├── LICENSE ├── README.md ├── Supplemental_material.pdf ├── doc └── sample.png ├── file_parser.py ├── opticalflow_estimate.py ├── opticalflow_evaluate.py ├── trajectory_evaluate.py └── util.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | tf_smoe 635 | Copyright (C) 2018 bochinski 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | tf_smoe Copyright (C) 2018 bochinski 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Dataset samples](doc/sample.png) 2 | ## TUB CrowdFlow Dataset 3 | Optical Flow Dataset and Evaluation Kit for Visual Crowd Analysis developed at [Communication Systems Group](https://www.nue.tu-berlin.de/) at TU-Berlin desciribed in the AVSS 2018 paper 4 | [Optical Flow Dataset and Benchmark for Visual Crowd Analysis](http://elvera.nue.tu-berlin.de/files/1548Schr%C3%B6der2018.pdf) or [TUBCrowdFlow@arxiv.org](https://arxiv.org/abs/1811.07170). 5 | 6 | The Dataset contains 10 sequences showing 5 scenes. Each scene is rendered twice: with a static point of view and a dynamic camera to simulate drone/UAV based surveillance. We render at HD resolution (1280x720) at 25 fps, which is typical for current commercial CCTV surveillance systems. The total number of frames 3200. 7 | 8 | For each sequence we provide the following **ground-truth** data: 9 | * **Optical flow fields** 10 | * **Person trajectories (up to 1451)** 11 | * **Dense pixel trajectories** 12 | 13 | This evaluation framework is released under the MIT License (details in [LICENSE](LICENSE)). 14 | If you use the dataset or evaluation kit or think our work is useful in your research, please consider citing: 15 | 16 | ``` 17 | @INPROCEEDINGS{TUBCrowdFlow2018, 18 | AUTHOR = {Gregory Schr{\"o}der and Tobias Senst and Erik Bochinski and Thomas Sikora}, 19 | TITLE = {Optical Flow Dataset and Benchmark for Visual Crowd Analysis}, 20 | BOOKTITLE = {IEEE International Conference on Advanced Video and Signals-based Surveillance}, 21 | YEAR = {2018}, 22 | } 23 | ``` 24 | 25 | Download the dataset via the following direct link 26 | - [https://hidrive.ionos.com/lnk/LUiCHfYG](https://hidrive.ionos.com/lnk/LUiCHfYG) 27 | 28 | The password is the case sensitive name of the repository. 29 | 30 | Unpack the dataset by: 31 | ``` 32 | sudo apt-get install unrar 33 | unrar x TUBCrowdFlow 34 | ``` 35 | **The TUB CrowdFlow dataset is made available for academic use only.** If you wish to use this dataset commercially please contact [sikora@nue.tu-berlin.de](mailto:sikora@nue.tu-berlin.de). 36 | 37 | ## Contact 38 | If you have any questions or encounter problems regarding the method/code or want to send us your optical flow benchmark 39 | results feel free to contact me 40 | at [tobias.senst@gmail.com](mailto:tobias.senst@gmail.com) 41 | 42 | ### Installation 43 | Minimum required python version: 3.5 44 | 45 | **Install dependencies on Ubuntu:** 46 | ``` 47 | sudo apt-get install python3-dev python3-virtualenv virtualenv 48 | ``` 49 | Create a virtual environment and install python requirements: 50 | ``` 51 | virtualenv -p python3 crowdflow_env 52 | source crowdflow_env/bin/activate 53 | pip3 install numpy progressbar2 opencv-contrib-python 54 | ``` 55 | 56 | ## Evaluation Framework 57 | To evaluate an optical flow method with the providen framework perform these step: 58 | * create a new directory in the `/TUBCrowdFlow/estimate` directory. 59 | * compute flow fields and save them in *.flo* fileformat with the structure given in by the 60 | `/TUBCrowdFlow/images` directory. For example optical flow results from the image pair `/TUBCrowdFlow/images/IM01/frame_0000.png` and `/TUBCrowdFlow/images/IM01/frame_0001.png` 61 | must be stored as `/estimate/[mymethod]/images/IM01/frame_0000.flo 62 | * run `opticalflow_evaluate.py` to compute EPE and R2 short-term metrics. 63 | * run `trajectory_evaluate.py` to compute tracking accuracy long-term metrics. 64 | 65 | **Optical Flow Samples** 66 | 67 | ` 68 | opticalflow_estimate.py ... 69 | ` 70 | 71 | With the following program optical flow fields for the TUB CrowdFlow dataset will be estimated with Dual-TVL1. 72 | ``` 73 | source crowdflow_env/bin/activate 74 | python3 opticalflow_estimate.py TUBCrowdFlow/ dual farneback plk 75 | ``` 76 | The optical flow files will be stored in the directory `/estimate/dual/` . 77 | 78 | **Short-Term Evaluation** 79 | 80 | Short-term evaluation performs classical approch for optical flow evaluation, i.e. measures based on ground-truth optical flow fields (e.g. end-point error, RX measures) 81 | 82 | ` 83 | opticalflow_evaluate.py ... 84 | ` 85 | 86 | Example: 87 | ``` 88 | source crowdflow_env/bin/activate 89 | python3 opticalflow_evaluate.py TUBCrowdFlow/ dual plk farneback 90 | ``` 91 | After execution the file *short_term_results.tex* will contain the evaluation results (*method 1 - method n*) in form of a latex table. 92 | *short_term_results.pb* will contain the evaluation results stored with pickle. 93 | 94 | **Long-Term Evaluation** 95 | 96 | Long-term evaluation performs evaluation based on ground-truth trajectories, i.e. **person trajectories** and **dense pixel trajectories** (see paper). 97 | 98 | ` 99 | trajectory_evaluate.py ... 100 | ` 101 | 102 | Example: 103 | ``` 104 | source crowdflow_env/bin/activate 105 | python3 trajectory_evaluate.py TUBCrowdFlow/ dual plk farneback 106 | ``` 107 | After execution the file *long_term_results.tex* will contain the evaluation results (*method 1 - method n*) in form of a latex table. 108 | *long_term_results.pb* will contain the evaluation results stored with pickle. 