constant_(m.bias, 0... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/dla.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import logging
import numpy as np
from os.path import join
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from .base_model im... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/dlav0.py | Python | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from os.path import join
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
from .base_model import BaseModel
import numpy as ... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/generic_network.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
from torch import nn
from .backbones.dla import dla34
from .backbones.resnet import Resnet
from .backbones.mobilenet import MobileNetV2
from .necks.dlaup import DLASeg
from .necks.msraup import MSR... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/necks/dlaup.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import logging
import numpy as np
from os.path import join
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
try:
from ..DCNv2... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/necks/msraup.py | Python | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Dequan Wang and Xingyi Zhou
# ------------------------------------------------------------------------------
from __f... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/resdcn.py | Python | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Dequan Wang and Xingyi Zhou
# ------------------------------------------------------------------------------
from __f... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/networks/resnet.py | Python | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Xingyi Zhou
# ------------------------------------------------------------------------------
from __future__ import a... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/scatter_gather.py | Python | import torch
from torch.autograd import Variable
from torch.nn.parallel._functions import Scatter, Gather
def scatter(inputs, target_gpus, dim=0, chunk_sizes=None):
r"""
Slices variables into approximately equal chunks and
distributes them across given GPUs. Duplicates
references to objects that are n... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/model/utils.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
def _sigmoid(x):
y = torch.clamp(x.sigmoid_(), min=1e-4, max=1-1e-4)
return y
def _sigmoid12(x):
y = torch.clamp(x.sigmoid_(), 1e-12)
return y
def _gather_feat(feat... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/opts.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
class opts(object):
def __init__(self):
self.parser = argparse.ArgumentParser()
# basic experiment setting
self.parser.add_argument('task', default='',
... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/trainer.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import torch
import numpy as np
from progress.bar import Bar
from model.data_parallel import DataParallel
from utils.utils import AverageMeter
from model.losses import FastFocalLoss, RegWeightedL1... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/utils/ddd_utils.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import cv2
def comput_corners_3d(dim, rotation_y):
# dim: 3
# location: 3
# rotation_y: 1
# return: 8 x 3
c, s = np.cos(rotation_y), np.sin(rotation_y)
R = np.array([[c, 0, s], [... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/utils/debugger.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
from mpl_toolkits.mplot3d import Axes3D
import time
import numpy as np
import cv2
from .ddd_utils import compute_box_3d, project_to_image, draw_box_3d... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/utils/image.py | Python | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Xingyi Zhou
# ------------------------------------------------------------------------------
from __future__ import a... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/utils/post_process.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import cv2
from .image import transform_preds_with_trans, get_affine_transform
from .ddd_utils import ddd2locrot, comput_corners_3d
from .ddd_utils import project_to_image, rot_y2alpha
import... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
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