├── README.md ├── experiments └── phtrack │ └── config.yaml ├── pretrained_models └── README.txt ├── pysot ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ └── __init__.cpython-39.pyc ├── core │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── config.cpython-37.pyc │ │ └── config.cpython-39.pyc │ └── config.py ├── datasets │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── augmentation.cpython-37.pyc │ │ ├── augmentation.cpython-39.pyc │ │ ├── dataset.cpython-37.pyc │ │ └── dataset.cpython-39.pyc │ ├── augmentation.py │ └── dataset.py ├── models │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── loss_car.cpython-37.pyc │ │ ├── loss_car.cpython-39.pyc │ │ ├── model_builder.cpython-37.pyc │ │ └── model_builder.cpython-39.pyc │ ├── backbone │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── alexnet.cpython-37.pyc │ │ │ ├── alexnet.cpython-39.pyc │ │ │ ├── mobile_v2.cpython-37.pyc │ │ │ ├── mobile_v2.cpython-39.pyc │ │ │ ├── resnet_atrous.cpython-37.pyc │ │ │ └── resnet_atrous.cpython-39.pyc │ │ ├── alexnet.py │ │ ├── mobile_v2.py │ │ └── resnet_atrous.py │ ├── channelsplit │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ └── msc.cpython-39.pyc │ │ └── msc.py │ ├── fusion │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ └── transfusion.cpython-39.pyc │ │ └── transfusion.py │ ├── head │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── car_head.cpython-37.pyc │ │ │ └── car_head.cpython-39.pyc │ │ └── car_head.py │ ├── init_weight.py │ ├── loss_car.py │ ├── model_builder.py │ └── neck │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── neck.cpython-37.pyc │ │ └── neck.cpython-39.pyc │ │ └── neck.py ├── tracker │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── base_tracker.cpython-37.pyc │ │ ├── base_tracker.cpython-39.pyc │ │ ├── phtrack_tracker.cpython-39.pyc │ │ └── tracker_builder.cpython-39.pyc │ ├── base_tracker.py │ ├── phtrack_tracker.py │ └── tracker_builder.py └── utils │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── __init__.cpython-39.pyc │ ├── average_meter.cpython-37.pyc │ ├── average_meter.cpython-39.pyc │ ├── bbox.cpython-37.pyc │ ├── bbox.cpython-39.pyc │ ├── distributed.cpython-37.pyc │ ├── distributed.cpython-39.pyc │ ├── location_grid.cpython-37.pyc │ ├── location_grid.cpython-39.pyc │ ├── log_helper.cpython-37.pyc │ ├── log_helper.cpython-39.pyc │ ├── lr_scheduler.cpython-37.pyc │ ├── lr_scheduler.cpython-39.pyc │ ├── misc.cpython-37.pyc │ ├── misc.cpython-39.pyc │ ├── model_load.cpython-37.pyc │ ├── model_load.cpython-39.pyc │ ├── xcorr.cpython-37.pyc │ └── xcorr.cpython-39.pyc │ ├── average_meter.py │ ├── bbox.py │ ├── distributed.py │ ├── location_grid.py │ ├── log_helper.py │ ├── lr_scheduler.py │ ├── misc.py │ ├── model_load.py │ └── xcorr.py ├── toolkit ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ └── __init__.cpython-39.pyc ├── datasets │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── dataset.cpython-37.pyc │ │ ├── dataset.cpython-39.pyc │ │ ├── got10k.cpython-37.pyc │ │ ├── got10k.cpython-39.pyc │ │ ├── lasot.cpython-37.pyc │ │ ├── lasot.cpython-39.pyc │ │ ├── otb.cpython-37.pyc │ │ ├── otb.cpython-39.pyc │ │ ├── uav.cpython-37.pyc │ │ ├── uav.cpython-39.pyc │ │ ├── video.cpython-37.pyc │ │ └── video.cpython-39.pyc │ ├── dataset.py │ ├── got10k.py │ ├── lasot.py │ ├── otb.py │ ├── uav.py │ └── video.py ├── evaluation │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-39.pyc │ │ ├── ope_benchmark.cpython-37.pyc │ │ └── ope_benchmark.cpython-39.pyc │ └── ope_benchmark.py ├── utils │ ├── __pycache__ │ │ ├── statistics.cpython-37.pyc │ │ └── statistics.cpython-39.pyc │ └── statistics.py └── visualization │ ├── __init__.py │ ├── draw_success_precision.py │ └── draw_utils.py ├── tools ├── eval.py ├── test.py └── train.py └── tracking_results ├── README.txt ├── hotc20test ├── ball.txt ├── basketball.txt ├── board.txt ├── book.txt ├── bus.txt ├── bus2.txt ├── campus.txt ├── car.txt ├── car2.txt ├── car3.txt ├── card.txt ├── coin.txt ├── coke.txt ├── drive.txt ├── excavator.txt ├── face.txt ├── face2.txt ├── forest.txt ├── forest2.txt ├── fruit.txt ├── hand.txt ├── kangaroo.txt ├── paper.txt ├── pedestrain.txt ├── pedestrian2.txt ├── player.txt ├── playground.txt ├── rider1.txt ├── rider2.txt ├── rubik.txt ├── student.txt ├── toy1.txt ├── toy2.txt ├── trucker.txt └── worker.txt ├── hotc23val_nir ├── basketball3.txt ├── car11.txt ├── car12.txt ├── car49.txt ├── car50.txt ├── car51.txt ├── car52.txt ├── car53.txt ├── car59.txt ├── car60.txt ├── car61.txt ├── car62.txt ├── car63.txt ├── car64.txt ├── car76.txt ├── car77.txt ├── car78.txt ├── car79.txt ├── car80.txt ├── car81.txt ├── car82.txt ├── car83.txt ├── car84.txt ├── car85.txt ├── pedestrian7.txt ├── rider11.txt ├── rider16.txt ├── rider17.txt ├── rider18.txt └── rider19.txt ├── hotc23val_rednir ├── ball&mirror9.txt ├── cards16.txt ├── cards19.txt ├── dice2.txt ├── duck5.txt ├── partylights6.txt ├── pool10.txt ├── pool11.txt ├── rainystreet10.txt ├── rainystreet16.txt └── whitecup1.txt ├── hotc23val_vis ├── ball&mirror9.txt ├── ball.txt ├── basketball.txt ├── board.txt ├── book.txt ├── bus.txt ├── bus2.txt ├── campus.txt ├── car.txt ├── car2.txt ├── car3.txt ├── card.txt ├── cards16.txt ├── cards19.txt ├── coin.txt ├── coke.txt ├── dice2.txt ├── drive.txt ├── duck5.txt ├── excavator.txt ├── face.txt ├── face2.txt ├── forest.txt ├── forest2.txt ├── fruit.txt ├── hand.txt ├── kangaroo.txt ├── paper.txt ├── partylights6.txt ├── pedestrain.txt ├── pedestrian2.txt ├── player.txt ├── playground.txt ├── pool10.txt ├── pool11.txt ├── rainystreet10.txt ├── rainystreet16.txt ├── rider1.txt ├── rider2.txt ├── rubik.txt ├── student.txt ├── toy1.txt ├── toy2.txt ├── trucker.txt ├── whitecup1.txt └── worker.txt ├── hotc24val_nir ├── basketball2.txt ├── car42.txt ├── car43.txt ├── car44.txt ├── car45.txt ├── car46.txt ├── car47.txt ├── car48.txt ├── car54.txt ├── car55.txt ├── car56.txt ├── car57.txt ├── car58.txt ├── car65.txt ├── car66.txt ├── car67.txt ├── car68.txt ├── car69.txt ├── car70.txt ├── car71.txt ├── car72.txt ├── car73.txt ├── car74.txt ├── car75.txt ├── car86.txt ├── pedestrian6.txt ├── rider10.txt ├── rider14.txt ├── rider15.txt └── rider9.txt ├── hotc24val_rednir ├── backpack4.txt ├── ball&mirror10.txt ├── cards6.txt ├── cloth3.txt ├── cranberries7.txt ├── dice3.txt ├── drone4.txt ├── dronecam4.txt ├── droneshow2.txt ├── duck4.txt ├── football1.txt ├── football2.txt ├── leaves5.txt ├── officechair2.txt ├── oranges5.txt ├── pills5.txt ├── pool12.txt ├── pool9.txt ├── rainystreet12.txt └── whitecup2.txt ├── hotc24val_vis ├── L_basketball.txt ├── L_basketball2.txt ├── L_basketball_person.txt ├── L_car2.txt ├── L_car3.txt ├── L_person.txt ├── L_person7.txt ├── L_person8.txt ├── L_person9.txt ├── L_runner.txt ├── S_jump2.txt ├── S_person2.txt ├── S_runner1.txt ├── S_runner2.txt ├── S_soccer1.txt ├── S_soccer3.txt ├── S_soccer4.txt ├── S_volleyball1.txt ├── S_walker.txt ├── S_warmup.txt ├── backpack4.txt ├── ball&mirror10.txt ├── cards6.txt ├── clamp.txt ├── clamp2.txt ├── clamp3.txt ├── cloth3.txt ├── cranberries7.txt ├── cup.txt ├── cup2.txt ├── cup3.txt ├── cup4.txt ├── cup5.txt ├── cup6.txt ├── dice3.txt ├── drone4.txt ├── dronecam4.txt ├── droneshow2.txt ├── duck4.txt ├── esophagectomy1.txt ├── football1.txt ├── football2.txt ├── hat.txt ├── heartsurgery2.txt ├── leaf.txt ├── leaves5.txt ├── officechair2.txt ├── oranges5.txt ├── pen.txt ├── pen2.txt ├── pen3.txt ├── pen4.txt ├── people.txt ├── people2.txt ├── people3.txt ├── people4.txt ├── pills5.txt ├── pool12.txt ├── pool9.txt ├── rainystreet12.txt ├── rubik.txt ├── rubik2.txt ├── rubik3.txt ├── rubik4.txt ├── rubik5.txt ├── rubik6.txt └── whitecup2.txt ├── mssot ├── airplane-0.txt ├── airplane-1.txt ├── airplane-11.txt ├── airplane-2.txt ├── airplane-20.txt ├── airplane-21.txt ├── basketball-0.txt ├── basketball-1.txt ├── bicycle-1n.txt ├── bicycle-7n.txt ├── boat-11.txt ├── boat-13.txt ├── boat-3.txt ├── boat-4.txt ├── boat-6.txt ├── car-15.txt ├── car-16.txt ├── car-24.txt ├── car-25.txt ├── car-26.txt ├── car-27.txt ├── double-6.txt ├── double-8.txt ├── double-9.txt ├── doublebasketball-0.txt ├── doublecar-1.txt ├── doublecar-9.txt ├── electriccar-6.txt ├── human-18.txt ├── human-19.txt ├── motorcycle-0.txt ├── motorcycle-3.txt ├── motorcycle-5.txt ├── motorcycle-6.txt ├── motorcycle-7.txt ├── motorcycle-8.txt ├── person-2.txt ├── person-3.txt ├── triple-2.txt └── triple-4.txt └── msvt ├── airplane.txt ├── airplane1.txt ├── airplane10.txt ├── airplane11.txt ├── airplane12.txt ├── airplane13.txt ├── airplane14.txt ├── airplane2.txt ├── airplane3.txt ├── airplane4.txt ├── airplane5.txt ├── airplane6.txt ├── airplane7.txt ├── airplane8.txt ├── airplane9.txt ├── bicycle.txt ├── bicycle1.txt ├── bicycle2.txt ├── bicycle3.txt ├── boat.txt ├── boat1.txt ├── boat10.txt ├── boat11.txt ├── boat12.txt ├── boat2.txt ├── boat3.txt ├── boat4.txt ├── boat5.txt ├── boat6.txt ├── boat7.txt ├── boat8.txt ├── boat9.txt ├── bottle.txt ├── camera.txt ├── car.txt ├── car1.txt ├── car2.txt ├── car3.txt ├── car4.txt ├── car5.txt ├── car6.txt ├── double.txt ├── double1.txt ├── double2.txt ├── double3.txt ├── double4.txt ├── double5.txt ├── doublebicycle.txt ├── doublecar.txt ├── doublecar1.txt ├── doublecar2.txt ├── doublecar3.txt ├── doublecar4.txt ├── doublecar5.txt ├── doublecar6.txt ├── doublecar7.txt ├── doublecar8.txt ├── electriccar.txt ├── electriccar1.txt ├── girl.txt ├── human.txt ├── human1.txt ├── human2.txt ├── human3.txt ├── human4.txt ├── human5.txt ├── human6.txt ├── human7.txt ├── human8.txt ├── human9.txt ├── man.txt ├── man1.txt ├── man2.txt ├── man3.txt ├── many_man.txt ├── motorcycle.txt ├── motorcycle1.txt ├── triple.txt ├── triple1.txt └── triple2.txt /README.md: -------------------------------------------------------------------------------- 1 | # [PHTrack: Prompting for Hyperspectral Video Tracking](https://ieeexplore.ieee.org/abstract/document/10680554) 2 | The official implementation for "**PHTrack: Prompting for Hyperspectral Video Tracking**" 3 | 4 | # Abstract 5 | Hyperspectral (HS) video captures continuous spectral information of objects, enhancing material identification in tracking tasks. It is expected to overcome the inherent limitations of RGB and multi-modal tracking, such as finite spectral cues and cumbersome modality alignment. However, HS tracking faces challenges like data anxiety, band gaps, and huge volumes. In this study, inspired by prompt learning in language models, we propose the Prompting for Hyperspectral Video Tracking (PHTrack) framework. PHTrack learns prompts to adapt foundation models, mitigating data anxiety and enhancing performance and efficiency. First, the modality prompter (MOP) is proposed to capture rich spectral cues and bridge band gaps for improved model adaptation and knowledge enhancement. Additionally, the distillation prompter (DIP) is developed to refine cross-modal features. PHTrack follows feature-level fusion, effectively managing huge volumes compared to traditional decision-level fusion fashions. Extensive experiments validate the proposed framework, offering valuable insights for future research. The code and data will be available at https://github.com/YZCU/PHTrack. 6 | ## Install 7 | ``` 8 | git clone https://github.com/YZCU/PHTrack.git 9 | ``` 10 | ## Environment 11 | > * CUDA 11.8 12 | > * Python 3.9.18 13 | > * PyTorch 2.0.0 14 | > * Torchvision 0.15.0 15 | > * numpy 1.25.0 16 | ## Usage 17 | - Download the RGB/Hyperspectral training/test datasets [GOT-10K](http://got-10k.aitestunion.com/downloads), [DET](http://image-net.org/challenges/LSVRC/2017/), [LaSOT](https://cis.temple.edu/lasot/), [COCO](http://cocodataset.org), [YOUTUBEBB](https://pan.baidu.com/s/1gQKmi7o7HCw954JriLXYvg) (code: v7s6), [VID](http://image-net.org/challenges/LSVRC/2017/), [HOTC](https://www.hsitracking.com/hot2020/). 18 | - Download the pretrained model: [pretrained model](https://pan.baidu.com/s/19pmFUAA0Bvj0s0GP_4xccA) (code: abcd) to `pretrained_models/`. 19 | - Please train the PHTrack based on the [foundation model](https://pan.baidu.com/s/19pmFUAA0Bvj0s0GP_4xccA) (code: abcd). 20 | - We will release the well-trained model of [PHTrack](https://pan.baidu.com/s/1TpODrs5IbnfXKyNC1N0uOAA) (code: abcd). 21 | - The generated model will be saved to the path of `tools/snapshot`. 22 | - Please test the model. The results will be saved in the path of `tools/results/OTB100`. 23 | - For evaluation, please download the evaluation benchmark [Toolkit](http://cvlab.hanyang.ac.kr/tracker_benchmark/) and [vlfeat](http://www.vlfeat.org/index.html) for more precision performance evaluation. 24 | - Refer to [HOTC](https://www.hsitracking.com/hot2022/) for evaluation. 25 | - Evaluation of the PHTrack tracker. Run `\tracker_benchmark_v1.0\perfPlot.m` 26 | - Relevant tracking results are provided in `PHTrack\tracking_results\hotc20test`. More evaluation results are provided in a `PHTrack\tracking_results`. 27 | -------------------------------------------------------------------------------------- 28 | :running:Keep updating:running:: More detailed tracking results for PHTrack have been released. 29 | - [hotc20test](https://www.hsitracking.com/) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 30 | - [hotc23val_nir](https://www.hsitracking.com/) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 31 | - [hotc23val_rednir](https://www.hsitracking.com/) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 32 | - [hotc23val_vis](https://www.hsitracking.com/) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 33 | - [hotc24val_nir](https://www.hsitracking.com/) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 34 | - [hotc24val_rednir](https://www.hsitracking.com/) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 35 | - [hotc24val_vis](https://www.hsitracking.com/) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 36 | - [mssot](https://www.sciencedirect.com/science/article/pii/S0924271623002551) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 37 | - [msvt](https://www.sciencedirect.com/science/article/pii/S0924271621002860) ([results](https://github.com/YZCU/PHTrack/tree/master/tracking_results)) 38 | -------------------------------------------------------------------------------------- 39 | For more comprehensive results and codes, please review the upcoming manuscript. 