109 | 110 | ## Results 111 | To assess the quality of the optical flow we propose to use two types of metrics: i) common optical flow metrics, i.e. average endpoint error (EPE) and percentage of erroneous 112 | pixel (RX) and ii) long-term motion metrics based on trajectories. An detailed overview of the optical flow parameters can be found in the document: [Supplemental_materials.pdf](./Supplemental_material.pdf)). 113 | ### Common optical flow metrics (short-term) 114 | 115 | | |FG (Static)| FG (Static)| BG (Static)|BG (Static) |FG (Dynamic)| FG (Dynamic) | BG (Dynamic) | BG (Dynamic) | FG(Avg.)| FG(Avg.) | BG(Avg.) | BG(Avg.) | Avg. |Avg. || 116 | | ---------- | ----- | ----- | ----- | ---- | ----- | ----- | ------ | ----- | ----- | ------ | ----- | ----- | ----- | ----- | ----- | 117 | | | EPE |R2[%] |EPE |R2[%] |EPE |R2[%] |EPE |R2[%] |EPE |R2[%] |EPE |R2[%] |EPE |R2[%] |t[sec]| 118 | | **[FlowFields (Bailer2015)](https://av.dfki.de/publications/flow-fields-dense-correspondence-fields-for-highly-accurate-large-displacement-optical-flow-estimation/)** | 0.756 | 8.27 | 0.213 | 2.79 | 1.069 | 14.92 | 2.571 | 51.42 | 0.913 | 11.595 | 1.392 | 27.10 | 0.915 | 11.74 | 43.53 | 119 | | **[RIC (Hu2017)](https://github.com/YinlinHu/Ric)** | 0.859 | 8.64 | 0.243 | 3.31 | 1.166 | 15.69 | 2.623 | 53.58 | 1.013 | 12.164 | 1.433 | 28.45 | 1.015 | 12.32 | 8.30 | 120 | |**[CPM (Li2018)](https://github.com/YinlinHu/CPM)** |0.701 |7.09 |0.247 |3.63 |1.026 |13.94 |2.585 |51.78 |0.864 |10.517 |1.416 |27.71 |0.868 |10.69 |14.74| 121 | |**[DeepFlow (Weinzaepfel2013)](https://thoth.inrialpes.fr/src/deepflow/)** |0.629 |6.19 |0.237 |3.67 |1.005 |13.95 |2.594 |51.67 |0.817 |10.069 |1.416 |27.67 |0.822 |10.25 |39.63| 122 | |**[RLOF6 (Geistert2016)](https://github.com/tsenst/RLOFLib)** |0.753 |8.61 |0.315 |5.00 |1.088 |15.61 |2.655 |53.47 |0.921 |12.112 |1.485 |29.23 |0.924 |12.27 |1.49| 123 | |**[RLOF10 (Geistert2016)](https://github.com/tsenst/RLOFLib)** |0.772 |8.80 |0.324 |5.10 |1.104 |15.80 |2.658 |53.60 |0.938 |12.303 |1.491 |29.35 |0.941 |12.46 |0.80| 124 | |**[DIS4 (Kroeger2016)](https://github.com/tikroeger/OF_DIS)** |0.627 |5.72 |0.356 |5.85 |0.928 |11.86 |2.665 |53.67 |0.777 |8.790 |1.511 |29.76 |0.784 |9.01 |1.70| 125 | |**[DIS2 (Kroeger2016)](https://github.com/tikroeger/OF_DIS)** |1.441 |20.40|0.528 |8.24 |1.726 |27.41 |3.001 |64.01 |1.583 |23.903 |1.765 |36.13 |1.579 |23.92 |0.28| 126 | |**[Farneback (Farneback2003)](http://www.diva-portal.org/smash/get/diva2:273847/FULLTEXT01.pdf)** |0.737 |7.21 |0.441 |7.30 |0.996 |12.67 |2.491 |50.60 |0.867 |9.940 |1.466 | 28.95|0.872 |10.13 | | 127 | |**[Sparse to Dense PLK (Bouguet2000)](http://robots.stanford.edu/cs223b04/algo_tracking.pdf)** | 0.793 | 8.07 | 0.563 | 9.12 | 1.041 | 13.24 | 2.875 | 56.29 | 0.917 | 10.653 | 1.719 | 32.71 | 0.925 | 10.88 | | 128 | 129 | ### Tracking Accuracy (long-term) 130 | 131 | **Dense Trajectories** 132 | 133 | | |IM01 |(Dyn) |IM02 |(Dyn)|IM03 |(Dyn) |IM04 |(Dyn) |IM05 |(Dyn) |Avg. | 134 | | --------- | --- | ---- | ---- | ---- | ---- | ---- | ---- | --- | ---- | ---- | --- | 135 | | **[FlowFields (Bailer2015)](https://av.dfki.de/publications/flow-fields-dense-correspondence-fields-for-highly-accurate-large-displacement-optical-flow-estimation/)** |70.63| 61.79| 56.69 |45.93| 71.46| 68.35| 42.27| 37.63| 65.15| 59.61| 57.95| 136 | | **[RIC (Hu2017)](https://github.com/YinlinHu/Ric)** |74.39| 69.41| 58.72 |50.33| 54.18| 73.80| 44.21| 39.52| 60.23| 60.28| 58.51| 137 | |**[CPM (Li2018)](https://github.com/YinlinHu/CPM)** |73.41| 65.16| 58.31 |47.57| 74.41| 71.13| 46.23| 41.15| 67.97| 61.68| 60.70| 138 | |**[DeepFlow (Weinzaepfel2013)](https://thoth.inrialpes.fr/src/deepflow/)** |83.84| 81.90| 63.33 |55.52| 83.38| 80.87| 57.08| 56.65| 71.25| 64.67| 69.85| 139 | |**[RLOF6 (Geistert2016)](https://github.com/tsenst/RLOFLib)** |82.80| 78.31| 63.16 |57.68| 87.46| 86.76| 50.56| 50.53| 69.86| 68.73| 69.59| 140 | |**[RLOF10 (Geistert2016)](https://github.com/tsenst/RLOFLib)** |80.14| 73.95| 62.05 |55.54| 85.44| 84.39| 48.80| 47.84| 67.53| 67.41| 67.31| 141 | |**[DIS4 (Kroeger2016)](https://github.com/tikroeger/OF_DIS)** |80.44| 76.19| 64.11 |56.99| 82.89| 82.24| 53.91| 52.75| 72.11| 70.71| 69.23| 142 | |**[DIS2 (Kroeger2016)](https://github.com/tikroeger/OF_DIS)** |47.55| 33.03| 36.52 |25.32| 22.59| 19.76| 26.79| 20.89| 27.63| 27.91| 28.80| 143 | |**[Farneback (Farneback2003)](http://www.diva-portal.org/smash/get/diva2:273847/FULLTEXT01.pdf)** |78.69| 74.24| 65.22 |59.43| 86.89| 87.17| 52.85| 55.29| 70.22| 68.94| 69.89| 144 | |**[Sparse to Dense PLK (Bouguet2000)](http://robots.stanford.edu/cs223b04/algo_tracking.pdf)** |75.15| 68.54| 64.71 |57.88| 84.71| 84.11| 50.08| 49.26| 68.45| 69.75| 67.26| 145 | 146 | **Person Trajectories** 147 | 148 | | | IM01|(Dyn)|IM02 |(Dyn)|IM03 |(Dyn)| IM04 |(Dyn)|IM05 |(Dyn) | Avg. | 149 | | --------- | --- | --- | --- | --- | --- | --- | ---- | --- | ---- | ---- | ------ | 150 | | **[FlowFields (Bailer2015)](https://av.dfki.de/publications/flow-fields-dense-correspondence-fields-for-highly-accurate-large-displacement-optical-flow-estimation/)** | 77.94| 62.68| 52.35| 38.22| 66.76| 63.17| 30.09| 25.24| 65.67| 68.20| 55.03 | 151 | | **[RIC (Hu2017)](https://github.com/YinlinHu/Ric)** | 87.88| 80.87| 56.56| 48.14| 43.49| 70.98| 32.48| 27.81| 57.47| 68.56| 57.42| 152 | |**[CPM (Li2018)](https://github.com/YinlinHu/CPM)** | 82.17| 68.82| 54.56| 40.99| 70.37| 66.69| 35.98| 30.00| 69.64| 71.58| 59.08| 153 | |**[DeepFlow (Weinzaepfel2013)](https://thoth.inrialpes.fr/src/deepflow/)** | 99.19| 95.32| 68.60| 63.04| 83.18| 81.20| 53.82| 52.22| 76.32| 79.15| 75.20| 154 | |**[RLOF6 (Geistert2016)](https://github.com/tsenst/RLOFLib)** | 97.70| 92.37| 66.70| 65.08| 88.73| 90.22| 43.56| 46.47| 72.60| 80.12| 74.36| 155 | |**[RLOF10 (Geistert2016)](https://github.com/tsenst/RLOFLib)** |96.00 | 85.02| 63.08| 59.77| 85.97| 86.69| 39.41| 40.48| 69.09| 78.70| 70.42| 156 | |**[DIS4 (Kroeger2016)](https://github.com/tikroeger/OF_DIS)** | 92.22| 85.98| 63.97| 56.35| 81.59| 81.61| 44.58| 42.64| 74.95| 82.09| 70.60| 157 | |**[DIS2 (Kroeger2016)](https://github.com/tikroeger/OF_DIS)** | 40.81| 22.39| 22.86| 15.37| 9.05 | 6.72 | 13.63| 9.72 | 17.86| 18.10| 17.65 | 158 | |**[Farneback (Farneback2003)](http://www.diva-portal.org/smash/get/diva2:273847/FULLTEXT01.pdf)** | 88.75| 81.33| 64.69| 59.05| 85.92| 87.44| 42.42| 45.35| 71.51| 79.63| 70.61| 159 | |**[Sparse to Dense PLK (Bouguet2000)](http://robots.stanford.edu/cs223b04/algo_tracking.pdf)** | 79.31| 66.83| 61.05| 52.41| 82.63| 83.11| 37.92| 36.81| 67.53| 76.18| 64.38| 160 | |**[NMC (IDREES2014)](https://www.sciencedirect.com/science/article/pii/S0262885613001637)** | 96.96 | 90.33 | 72.18 | 71.44 | 92.28 | 20.70 | 32.72 | 42.38 | 60.15 | 56.02 | 63.52 | 161 | 162 | 163 | 164 | 165 | ## References - Optical Flow Algorithm 166 | 167 | ``` 168 | @inproceedings 169 | {Bailer2015, 170 | title = {Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation}, 171 | author={Bailer, C. and Taetz, B. and Stricker, D.}, 172 | booktitle = {International Conference on Computer Vision}, 173 | pages={4015--4023}, 174 | year = {2015} 175 | } 176 | ``` 177 | ``` 178 | @inproceedings{Hu2017, 179 | title={Robust interpolation of correspondences for large displacement optical flow}, 180 | author={Hu, Y. and Li, Y. and Song, R.}, 181 | booktitle={Conference on Computer Vision and Pattern Recognition}, 182 | pages={4791--4799}, 183 | year={2017}, 184 | } 185 | ``` 186 | ``` 187 | @article{Li2018, 188 | author={Y. Li and Y. Hu and R. Song and P. Rao and Y. Wang}, 189 | journal={IEEE Transactions on Circuits and Systems for Video Technology}, 190 | title={Coarse-to-Fine PatchMatch for Dense Correspondence}, 191 | year={2018}, 192 | volume={28}, 193 | number={9}, 194 | pages={2233-2245}, 195 | } 196 | ``` 197 | ``` 198 | @inproceedings{Weinzaepfel2013, 199 | AUTHOR = {Weinzaepfel, Philippe and Revaud, Jerome and Harchaoui, Zaid and Schmid, Cordelia}, 200 | TITLE = {{DeepFlow: Large displacement optical flow with deep matching}}, 201 | BOOKTITLE = {{Intenational Conference on Computer Vision }}, 202 | YEAR = {2013}, 203 | } 204 | ``` 205 | ``` 206 | @inproceedings{Geistert2016, 207 | AUTHOR = {Jonas Geistert and Tobias Senst and Thomas Sikora}, 208 | TITLE = {Robust Local Optical Flow: Dense Motion Vector Field Interpolation}, 209 | BOOKTITLE = {Picture Coding Symposium}, 210 | YEAR = {2016}, 211 | PAGES = {1--5}, 212 | } 213 | ``` 214 | ``` 215 | @inproceedings{Kroeger2016, 216 | Author = {Till Kroeger and Radu Timofte and Dengxin Dai and Luc Van Gool}, 217 | Title = {Fast Optical Flow using Dense Inverse Search}, 218 | Booktitle = {European Conference on Computer Vision }, 219 | Year = {2016} 220 | } 221 | ``` 222 | ``` 223 | @inproceedings{Farneback2003, 224 | Author = {Gunnar Farneb{\"a}ck}, 225 | Title = {Two-Frame Motion Estimation Based on Polynomial Expansion}, 226 | Booktitle = {Proceedings of the 13th Scandinavian Conference on Image Analysis}, 227 | Pages = {363--370}, 228 | Year = {2003}, 229 | } 230 | ``` 231 | ``` 232 | @TECHREPORT{Bouguet2000, 233 | author = {J.-Y. Bouguet}, 234 | title = {Pyramidal Implementation of the Lucas Kanade Feature Tracker}, 235 | institution = {Intel Corporation Microprocessor Research Lab}, 236 | year = {2000}, 237 | type = {Technical {R}eport}, 238 | publisher = {Intel Corporation Microprocessor Research Labs}, 239 | timestamp = {2013.04.03} 240 | } 241 | ``` 242 | 243 | ## References - Person Tracking Algorithm 244 | 245 | ``` 246 | @article{IDREES2014, 247 | title = "Tracking in dense crowds using prominence and neighborhood motion concurrence", 248 | journal = "Image and Vision Computing", 249 | volume = "32", 250 | number = "1", 251 | pages = "14 - 26", 252 | year = "2014", 253 | author = "Haroon Idrees and Nolan Warner and Mubarak Shah", 254 | } 255 | ``` 256 | -------------------------------------------------------------------------------- /Supplemental_material.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsenst/CrowdFlow/086517805fe557b44ad1d529e384634a5fa048b3/Supplemental_material.pdf -------------------------------------------------------------------------------- /doc/sample.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tsenst/CrowdFlow/086517805fe557b44ad1d529e384634a5fa048b3/doc/sample.png -------------------------------------------------------------------------------- /file_parser.py: -------------------------------------------------------------------------------- 1 | # --------------------------------------------------------------------- 2 | # Copyright (c) 2018 TU Berlin, Communication Systems Group 3 | # Written by Tobias Senst 4 | # 5 | # This program is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | # 10 | # This program is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | # 15 | # You should have received a copy of the GNU General Public License 16 | # along with this program. If not, see . 17 | # --------------------------------------------------------------------- 18 | import os 19 | import glob 20 | try: 21 | from itertools import tee, izip 22 | except ImportError: 23 | pass 24 | 25 | def window(iterable, size): 26 | iters = tee(iterable, size) 27 | for i in range(1, size): 28 | for each in iters[i:]: 29 | next(each, None) 30 | return zip(*iters) 31 | 32 | class SDataBaseParser: 33 | def __init__(self): 34 | self.filenames_dict = list() 35 | 36 | class CrowdFileParser(SDataBaseParser): 37 | def __init__(self): 38 | SDataBaseParser.__init__(self) 39 | 40 | def parseFilenames(self, basepaths): 41 | self.filenames_dict = [] 42 | for dirpath, dirnames, filenames in os.walk(basepaths["images"]): 43 | for dirs in dirnames: 44 | image_paths = glob.glob("{}/*.png".format(dirpath + dirs)) 45 | image_paths1 = glob.glob("{}/*.jpg".format(dirpath + dirs)) 46 | image_paths = image_paths + image_paths1 47 | image_paths = sorted(image_paths) # assume filenames are sortable 48 | image_pairs = list(window(image_paths, 2)) 49 | for image_pair in image_pairs: 50 | #print(image_pair) 51 | filename_dict = dict() 52 | filename_dict["prevImg"] = image_pair[0] 53 | filename_dict["currImg"] = image_pair[1] 54 | filename_base = os.path.relpath(filename_dict["prevImg"], dirpath + dirs ) 55 | filename_base = os.path.splitext(filename_base)[0] 56 | 57 | 58 | 59 | if "groundtruth" in basepaths: 60 | filenumber = filename_base.split("_")[1] 61 | filename_dict["gtflow"] = basepaths["groundtruth"] + "/" + dirs + "/frameGT_" + filenumber + ".flo" 62 | if "masks" in basepaths: 63 | filenumber = filename_base.split("_")[1] 64 | filename_dict["mask"] = basepaths["masks"] + "/" + dirs + "/maskGT_" + filenumber + ".png" 65 | 66 | if "estimate" in basepaths : 67 | filename_dict["estimatepath"] = basepaths["estimate"] + "/" + dirs + "/" 68 | filename_dict["estflow"] = basepaths["estimate"] + "/" + dirs + "/frame_" + filenumber + ".flo" 69 | 70 | filename_dict["filename"] = "frame_" + filenumber 71 | filename_dict["dir"] = dirs 72 | filename_dict["basepath"] = basepaths["basepath"] 73 | self.filenames_dict.append(filename_dict) 74 | 75 | """ 76 | create_filename_list(basepath) -> retval 77 | . @brief Create a list of dictionaries containing file-paths for the optical flow dataset 78 | . @param basepath is a dictionary containing sub path. 79 | """ 80 | def create_filename_list(basepath): 81 | fileparser = CrowdFileParser() 82 | fileparser.parseFilenames(basepath) 83 | return fileparser.filenames_dict -------------------------------------------------------------------------------- /opticalflow_estimate.py: -------------------------------------------------------------------------------- 1 | # --------------------------------------------------------------------- 2 | # Copyright (c) 2018 TU Berlin, Communication Systems Group 3 | # Written by Tobias Senst 4 | # 5 | # This program is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | # 10 | # This program is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | # 15 | # You should have received a copy of the GNU General Public License 16 | # along with this program. If not, see . 17 | # --------------------------------------------------------------------- 18 | import file_parser as fp 19 | import cv2 20 | import sys 21 | import os 22 | import util as ut 23 | import numpy as np 24 | 25 | 26 | def run_parameter(config_item): 27 | prev_img = cv2.imread(config_item["files"]["prevImg"]) 28 | curr_img = cv2.imread(config_item["files"]["currImg"]) 29 | flow_method = config_item["parameter"]["flow_method"] 30 | estimate_base = config_item["files"]["estimatepath"] + "/" 31 | 32 | if os.path.exists(estimate_base) == False: 33 | os.makedirs(estimate_base) 34 | 35 | if os.path.exists(config_item["files"]["estflow"]): 36 | return 37 | # compute optical flow 38 | if flow_method.find("dual") >= 0: 39 | dual_proc = cv2.DualTVL1OpticalFlow_create(config_item["parameter"]["tau"], 40 | config_item["parameter"]["lambda"], 41 | config_item["parameter"]["theta"], 42 | config_item["parameter"]["nscales"], 43 | config_item["parameter"]["warps"]) 44 | est_flow = np.zeros(shape=(prev_img.shape[0], prev_img.shape[1],2), dtype=np.float32) 45 | dual_proc.calc(cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY), cv2.cvtColor(curr_img, cv2.COLOR_BGR2GRAY), est_flow) 46 | # 47 | elif flow_method.find("farneback") >= 0: 48 | est_flow = cv2.calcOpticalFlowFarneback(cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY), 49 | cv2.cvtColor(curr_img, cv2.COLOR_BGR2GRAY), 50 | None, 0.5, 3, 15, 3, 5, 1.2, 0) 51 | elif flow_method.find("plk") >= 0: 52 | prev_pts = list() 53 | for r in range(prev_img.shape[0]): 54 | for c in range(prev_img.shape[1]): 55 | prev_pts.append((c,r)) 56 | prev_pts = np.array(prev_pts, dtype=np.float32) 57 | curr_pts, st, err = cv2.calcOpticalFlowPyrLK(cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY), 58 | cv2.cvtColor(curr_img, cv2.COLOR_BGR2GRAY), 59 | prev_pts, None, 60 | winSize=(21,21), maxLevel=3, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 20, 0.001)) 61 | est_flow = np.zeros(shape=(prev_img.shape[0], prev_img.shape[1],2), dtype=np.float32) 62 | n = 0 63 | flow_pts = curr_pts - prev_pts 64 | for r in range(prev_img.shape[0]): 65 | for c in range(prev_img.shape[1]): 66 | est_flow[r, c, :] = flow_pts[n,:] 67 | n = n + 1 68 | #here alternative optical flow methods can be applied 69 | # 70 | else: 71 | raise ValueError("flow method has not been implemented") 72 | 73 | ut.writeFlowFile(config_item["files"]["estflow"], est_flow) 74 | ut.drawFlowField(config_item["files"]["estflow"][:-3] + "png", est_flow) 75 | print("Done -> ", config_item["files"]["estflow"]) 76 | 77 | def create_dual_parameter(parameter_list): 78 | parameter_list.append({"flow_method": "dual", 79 | "tau" : 0.25, 80 | "lambda" : 0.08, 81 | "theta" : 0.37, 82 | "nscales" : 6, 83 | "warps" : 5}) 84 | return parameter_list 85 | 86 | def main(): 87 | if len(sys.argv) < 2: 88 | print("Please provide the first argument that is the root path of the CrowdFlow dataset.") 