40 | 41 | ## Contact 42 | If you have any questions or suggestions, feel free to contact me. 43 | Email: yuzeng_chen@whu.edu.cn 44 | ## Citation 45 | If you find our work helpful in your research, kindly consider citing it. We appreciate your support. 46 | 47 | ``` 48 | @ARTICLE{10680554, 49 | author={Chen, Yuzeng and Tang, Yuqi and Su, Xin and Li, Jie and Xiao, Yi and He, Jiang and Yuan, Qiangqiang}, 50 | journal={IEEE Transactions on Geoscience and Remote Sensing}, 51 | title={PHTrack: Prompting for Hyperspectral Video Tracking}, 52 | year={2024}, 53 | volume={}, 54 | number={}, 55 | pages={1-1}, 56 | keywords={Feature extraction;Video tracking;Photonic band gap;Adaptation models;Visualization;Correlation;Anxiety disorders;Hyperspectral video tracking;Prompt learning;Self-expression model;Material information}, 57 | doi={10.1109/TGRS.2024.3461316}} 58 | keywords={}, 59 | doi={} 60 | ``` 61 | 62 | -------------------------------------------------------------------------------- /experiments/phtrack/config.yaml: -------------------------------------------------------------------------------- 1 | META_ARC: "phtrack_r50" 2 | 3 | BS: 4 | SELECT: true 5 | TYPE: "msc" 6 | 7 | FS: 8 | FUSION: true 9 | TYPE: "transfusion" 10 | 11 | BACKBONE: 12 | TYPE: "resnet50" 13 | KWARGS: 14 | used_layers: [2, 3, 4] 15 | PRETRAINED: '' 16 | TRAIN_LAYERS: ['layer2','layer3','layer4'] 17 | TRAIN_EPOCH: 30 18 | LAYERS_LR: 0.1 19 | 20 | ADJUST: 21 | ADJUST: true 22 | TYPE: "AdjustAllLayer" 23 | KWARGS: 24 | in_channels: [512, 1024, 2048] 25 | out_channels: [256, 256, 256] 26 | 27 | 28 | TRACK: 29 | TYPE: 'phtrackTracker' 30 | LR: 0.0 31 | PENALTY_K: 0.0 32 | WINDOW_INFLUENCE: 0.0 33 | CONTEXT_AMOUNT: 0.0 34 | 35 | STRIDE: 8 36 | EXEMPLAR_SIZE: 127 37 | INSTANCE_SIZE: 255 38 | 39 | TRAIN: 40 | PRINT_FREQ: 10 41 | PRETRAINED: 'model_general.pth' 42 | EPOCH: 20 43 | START_EPOCH: 0 44 | BATCH_SIZE: 32 45 | BASE_LR: 0.005 46 | CLS_WEIGHT: 1.0 47 | LOC_WEIGHT: 3.0 48 | CEN_WEIGHT: 1.0 49 | CG_WEIGHT: 3.0 50 | RESUME: 'true' 51 | NUM_CLASSES: 2 52 | NUM_CONVS: 4 53 | PRIOR_PROB: 0.01 54 | OUTPUT_SIZE: 25 55 | LR: 56 | TYPE: 'log' 57 | KWARGS: 58 | start_lr: 0.005 59 | end_lr: 0.0005 60 | LR_WARMUP: 61 | TYPE: 'step' 62 | EPOCH: 5 63 | KWARGS: 64 | start_lr: 0.001 65 | end_lr: 0.005 66 | step: 1 67 | 68 | DATASET: 69 | NAMES: 70 | - 'GOT' 71 | 72 | TEMPLATE: 73 | SHIFT: 4 74 | SCALE: 0.05 75 | BLUR: 0.0 76 | FLIP: 0.0 77 | COLOR: 0.0 78 | 79 | SEARCH: 80 | SHIFT: 64 81 | SCALE: 0.18 82 | BLUR: 0.2 83 | FLIP: 0.0 84 | COLOR: 0.0 85 | 86 | NEG: 0.0 87 | GRAY: 0.0 88 | -------------------------------------------------------------------------------- /pretrained_models/README.txt: -------------------------------------------------------------------------------- 1 | Download model_general.pth and put it here. -------------------------------------------------------------------------------- /pysot/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/__init__.py -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /pysot/datasets/augmentation.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) SenseTime. All Rights Reserved. 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | from __future__ import unicode_literals 7 | 8 | import numpy as np 9 | import cv2 10 | 11 | from pysot.utils.bbox import corner2center, \ 12 | Center, center2corner, Corner 13 | 14 | 15 | class Augmentation: 16 | def __init__(self, shift, scale, blur, flip, color): 17 | self.shift = shift 18 | self.scale = scale 19 | self.blur = blur 20 | self.flip = flip 21 | self.color = color 22 | self.rgbVar = np.array( 23 | [[-0.55919361, 0.98062831, - 0.41940627], 24 | [1.72091413, 0.19879334, - 1.82968581], 25 | [4.64467907, 4.73710203, 4.88324118]], dtype=np.float32) 26 | 27 | @staticmethod 28 | def random(): 29 | return np.random.random() * 2 - 1.0 30 | 31 | def _crop_roi(self, image, bbox, out_sz, padding=(0, 0, 0)): 32 | bbox = [float(x) for x in bbox] 33 | a = (out_sz-1) / (bbox[2]-bbox[0]) 34 | b = (out_sz-1) / (bbox[3]-bbox[1]) 35 | c = -a * bbox[0] 36 | d = -b * bbox[1] 37 | mapping = np.array([[a, 0, c], 38 | [0, b, d]]).astype(np.float) 39 | crop = cv2.warpAffine(image, mapping, (out_sz, out_sz), 40 | borderMode=cv2.BORDER_CONSTANT, 41 | borderValue=padding) 42 | return crop 43 | 44 | def _blur_aug(self, image): 45 | def rand_kernel(): 46 | sizes = np.arange(5, 46, 2) 47 | size = np.random.choice(sizes) 48 | kernel = np.zeros((size, size)) 49 | c = int(size/2) 50 | wx = np.random.random() 51 | kernel[:, c] += 1. / size * wx 52 | kernel[c, :] += 1. / size * (1-wx) 53 | return kernel 54 | kernel = rand_kernel() 55 | image = cv2.filter2D(image, -1, kernel) 56 | return image 57 | 58 | def _color_aug(self, image): 59 | offset = np.dot(self.rgbVar, np.random.randn(3, 1)) 60 | offset = offset[::-1] # bgr 2 rgb 61 | offset = offset.reshape(3) 62 | image = image - offset 63 | return image 64 | 65 | def _gray_aug(self, image): 66 | grayed = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 67 | image = cv2.cvtColor(grayed, cv2.COLOR_GRAY2BGR) 68 | return image 69 | 70 | def _shift_scale_aug(self, image, bbox, crop_bbox, size): 71 | im_h, im_w = image.shape[:2] 72 | 73 | # adjust crop bounding box 74 | crop_bbox_center = corner2center(crop_bbox) 75 | if self.scale: 76 | scale_x = (1.0 + Augmentation.random() * self.scale) 77 | scale_y = (1.0 + Augmentation.random() * self.scale) 78 | h, w = crop_bbox_center.h, crop_bbox_center.w 79 | scale_x = min(scale_x, float(im_w) / w) 80 | scale_y = min(scale_y, float(im_h) / h) 81 | crop_bbox_center = Center(crop_bbox_center.x, 82 | crop_bbox_center.y, 83 | crop_bbox_center.w * scale_x, 84 | crop_bbox_center.h * scale_y) 85 | 86 | crop_bbox = center2corner(crop_bbox_center) 87 | if self.shift: 88 | sx = Augmentation.random() * self.shift 89 | sy = Augmentation.random() * self.shift 90 | 91 | x1, y1, x2, y2 = crop_bbox 92 | 93 | sx = max(-x1, min(im_w - 1 - x2, sx)) 94 | sy = max(-y1, min(im_h - 1 - y2, sy)) 95 | 96 | crop_bbox = Corner(x1 + sx, y1 + sy, x2 + sx, y2 + sy) 97 | 98 | # adjust target bounding box 99 | x1, y1 = crop_bbox.x1, crop_bbox.y1 100 | bbox = Corner(bbox.x1 - x1, bbox.y1 - y1, 101 | bbox.x2 - x1, bbox.y2 - y1) 102 | 103 | if self.scale: 104 | bbox = Corner(bbox.x1 / scale_x, bbox.y1 / scale_y, 105 | bbox.x2 / scale_x, bbox.y2 / scale_y) 106 | 107 | image = self._crop_roi(image, crop_bbox, size) 108 | return image, bbox 109 | 110 | def _flip_aug(self, image, bbox): 111 | image = cv2.flip(image, 1) 112 | width = image.shape[1] 113 | bbox = Corner(width - 1 - bbox.x2, bbox.y1, 114 | width - 1 - bbox.x1, bbox.y2) 115 | return image, bbox 116 | 117 | def __call__(self, image, bbox, size, gray=False): 118 | shape = image.shape 119 | crop_bbox = center2corner(Center(shape[0]//2, shape[1]//2, 120 | size-1, size-1)) 121 | # gray augmentation 122 | if gray: 123 | image = self._gray_aug(image) 124 | 125 | # shift scale augmentation 126 | image, bbox = self._shift_scale_aug(image, bbox, crop_bbox, size) # 原来强行执行shift scale增强 127 | 128 | # color augmentation 129 | if self.color > np.random.random(): # 永远为true, 永远增强 130 | image = self._color_aug(image) 131 | 132 | # blur augmentation 133 | if self.blur > np.random.random(): 134 | image = self._blur_aug(image) 135 | 136 | # flip augmentation 137 | if self.flip and self.flip > np.random.random(): 138 | image, bbox = self._flip_aug(image, bbox) 139 | return image, bbox 140 | -------------------------------------------------------------------------------- /pysot/models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/__init__.py -------------------------------------------------------------------------------- /pysot/models/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /pysot/models/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /pysot/models/__pycache__/loss_car.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/__pycache__/loss_car.cpython-37.pyc -------------------------------------------------------------------------------- /pysot/models/__pycache__/loss_car.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/__pycache__/loss_car.cpython-39.pyc -------------------------------------------------------------------------------- /pysot/models/__pycache__/model_builder.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/__pycache__/model_builder.cpython-37.pyc -------------------------------------------------------------------------------- /pysot/models/__pycache__/model_builder.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/__pycache__/model_builder.cpython-39.pyc -------------------------------------------------------------------------------- /pysot/models/backbone/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) SenseTime. All Rights Reserved. 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | from __future__ import unicode_literals 7 | 8 | from pysot.models.backbone.alexnet import alexnetlegacy, alexnet 9 | from pysot.models.backbone.mobile_v2 import mobilenetv2 10 | from pysot.models.backbone.resnet_atrous import resnet18, resnet34, resnet50 11 | 12 | BACKBONES = { 13 | 'alexnetlegacy': alexnetlegacy, 14 | 'mobilenetv2': mobilenetv2, 15 | 'resnet18': resnet18, 16 | 'resnet34': resnet34, 17 | 'resnet50': resnet50, 18 | 'alexnet': alexnet, 19 | } 20 | 21 | 22 | def get_backbone(name, **kwargs): 23 | return BACKBONES[name](**kwargs) 24 | -------------------------------------------------------------------------------- /pysot/models/backbone/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- 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/pysot/models/backbone/alexnet.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | 6 | import torch.nn as nn 7 | 8 | 9 | class AlexNetLegacy(nn.Module): 10 | configs = [3, 96, 256, 384, 384, 256] 11 | 12 | def __init__(self, width_mult=1): 13 | configs = list(map(lambda x: 3 if x == 3 else 14 | int(x*width_mult), AlexNet.configs)) 15 | super(AlexNetLegacy, self).__init__() 16 | self.features = nn.Sequential( 17 | nn.Conv2d(configs[0], configs[1], kernel_size=11, stride=2), 18 | nn.BatchNorm2d(configs[1]), 19 | nn.MaxPool2d(kernel_size=3, stride=2), 20 | nn.ReLU(inplace=True), 21 | nn.Conv2d(configs[1], configs[2], kernel_size=5), 22 | nn.BatchNorm2d(configs[2]), 23 | nn.MaxPool2d(kernel_size=3, stride=2), 24 | nn.ReLU(inplace=True), 25 | nn.Conv2d(configs[2], configs[3], kernel_size=3), 26 | nn.BatchNorm2d(configs[3]), 27 | nn.ReLU(inplace=True), 28 | nn.Conv2d(configs[3], configs[4], kernel_size=3), 29 | nn.BatchNorm2d(configs[4]), 30 | nn.ReLU(inplace=True), 31 | nn.Conv2d(configs[4], configs[5], kernel_size=3), 32 | nn.BatchNorm2d(configs[5]), 33 | ) 34 | self.feature_size = configs[5] 35 | 36 | def forward(self, x): 37 | x = self.features(x) 38 | return x 39 | 40 | 41 | class AlexNet(nn.Module): 42 | configs = [3, 96, 256, 384, 384, 256] 43 | 44 | def __init__(self, width_mult=1): 45 | configs = list(map(lambda x: 3 if x == 3 else 46 | int(x*width_mult), AlexNet.configs)) 47 | super(AlexNet, self).__init__() 48 | self.layer1 = nn.Sequential( 49 | nn.Conv2d(configs[0], configs[1], kernel_size=11, stride=2), 50 | nn.BatchNorm2d(configs[1]), 51 | nn.MaxPool2d(kernel_size=3, stride=2), 52 | nn.ReLU(inplace=True), 53 | ) 54 | self.layer2 = nn.Sequential( 55 | nn.Conv2d(configs[1], configs[2], kernel_size=5), 56 | nn.BatchNorm2d(configs[2]), 57 | nn.MaxPool2d(kernel_size=3, stride=2), 58 | nn.ReLU(inplace=True), 59 | ) 60 | self.layer3 = nn.Sequential( 61 | nn.Conv2d(configs[2], configs[3], kernel_size=3), 62 | nn.BatchNorm2d(configs[3]), 63 | nn.ReLU(inplace=True), 64 | ) 65 | self.layer4 = nn.Sequential( 66 | nn.Conv2d(configs[3], configs[4], kernel_size=3), 67 | nn.BatchNorm2d(configs[4]), 68 | nn.ReLU(inplace=True), 69 | ) 70 | 71 | self.layer5 = nn.Sequential( 72 | nn.Conv2d(configs[4], configs[5], kernel_size=3), 73 | nn.BatchNorm2d(configs[5]), 74 | ) 75 | self.feature_size = configs[5] 76 | 77 | def forward(self, x): 78 | x = self.layer1(x) 79 | x = self.layer2(x) 80 | x = self.layer3(x) 81 | x = self.layer4(x) 82 | x = self.layer5(x) 83 | return x 84 | 85 | 86 | def alexnetlegacy(**kwargs): 87 | return AlexNetLegacy(**kwargs) 88 | 89 | 90 | def alexnet(**kwargs): 91 | return AlexNet(**kwargs) 92 | -------------------------------------------------------------------------------- /pysot/models/backbone/mobile_v2.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | 6 | import torch 7 | import torch.nn as nn 8 | 9 | 10 | def conv_bn(inp, oup, stride, padding=1): 11 | return nn.Sequential( 12 | nn.Conv2d(inp, oup, 3, stride, padding, bias=False), 13 | nn.BatchNorm2d(oup), 14 | nn.ReLU6(inplace=True) 15 | ) 16 | 17 | 18 | def conv_1x1_bn(inp, oup): 19 | return nn.Sequential( 20 | nn.Conv2d(inp, oup, 1, 1, 0, bias=False), 21 | nn.BatchNorm2d(oup), 22 | nn.ReLU6(inplace=True) 23 | ) 24 | 25 | 26 | class InvertedResidual(nn.Module): 27 | def __init__(self, inp, oup, stride, expand_ratio, dilation=1): 28 | super(InvertedResidual, self).__init__() 29 | self.stride = stride 30 | 31 | self.use_res_connect = self.stride == 1 and inp == oup 32 | 33 | padding = 2 - stride 34 | if dilation > 1: 35 | padding = dilation 36 | 37 | self.conv = nn.Sequential( 38 | 39 | nn.Conv2d(inp, inp * expand_ratio, 1, 1, 0, bias=False), 40 | nn.BatchNorm2d(inp * expand_ratio), 41 | nn.ReLU6(inplace=True), 42 | 43 | nn.Conv2d(inp * expand_ratio, inp * expand_ratio, 3, 44 | stride, padding, dilation=dilation, 45 | groups=inp * expand_ratio, bias=False), 46 | nn.BatchNorm2d(inp * expand_ratio), 47 | nn.ReLU6(inplace=True), 48 | 49 | nn.Conv2d(inp * expand_ratio, oup, 1, 1, 0, bias=False), 50 | nn.BatchNorm2d(oup), 51 | ) 52 | 53 | def forward(self, x): 54 | if self.use_res_connect: 55 | return x + self.conv(x) 56 | else: 57 | return self.conv(x) 58 | 59 | 60 | class MobileNetV2(nn.Sequential): 61 | def __init__(self, width_mult=1.0, used_layers=[3, 5, 7]): 62 | super(MobileNetV2, self).__init__() 63 | 64 | self.interverted_residual_setting = [ 65 | 66 | [1, 16, 1, 1, 1], 67 | [6, 24, 2, 2, 1], 68 | [6, 32, 3, 2, 1], 69 | [6, 64, 4, 2, 1], 70 | [6, 96, 3, 1, 1], 71 | [6, 160, 3, 2, 1], 72 | [6, 320, 1, 1, 1], 73 | ] 74 | 75 | self.interverted_residual_setting = [ 76 | 77 | [1, 16, 1, 1, 1], 78 | [6, 24, 2, 2, 1], 79 | [6, 32, 3, 2, 1], 80 | [6, 64, 4, 1, 2], 81 | [6, 96, 3, 1, 2], 82 | [6, 160, 3, 1, 4], 83 | [6, 320, 1, 1, 4], 84 | ] 85 | 86 | self.channels = [24, 32, 96, 320] 87 | self.channels = [int(c * width_mult) for c in self.channels] 88 | 89 | input_channel = int(32 * width_mult) 90 | self.last_channel = int(1280 * width_mult) \ 91 | if width_mult > 1.0 else 1280 92 | 93 | self.add_module('layer0', conv_bn(3, input_channel, 2, 0)) 94 | 95 | last_dilation = 1 96 | 97 | self.used_layers = used_layers 98 | 99 | for idx, (t, c, n, s, d) in \ 100 | enumerate(self.interverted_residual_setting, start=1): 101 | output_channel = int(c * width_mult) 102 | 103 | layers = [] 104 | 105 | for i in range(n): 106 | if i == 0: 107 | if d == last_dilation: 108 | dd = d 109 | else: 110 | dd = max(d // 2, 1) 111 | layers.append(InvertedResidual(input_channel, 112 | output_channel, s, t, dd)) 113 | else: 114 | layers.append(InvertedResidual(input_channel, 115 | output_channel, 1, t, d)) 116 | input_channel = output_channel 117 | 118 | last_dilation = d 119 | 120 | self.add_module('layer%d' % (idx), nn.Sequential(*layers)) 121 | 122 | def forward(self, x): 123 | outputs = [] 124 | for idx in range(8): 125 | name = "layer%d" % idx 126 | x = getattr(self, name)(x) 127 | outputs.append(x) 128 | p0, p1, p2, p3, p4 = [outputs[i] for i in [1, 2, 3, 5, 7]] 129 | out = [outputs[i] for i in self.used_layers] 130 | return out 131 | 132 | 133 | def mobilenetv2(**kwargs): 134 | model = MobileNetV2(**kwargs) 135 | return model 136 | 137 | 138 | if __name__ == '__main__': 139 | net = mobilenetv2() 140 | 141 | print(net) 142 | 143 | from torch.autograd import Variable 144 | 145 | tensor = Variable(torch.Tensor(1, 3, 255, 255)).cuda() 146 | 147 | net = net.cuda() 148 | 149 | out = net(tensor) 150 | 151 | for i, p in enumerate(out): 152 | print(i, p.size()) 153 | -------------------------------------------------------------------------------- /pysot/models/channelsplit/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | from pysot.models.channelsplit.msc import MSC 6 | 7 | BS = { 8 | 'msc': MSC, 9 | } 10 | 11 | 12 | def get_BS(name, **kwargs): 13 | return BS[name](**kwargs) 14 | -------------------------------------------------------------------------------- /pysot/models/channelsplit/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/channelsplit/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /pysot/models/channelsplit/__pycache__/msc.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/channelsplit/__pycache__/msc.cpython-39.pyc -------------------------------------------------------------------------------- /pysot/models/channelsplit/msc.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | import torch.nn.functional as F 6 | import torch.nn as nn 7 | import torch 8 | 9 | 10 | def scl(rc, order): 11 | br = [] 12 | b = rc.size()[0] 13 | ofr = [[None for j in range(b)] for i in range(5)] 14 | obi = order[0] 15 | ob = order[1] 16 | for i in range(5): 17 | select_three_band = ob[0, i * 3:i * 3 + 3] 18 | gg = rc[None, 0, select_three_band, :, :] 19 | ofr[i][0] = obi[0, i * 3:i * 3 + 3].detach().mean().item() 20 | for k in range(1, b): 21 | stbo = ob[k, i * 3:i * 3 + 3] 22 | gg = torch.cat((gg, rc[None, k, stbo, :, :]), dim=0) 23 | ofr[i][k] = obi[k, i * 3:i * 3 + 3].detach().mean().item() 24 | br.append(gg) 25 | return br, ofr 26 | 27 | 28 | class cbr_1(nn.Module): 29 | def __init__(self, in_channels, out_channels, kernel_size=1, padding=0, stride=1, dilation=1, groups=1, bias=False): 30 | super(cbr_1, self).__init__() 31 | self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=padding, stride=stride, 32 | dilation=dilation, groups=groups, bias=bias) 33 | self.bn = nn.BatchNorm2d(out_channels) 34 | 35 | def forward(self, x): 36 | return F.relu(self.bn(self.conv(x))) 37 | 38 | 39 | class db_3(nn.Module): 40 | def __init__(self, in_channels, out_channels, dilation, padding, kernel_size=3, stride=1, groups=1, bias=False): 41 | super(db_3, self).__init__() 42 | self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=padding, stride=stride, 43 | dilation=dilation, groups=groups, bias=bias) 44 | self.bn = nn.BatchNorm2d(out_channels) 45 | 46 | def forward(self, x): 47 | return F.relu(self.bn(self.conv(x))) 48 | 49 | 50 | class msp(nn.Module): 51 | def __init__(self, in_channels, mid_channels): 52 | super(msp, self).__init__() 53 | self.dc_1 = db_3(mid_channels, mid_channels, dilation=1, padding=1) 54 | self.dc_2 = db_3(mid_channels, mid_channels, dilation=6, padding=6) 55 | self.dc_3 = db_3(mid_channels, mid_channels, dilation=12, padding=12) 56 | self.c_0 = cbr_1(in_channels, mid_channels) 57 | self.c4_0 = cbr_1(mid_channels, mid_channels) 58 | self.c5_0 = cbr_1(mid_channels, mid_channels) 59 | self.c_4 = cbr_1(mid_channels, mid_channels) 60 | self.c_5 = cbr_1(mid_channels, mid_channels) 61 | self.fd = cbr_1(4 * mid_channels, in_channels) 62 | 63 | def forward(self, x): 64 | x_1 = self.dc_1(self.c_0(x)) 65 | x_2 = self.dc_2(self.c_0(x)) 66 | x_3 = self.dc_3(self.c_0(x)) 67 | x_0 = self.c_0(x) 68 | x_1_cos = self.c5_0(x_1 * torch.cos(x_1)) 69 | x_1_sin = self.c4_0(x_1 * torch.sin(x_1)) 70 | m_1 = self.c_4(x_1_sin * x_1_cos) 71 | s_1 = self.c_5(x_1_sin + x_1_cos) 72 | fus_1 = s_1 + m_1 73 | x_2_c = self.c5_0(x_2 * torch.cos(x_2)) 74 | x_2_s = self.c4_0(x_2 * torch.sin(x_2)) 75 | mn_2 = self.c_4(x_2_s * x_2_c) 76 | sn_2 = self.c_5(x_2_s + x_2_c) 77 | fus_2 = sn_2 + mn_2 78 | x_3_c = self.c5_0(x_3 * torch.cos(x_3)) 79 | x_3_s = self.c4_0(x_3 * torch.sin(x_3)) 80 | mn_3 = self.c_4(x_3_s * x_3_c) 81 | sn_3 = self.c_5(x_3_s + x_3_c) 82 | fus_3 = sn_3 + mn_3 83 | oc = torch.cat((fus_1, fus_2, fus_3, x_0), dim=1) 84 | out = self.fd(oc) 85 | return out 86 | 87 | 88 | class MSC(nn.Module): 89 | def __init__(self): 90 | super(MSC, self).__init__() 91 | in_channels = 16 92 | mid_channels = 8 93 | self.msp = msp(in_channels=in_channels, mid_channels=mid_channels) 94 | self.mlp = nn.Sequential(nn.Linear(in_channels, in_channels, bias=True), nn.Tanh(), 95 | nn.Linear(in_channels, in_channels, bias=True), nn.Tanh()) 96 | 97 | def forward(self, x): 98 | xx = x.mul(1 / 255.0).clamp(0.0, 1.0) 99 | x_assp = self.msp(xx) 100 | b, c, w, h = x_assp.size() 101 | x2 = x_assp.view(b, c, -1) 102 | x3 = x2.permute(0, 2, 1) 103 | ms_mlp = self.mlp(x3) 104 | ms_mlp_ = ms_mlp.transpose(1, 2) 105 | cm = torch.matmul(ms_mlp_, ms_mlp) 106 | for i in range(16): 107 | cm[:, i, i] = 0.0 108 | cm = F.normalize(cm, p=2, dim=-1) 109 | for i in range(16): 110 | cm[:, i, i] = 0.0 111 | w = torch.norm(cm, p=1, dim=-1) 112 | w_reshape = w.contiguous().view(w.shape[0], w.shape[1], 1, 1) 113 | x_att = w_reshape.expand_as(x) 114 | x_cg = xx * x_att 115 | x_cg = x_cg.view(x_cg.shape[0], x_cg.shape[1], -1) 116 | x_orig = xx.view(xx.shape[0], xx.shape[1], -1) 117 | cg_list = [x_orig, x_cg] 118 | order_Y = torch.sort(w, dim=-1, descending=True, out=None) 119 | fc_list, w_list = scl(x, order_Y) 120 | return fc_list, w_list, order_Y, cg_list 121 | -------------------------------------------------------------------------------- /pysot/models/fusion/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | from pysot.models.fusion.transfusion import transformerfusion # 6 | 7 | FS = { 8 | 'transfusion': transformerfusion, 9 | } 10 | 11 | 12 | def get_FS(name, **kwargs): 13 | return FS[name](**kwargs) 14 | -------------------------------------------------------------------------------- /pysot/models/fusion/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/fusion/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /pysot/models/fusion/__pycache__/transfusion.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/fusion/__pycache__/transfusion.cpython-39.pyc -------------------------------------------------------------------------------- /pysot/models/fusion/transfusion.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | import torch.nn.functional as F 6 | import torch 7 | from torch import nn 8 | 9 | 10 | class transformerfusion(nn.Module): 11 | def __init__(self): 12 | super(transformerfusion, self).__init__() 13 | self.block0 = Block() 14 | 15 | def forward(self, down_x): 16 | x0 = down_x[0] 17 | x1 = down_x[1] 18 | x2 = down_x[2] 19 | x3 = down_x[3] 20 | x4 = down_x[4] 21 | nd_01 = self.block0(x0, x1) 22 | nd_12 = self.block0(x1, x2) 23 | nd_23 = self.block0(x2, x3) 24 | nd_34 = self.block0(x3, x4) 25 | f = nd_01 + nd_12 + nd_23 + nd_34 26 | return f 27 | 28 | 29 | class Block(nn.Module): 30 | def __init__(self): 31 | super(Block, self).__init__() 32 | channels = 256 33 | self.out_channels = channels // 4 34 | self.a_key = nn.Sequential( 35 | nn.Conv2d(in_channels=channels, out_channels=self.out_channels, kernel_size=1, stride=1, padding=0), 36 | nn.BatchNorm2d(self.out_channels), 37 | nn.ReLU(), ) 38 | self.a_query = nn.Sequential( 39 | nn.Conv2d(in_channels=channels, out_channels=self.out_channels, kernel_size=1, stride=1, padding=0), 40 | nn.BatchNorm2d(self.out_channels), 41 | nn.ReLU(), ) 42 | self.a_value = nn.Conv2d(in_channels=channels, out_channels=self.out_channels, kernel_size=1, stride=1, 43 | padding=0) 44 | self.a_W = nn.Conv2d(in_channels=self.out_channels, out_channels=self.out_channels * 2, kernel_size=1, stride=1, 45 | padding=0) 46 | self.b_key = nn.Sequential( 47 | nn.Conv2d(in_channels=channels, out_channels=self.out_channels, kernel_size=1, stride=1, padding=0), 48 | nn.BatchNorm2d(self.out_channels), 49 | nn.ReLU(), ) 50 | self.b_query = nn.Sequential( 51 | nn.Conv2d(in_channels=channels, out_channels=self.out_channels, kernel_size=1, stride=1, padding=0), 52 | nn.BatchNorm2d(self.out_channels), 53 | nn.ReLU(), ) 54 | self.b_value = nn.Conv2d(in_channels=channels, out_channels=self.out_channels, kernel_size=1, stride=1, 55 | padding=0) 56 | self.b_W = nn.Conv2d(in_channels=self.out_channels, out_channels=self.out_channels * 2, kernel_size=1, stride=1, 57 | padding=0) 58 | self.gate_a = nn.Sequential( 59 | nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=1, stride=1, padding=0), 60 | nn.Sigmoid(), ) 61 | 62 | def forward(self, a, b): 63 | ns = self.fuse(a, b) 64 | return ns 65 | 66 | def fuse(self, a, b): 67 | a_m = a 68 | b_m = b 69 | adapt_channels = self.out_channels 70 | batch_size = a_m.size(0) 71 | a_query = self.a_query(a_m).view(batch_size, adapt_channels, -1).permute(0, 2, 1) 72 | a_key = self.a_key(a_m).view(batch_size, adapt_channels, -1) 73 | a_value = self.a_value(a_m).view(batch_size, adapt_channels, -1).permute(0, 2, 1) 74 | batch_size = b_m.size(0) 75 | b_query = self.b_query(b_m).view(batch_size, adapt_channels, -1).permute(0, 2, 1) 76 | b_key = self.b_key(b_m).view(batch_size, adapt_channels, -1) 77 | b_value = self.b_value(b_m).view(batch_size, adapt_channels, -1).permute(0, 2, 1) 78 | a_sim_map = torch.matmul(b_query, a_key) 79 | a_sim_map = F.softmax(a_sim_map, dim=-1) 80 | a_context = torch.matmul(a_sim_map, a_value) 81 | a_context = a_context.permute(0, 2, 1).contiguous() 82 | a_context = a_context.view(batch_size, self.out_channels, *a_m.size()[2:]) 83 | a_context = self.a_W(a_context) 84 | b_sim_map = torch.matmul(a_query, b_key) 85 | b_sim_map = F.softmax(b_sim_map, dim=-1) 86 | b_context = torch.matmul(b_sim_map, b_value) 87 | b_context = b_context.permute(0, 2, 1).contiguous() 88 | b_context = b_context.view(batch_size, self.out_channels, *b_m.size()[2:]) 89 | b_context = self.b_W(b_context) 90 | cat_fea = torch.cat([b_context, a_context], dim=1) 91 | attention_vector_a = self.gate_a(cat_fea) 92 | attention_vector_T = 1 - attention_vector_a 93 | ns = a * attention_vector_a + b * attention_vector_T 94 | return ns 95 | -------------------------------------------------------------------------------- /pysot/models/head/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /pysot/models/head/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/head/__pycache__/car_head.cpython-39.pyc -------------------------------------------------------------------------------- /pysot/models/head/car_head.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | import math 4 | 5 | 6 | class CARHead(torch.nn.Module): 7 | def __init__(self, cfg, in_channels): 8 | """ 9 | Arguments: 10 | in_channels (int): number of channels of the input feature 11 | """ 12 | super(CARHead, self).__init__() 13 | 14 | num_classes = cfg.TRAIN.NUM_CLASSES 15 | 16 | cls_tower = [] 17 | bbox_tower = [] 18 | for i in range(cfg.TRAIN.NUM_CONVS): 19 | cls_tower.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)) 20 | cls_tower.append(nn.GroupNorm(32, in_channels)) 21 | cls_tower.append(nn.ReLU()) 22 | bbox_tower.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)) 23 | bbox_tower.append(nn.GroupNorm(32, in_channels)) 24 | bbox_tower.append(nn.ReLU()) 25 | 26 | self.add_module('cls_tower', nn.Sequential(*cls_tower)) 27 | 28 | self.add_module('bbox_tower', nn.Sequential(*bbox_tower)) 29 | 30 | self.cls_logits = nn.Conv2d(in_channels, num_classes, kernel_size=3, stride=1, padding=1) 31 | self.bbox_pred = nn.Conv2d(in_channels, 4, kernel_size=3, stride=1, padding=1) 32 | self.centerness = nn.Conv2d(in_channels, 1, kernel_size=3, stride=1, padding=1) 33 | 34 | for modules in [self.cls_tower, self.bbox_tower, self.cls_logits, self.bbox_pred, self.centerness]: 35 | for l in modules.modules(): 36 | if isinstance(l, nn.Conv2d): 37 | torch.nn.init.normal_(l.weight, std=0.01) 38 | torch.nn.init.constant_(l.bias, 0) 39 | 40 | prior_prob = cfg.TRAIN.PRIOR_PROB 41 | bias_value = -math.log((1 - prior_prob) / prior_prob) 42 | torch.nn.init.constant_(self.cls_logits.bias, bias_value) 43 | 44 | def forward(self, x): 45 | 46 | cls_tower = self.cls_tower(x) 47 | logits = self.cls_logits(cls_tower) 48 | centerness = self.centerness(cls_tower) 49 | 50 | bbox_reg = torch.exp(self.bbox_pred(self.bbox_tower(x))) 51 | 52 | return logits, bbox_reg, centerness 53 | 54 | 55 | class Scale(nn.Module): 56 | def __init__(self, init_value=1.0): 57 | super(Scale, self).