89 | return 90 | basepath = sys.argv[1] 91 | 92 | flow_method_list = list() 93 | for n in range(2, len(sys.argv)): 94 | flow_method_list.append(sys.argv[n]) 95 | 96 | 97 | parameter_list = list() 98 | for flow_method in flow_method_list: 99 | if flow_method == "dual": 100 | parameter_list = create_dual_parameter(parameter_list) 101 | else: 102 | parameter_list.append({"flow_method": flow_method}) 103 | 104 | for parameter in parameter_list: 105 | basepath_dict = {"basepath": basepath, 106 | "images" : basepath + "/images/", 107 | "groundtruth": basepath + "/gt_flow/", 108 | "estimate": basepath + "/estimate/" + parameter["flow_method"], 109 | "masks": basepath + "/masks/", 110 | } 111 | 112 | filenames = fp.create_filename_list(basepath_dict) 113 | config_list = ut.create_config(parameter, filenames) 114 | 115 | for config_item in config_list: 116 | run_parameter(config_item) 117 | 118 | main() -------------------------------------------------------------------------------- /opticalflow_evaluate.py: -------------------------------------------------------------------------------- 1 | # --------------------------------------------------------------------- 2 | # Copyright (c) 2018 TU Berlin, Communication Systems Group 3 | # Written by Tobias Senst 4 | # 5 | # This program is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | # 10 | # This program is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | # 15 | # You should have received a copy of the GNU General Public License 16 | # along with this program. If not, see . 17 | # --------------------------------------------------------------------- 18 | import file_parser as fp 19 | import cv2 20 | import sys 21 | import pickle 22 | import copy 23 | import util as ut 24 | import progressbar 25 | 26 | def run_parameter(config_item): 27 | result = dict() 28 | result["ee"] = 0 29 | result["R1"] = 0 30 | result["R2"] = 0 31 | result["R3"] = 0 32 | result["no_points"] = 0 33 | # load ground truth optical flow 34 | flow_gt = ut.readFlowFiles(config_item["files"]["gtflow"]) 35 | # load ground truth mask indicating foreground and background flow vectors 36 | mask_rgb = cv2.imread(config_item["files"]["mask"]) 37 | # load estimated optical flow 38 | est_flow = ut.readFlowFiles(config_item["files"]["estflow"]) 39 | # compute short term errors 40 | result = ut.compute_error(est_flow, flow_gt, mask_rgb) 41 | 42 | return (config_item, result) 43 | 44 | 45 | def main(): 46 | if len(sys.argv) < 2: 47 | print("Please provide the first argument that is the root path of the CrowdFlow dataset.") 48 | return 49 | basepath = sys.argv[1] 50 | 51 | parameter_list = list() 52 | 53 | for n in range(2, len(sys.argv)): 54 | parameter_list.append({"flow_method": "/" + sys.argv[n] + "/" }) 55 | 56 | latex_filename = "short_term_results.tex" 57 | result_filename = "short_term_results.pb" 58 | 59 | 60 | result_list = list() 61 | bar = progressbar.ProgressBar() 62 | 63 | for n, parameter in enumerate(parameter_list): 64 | basepath_dict = {"basepath": basepath, 65 | "images" : basepath + "/images/", 66 | "groundtruth": basepath + "/gt_flow/", 67 | "estimate": basepath + "/estimate/" + parameter["flow_method"] + "/", 68 | "masks": basepath + "/masks/", 69 | } 70 | 71 | filenames = fp.create_filename_list(basepath_dict) 72 | config_list = ut.create_config(parameter, filenames) 73 | 74 | bar.start(max_value=len(config_list) * len(parameter_list)) 75 | for c, config_item in enumerate(config_list): 76 | result = run_parameter(config_item) 77 | result_list.append(copy.deepcopy(result)) 78 | bar.update(c + n * len(config_list)) 79 | 80 | bar.finish() 81 | print("\n") 82 | print("\n") 83 | print("Save short term evaluation file ", result_filename) 84 | out_dict = {"result": result_list} 85 | pickle.dump(out_dict, open( result_filename, "wb")) 86 | 87 | result_str = ut.getLatexTable(result_filename) 88 | print(result_str) 89 | if len(latex_filename) > 0: 90 | with open(latex_filename, "w") as f: 91 | f.write(result_str) 92 | main() -------------------------------------------------------------------------------- /trajectory_evaluate.py: -------------------------------------------------------------------------------- 1 | # --------------------------------------------------------------------- 2 | # Copyright (c) 2018 TU Berlin, Communication Systems Group 3 | # Written by Tobias Senst 4 | # 5 | # This program is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | # 10 | # This program is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | # 15 | # You should have received a copy of the GNU General Public License 16 | # along with this program. If not, see . 17 | # --------------------------------------------------------------------- 18 | import copy 19 | import time 20 | import sys 21 | import numpy as np 22 | import pickle 23 | import util as ut 24 | import file_parser as fp 25 | 26 | 27 | class Trajectory: 28 | def __init__(self, pos, from_idx, to_idx): 29 | self.points = np.ones(shape=(int(to_idx) - int(from_idx) + 1, 2), dtype=np.float32) * 99999 30 | self.points[0,:] = pos 31 | self.start_index = int(from_idx) 32 | 33 | def run(self, flow, index): 34 | idx = index - self.start_index 35 | if idx >= 0 and idx + 1 < self.points.shape[0]: 36 | # position is in (x,y) 37 | pos = self.points[idx,:] 38 | ipos = np.floor(pos).astype(np.int32) 39 | if ipos[0] >= 0 and ipos[1] >= 0 and ipos[0] + 1 < flow.shape[1] and ipos[1] + 1 < flow.shape[0]: 40 | a = pos - ipos 41 | iw00 = (1 - a[0]) * (1 - a[1]) 42 | iw01 = a[0] * (1 - a[1]) 43 | iw10 = (1 - a[0]) * a[1] 44 | iw11 = 1 - iw00 - iw01 - iw10 45 | self.points[idx + 1, :] = pos + \ 46 | flow[ipos[1], ipos[0]] * iw00 + \ 47 | flow[ipos[1], ipos[0] + 1] * iw01 + \ 48 | flow[ipos[1] + 1, ipos[0]] * iw10 + \ 49 | flow[ipos[1] + 1, ipos[0] + 1] * iw11 50 | 51 | class TrajectoryEvaluator: 52 | def __init__(self, 53 | thresholds=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25], 54 | dense_person_groundtruth_filename_base="DenseTracks.pb", 55 | person_groundtruth_filename="PersonTracks.pb"): 56 | self.dense_person_groundtruth_filename_base = None 57 | self.person_groundtruth_filename = None 58 | 59 | if dense_person_groundtruth_filename_base is not None: 60 | self.dense_person_groundtruth_filename_base = copy.deepcopy(dense_person_groundtruth_filename_base) 61 | if person_groundtruth_filename is not None: 62 | self.person_groundtruth_filename = copy.deepcopy(person_groundtruth_filename) 63 | 64 | self.threshold_list = copy.deepcopy(thresholds) 65 | 66 | def compute_differenz_trajectories(self, flow_filenames, groundtruth_filename): 67 | print(" Load ", groundtruth_filename) 68 | ret = pickle.load(open(groundtruth_filename, "rb")) 69 | start_points_trajectory = ret["GT_StartPoints"] 70 | gt_trajectory = ret["GT_Trajectories"] 71 | print(" Start TrajectoryEstimator ", flow_filenames[0]) 72 | 73 | trajectory_list = list() 74 | for r in range(0,start_points_trajectory.shape[0]): 75 | trajectory_list.append(Trajectory(pos = (start_points_trajectory[r,3], start_points_trajectory[r,2]) , 76 | from_idx = start_points_trajectory[r,0], to_idx= start_points_trajectory[r,1])) 77 | 78 | for i, flow_file in enumerate(flow_filenames): 79 | flow = ut.readFlowFiles(flow_file) 80 | for trajectory_item in trajectory_list: 81 | trajectory_item.run(flow, i) 82 | 83 | estimated_trajectory = list() 84 | for trajectory_item in trajectory_list: 85 | estimated_trajectory.append(list()) 86 | for n in range(trajectory_item.points.shape[0]): 87 | estimated_trajectory[-1].append(trajectory_item.points[n,1]) 88 | estimated_trajectory[-1].append(trajectory_item.points[n,0]) 89 | 90 | return ut.differenz_trajectory_list(gt_trajectory, estimated_trajectory) 91 | 92 | def run_sequence(self, sequence_name): 93 | start = time.time() 94 | print("Start ", sequence_name) 95 | flow_filenames = list() 96 | filebase = None 97 | for f in self.file_dict: 98 | if f["dir"] == sequence_name: 99 | filebase = f["basepath"] 100 | 101 | flow_filenames.append(f["estflow"]) 102 | 103 | flow_filenames = sorted(flow_filenames) 104 | result = dict() 105 | gt_filebase = filebase + "/gt_trajectories/" + sequence_name + "/" 106 | if self.person_groundtruth_filename is not None: 107 | person_diff_trajectory = self.compute_differenz_trajectories(flow_filenames, 108 | gt_filebase + self.person_groundtruth_filename) 109 | result["person"] = ut.compute_tracking_error(person_diff_trajectory, self.threshold_list) 110 | print("Done person_groundtruth_filename ") 111 | 112 | if self.dense_person_groundtruth_filename_base is not None: 113 | print("Start next") 114 | dense_person_diff_trajectory = self.compute_differenz_trajectories(flow_filenames, 115 | gt_filebase + self.dense_person_groundtruth_filename_base) 116 | result["dense_person"] = ut.compute_tracking_error(dense_person_diff_trajectory, self.threshold_list) 117 | print("Done dense_person_groundtruth_filename_base ") 118 | 119 | print(time.time() - start) 120 | return result 121 | 122 | def average(self, sequences): 123 | keys = [] 124 | if self.dense_person_groundtruth_filename_base is not None: 125 | keys.append("dense_person") 126 | if self.person_groundtruth_filename is not None: 127 | keys.append("person") 128 | result = dict() 129 | no_sequences = 1.0 / len(sequences) 130 | for seq_key in sequences.keys(): 131 | for key in keys: 132 | if key in result: 133 | result[key] = result[key] + np.array(sequences[seq_key][key]) * no_sequences 134 | else: 135 | result[key] = np.array(sequences[seq_key][key]) * no_sequences 136 | 137 | return result 138 | 139 | def run(self, file_dict): 140 | self.file_dict = copy.deepcopy(file_dict) 141 | result = dict() 142 | 143 | sequence_name = ["IM01", "IM02", "IM03", "IM04", "IM05"] 144 | static_ret = dict() 145 | for s in sequence_name: 146 | static_ret[s] = self.run_sequence(s) 147 | result["static"] = self.average(static_ret) 148 | result = {**result, **static_ret} 149 | 150 | sequence_name = ["IM01_hDyn", "IM02_hDyn", "IM03_hDyn", "IM04_hDyn", "IM05_hDyn"] 151 | dynamic_ret = dict() 152 | for s in sequence_name: 153 | dynamic_ret[s] = self.run_sequence(s) 154 | result["dynamic"] = self.average(dynamic_ret) 155 | result = {**result, **dynamic_ret} 156 | result["all"] = self.average({**dynamic_ret, **static_ret}) 157 | return result 158 | 159 | def get_statistics(self, basepath): 160 | static_sequence_name = ["IM01", "IM02", "IM03", "IM04", "IM05"] 161 | dynamic_sequence_name = ["IM01_hDyn", "IM02_hDyn", "IM03_hDyn", "IM04_hDyn", "IM05_hDyn"] 162 | sequence_name = static_sequence_name + dynamic_sequence_name 163 | 164 | length_list = list() 165 | for seq in sequence_name: 166 | filename_base = basepath + "/gt_trajectories/" + seq + "/" 167 | ret = pickle.load(open(filename_base + self.person_groundtruth_filename, "rb")) 168 | retl = ut.get_trajectory_lengths(ret["GT_Trajectories"]) 169 | length_list = length_list + retl 170 | 171 | print("Max Length", np.max(length_list)) 172 | np.save("Person_Trajectory_Length", np.array(length_list)) 173 | print("Done Stored") 174 | 175 | def run_parameter(flow_method, basepath): 176 | 177 | basepath_dict = {"basepath": basepath, 178 | "images" : basepath + "/images/", 179 | "groundtruth": basepath + "/gt_flow/", 180 | "estimate": basepath + "/estimate/" + flow_method + "/", 181 | "masks": basepath + "/masks/", 182 | } 183 | 184 | filenames = fp.create_filename_list(basepath_dict) 185 | evaluator = TrajectoryEvaluator() 186 | ret_dict = evaluator.run(filenames) 187 | ret_dict["name"] = flow_method.replace("/", "") 188 | return ret_dict 189 | 190 | 191 | def main(): 192 | if len(sys.argv) < 2: 193 | print("Please provide the first argument. Root path of the CrowdFlow dataset.") 194 | return 195 | basepath = sys.argv[1] 196 | 197 | method_list = list() 198 | for n in range(2, len(sys.argv)): 199 | method_list.append("/" + sys.argv[n] + "/" ) 200 | 201 | latex_filename = "long_term_results.tex" 202 | result_filename = "long_term_results.pb" 203 | 204 | 205 | 206 | result_dict = list() 207 | 208 | for method in method_list: 209 | result_dict.append(run_parameter(method, basepath)) 210 | 211 | print("Try to save file ", result_filename) 212 | pickle.dump(result_dict, open( result_filename, "wb")) 213 | print("Person Trajectories") 214 | result_str = ut.genTrajectoryLatexTable(filename=result_filename, item_key="dense_person") 215 | print("Dense Person Trajectories") 216 | result_str = result_str + ut.genTrajectoryLatexTable(filename=result_filename, item_key="person") 217 | if len(latex_filename) > 0: 218 | with open(latex_filename, "w") as f: 219 | f.write(result_str) 220 | 221 | main() 222 | -------------------------------------------------------------------------------- /util.py: -------------------------------------------------------------------------------- 1 | # --------------------------------------------------------------------- 2 | # Copyright (c) 2018 TU Berlin, Communication Systems Group 3 | # Written by Tobias Senst 4 | # 5 | # This program is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | # 10 | # This program is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | # 15 | # You should have received a copy of the GNU General Public License 16 | # along with this program. If not, see . 17 | # --------------------------------------------------------------------- 18 | import numpy as np 19 | import cv2 20 | import copy 21 | import pickle 22 | import math 23 | 24 | def parameter_to_string(parameter_dict): 25 | out_str = str() 26 | for key in sorted(parameter_dict.keys()): 27 | try: 28 | str_value = str(parameter_dict[key]) 29 | except TypeError: 30 | str_value = parameter_dict[key] 31 | 32 | out_str = out_str + str_value + "_" 33 | return out_str 34 | 35 | def create_config(parameter, filelist): 36 | config_list = list() 37 | for id, f in enumerate(filelist): 38 | file_list = copy.deepcopy(f) 39 | config_list.append({ "files" : file_list, "parameter" : copy.deepcopy(parameter), "file_index": id}) 40 | 41 | return config_list 42 | 43 | 44 | def computeEE(src0, src1): 45 | diff_flow = src0 - src1 46 | res = (diff_flow[:, :, 0] * diff_flow[:, :, 0]) + (diff_flow[:, :, 1] * diff_flow[:, :, 1]) 47 | return cv2.sqrt(res) 48 | 49 | def computer_errors(ee_base, mask): 50 | ee_base = ee_base * mask 51 | ret, R1 = cv2.threshold(src=ee_base, thresh=1, maxval=1, type=cv2.THRESH_BINARY) 52 | ret, R2 = cv2.threshold(src=ee_base, thresh=2, maxval=1, type=cv2.THRESH_BINARY) 53 | ret, R3 = cv2.threshold(src=ee_base, thresh=3, maxval=1, type=cv2.THRESH_BINARY) 54 | R3 = R3 * mask 55 | R2 = R2 * mask 56 | R1 = R1 * mask 57 | r3_sum = cv2.sumElems(R3)[0] 58 | r2_sum = cv2.sumElems(R2)[0] 59 | r1_sum = cv2.sumElems(R1)[0] 60 | ee = cv2.sumElems(ee_base)[0] 61 | no_p = cv2.sumElems(mask)[0] 62 | result = {"ee" : ee, "R1": r1_sum, "R2": r2_sum, "R3" : r3_sum, "noPoints" : no_p} 63 | return result 64 | 65 | def compute_error(flow, gt_flow, invalid_mask): 66 | 67 | mag_flow = cv2.sqrt(gt_flow[:, :, 0] * gt_flow[:, :, 0] + gt_flow[:, :, 1] * gt_flow[:, :, 1]) 68 | 69 | 70 | ret, mask_to_large = cv2.threshold(src=mag_flow, thresh=900, maxval=1, type=cv2.THRESH_BINARY_INV) 71 | 72 | total_inp_mask = invalid_mask[:, :, 0] + invalid_mask[:, :, 1] + invalid_mask[:, :, 2] 73 | ret, fg_mask = cv2.threshold(src=invalid_mask[:, :, 1], thresh=0.5, maxval=1, 74 | type=cv2.THRESH_BINARY) 75 | ret, total_mask = cv2.threshold(src=total_inp_mask, thresh=0.5, maxval=1, 76 | type=cv2.THRESH_BINARY) 77 | #mask_to_large = np.ones(fg_mask.shape) 78 | bg_mask = total_mask - fg_mask 79 | ee_base = computeEE(flow, gt_flow) 80 | result = dict() 81 | result["FG"] = computer_errors(ee_base, fg_mask * mask_to_large) 82 | result["BG"] = computer_errors(ee_base, bg_mask * mask_to_large) 83 | result["Total"] = computer_errors(ee_base, total_mask * mask_to_large) 84 | return result 85 | 86 | 87 | def draw_trajectories(gt_trajectory, estimate_trajectory, img): 88 | 89 | h = img.shape[0] 90 | w = img.shape[1] 91 | for n in range(len(gt_trajectory)): 92 | pos = (int(gt_trajectory[n][0]),int(gt_trajectory[n][1])) 93 | if pos[0] >= 0 and pos[0] < h and pos[1] >= 0 and pos[1] < w: 94 | img[pos[0], pos[1],:] = [0,255,0] 95 | #cv2.circle(img,center=(int(gt_trajectory[n][0]),int(gt_trajectory[n][1])), 96 | # radius=1, color=(0,255,0), thickness=-1) 97 | for n in range(0,len(estimate_trajectory),2): 98 | pos = (int(estimate_trajectory[n]), int(estimate_trajectory[n+1])) 99 | if pos[0] >= 0 and pos[0] < h and pos[1] >= 0 and pos[1] < w: 100 | img[pos[0], pos[1], :] = [255, 0, 0] 101 | 102 | #cv2.circle(img, center=(int(estimate_trajectory[n]),int(estimate_trajectory[n+1])), 103 | # radius=2, color=(255,0,0), thickness=-1) 104 | return img 105 | 106 | def differenz_trajectory_list(gt_trajectories, estimate_trajectories): 107 | """ 108 | .