__init__() 58 | self.scale = nn.Parameter(torch.FloatTensor([init_value])) 59 | 60 | def forward(self, input): 61 | return input * self.scale 62 | -------------------------------------------------------------------------------- /pysot/models/init_weight.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | 4 | def init_weights(model): 5 | for m in model.modules(): 6 | if isinstance(m, nn.Conv2d): 7 | nn.init.kaiming_normal_(m.weight.data, 8 | mode='fan_out', 9 | nonlinearity='relu') 10 | elif isinstance(m, nn.BatchNorm2d): 11 | m.weight.data.fill_(1) 12 | m.bias.data.zero_() 13 | -------------------------------------------------------------------------------- /pysot/models/model_builder.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | import torch 6 | import torch.nn as nn 7 | import torch.nn.functional as F 8 | from pysot.core.config import cfg 9 | from pysot.models.loss_car import make_phtrack_loss_evaluator 10 | from ..utils.location_grid import compute_locations 11 | from pysot.utils.xcorr import xcorr_depthwise 12 | from pysot.models.channelsplit import get_BS 13 | from pysot.models.backbone import get_backbone 14 | from pysot.models.neck import get_neck 15 | from pysot.models.fusion import get_FS 16 | from pysot.models.head.car_head import CARHead 17 | 18 | 19 | class ModelBuilder(nn.Module): 20 | def __init__(self): 21 | super(ModelBuilder, self).__init__() 22 | self.zf = None 23 | 24 | self.channelsplit = get_BS(cfg.BS.TYPE) 25 | 26 | self.backbone = get_backbone(cfg.BACKBONE.TYPE, **cfg.BACKBONE.KWARGS) 27 | 28 | self.neck = get_neck(cfg.ADJUST.TYPE, **cfg.ADJUST.KWARGS) 29 | 30 | self.xcorr_depthwise = xcorr_depthwise 31 | 32 | self.down = nn.ConvTranspose2d(256 * 3, 256, 1, 1) 33 | 34 | self.fs = get_FS(cfg.FS.TYPE) 35 | 36 | self.car_head = CARHead(cfg, 256) 37 | 38 | self.loss_evaluator = make_phtrack_loss_evaluator(cfg) 39 | 40 | def template(self, z): 41 | 42 | bs_z = self.channelsplit(z) 43 | 44 | for i in range(len(bs_z[0])): 45 | z = bs_z[0][i] 46 | 47 | zf = self.backbone(z) 48 | 49 | zf = self.neck(zf) 50 | 51 | if i == 0: 52 | zff = [zf] 53 | else: 54 | zff.append(zf) 55 | 56 | self.zf = zff 57 | 58 | def track(self, x): 59 | 60 | bs_x = self.channelsplit(x) 61 | 62 | for i in range(len(bs_x[0])): 63 | 64 | x = bs_x[0][i] 65 | 66 | xf = self.backbone(x) 67 | 68 | xf = self.neck(xf) 69 | 70 | features = self.xcorr_depthwise(xf[0], self.zf[i][0]) 71 | 72 | for j in range(len(xf) - 1): 73 | features_new = self.xcorr_depthwise(xf[j + 1], self.zf[i][j + 1]) 74 | 75 | features = torch.cat([features, features_new], 1) 76 | 77 | f_down = self.down(features) 78 | 79 | if i == 0: 80 | f_list = [f_down] 81 | else: 82 | f_list.append(f_down) 83 | 84 | f_f = self.fs(f_list) 85 | 86 | cls, loc, cen = self.car_head(f_f) 87 | bs_order = bs_x[2][1][0][0:3] 88 | 89 | w_16band = bs_x[2][0] 90 | 91 | w_std = torch.std(w_16band) 92 | 93 | return {'cls': cls, 'loc': loc, 'cen': cen, 'bs_order': bs_order, 'w_std': w_std} 94 | 95 | def log_softmax(self, cls): 96 | b, a2, h, w = cls.size() 97 | cls = cls.view(b, 2, a2 // 2, h, w) 98 | cls = cls.permute(0, 2, 3, 4, 1).contiguous() 99 | cls = F.log_softmax(cls, dim=4) 100 | return cls 101 | 102 | def forward(self, data): 103 | """ 104 | only used in training 105 | 输出为损失 106 | """ 107 | template = data['template'].cuda() 108 | search = data['search'].cuda() 109 | label_cls = data['label_cls'].cuda() 110 | label_loc = data['bbox'].cuda() 111 | 112 | template_cs = self.channelsplit(template) 113 | search_cs = self.channelsplit(search) 114 | 115 | for i in range(5): 116 | template_3 = template_cs[0][i] 117 | search_3 = search_cs[0][i] 118 | 119 | zf0 = self.backbone(template_3) 120 | 121 | xf0 = self.backbone(search_3) 122 | 123 | zf = self.neck(zf0) 124 | 125 | xf = self.neck(xf0) 126 | 127 | f_0 = self.xcorr_depthwise(xf[0], zf[0]) 128 | f_1 = self.xcorr_depthwise(xf[1], zf[1]) 129 | f_2 = self.xcorr_depthwise(xf[2], zf[2]) 130 | 131 | fea = torch.cat([f_0, f_1, f_2], dim=1) 132 | 133 | f_down = self.down(fea) 134 | 135 | if i == 0: 136 | f_five = [f_down] 137 | else: 138 | f_five.append(f_down) 139 | 140 | f_fs = self.fs(f_five) 141 | 142 | cls, loc, cen = self.car_head(f_fs) 143 | 144 | locations = compute_locations(cls, cfg.TRACK.STRIDE) 145 | cls = self.log_softmax(cls) 146 | 147 | cls_loss, loc_loss, cen_loss = self.loss_evaluator(locations, cls, loc, cen, label_cls, label_loc) 148 | 149 | t_cg_loss = F.l1_loss(template_cs[3][0], template_cs[3][1]) 150 | 151 | s_cg_loss = F.l1_loss(search_cs[3][0], search_cs[3][1]) 152 | cg_loss = (t_cg_loss.data + s_cg_loss.data) / 2 153 | 154 | total_loss = cfg.TRAIN.CLS_WEIGHT * cls_loss + cfg.TRAIN.LOC_WEIGHT * loc_loss + cfg.TRAIN.CEN_WEIGHT * cen_loss + cfg.TRAIN.CG_WEIGHT * cg_loss 155 | 156 | outputs = {'total_loss': total_loss, 'cls_loss': cls_loss, 'loc_loss': loc_loss, 'cen_loss': cen_loss, 157 | 'cg_loss': cg_loss} 158 | 159 | return outputs 160 | -------------------------------------------------------------------------------- /pysot/models/neck/__init__.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | 6 | import torch 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | 10 | from pysot.models.neck.neck import AdjustLayer, AdjustAllLayer 11 | 12 | NECKS = { 13 | 'AdjustLayer': AdjustLayer, 14 | 'AdjustAllLayer': AdjustAllLayer 15 | } 16 | 17 | 18 | def get_neck(name, **kwargs): 19 | return NECKS[name](**kwargs) 20 | -------------------------------------------------------------------------------- /pysot/models/neck/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/models/neck/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /pysot/models/neck/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | 6 | import torch.nn as nn 7 | 8 | 9 | class AdjustLayer(nn.Module): 10 | def __init__(self, in_channels, out_channels): 11 | super(AdjustLayer, self).__init__() 12 | self.downsample = nn.Sequential( 13 | nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), 14 | nn.BatchNorm2d(out_channels), 15 | ) 16 | 17 | def forward(self, x): 18 | x = self.downsample(x) 19 | 20 | if x.size(3) < 20: 21 | l = 4 22 | r = l + 7 23 | x = x[:, :, l:r, l:r] 24 | return x 25 | 26 | 27 | class AdjustAllLayer(nn.Module): 28 | def __init__(self, in_channels, out_channels): 29 | super(AdjustAllLayer, self).__init__() 30 | self.num = len(out_channels) 31 | if self.num == 1: 32 | self.downsample = AdjustLayer(in_channels[0], out_channels[0]) 33 | 34 | 35 | else: 36 | for i in range(self.num): 37 | self.add_module('downsample' + str(i + 2), AdjustLayer(in_channels[i], out_channels[i])) 38 | 39 | def forward(self, features): 40 | if self.num == 1: 41 | return self.downsample(features) 42 | 43 | else: 44 | out = [] 45 | for i in range(self.num): 46 | adj_layer = getattr(self, 'downsample' + str(i + 2)) 47 | 48 | out.append(adj_layer(features[i]).contiguous()) 49 | return out 50 | -------------------------------------------------------------------------------- /pysot/tracker/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/tracker/__init__.py -------------------------------------------------------------------------------- /pysot/tracker/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /pysot/tracker/base_tracker.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | 6 | import cv2 7 | import numpy as np 8 | import torch 9 | 10 | from pysot.core.config import cfg 11 | 12 | 13 | class BaseTracker(object): 14 | """ Base tracker of single object tracking 15 | """ 16 | 17 | def init(self, img, bbox): 18 | """ 19 | args: 20 | img(np.ndarray): BGR image 21 | bbox(list): [x, y, width, height] 22 | x, y need to be 0-based 23 | """ 24 | raise NotImplementedError 25 | 26 | def track(self, img): 27 | """ 28 | args: 29 | img(np.ndarray): BGR image 30 | return: 31 | bbox(list):[x, y, width, height] 32 | """ 33 | raise NotImplementedError 34 | 35 | 36 | class SiameseTracker(BaseTracker): 37 | def get_subwindow(self, im, pos, model_sz, original_sz, avg_chans): 38 | """ 39 | args: 40 | im: bgr based image 41 | pos: center position 42 | model_sz: exemplar size 43 | s_z: original size 44 | avg_chans: channel average 45 | """ 46 | if isinstance(pos, float): 47 | pos = [pos, pos] 48 | sz = original_sz 49 | im_sz = im.shape 50 | c = (original_sz + 1) / 2 51 | 52 | context_xmin = np.floor(pos[0] - c + 0.5) 53 | context_xmax = context_xmin + sz - 1 54 | 55 | context_ymin = np.floor(pos[1] - c + 0.5) 56 | context_ymax = context_ymin + sz - 1 57 | left_pad = int(max(0., -context_xmin)) 58 | top_pad = int(max(0., -context_ymin)) 59 | right_pad = int(max(0., context_xmax - im_sz[1] + 1)) 60 | bottom_pad = int(max(0., context_ymax - im_sz[0] + 1)) 61 | 62 | context_xmin = context_xmin + left_pad 63 | context_xmax = context_xmax + left_pad 64 | context_ymin = context_ymin + top_pad 65 | context_ymax = context_ymax + top_pad 66 | 67 | r, c, k = im.shape 68 | if any([top_pad, bottom_pad, left_pad, right_pad]): 69 | size = (r + top_pad + bottom_pad, c + left_pad + right_pad, k) 70 | te_im = np.zeros(size, np.uint8) 71 | te_im[top_pad:top_pad + r, left_pad:left_pad + c, :] = im 72 | if top_pad: 73 | te_im[0:top_pad, left_pad:left_pad + c, :] = avg_chans 74 | if bottom_pad: 75 | te_im[r + top_pad:, left_pad:left_pad + c, :] = avg_chans 76 | if left_pad: 77 | te_im[:, 0:left_pad, :] = avg_chans 78 | if right_pad: 79 | te_im[:, c + left_pad:, :] = avg_chans 80 | im_patch = te_im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] 81 | else: 82 | im_patch = im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] 83 | 84 | if not np.array_equal(model_sz, original_sz): 85 | im_patch = cv2.resize(im_patch, (model_sz, model_sz)) 86 | im_patch = im_patch.transpose(2, 0, 1) 87 | im_patch = im_patch[np.newaxis, :, :, :] 88 | im_patch = im_patch.astype(np.float32) 89 | im_patch = torch.from_numpy(im_patch) 90 | if cfg.CUDA: 91 | im_patch = im_patch.cuda() 92 | return im_patch 93 | -------------------------------------------------------------------------------- /pysot/tracker/tracker_builder.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | 6 | from pysot.core.config import cfg 7 | from pysot.tracker.phtrack_tracker import phtrackTracker 8 | 9 | TRACKS = { 10 | 'phtrackTracker': phtrackTracker 11 | } 12 | 13 | 14 | def build_tracker(model, cfg): 15 | return TRACKS[model, cfg] -------------------------------------------------------------------------------- /pysot/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/utils/__init__.py 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All Rights Reserved. 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | from __future__ import unicode_literals 7 | 8 | 9 | class Meter(object): 10 | def __init__(self, name, val, avg): 11 | self.name = name 12 | self.val = val 13 | self.avg = avg 14 | 15 | def __repr__(self): 16 | return "{name}: {val:.6f} ({avg:.6f})".format( 17 | name=self.name, val=self.val, avg=self.avg 18 | ) 19 | 20 | def __format__(self, *tuples, **kwargs): 21 | return self.__repr__() 22 | 23 | 24 | class AverageMeter: 25 | """Computes and stores the average and current value""" 26 | def __init__(self, num=100): 27 | self.num = num 28 | self.reset() 29 | 30 | def reset(self): 31 | self.val = {} 32 | self.sum = {} 33 | self.count = {} 34 | self.history = {} 35 | 36 | def update(self, batch=1, **kwargs): 37 | val = {} 38 | for k in kwargs: 39 | val[k] = kwargs[k] / float(batch) 40 | self.val.update(val) 41 | for k in kwargs: 42 | if k not in self.sum: 43 | self.sum[k] = 0 44 | self.count[k] = 0 45 | self.history[k] = [] 46 | self.sum[k] += kwargs[k] 47 | self.count[k] += batch 48 | for _ in range(batch): 49 | self.history[k].append(val[k]) 50 | 51 | if self.num <= 0: 52 | # < 0, average all 53 | self.history[k] = [] 54 | 55 | # == 0: no average 56 | if self.num == 0: 57 | self.sum[k] = self.val[k] 58 | self.count[k] = 1 59 | 60 | elif len(self.history[k]) > self.num: 61 | pop_num = len(self.history[k]) - self.num 62 | for _ in range(pop_num): 63 | self.sum[k] -= self.history[k][0] 64 | del self.history[k][0] 65 | self.count[k] -= 1 66 | 67 | def __repr__(self): 68 | s = '' 69 | for k in self.sum: 70 | s += self.format_str(k) 71 | return s 72 | 73 | def format_str(self, attr): 74 | return "{name}: {val:.6f} ({avg:.6f}) ".format( 75 | name=attr, 76 | val=float(self.val[attr]), 77 | avg=float(self.sum[attr]) / self.count[attr]) 78 | 79 | def __getattr__(self, attr): 80 | if attr in self.__dict__: 81 | return super(AverageMeter, self).__getattr__(attr) 82 | if attr not in self.sum: 83 | print("invalid key '{}'".format(attr)) 84 | return Meter(attr, 0, 0) 85 | return Meter(attr, self.val[attr], self.avg(attr)) 86 | 87 | def avg(self, attr): 88 | return float(self.sum[attr]) / self.count[attr] 89 | 90 | 91 | if __name__ == '__main__': 92 | avg1 = AverageMeter(10) 93 | avg2 = AverageMeter(0) 94 | avg3 = AverageMeter(-1) 95 | 96 | for i in range(20): 97 | avg1.update(s=i) 98 | avg2.update(s=i) 99 | avg3.update(s=i) 100 | 101 | print('iter {}'.format(i)) 102 | print(avg1.s) 103 | print(avg2.s) 104 | print(avg3.s) 105 | -------------------------------------------------------------------------------- /pysot/utils/bbox.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) SenseTime. All Rights Reserved. 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | from __future__ import unicode_literals 7 | 8 | from collections import namedtuple 9 | 10 | import numpy as np 11 | 12 | 13 | Corner = namedtuple('Corner', 'x1 y1 x2 y2') 14 | # alias 15 | BBox = Corner 16 | Center = namedtuple('Center', 'x y w h') 17 | 18 | 19 | def corner2center(corner): 20 | """ convert (x1, y1, x2, y2) to (cx, cy, w, h) 21 | Args: 22 | conrner: Corner or np.array (4*N) 23 | Return: 24 | Center or np.array (4 * N) 25 | """ 26 | if isinstance(corner, Corner): 27 | x1, y1, x2, y2 = corner 28 | return Center((x1 + x2) * 0.5, (y1 + y2) * 0.5, (x2 - x1), (y2 - y1)) 29 | else: 30 | x1, y1, x2, y2 = corner[0], corner[1], corner[2], corner[3] 31 | x = (x1 + x2) * 0.5 32 | y = (y1 + y2) * 0.5 33 | w = x2 - x1 34 | h = y2 - y1 35 | return x, y, w, h 36 | 37 | 38 | def center2corner(center): 39 | """ convert (cx, cy, w, h) to (x1, y1, x2, y2) 40 | Args: 41 | center: Center or np.array (4 * N) 42 | Return: 43 | center or np.array (4 * N) 44 | """ 45 | if isinstance(center, Center): 46 | x, y, w, h = center 47 | return Corner(x - w * 0.5, y - h * 0.5, x + w * 0.5, y + h * 0.5) 48 | else: 49 | x, y, w, h = center[0], center[1], center[2], center[3] 50 | x1 = x - w * 0.5 51 | y1 = y - h * 0.5 52 | x2 = x + w * 0.5 53 | y2 = y + h * 0.5 54 | return x1, y1, x2, y2 55 | 56 | 57 | def IoU(rect1, rect2): 58 | """ caculate interection over union 59 | Args: 60 | rect1: (x1, y1, x2, y2) 61 | rect2: (x1, y1, x2, y2) 62 | Returns: 63 | iou 64 | """ 65 | # overlap 66 | x1, y1, x2, y2 = rect1[0], rect1[1], rect1[2], rect1[3] 67 | tx1, ty1, tx2, ty2 = rect2[0], rect2[1], rect2[2], rect2[3] 68 | 69 | xx1 = np.maximum(tx1, x1) 70 | yy1 = np.maximum(ty1, y1) 71 | xx2 = np.minimum(tx2, x2) 72 | yy2 = np.minimum(ty2, y2) 73 | 74 | ww = np.maximum(0, xx2 - xx1) 75 | hh = np.maximum(0, yy2 - yy1) 76 | 77 | area = (x2-x1) * (y2-y1) 78 | target_a = (tx2-tx1) * (ty2 - ty1) 79 | inter = ww * hh 80 | iou = inter / (area + target_a - inter) 81 | return iou 82 | 83 | 84 | def cxy_wh_2_rect(pos, sz): 85 | """ convert (cx, cy, w, h) to (x1, y1, w, h), 0-index 86 | """ 87 | return np.array([pos[0]-sz[0]/2, pos[1]-sz[1]/2, sz[0], sz[1]]) 88 | 89 | 90 | def rect_2_cxy_wh(rect): 91 | """ convert (x1, y1, w, h) to (cx, cy, w, h), 0-index 92 | """ 93 | return np.array([rect[0]+rect[2]/2, rect[1]+rect[3]/2]), \ 94 | np.array([rect[2], rect[3]]) 95 | 96 | 97 | def cxy_wh_2_rect1(pos, sz): 98 | """ convert (cx, cy, w, h) to (x1, y1, w, h), 1-index 99 | """ 100 | return np.array([pos[0]-sz[0]/2+1, pos[1]-sz[1]/2+1, sz[0], sz[1]]) 101 | 102 | 103 | def rect1_2_cxy_wh(rect): 104 | """ convert (x1, y1, w, h) to (cx, cy, w, h), 1-index 105 | """ 106 | return np.array([rect[0]+rect[2]/2-1, rect[1]+rect[3]/2-1]), \ 107 | np.array([rect[2], rect[3]]) 108 | 109 | 110 | def get_axis_aligned_bbox(region): 111 | """ convert region to (cx, cy, w, h) that represent by axis aligned box 112 | """ 113 | nv = region.size 114 | if nv == 8: 115 | cx = np.mean(region[0::2]) 116 | cy = np.mean(region[1::2]) 117 | x1 = min(region[0::2]) 118 | x2 = max(region[0::2]) 119 | y1 = min(region[1::2]) 120 | y2 = max(region[1::2]) 121 | A1 = np.linalg.norm(region[0:2] - region[2:4]) * \ 122 | np.linalg.norm(region[2:4] - region[4:6]) 123 | A2 = (x2 - x1) * (y2 - y1) 124 | s = np.sqrt(A1 / A2) 125 | w = s * (x2 - x1) + 1 126 | h = s * (y2 - y1) + 1 127 | else: 128 | x = region[0] 129 | y = region[1] 130 | w = region[2] 131 | h = region[3] 132 | cx = x+w/2 133 | cy = y+h/2 134 | return cx, cy, w, h 135 | 136 | 137 | def get_min_max_bbox(region): 138 | """ convert region to (cx, cy, w, h) that represent by mim-max box 139 | """ 140 | nv = region.size 141 | if nv == 8: 142 | cx = np.mean(region[0::2]) 143 | cy = np.mean(region[1::2]) 144 | x1 = min(region[0::2]) 145 | x2 = max(region[0::2]) 146 | y1 = min(region[1::2]) 147 | y2 = max(region[1::2]) 148 | w = x2 - x1 149 | h = y2 - y1 150 | else: 151 | x = region[0] 152 | y = region[1] 153 | w = region[2] 154 | h = region[3] 155 | cx = x+w/2 156 | cy = y+h/2 157 | return cx, cy, w, h 158 | -------------------------------------------------------------------------------- /pysot/utils/distributed.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) SenseTime. All Rights Reserved. 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | from __future__ import unicode_literals 7 | 8 | import os 9 | import socket 10 | import logging 11 | 12 | import torch 13 | import torch.nn as nn 14 | import torch.distributed as dist 15 | 16 | from pysot.utils.log_helper import log_once 17 | 18 | logger = logging.getLogger('global') 19 | 20 | 21 | def average_reduce(v): 22 | if get_world_size() == 1: 23 | return v 24 | tensor = torch.cuda.FloatTensor(1) 25 | tensor[0] = v 26 | dist.all_reduce(tensor) 27 | v = tensor[0] / get_world_size() 28 | return v 29 | 30 | 31 | class DistModule(nn.Module): 32 | def __init__(self, module, bn_method=0): 33 | super(DistModule, self).__init__() 34 | self.module = module 35 | self.bn_method = bn_method 36 | if get_world_size() > 1: 37 | broadcast_params(self.module) 38 | else: 39 | self.bn_method = 0 # single proccess 40 | 41 | def forward(self, *args, **kwargs): 42 | broadcast_buffers(self.module, self.bn_method) 43 | return self.module(*args, **kwargs) 44 | 45 | def train(self, mode=True): 46 | super(DistModule, self).train(mode) 47 | self.module.train(mode) 48 | return self 49 | 50 | 51 | def broadcast_params(model): 52 | """ broadcast model parameters """ 53 | for p in model.state_dict().values(): 54 | dist.broadcast(p, 0) 55 | 56 | 57 | def broadcast_buffers(model, method=0): 58 | """ broadcast model buffers """ 59 | if method == 0: 60 | return 61 | 62 | world_size = get_world_size() 63 | 64 | for b in model._all_buffers(): 65 | if method == 1: # broadcast from main proccess 66 | dist.broadcast(b, 0) 67 | elif method == 2: # average 68 | dist.all_reduce(b) 69 | b /= world_size 70 | else: 71 | raise Exception('Invalid buffer broadcast code {}'.format(method)) 72 | 73 | 74 | inited = False 75 | 76 | 77 | def _dist_init(): 78 | ''' 79 | if guess right: 80 | ntasks: world_size (process num) 81 | proc_id: rank 82 | ''' 83 | # rank = int(os.environ['RANK']) 84 | rank = 0 85 | num_gpus = torch.cuda.device_count() 86 | torch.cuda.set_device(rank % num_gpus) 87 | dist.init_process_group(backend='nccl') 88 | world_size = dist.get_world_size() 89 | return rank, world_size 90 | 91 | 92 | def _get_local_ip(): 93 | try: 94 | s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) 95 | s.connect(('8.8.8.8', 80)) 96 | ip = s.getsockname()[0] 97 | finally: 98 | s.close() 99 | return ip 100 | 101 | 102 | def dist_init(): 103 | global rank, world_size, inited 104 | # try: #看来作者和我调整的一样，都屏蔽了这一段落 105 | # rank, world_size = _dist_init() 106 | # except RuntimeError as e: 107 | # if 'public' in e.args[0]: 108 | # logger.info(e) 109 | # logger.info('Warning: use single process') 110 | # rank, world_size = 0, 1 111 | # else: 112 | # raise RuntimeError(*e.args) 113 | rank, world_size = 0, 1 114 | inited = True 115 | return rank, world_size 116 | 117 | 118 | def get_rank(): 119 | if not inited: 120 | raise(Exception('dist not inited')) 121 | return rank 122 | 123 | 124 | def get_world_size(): 125 | if not inited: 126 | raise(Exception('dist not inited')) 127 | return world_size 128 | 129 | 130 | def reduce_gradients(model, _type='sum'): 131 | types = ['sum', 'avg'] 132 | assert _type in types, 'gradients method must be in "{}"'.format(types) 133 | log_once("gradients method is {}".format(_type)) 134 | if get_world_size() > 1: 135 | for param in model.parameters(): 136 | if param.requires_grad: 137 | dist.all_reduce(param.grad.data) 138 | if _type == 'avg': 139 | param.grad.data /= get_world_size() 140 | else: 141 | return None 142 | -------------------------------------------------------------------------------- /pysot/utils/location_grid.py: -------------------------------------------------------------------------------- 1 | import torch 2 | def compute_locations(features,stride): 3 | h, w = features.size()[-2:] 4 | locations_per_level = compute_locations_per_level( 5 | h, w, stride, 6 | features.device 7 | ) 8 | return locations_per_level # 计算每像素-对应原图中位置 9 | 10 | 11 | def compute_locations_per_level(h, w, stride, device): 12 | shifts_x = torch.arange( 13 | 0, w * stride, step=stride, 14 | dtype=torch.float32, device=device 15 | ) 16 | shifts_y = torch.arange( 17 | 0, h * stride, step=stride, 18 | dtype=torch.float32, device=device 19 | ) 20 | shift_y, shift_x = torch.meshgrid((shifts_y, shifts_x)) 21 | shift_x = shift_x.reshape(-1) 22 | shift_y = shift_y.reshape(-1) 23 | # locations = torch.stack((shift_x, shift_y), dim=1) + stride + 3*stride # (size_z-1)/2*size_z 28 24 | # locations = torch.stack((shift_x, shift_y), dim=1) + stride 25 | locations = torch.stack((shift_x, shift_y), dim=1) + 32 #alex:48 // 32 26 | return locations 27 | 28 | 29 | 30 | -------------------------------------------------------------------------------- /pysot/utils/log_helper.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/utils/log_helper.py -------------------------------------------------------------------------------- /pysot/utils/misc.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/pysot/utils/misc.py -------------------------------------------------------------------------------- /pysot/utils/model_load.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | 6 | import logging 7 | 8 | import torch 9 | 10 | logger = logging.getLogger('global') 11 | 12 | 13 | def check_keys(model, pretrained_state_dict): 14 | ckpt_keys = set(pretrained_state_dict.keys()) 15 | model_keys = set(model.state_dict().keys()) 16 | used_pretrained_keys = model_keys & ckpt_keys 17 | unused_pretrained_keys = ckpt_keys - model_keys 18 | missing_keys = model_keys - ckpt_keys 19 | 20 | missing_keys = [x for x in missing_keys if not x.endswith('num_batches_tracked')] 21 | 22 | if len(missing_keys) > 0: 23 | logger.info('[Warning] missing keys: {}'.format(missing_keys)) 24 | logger.info('missing keys:{}'.format(len(missing_keys))) 25 | if len(unused_pretrained_keys) > 0: 26 | logger.info('[Warning] unused_pretrained_keys: {}'.format(unused_pretrained_keys)) 27 | logger.info('unused checkpoint keys:{}'.format(len(unused_pretrained_keys))) 28 | logger.info('used keys:{}'.format(len(used_pretrained_keys))) 29 | assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' 30 | 31 | return True 32 | 33 | 34 | def remove_prefix(state_dict, prefix): 35 | ''' Old style model is stored with all names of parameters 36 | share common prefix 'module.' ''' 37 | logger.info('remove prefix \'{}\''.format(prefix)) 38 | f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x 39 | return {f(key): value for key, value in state_dict.items()} 40 | 41 | 42 | def load_pretrain(model, pretrained_path): 43 | logger.info('load pretrained model from {}'.format(pretrained_path)) 44 | device = torch.cuda.current_device() 45 | pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device)) 46 | 47 | if "state_dict" in pretrained_dict.keys(): 48 | pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.') 49 | else: 50 | pretrained_dict = remove_prefix(pretrained_dict, 'module.') 51 | 52 | try: 53 | check_keys(model, pretrained_dict) 54 | except: 55 | logger.info('[Warning]: using pretrain as features. Adding "features." as prefix') 56 | new_dict = {} 57 | for k, v in pretrained_dict.items(): 58 | k = 'features.' + k 59 | new_dict[k] = v 60 | pretrained_dict = new_dict 61 | check_keys(model, pretrained_dict) 62 | model.load_state_dict(pretrained_dict, strict=False) 63 | 64 | return model 65 | 66 | 67 | def restore_from(model, optimizer, ckpt_path): 68 | device = torch.cuda.current_device() 69 | ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage.cuda(device)) 70 | epoch = ckpt['epoch'] 71 | 72 | ckpt_model_dict = remove_prefix(ckpt['state_dict'], 'module.') 73 | check_keys(model, ckpt_model_dict) 74 | model.load_state_dict(ckpt_model_dict, strict=False) 75 | 76 | check_keys(optimizer, ckpt['optimizer']) 77 | optimizer.load_state_dict(ckpt['optimizer']) 78 | return model, optimizer, epoch 79 | -------------------------------------------------------------------------------- /pysot/utils/xcorr.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) SenseTime. All Rights Reserved. 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | from __future__ import unicode_literals 7 | 8 | import torch 9 | import torch.nn.functional as F 10 | 11 | 12 | def xcorr_slow(x, kernel): 13 | """for loop to calculate cross correlation, slow version 14 | """ 15 | batch = x.size()[0] 16 | out = [] 17 | for i in range(batch): 18 | px = x[i] 19 | pk = kernel[i] 20 | px = px.view(1, px.size()[0], px.size()[1], px.size()[2]) 21 | pk = pk.view(-1, px.size()[1], pk.size()[1], pk.size()[2]) 22 | po = F.conv2d(px, pk) 23 | out.append(po) 24 | out = torch.cat(out, 0) 25 | return out 26 | 27 | 28 | def xcorr_fast(x, kernel): 29 | """group conv2d to calculate cross correlation, fast version 30 | """ 31 | batch = kernel.size()[0] 32 | pk = kernel.view(-1, x.size()[1], kernel.size()[2], kernel.size()[3]) 33 | px = x.view(1, -1, x.size()[2], x.size()[3]) 34 | po = F.conv2d(px, pk, groups=batch) 35 | po = po.view(batch, -1, po.size()[2], po.size()[3]) 36 | return po 37 | 38 | 39 | def xcorr_depthwise(x, kernel): 40 | """depthwise cross correlation 41 | """ 42 | # x [32, 256, 31, 31], kernel:[32, 256, 7, 7] 43 | batch = kernel.size(0) 44 | channel = kernel.size(1) 45 | x = x.view(1, batch*channel, x.size(2), x.size(3)) # [32, 256, 31, 31] --> [1, 32*256, 31, 31] 46 | kernel = kernel.view(batch*channel, 1, kernel.size(2), kernel.size(3)) # [32, 256, 7, 7] --> [32*256, 1, 7, 7] 47 | 48 | # 互相换DW逐层卷积, out:[1, 32*256, 25, 25]; x:[1, 32*256, 31, 31], kernel:[32*256, 1, 7, 7] 49 | out = F.conv2d(x, kernel, groups=batch*channel) # ,互相关-创新点，多尺度互相关; 50 | 51 | # 展开互相关结果out 52 | out = out.view(batch, channel, out.size(2), out.size(3)) # [1, 32*256, 25, 25] --> [32, 256, 25, 25] 53 | return out 54 | -------------------------------------------------------------------------------- /toolkit/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/__init__.py -------------------------------------------------------------------------------- /toolkit/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /toolkit/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /toolkit/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | from .otb import OTBDataset 2 | from .uav import UAVDataset 3 | from .lasot import LaSOTDataset 4 | from .got10k import GOT10kDataset 5 | 6 | class DatasetFactory(object): 7 | @staticmethod 8 | def create_dataset(**kwargs): 9 | """ 10 | Args: 11 | name: dataset name 'OTB2015', 'LaSOT', 'UAV123', 'NFS240', 'NFS30', 12 | 'VOT2018', 'VOT2016', 'VOT2018-LT' 13 | dataset_root: dataset root 14 | load_img: wether to load image 15 | Return: 16 | dataset 17 | """ 18 | assert 'name' in kwargs, "should provide dataset name" 19 | name = kwargs['name'] 20 | if 'OTB' in name: 21 | dataset = OTBDataset(**kwargs) # 22 | elif 'LaSOT' == name: 23 | dataset = LaSOTDataset(**kwargs) 24 | elif 'UAV' in name: 25 | dataset = UAVDataset(**kwargs) 26 | elif 'GOT-10k' == name: 27 | dataset = GOT10kDataset(**kwargs) 28 | else: 29 | raise Exception("unknow dataset {}".format(kwargs['name'])) 30 | return dataset 31 | 32 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /toolkit/datasets/dataset.py: -------------------------------------------------------------------------------- 1 | from tqdm import tqdm 2 | 3 | 4 | class Dataset(object): 5 | def __init__(self, name, dataset_root): 6 | self.name = name 7 | self.dataset_root = dataset_root 8 | self.videos = None 9 | 10 | def __getitem__(self, idx): 11 | if isinstance(idx, str): 12 | return self.videos[idx] 13 | elif isinstance(idx, int): 14 | return self.videos[sorted(list(self.videos.keys()))[idx]] 15 | 16 | def __len__(self): 17 | return len(self.videos) 18 | 19 | def __iter__(self): 20 | keys = sorted(list(self.videos.keys())) 21 | for key in keys: 22 | yield self.videos[key] 23 | 24 | def set_tracker(self, path, tracker_names): 25 | """ 26 | Args: 27 | path: path to tracker results, 28 | tracker_names: list of tracker name 29 | """ 30 | self.tracker_path = path 31 | self.tracker_names = tracker_names 32 | -------------------------------------------------------------------------------- /toolkit/datasets/got10k.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | 4 | from tqdm import tqdm 5 | 6 | from .dataset import Dataset 7 | from .video import Video 8 | 9 | 10 | class GOT10kVideo(Video): 11 | """ 12 | Args: 13 | name: video name 14 | root: dataset root 15 | video_dir: video directory 16 | init_rect: init rectangle 17 | img_names: image names 18 | gt_rect: groundtruth rectangle 19 | attr: attribute of video 20 | """ 21 | 22 | def __init__(self, name, root, video_dir, init_rect, img_names, 23 | gt_rect, attr, load_img=False): 24 | super(GOT10kVideo, self).__init__(name, root, video_dir, init_rect, img_names, gt_rect, attr, load_img) 25 | 26 | 27 | class GOT10kDataset(Dataset): 28 | """ 29 | Args: 30 | name: dataset name, should be "NFS30" or "NFS240" 31 | dataset_root, dataset root dir 32 | """ 33 | 34 | def __init__(self, name, dataset_root, load_img=False): 35 | super(GOT10kDataset, self).__init__(name, dataset_root) 36 | with open(os.path.join(dataset_root, name + '.json'), 'r') as f: 37 | meta_data = json.load(f) 38 | 39 | pbar = tqdm(meta_data.keys(), desc='loading ' + name, ncols=100) 40 | self.videos = {} 41 | for video in pbar: 42 | pbar.set_postfix_str(video) 43 | self.videos[video] = GOT10kVideo(video, 44 | dataset_root, 45 | meta_data[video]['video_dir'], 46 | meta_data[video]['init_rect'], 47 | meta_data[video]['img_names'], 48 | meta_data[video]['gt_rect'], 49 | None) 50 | self.attr = {} 51 | self.attr['ALL'] = list(self.videos.keys()) 52 | -------------------------------------------------------------------------------- /toolkit/datasets/lasot.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import numpy as np 4 | 5 | from tqdm import tqdm 6 | from glob import glob 7 | 8 | from .dataset import Dataset 9 | from .video import Video 10 | 11 | class LaSOTVideo(Video): 12 | """ 13 | Args: 14 | name: video name 15 | root: dataset root 16 | video_dir: video directory 17 | init_rect: init rectangle 18 | img_names: image names 19 | gt_rect: groundtruth rectangle 20 | attr: attribute of video 21 | """ 22 | def __init__(self, name, root, video_dir, init_rect, img_names, 23 | gt_rect, attr, absent, load_img=False): 24 | super(LaSOTVideo, self).__init__(name, root, video_dir, 25 | init_rect, img_names, gt_rect, attr, load_img) 26 | self.absent = np.array(absent, np.int8) 27 | 28 | def load_tracker(self, path, tracker_names=None, store=True): 29 | """ 30 | Args: 31 | path(str): path to result 32 | tracker_name(list): name of tracker 33 | """ 34 | if not tracker_names: 35 | tracker_names = [x.split('/')[-1] for x in glob(path) 36 | if os.path.isdir(x)] 37 | if isinstance(tracker_names, str): 38 | tracker_names = [tracker_names] 39 | for name in tracker_names: 40 | traj_file = os.path.join(path, name, self.name+'.txt') 41 | if os.path.exists(traj_file): 42 | with open(traj_file, 'r') as f : 43 | pred_traj = [list(map(float, x.strip().split(','))) 44 | for x in f.readlines()] 45 | else: 46 | print("File not exists: ", traj_file) 47 | if self.name == 'monkey-17': 48 | pred_traj = pred_traj[:len(self.gt_traj)] 49 | if store: 50 | self.pred_trajs[name] = pred_traj 51 | else: 52 | return pred_traj 53 | self.tracker_names = list(self.pred_trajs.keys()) 54 | 55 | 56 | 57 | class LaSOTDataset(Dataset): 58 | """ 59 | Args: 60 | name: dataset name, should be 'phtrack_model_general', 'CVPR13', 'OTB50' 61 | dataset_root: dataset root 62 | load_img: wether to load all imgs 63 | """ 64 | def __init__(self, name, dataset_root, load_img=False): 65 | super(LaSOTDataset, self).__init__(name, dataset_root) 66 | with open(os.path.join(dataset_root, name+'.json'), 'r') as f: 67 | meta_data = json.load(f) 68 | 69 | # load videos 70 | pbar = tqdm(meta_data.keys(), desc='loading '+name, ncols=100) 71 | self.videos = {} 72 | for video in pbar: 73 | pbar.set_postfix_str(video) 74 | self.videos[video] = LaSOTVideo(video, 75 | dataset_root, 76 | meta_data[video]['video_dir'], 77 | meta_data[video]['init_rect'], 78 | meta_data[video]['img_names'], 79 | meta_data[video]['gt_rect'], 80 | meta_data[video]['attr'], 81 | meta_data[video]['absent']) 82 | 83 | # set attr 84 | attr = [] 85 | for x in self.videos.values(): 86 | attr += x.attr 87 | attr = set(attr) 88 | self.attr = {} 89 | self.attr['ALL'] = list(self.videos.keys()) 90 | for x in attr: 91 | self.attr[x] = [] 92 | for k, v in self.videos.items(): 93 | for attr_ in v.attr: 94 | self.attr[attr_].append(k) 95 | 96 | 97 | -------------------------------------------------------------------------------- /toolkit/datasets/otb.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import numpy as np 4 | 5 | from PIL import Image 6 | from tqdm import tqdm 7 | from glob import glob 8 | 9 | from .dataset import Dataset 10 | from .video import Video 11 | 12 | 13 | class OTBVideo(Video): 14 | """ 15 | Args: 16 | name: video name 17 | root: dataset root 18 | video_dir: video directory 19 | init_rect: init rectangle 20 | img_names: image names 21 | gt_rect: groundtruth rectangle 22 | attr: attribute of video 23 | """ 24 | 25 | def __init__(self, name, root, video_dir, init_rect, img_names, gt_rect, attr, load_img=False): 26 | super(OTBVideo, self).__init__(name, root, video_dir, init_rect, img_names, gt_rect, attr, load_img) 27 | 28 | def load_tracker(self, path, tracker_names=None, store=True): 29 | """ 30 | Args: 31 | path(str): path to result 32 | tracker_name(list): name of tracker 33 | """ 34 | if not tracker_names: 35 | tracker_names = [x.split('/')[-1] for x in glob(path) 36 | if os.path.isdir(x)] 37 | if isinstance(tracker_names, str): 38 | tracker_names = [tracker_names] 39 | for name in tracker_names: 40 | traj_file = os.path.join(path, name, self.name + '.txt') 41 | if not os.path.exists(traj_file): 42 | if self.name == 'FleetFace': 43 | txt_name = 'fleetface.txt' 44 | elif self.name == 'Jogging-1': 45 | txt_name = 'jogging_1.txt' 46 | elif self.name == 'Jogging-2': 47 | txt_name = 'jogging_2.txt' 48 | elif self.name == 'Skating2-1': 49 | txt_name = 'skating2_1.txt' 50 | elif self.name == 'Skating2-2': 51 | txt_name = 'skating2_2.txt' 52 | elif self.name == 'FaceOcc1': 53 | txt_name = 'faceocc1.txt' 54 | elif self.name == 'FaceOcc2': 55 | txt_name = 'faceocc2.txt' 56 | elif self.name == 'Human4-2': 57 | txt_name = 'human4_2.txt' 58 | else: 59 | txt_name = self.name[0].lower() + self.name[1:] + '.txt' 60 | traj_file = os.path.join(path, name, txt_name) 61 | if os.path.exists(traj_file): 62 | with open(traj_file, 'r') as f: 63 | pred_traj = [list(map(float, x.strip().split(','))) 64 | for x in f.readlines()] 65 | if len(pred_traj) != len(self.gt_traj): 66 | print(name, len(pred_traj), len(self.gt_traj), self.name) 67 | if store: 68 | self.pred_trajs[name] = pred_traj 69 | else: 70 | return pred_traj 71 | else: 72 | print(traj_file) 73 | self.tracker_names = list(self.pred_trajs.keys()) 74 | 75 | 76 | class OTBDataset(Dataset): 77 | """ 78 | Args: 79 | name: dataset name, should be 'SiamCAR_model_general', 'CVPR13', 'OTB50' 80 | dataset_root: dataset root 81 | load_img: wether to load all imgs 82 | """ 83 | 84 | def __init__(self, name, dataset_root, load_img=False): 85 | super(OTBDataset, self).__init__(name, dataset_root) 86 | 87 | with open(os.path.join(dataset_root, name + '.json'), 'r') as f: 88 | meta_data = json.load(f) 89 | 90 | pbar = tqdm(meta_data.keys(), desc='loading ' + name, ncols=100) 91 | self.videos = {} 92 | for video in pbar: 93 | pbar.set_postfix_str(video) 94 | self.videos[video] = OTBVideo(video, 95 | dataset_root, 96 | meta_data[video]['video_dir'], 97 | meta_data[video]['init_rect'], 98 | meta_data[video]['img_names'], 99 | meta_data[video]['gt_rect'], 100 | meta_data[video]['attr'], 101 | load_img) 102 | 103 | attr = [] 104 | for x in self.videos.values(): 105 | attr += x.attr 106 | attr = set(attr) 107 | self.attr = {} 108 | self.attr['ALL'] = list(self.videos.keys()) 109 | for x in attr: 110 | self.attr[x] = [] 111 | for k, v in self.videos.items(): 112 | for attr_ in v.attr: 113 | self.attr[attr_].append(k) 114 | -------------------------------------------------------------------------------- /toolkit/datasets/uav.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | from tqdm import tqdm 5 | from glob import glob 6 | 7 | from .dataset import Dataset 8 | from .video import Video 9 | 10 | class UAVVideo(Video): 11 | """ 12 | Args: 13 | name: video name 14 | root: dataset root 15 | video_dir: video directory 16 | init_rect: init rectangle 17 | img_names: image names 18 | gt_rect: groundtruth rectangle 19 | attr: attribute of video 20 | """ 21 | def __init__(self, name, root, video_dir, init_rect, img_names, 22 | gt_rect, attr, load_img=False): 23 | super(UAVVideo, self).__init__(name, root, video_dir, 24 | init_rect, img_names, gt_rect, attr, load_img) 25 | 26 | 27 | class UAVDataset(Dataset): 28 | """ 29 | Args: 30 | name: dataset name, should be 'UAV123', 'UAV20L' 31 | dataset_root: dataset root 32 | load_img: wether to load all imgs 33 | """ 34 | def __init__(self, name, dataset_root, load_img=False): 35 | super(UAVDataset, self).__init__(name, dataset_root) 36 | with open(os.path.join(dataset_root, name+'.json'), 'r') as f: 37 | meta_data = json.load(f) 38 | 39 | # load videos 40 | pbar = tqdm(meta_data.keys(), desc='loading '+name, ncols=100) 41 | self.videos = {} 42 | for video in pbar: 43 | pbar.set_postfix_str(video) 44 | self.videos[video] = UAVVideo(video, 45 | dataset_root, 46 | meta_data[video]['video_dir'], 47 | meta_data[video]['init_rect'], 48 | meta_data[video]['img_names'], 49 | meta_data[video]['gt_rect'], 50 | meta_data[video]['attr']) 51 | 52 | # set attr 53 | attr = [] 54 | for x in self.videos.values(): 55 | attr += x.attr 56 | attr = set(attr) 57 | self.attr = {} 58 | self.attr['ALL'] = list(self.videos.keys()) 59 | for x in attr: 60 | self.attr[x] = [] 61 | for k, v in self.videos.items(): 62 | for attr_ in v.attr: 63 | self.attr[attr_].append(k) 64 | 65 | -------------------------------------------------------------------------------- /toolkit/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from .ope_benchmark import OPEBenchmark 2 | -------------------------------------------------------------------------------- /toolkit/evaluation/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/evaluation/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /toolkit/evaluation/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/evaluation/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /toolkit/evaluation/__pycache__/ope_benchmark.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/evaluation/__pycache__/ope_benchmark.cpython-37.pyc -------------------------------------------------------------------------------- /toolkit/evaluation/__pycache__/ope_benchmark.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/evaluation/__pycache__/ope_benchmark.cpython-39.pyc -------------------------------------------------------------------------------- /toolkit/utils/__pycache__/statistics.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/utils/__pycache__/statistics.cpython-37.pyc -------------------------------------------------------------------------------- /toolkit/utils/__pycache__/statistics.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YZCU/PHTrack/8e8abdedd399e9729f92ca63ec15c908bfa177a8/toolkit/utils/__pycache__/statistics.cpython-39.pyc -------------------------------------------------------------------------------- /toolkit/utils/statistics.py: -------------------------------------------------------------------------------- 1 | """ 2 | @author fangyi.zhang@vipl.ict.ac.cn 3 | """ 4 | import numpy as np 5 | 6 | def overlap_ratio(rect1, rect2): 7 | '''Compute overlap ratio between two rects 8 | Args 9 | rect:2d array of N x [x,y,w,h] 10 | Return: 11 | iou 12 | ''' 13 | # if rect1.ndim==1: 14 | # rect1 = rect1[np.newaxis, :] 15 | # if rect2.ndim==1: 16 | # rect2 = rect2[np.newaxis, :] 17 | left = np.maximum(rect1[:,0], rect2[:,0]) 18 | right = np.minimum(rect1[:,0]+rect1[:,2], rect2[:,0]+rect2[:,2]) 19 | top = np.maximum(rect1[:,1], rect2[:,1]) 20 | bottom = np.minimum(rect1[:,1]+rect1[:,3], rect2[:,1]+rect2[:,3]) 21 | 22 | intersect = np.maximum(0,right - left) * np.maximum(0,bottom - top) 23 | union = rect1[:,2]*rect1[:,3] + rect2[:,2]*rect2[:,3] - intersect 24 | iou = intersect / union 25 | iou = np.maximum(np.minimum(1, iou), 0) 26 | return iou 27 | 28 | def success_overlap(gt_bb, result_bb, n_frame): 29 | thresholds_overlap = np.arange(0, 1.05, 0.05) 30 | success = np.zeros(len(thresholds_overlap)) 31 | iou = np.ones(len(gt_bb)) * (-1) 32 | # mask = np.sum(gt_bb > 0, axis=1) == 4 #TODO check all dataset 33 | mask = np.sum(gt_bb[:, 2:] > 0, axis=1) == 2 34 | iou[mask] = overlap_ratio(gt_bb[mask], result_bb[mask]) 35 | for i in range(len(thresholds_overlap)): 36 | success[i] = np.sum(iou > thresholds_overlap[i]) / float(n_frame) 37 | return success 38 | 39 | def success_error(gt_center, result_center, thresholds, n_frame): 40 | # n_frame = len(gt_center) 41 | success = np.zeros(len(thresholds)) 42 | dist = np.ones(len(gt_center)) * (-1) 43 | mask = np.sum(gt_center > 0, axis=1) == 2 44 | dist[mask] = np.sqrt(np.sum( 45 | np.power(gt_center[mask] - result_center[mask], 2), axis=1)) 46 | for i in range(len(thresholds)): 47 | success[i] = np.sum(dist <= thresholds[i]) / float(n_frame) 48 | return success 49 | 50 | 51 | -------------------------------------------------------------------------------- /toolkit/visualization/__init__.py: -------------------------------------------------------------------------------- 1 | from .draw_success_precision import draw_success_precision 2 | -------------------------------------------------------------------------------- /toolkit/visualization/draw_success_precision.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | 4 | from .draw_utils import COLOR, LINE_STYLE 5 | 6 | 7 | def draw_success_precision(success_ret, name, videos, attr, precision_ret=None, 8 | norm_precision_ret=None, bold_name=None, axis=[0, 1]): 9 | fig, ax = plt.subplots() 10 | ax.grid(b=True) 11 | ax.set_aspect(1) 12 | plt.xlabel('Overlap threshold') 13 | plt.ylabel('Success rate') 14 | if attr == 'ALL': 15 | plt.title(r'\textbf{Success plots of OPE on %s}' % (name)) 16 | else: 17 | plt.title(r'\textbf{Success plots of OPE - %s}' % (attr)) 18 | plt.axis([0, 1] + axis) 19 | success = {} 20 | thresholds = np.arange(0, 1.05, 0.05) 21 | for tracker_name in success_ret.keys(): 22 | value = [v for k, v in success_ret[tracker_name].items() if k in videos] 23 | success[tracker_name] = np.mean(value) 24 | for idx, (tracker_name, auc) in \ 25 | enumerate(sorted(success.items(), key=lambda x: x[1], reverse=True)): 26 | if tracker_name == bold_name: 27 | label = r"\textbf{[%.3f] %s}" % (auc, tracker_name) 28 | else: 29 | label = "[%.3f] " % (auc) + tracker_name 30 | value = [v for k, v in success_ret[tracker_name].items() if k in videos] 31 | plt.plot(thresholds, np.mean(value, axis=0), 32 | color=COLOR[idx], linestyle=LINE_STYLE[idx], label=label, linewidth=2) 33 | ax.legend(loc='lower left', labelspacing=0.2) 34 | ax.autoscale(enable=True, axis='both', tight=True) 35 | xmin, xmax, ymin, ymax = plt.axis() 36 | ax.autoscale(enable=False) 37 | ymax += 0.03 38 | plt.axis([xmin, xmax, ymin, ymax]) 39 | plt.xticks(np.arange(xmin, xmax + 0.01, 0.1)) 40 | plt.yticks(np.arange(ymin, ymax, 0.1)) 41 | ax.set_aspect((xmax - xmin) / (ymax - ymin)) 42 | plt.show() 43 | 44 | if precision_ret: 45 | 46 | fig, ax = plt.subplots() 47 | ax.grid(b=True) 48 | ax.set_aspect(50) 49 | plt.xlabel('Location error threshold') 50 | plt.ylabel('Precision') 51 | if attr == 'ALL': 52 | plt.title(r'\textbf{Precision plots of OPE on %s}' % (name)) 53 | else: 54 | plt.title(r'\textbf{Precision plots of OPE - %s}' % (attr)) 55 | plt.axis([0, 50] + axis) 56 | precision = {} 57 | thresholds = np.arange(0, 51, 1) 58 | for tracker_name in precision_ret.keys(): 59 | value = [v for k, v in precision_ret[tracker_name].items() if k in videos] 60 | precision[tracker_name] = np.mean(value, axis=0)[20] 61 | for idx, (tracker_name, pre) in \ 62 | enumerate(sorted(precision.items(), key=lambda x: x[1], reverse=True)): 63 | if tracker_name == bold_name: 64 | label = r"\textbf{[%.3f] %s}" % (pre, tracker_name) 65 | else: 66 | label = "[%.3f] " % (pre) + tracker_name 67 | value = [v for k, v in precision_ret[tracker_name].items() if k in videos] 68 | plt.plot(thresholds, np.mean(value, axis=0), 69 | color=COLOR[idx], linestyle=LINE_STYLE[idx], label=label, linewidth=2) 70 | ax.legend(loc='lower right', labelspacing=0.2) 71 | ax.autoscale(enable=True, axis='both', tight=True) 72 | xmin, xmax, ymin, ymax = plt.axis() 73 | ax.autoscale(enable=False) 74 | ymax += 0.03 75 | plt.axis([xmin, xmax, ymin, ymax]) 76 | plt.xticks(np.arange(xmin, xmax + 0.01, 5)) 77 | plt.yticks(np.arange(ymin, ymax, 0.1)) 78 | ax.set_aspect((xmax - xmin) / (ymax - ymin)) 79 | plt.show() 80 | 81 | if norm_precision_ret: 82 | fig, ax = plt.subplots() 83 | ax.grid(b=True) 84 | plt.xlabel('Location error threshold') 85 | plt.ylabel('Precision') 86 | if attr == 'ALL': 87 | plt.title(r'\textbf{Normalized Precision plots of OPE on %s}' % (name)) 88 | else: 89 | plt.title(r'\textbf{Normalized Precision plots of OPE - %s}' % (attr)) 90 | norm_precision = {} 91 | thresholds = np.arange(0, 51, 1) / 100 92 | for tracker_name in precision_ret.keys(): 93 | value = [v for k, v in norm_precision_ret[tracker_name].items() if k in videos] 94 | norm_precision[tracker_name] = np.mean(value, axis=0)[20] 95 | for idx, (tracker_name, pre) in \ 96 | enumerate(sorted(norm_precision.items(), key=lambda x: x[1], reverse=True)): 97 | if tracker_name == bold_name: 98 | label = r"\textbf{[%.3f] %s}" % (pre, tracker_name) 99 | else: 100 | label = "[%.3f] " % (pre) + tracker_name 101 | value = [v for k, v in norm_precision_ret[tracker_name].items() if k in videos] 102 | plt.plot(thresholds, np.mean(value, axis=0), 103 | color=COLOR[idx], linestyle=LINE_STYLE[idx], label=label, linewidth=2) 104 | ax.legend(loc='lower right', labelspacing=0.2) 105 | ax.autoscale(enable=True, axis='both', tight=True) 106 | xmin, xmax, ymin, ymax = plt.axis() 107 | ax.autoscale(enable=False) 108 | ymax += 0.03 109 | plt.axis([xmin, xmax, ymin, ymax]) 110 | plt.xticks(np.arange(xmin, xmax + 0.01, 0.05)) 111 | plt.yticks(np.arange(ymin, ymax, 0.1)) 112 | ax.set_aspect((xmax - xmin) / (ymax - ymin)) 113 | plt.show() 114 | -------------------------------------------------------------------------------- /toolkit/visualization/draw_utils.py: -------------------------------------------------------------------------------- 1 | 2 | COLOR = ((1, 0, 0), 3 | (0, 1, 0), 4 | (1, 0, 1), 5 | (1, 1, 0), 6 | (0 , 162/255, 232/255), 7 | (0.5, 0.5, 0.5), 8 | (0, 0, 1), 9 | (0, 1, 1), 10 | (136/255, 0 , 21/255), 11 | (255/255, 127/255, 39/255), 12 | (0, 0, 0)) 13 | 14 | LINE_STYLE = ['-', '--', ':', '-', '--', ':', '-', '--', ':', '-'] 15 | 16 | MARKER_STYLE = ['o', 'v', '<', '*', 'D', 'x', '.', 'x', '<', '.'] 17 | -------------------------------------------------------------------------------- /tools/eval.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | import os 6 | import argparse 7 | from glob import glob 8 | from tqdm import tqdm 9 | from multiprocessing import Pool 10 | from toolkit.datasets import OTBDataset, UAVDataset, LaSOTDataset 11 | from toolkit.evaluation import OPEBenchmark 12 | import sys 13 | 14 | sys.path.append('../') 15 | 16 | parser = argparse.ArgumentParser(description='tracking evaluation') 17 | parser.add_argument('--dataset', '-d', type=str, default='OTB100', help='dataset name') 18 | parser.add_argument('--show_video_level', '-s', dest='show_video_level', action='store_true') 19 | parser.add_argument('--num', '-n', default=1, type=int, help='number of thread to eval') 20 | 21 | parser.add_argument('--tracker_path', '-p', type=str, default='./results', help='tracker result path') 22 | 23 | parser.add_argument('--tracker_prefix', '-t', default='checkpoint', type=str, help='tracker name') 24 | 25 | parser.set_defaults(show_video_level=True) 26 | args = parser.parse_args() 27 | 28 | 29 | def main(): 30 | tracker_dir = os.path.join(args.tracker_path, args.dataset) 31 | trackers = glob(os.path.join(args.tracker_path, args.dataset, args.tracker_prefix + '*')) 32 | 33 | trackers = [x.split('\\')[-1] for x in trackers] 34 | assert len(trackers) > 0 35 | args.num = min(args.num, len(trackers)) 36 | 37 | root = r'G:\0hsi_train_test_data\hsi_test\OTB100' 38 | 39 | dataset = OTBDataset(args.dataset, root) 40 | dataset.set_tracker(tracker_dir, trackers) 41 | benchmark = OPEBenchmark(dataset) 42 | success_ret = {} 43 | with Pool(processes=args.num) as pool: 44 | for ret in tqdm(pool.imap_unordered(benchmark.eval_success, trackers), desc='eval success', total=len(trackers), 45 | ncols=100): success_ret.update(ret) 46 | precision_ret = {} 47 | with Pool(processes=args.num) as pool: 48 | for ret in tqdm(pool.imap_unordered(benchmark.eval_precision, trackers), desc='eval precision', 49 | total=len(trackers), ncols=100): 50 | precision_ret.update(ret) 51 | benchmark.show_result(success_ret, precision_ret, show_video_level=args.show_video_level) 52 | 53 | 54 | if __name__ == '__main__': 55 | main() 56 | -------------------------------------------------------------------------------- /tools/test.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | from __future__ import unicode_literals 5 | import argparse 6 | import os 7 | import cv2 8 | import torch 9 | import numpy as np 10 | from pysot.core.config import cfg 11 | from pysot.tracker.phtrack_tracker import phtrackTracker 12 | from pysot.utils.bbox import get_axis_aligned_bbox 13 | from pysot.utils.model_load import load_pretrain 14 | from pysot.models.model_builder import ModelBuilder 15 | from toolkit.datasets import DatasetFactory 16 | import math 17 | import logging 18 | from pysot.utils.log_helper import init_log 19 | import matplotlib.pyplot as plt 20 | import matplotlib 21 | import sys 22 | 23 | sys.path.append('../') 24 | logger = logging.getLogger('global') 25 | init_log('global', logging.INFO) 26 | imageFilter = '*.png*' 27 | 28 | parser = argparse.ArgumentParser(description='phtrack tracking') 29 | parser.add_argument('--dataset', type=str, default='OTB100', help='datasets') 30 | 31 | parser.add_argument('--video', default='', type=str, help='test one special video') 32 | 33 | parser.add_argument('--snapshot', type=str, default='snapshot/checkpoint_e16.pth', help='snapshot of models to eval') 34 | 35 | parser.add_argument('--config', type=str, default='../experiments/phtrack/config.yaml', help='config file') 36 | 37 | parser.add_argument('--vis', action='store_true', default=False, help='whether visualzie result') 38 | 39 | args = parser.parse_args() 40 | torch.set_num_threads(1) 41 | os.environ['CUDA_VISIBLE_DEVICES'] = '1' 42 | 43 | 44 | def main(): 45 | cfg.merge_from_file(args.config) 46 | 47 | params = getattr(cfg.HP_SEARCH, args.dataset) 48 | hp = {'lr': params[0], 'penalty_k': params[1], 'window_lr': params[2], 'context_amount': params[3]} 49 | 50 | dataset_root = r'G:\0hsi_train_test_data\hsi_test\OTB100' 51 | 52 | model = ModelBuilder() 53 | 54 | model = load_pretrain(model, args.snapshot).cuda().eval() 55 | 56 | tracker = phtrackTracker(model, cfg.TRACK) 57 | 58 | dataset = DatasetFactory.create_dataset(name=args.dataset, dataset_root=dataset_root, load_img=False) 59 | 60 | model_name = args.snapshot.split('.')[-2].split('/')[-1] + '_lr' + str(hp['lr']) + '_pk' + str( 61 | hp['penalty_k']) + '_wlr' + str(hp['window_lr']) + '_ca' + str(hp['context_amount']) 62 | 63 | for v_idx, video in enumerate(dataset): 64 | 65 | if args.video != '': 66 | if video.name != args.video: 67 | continue 68 | 69 | pred_bboxes = [] 70 | track_times = [] 71 | w_std = [] 72 | 73 | for idx, (img, gt_bbox) in enumerate(video): 74 | 75 | img = img / img.max() * 255 76 | img = img.astype('uint8') 77 | 78 | tic = cv2.getTickCount() 79 | 80 | if idx == 0: 81 | cx, cy, w, h = get_axis_aligned_bbox(np.array(gt_bbox)) 82 | gt_bbox_ = [cx - (w - 1) / 2, cy - (h - 1) / 2, w, h] 83 | pred_bbox = gt_bbox_ 84 | pred_bboxes.append(pred_bbox) 85 | tracker.init(img, gt_bbox_, hp) 86 | 87 | 88 | else: 89 | 90 | outputs = tracker.track(img, hp) 91 | 92 | pred_bbox = outputs['bbox'] 93 | pred_bboxes.append(pred_bbox) 94 | 95 | w_std.append(outputs['w_std'].item()) 96 | 97 | track_times.append((cv2.getTickCount() - tic) / cv2.getTickFrequency()) 98 | 99 | if idx == 0: 100 | cv2.destroyAllWindows() 101 | if args.vis and idx > 0: 102 | 103 | bs_order = outputs['bs_order'].cpu() 104 | img = img[:, :, bs_order] 105 | 106 | img_copy = img.copy() 107 | if not any(map(math.isnan, gt_bbox)): 108 | gt_bbox = list(map(int, gt_bbox)) 109 | pred_bbox = list(map(int, pred_bbox)) 110 | 111 | cv2.rectangle(img_copy, (gt_bbox[0], gt_bbox[1]), 112 | (gt_bbox[0] + gt_bbox[2], gt_bbox[1] + gt_bbox[3]), (0, 255, 0), 1) 113 | 114 | cv2.rectangle(img_copy, (pred_bbox[0], pred_bbox[1]), 115 | (pred_bbox[0] + pred_bbox[2], pred_bbox[1] + pred_bbox[3]), (0, 255, 255), 1) 116 | 117 | cv2.putText(img_copy, str(idx), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1) 118 | cv2.imshow(video.name, img_copy) 119 | cv2.waitKey(1) 120 | 121 | model_path = os.path.join('results', args.dataset, model_name) 122 | if not os.path.isdir(model_path): 123 | os.makedirs(model_path) 124 | fps_path = os.path.join('results', args.dataset, 'fps') 125 | if not os.path.isdir(fps_path): 126 | os.makedirs(fps_path) 127 | result_path = os.path.join(model_path, '{}.txt'.format(video.name)) 128 | result_fps_path = os.path.join(fps_path, '{}.txt'.format(video.name)) 129 | 130 | with open(result_path, 'w') as f: 131 | for x in pred_bboxes: 132 | f.write(','.join([str(i) for i in x]) + '\n') 133 | 134 | with open(result_fps_path, 'w') as f: 135 | for x in track_times: 136 | f.write(str(x) + '\n') 137 | fps = len(track_times) / sum(track_times) 138 | print('({:3d}) Video: {:12s} Time: {:5.1f}s Speed: {:3.1f}fps'.format(v_idx + 1, video.name, sum(track_times), 139 | fps)) 140 | 141 | 142 | if __name__ == '__main__': 143 | main() 144 | -------------------------------------------------------------------------------- /tracking_results/README.txt: -------------------------------------------------------------------------------- 1 | ---------------- Note ---------------- 2 | ***Training Stage*** 3 | Training set: hotc20 training set 4 | GPU: 2x NVIDIA GeForce RTX 3090 5 | CPU: Intel(R)Xeon(R) Silver 4210 CPU @ 2.20GHz 6 | 7 | ***Test Stage*** 8 | Test Sets: hotc20test, hotc23val_nir, hotc23val_rednir, hotc23val_vis, hotc24val_nir, hotc24val_rednir, hotc24val_vis, mssot, msvt 9 | GPU: NVIDIA GeForce RTX 3090 for hotc20test, NVIDIA GeForce RTX 4060 for others 10 | CPU: Intel(R)Xeon(R) Silver 4210 CPU @ 2.20GHz for hotc20test, 12th Gen Intel (R) Core (TM) i7-12700F for others 11 | 12 | - These results have been evaluated using a single model trained on the hotc2020 training set. 13 | - For the different kinds of hyperspectral data, we only use the first 16 bands in a uniform fashion. 14 | - Tracking performance is expected to be further improved by using corresponding training sets for training. 15 | 16 | - The mssot dataset is from 'SFA-guided mosaic transformer for tracking small objects in snapshot spectral imaging'. 17 | - The msvt dataset is from 'Histograms of oriented mosaic gradients for snapshot spectral image description'. 18 | - The other datasets are from 'https://www.hsitracking.com/'. 19 | -------------------------------------------------------------------------------- /tracking_results/hotc23val_nir/car64.txt: -------------------------------------------------------------------------------- 1 | 44.5,168.5,30.0,20.0 2 | 46.30875415763832,168.625790975594,30.12120389278019,20.327988784830964 3 | 46.99646609661764,168.81139454574816,30.340000184989776,20.75389172960675 4 | 49.71266472439123,168.5774729694755,30.560420085944205,21.221734882152 5 | 51.18090851202609,168.30789695206073,30.898712959885785,21.760886916981555 6 | 51.863900739808045,168.0903811969128,31.19836244223729,22.19591842727743 7 | 53.79215328508338,167.91767536460478,31.564918814038133,22.541330091893478 8 | 54.385112412706526,168.18812907059146,32.09087953379493,22.85636216742163 9 | 57.18681335526129,167.99115426289077,32.57185867272511,23.25031178282301 10 | 58.71986205539919,167.7792955024358,33.03806919631895,23.67402930373301 11 | 59.35892031575922,167.57373764004504,33.55440112164223,24.085145028514493 12 | 61.87352382574942,167.39505302093912,33.99676215458949,24.442514266726338 13 | 64.40465287389405,167.24775748907462,34.48216713074222,24.737105330455353 14 | 65.08741016534921,167.03398662713596,34.99047398212413,25.164647054332647 15 | 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135.4474507859228,166.04504213894705,58.086733931336866,36.70523277239289 40 | 141.08433348017263,166.44260669268863,60.270497854177236,37.4053846995031 41 | 144.91694456694663,166.06550548422817,61.845396666200806,38.15958711642399 42 | 149.66883593978648,166.51198266959094,63.3770240587624,38.843119908304345 43 | 155.35751509829038,166.88167335409452,64.88849618618198,39.714842344850595 44 | 159.28990094231318,167.32933565093086,66.91533384014154,40.46811930817874 45 | 164.5241285973318,166.8042142104208,68.28974789376016,41.518362189198854 46 | 170.86720016568577,167.23750744352688,69.44602438761238,42.38207817680672 47 | 178.1686543518047,168.87873847843906,70.7040281236408,42.62426324215264 48 | 184.42576324322368,168.3053898792465,72.4725012002485,43.770960440537735 49 | 188.65218435014756,167.7794950914219,75.00781201251716,44.82275001618696 50 | 195.91924595416938,167.17376356110185,77.45925365394861,46.03421307682703 51 | 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301.0839520624584,163.81713596509948,109.16378100443558,64.85880605408039 64 | 315.5649165971703,161.37165002854712,104.86473868672607,67.00945717699462 65 | 330.88268528219925,160.30088534051558,101.34922559995924,69.1509865530577 66 | 344.7568345053622,160.65580916155108,97.85252871843807,71.13576130707608 67 | 371.1746401195029,161.2907912781693,98.50214404913453,72.54005840178851 68 | 388.45072838958754,161.7212315519207,99.14642641369534,74.3865977700342 69 | -------------------------------------------------------------------------------- /tracking_results/hotc24val_nir/car47.txt: -------------------------------------------------------------------------------- 1 | 42.5,159.5,38.0,34.0 2 | 41.87518694520941,158.7878133282153,38.249626109581186,34.42437334356943 3 | 44.654974964880154,158.53882741062287,38.456224778368345,34.92234517875424 4 | 45.58422684595587,158.2551494368279,38.926681349563154,35.4897011263442 5 | 45.86891169547903,158.00927519794433,39.538288437468566,35.98144960411135 6 | 47.40069919808331,157.8076936222833,40.07013638857511,36.38461275543341 7 | 50.24331053251586,157.6677094171125,40.45139817012213,36.66458116577503 8 | 50.556381584759585,157.46450138304365,41.04903313720304,37.070997233912685 9 | 51.52580692028489,157.3033737528126,41.58954833469124,37.39325249437481 10 | 54.439041604324544,157.04332091730893,42.029772774595806,37.91335816538216 11 | 54.72176694320362,156.75531533556477,42.73292661315082,38.48936932887045 12 | 56.92347920502147,156.46687607698914,43.48506592684875,39.06624784602172 13 | 57.7720543766877,156.28586232632514,44.40783329619926,39.42827534734969 14 | 61.97075515122924,156.12875843667894,45.322097640284774,39.742483126642156 15 | 61.473491032457986,155.90456514333272,46.31662587782728,40.190869713334564 16 | 63.273445575863335,155.69747618332693,46.83383870921694,40.605047633346125 17 | 64.93987694143541,155.50502545864185,47.662394382050564,40.9899490827163 18 | 68.10648364305321,155.2126402048233,48.360619031096,41.57471959035344 19 | 68.51171434899452,154.95110231108674,48.97679234197511,42.09779537782654 20 | 71.838764140546,154.73500740849028,49.54621477955925,42.52998518301945 21 | 74.43612727376195,154.41417984977736,50.19378687238148,43.1716403004453 22 | 75.46059592743504,155.00659537079304,51.10695455826952,43.467861755031066 23 | 79.71051913073767,154.82863238161778,51.60756804265241,43.823787733381536 24 | 80.10719446217036,154.65603824585887,52.327855875996576,44.16897600489939 25 | 85.09431026888942,154.4534888549741,53.066735494612985,44.57407478666893 26 | 85.37835353786699,154.18812946083588,54.04715749129049,45.10479357494533 27 | 88.74180313947832,154.62556443053077,55.1820775325159,45.80228748444516 28 | 90.64471889303101,154.24963154286164,56.18022129882692,46.55415325978343 29 | 95.05779128744808,153.88334021561013,57.12834487809991,47.286735914286425 30 | 95.26460599499637,153.6143202889564,58.37038958211827,47.8247757675939 31 | 99.75624484735536,153.38185307281435,59.48929618261484,48.289710199878 32 | 100.87843768988162,152.96947886291633,60.66213332810749,49.114458619674025 33 | 104.53629559059783,152.62256130055846,62.047525085440896,49.8082937443898 34 | 107.536616732435,152.25269845674228,63.13881790941474,50.54801943202213 35 | 109.49644343839319,152.00035977995884,64.62498880304486,51.05269678558904 36 | 115.13979025291181,151.67246819950213,66.1703939239945,51.70847994650248 37 | 116.36728583435936,151.13129832046207,67.45088872372274,52.79081970458262 38 | 122.49360707550353,150.83895014269208,68.53482148957781,53.375516060122536 39 | 123.81225475730399,150.3091490533973,69.76056987138773,54.43511823871212 40 | 129.80150539570508,149.81291186609243,71.55658251563236,55.42759261332186 41 | 132.01514638794015,149.14629860278137,73.16453275948501,56.76081913994396 42 | 136.38298665096553,148.54691648344317,74.7207952126122,57.95958337862039 43 | 141.51524599805566,148.09963992996788,77.06846438119217,58.854136485570976 44 | 143.78094936645147,147.46493289454457,78.9956544788719,60.12355055641758 45 | 149.69520494753527,146.98332213231694,80.38739480507796,61.086772080872834 46 | 154.41449097159608,146.37793942834685,82.15189847664695,62.29753748881305 47 | 157.7606344867194,145.7155346778417,84.6102398245497,63.62234698982331 48 | 164.8768763172943,145.04123954843143,86.80840826239158,64.97093724864386 49 | 167.34880557265814,145.59602177447192,89.07416216149588,66.26457693317353 50 | 173.4987854977057,145.10710076552888,91.53000287769584,67.24241895105962 51 | 176.25439243925,145.86339101875902,93.55805874826675,68.24292836248588 52 | 185.59272716993738,145.4129850125829,95.36686793753086,69.14374037483813 53 | 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154.52703316480952,143.01090129575832,4.599725672959657,3.600203257648935 17 | 152.8379792777936,143.46564283281614,4.5997144877588525,3.600213749480536 18 | 152.18835029530726,144.6999494611393,4.599695936827026,3.600225873052833 19 | 151.34382662449767,144.57001800567346,4.599684946374977,3.60023365597354 20 | 149.84966367449903,145.9992184003134,4.59967784153616,3.600238349580343 21 | 149.6547840812488,146.25906853189136,4.5996545603457415,3.600248043345654 22 | 148.2905532226789,147.16855563863885,4.599643211125517,3.6002558740906516 23 | 146.79639424947416,147.7532239361031,4.599634590003339,3.6002644577615 24 | 145.9518750617718,148.0780380934561,4.599618719621653,3.600272391434876 25 | 145.36721013582167,149.11744922025076,4.599604358237474,3.60028429479575 26 | 144.26283655094585,149.63715167156923,4.599590687838484,3.600296258112033 27 | 143.1584670139384,150.93641545766354,4.599569175413946,3.6003105523227648 28 | 143.48328716359867,150.4816689050632,4.59956385410479,3.6003146883075154 29 | 142.8986203130007,149.89699447713366,4.599554723947006,3.600320712812799 30 | 142.4438809757747,147.55830557082854,4.599544824711316,3.60032871788632 31 | 140.36505609392816,144.3101283721391,4.599533228008268,3.6003372396463007 32 | 138.6110493236057,143.40063520294007,4.599521057144544,3.6003472831880434 33 | 137.57164436275863,144.1152241450656,4.599501299399777,3.6003648035512557 34 | 136.33734603173556,144.6349266781259,4.59948805545423,3.6003744346902877 35 | 134.84319163253912,145.8692291469226,4.599480669841324,3.6003785186669166 36 | 134.19356653355297,146.0641137917139,4.599463432103042,3.600389459797503 37 | 133.1541665750415,146.71373599868787,4.5994368392050475,3.600411614550099 38 | 132.17972752643797,147.16847226334986,4.5994157934650675,3.6004253519347436 39 | 130.6855796737513,148.3378066983682,4.5994017714549145,3.6004336598503586 40 | 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-------------------------------------------------------------------------------- /tracking_results/msvt/airplane3.txt: -------------------------------------------------------------------------------- 1 | 256.1,76.9,30.0,10.6 2 | 257.82255473295396,76.10087124530209,29.937732462094257,10.572137233966977 3 | 259.72940248361584,76.09630031674476,29.872318979409258,10.58127909108164 4 | 260.71939921167234,76.40179181178581,29.764137605197273,10.594233461633198 5 | 263.2295044909249,77.6395398890323,29.724834897238395,10.609191232413332 6 | 266.0726646410351,77.61624721626758,29.639388466893877,10.655776577942797 7 | 267.3220540750689,79.15857003680077,29.628351265338722,10.680808020016991 8 | 269.8503183229089,79.13586441156372,29.549679168325625,10.726219270491102 9 | 272.36967899561677,79.15955418067423,29.48633979411139,10.678839732270069 10 | 274.9245552968201,79.17841671529736,29.337754009674192,10.64111466302382 11 | 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-------------------------------------------------------------------------------- /tracking_results/msvt/boat3.txt: -------------------------------------------------------------------------------- 1 | 118.5,88.5,5.0,6.0 2 | 117.38955606770718,88.69763050499962,4.999832026778583,6.000231376065982 3 | 113.98815256822594,89.39523805799737,4.999579888627882,6.0005155802475265 4 | 113.37765707091887,90.00572183937301,4.9995067553983485,6.00061214533995 5 | 113.37771140586347,90.09286006240454,4.99939808550916,6.000773743786172 6 | 114.68607120027411,90.00554501831543,4.9992603373255635,6.0009650425885575 7 | 114.0755955871292,90.70322939120014,4.999134901385893,6.001112482224249 8 | 115.38392179244363,90.09264802772044,4.999079125572961,6.001196779602884 9 | 115.99450166227766,89.4820527264471,4.99900063877739,6.00130612927709 10 | 115.38399182923646,90.17976868017546,4.998936087418463,6.00139904175329 11 | 116.69233219722724,89.39473400132297,4.998870307298957,6.00149942594104 12 | 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153.53875280925396,83.80979150151565,24.435166860751522,22.494489261504462 9 | 151.3278570132817,81.27343585245353,24.38812629289283,22.353563039858262 10 | 151.0000495503241,80.93470142269388,24.301935873694134,22.28922655426367 11 | 148.80548983296902,78.4154515064816,24.254541140892798,22.151793191258186 12 | 148.8503175224751,77.71828536494334,24.164885761880615,22.07318789441159 13 | 148.1544808465173,75.93030676986467,24.088960917713766,21.980149594362743 14 | 146.71481533275352,75.59185304514389,24.043838951516566,21.925943795373108 15 | 145.28241950804207,73.07960290443565,23.990492685182026,21.84370272421402 16 | 145.314290712116,73.11268178378002,23.926750277034156,21.777544965525273 17 | 143.88917870038063,72.7560141563511,23.875721066202296,21.765566911807458 18 | 144.2854277718568,72.80576792975961,23.80754289983414,21.66605936499046 19 | 144.34231538286102,73.21792081585717,23.69376767782569,21.563405298977177 20 | 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