@brief gt_trajectories and estimate trajectories have to be aligned 109 | """ 110 | differenz_trajectory_list = list() 111 | assert len(gt_trajectories) == len(estimate_trajectories) 112 | for n in range(len(gt_trajectories)): 113 | if len(gt_trajectories[n]) != (len(estimate_trajectories[n])) / 2: 114 | print( "ID", n, len(gt_trajectories[n]), (len(estimate_trajectories[n])) / 2) 115 | for i in range(len(gt_trajectories[n])): 116 | 117 | diff_x = gt_trajectories[n][i][0] - estimate_trajectories[n][2*i] 118 | diff_y = gt_trajectories[n][i][1] - estimate_trajectories[n][2*i+1] 119 | differenz_trajectory_list.append(math.sqrt( diff_x * diff_x + diff_y * diff_y)) 120 | 121 | return np.array(differenz_trajectory_list) 122 | 123 | def compute_tracking_error(differenz_trajectory_list, thresholds_list): 124 | result = list() 125 | for thresholds in thresholds_list: 126 | ret, mask = cv2.threshold(src=differenz_trajectory_list, thresh=thresholds, maxval=1, type=cv2.THRESH_BINARY_INV) 127 | result.append(cv2.sumElems(mask)[0] / differenz_trajectory_list.shape[0]) 128 | return result 129 | 130 | 131 | def get_trajectory_lengths(groundtruth_trajectory_list): 132 | result = list() 133 | for item in groundtruth_trajectory_list: 134 | result.append(len(item)) 135 | return result 136 | 137 | def flow2RGB(flow, max_flow_mag = 5): 138 | """ Color-coded visualization of optical flow fields 139 | 140 | # Arguments 141 | flow: array of shape [:,:,2] containing optical flow 142 | max_flow_mag: maximal expected flow magnitude used to normalize. If max_flow_mag < 0 the maximal 143 | magnitude of the optical flow field will be used 144 | """ 145 | hsv_mat = np.ones(shape=(flow.shape[0], flow.shape[1], 3), dtype=np.float32) * 255 146 | ee = cv2.sqrt(flow[:, :, 0] * flow[:, :, 0] + flow[:, :, 1] * flow[:, :, 1]) 147 | angle = np.arccos(flow[:, :, 0]/ ee) 148 | angle[flow[:, :, 0] == 0] = 0 149 | angle[flow[:, :, 1] == 0] = 6.2831853 - angle[flow[:, :, 1] == 0] 150 | angle = angle * 180 / 3.141 151 | hsv_mat[:,:,0] = angle 152 | if max_flow_mag < 0: 153 | max_flow_mag = ee.max() 154 | hsv_mat[:,:,1] = ee * 255.0 / max_flow_mag 155 | ret, hsv_mat[:,:,1] = cv2.threshold(src=hsv_mat[:,:,1], maxval=255, thresh=255, type=cv2.THRESH_TRUNC ) 156 | rgb_mat = cv2.cvtColor(hsv_mat.astype(np.uint8), cv2.COLOR_HSV2BGR) 157 | return rgb_mat 158 | 159 | def readFlowFiles(filename): 160 | with open(filename, 'rb') as f: 161 | magic = np.fromfile(f, np.float32, count=1) 162 | if 202021.25 != magic: 163 | print( "Magic number incorrect. Invalid .flo file") 164 | else: 165 | w = np.fromfile(f, np.int32, count=1)[0] 166 | h = np.fromfile(f, np.int32, count=1)[0] 167 | data = np.fromfile(f, np.float32, count=2 * w * h) 168 | # Reshape data into 3D array (columns, rows, bands) 169 | data2D = np.resize(data, (h, w, 2)) 170 | return data2D 171 | 172 | def writeFlowFile(filename,flow): 173 | TAG_STRING = np.array(202021.25, dtype=np.float32) 174 | assert flow.shape[2] == 2 175 | h = np.array(flow.shape[0], dtype=np.int32) 176 | w = np.array(flow.shape[1], dtype=np.int32) 177 | with open(filename, 'wb') as f: 178 | f.write(TAG_STRING.tobytes()) 179 | f.write(w.tobytes()) 180 | f.write(h.tobytes()) 181 | f.write(flow.tobytes()) 182 | 183 | def flow2RGB(flow, max_flow_mag = 5): 184 | """ Color-coded visualization of optical flow fields 185 | 186 | # Arguments 187 | flow: array of shape [:,:,2] containing optical flow 188 | max_flow_mag: maximal expected flow magnitude used to normalize. If max_flow_mag < 0 the maximal 189 | magnitude of the optical flow field will be used 190 | """ 191 | hsv_mat = np.ones(shape=(flow.shape[0], flow.shape[1], 3), dtype=np.float32) * 255 192 | ee = cv2.sqrt(flow[:, :, 0] * flow[:, :, 0] + flow[:, :, 1] * flow[:, :, 1]) 193 | angle = np.arccos(flow[:, :, 0]/ ee) 194 | angle[flow[:, :, 0] == 0] = 0 195 | angle[flow[:, :, 1] == 0] = 6.2831853 - angle[flow[:, :, 1] == 0] 196 | angle = angle * 180 / 3.141 197 | hsv_mat[:,:,0] = angle 198 | if max_flow_mag < 0: 199 | max_flow_mag = ee.max() 200 | hsv_mat[:,:,1] = ee * 220.0 / max_flow_mag 201 | ret, hsv_mat[:,:,1] = cv2.threshold(src=hsv_mat[:,:,1], maxval=255, thresh=255, type=cv2.THRESH_TRUNC ) 202 | rgb_mat = cv2.cvtColor(hsv_mat.astype(np.uint8), cv2.COLOR_HSV2BGR) 203 | return rgb_mat 204 | 205 | def drawFlowField(filename, flow): 206 | cv2.imwrite(filename=filename, img = flow2RGB(flow)) 207 | 208 | def avg_sequence(src): 209 | sequence_result = dict() 210 | for seq_keys in src.keys(): 211 | result = dict() 212 | for item in src[seq_keys]: 213 | for key in item.keys(): 214 | if key != "FG" and key != "BG" and key != "Total" : 215 | continue 216 | if key not in result: 217 | result[key] = item[key] 218 | else: 219 | for key1 in item[key].keys(): 220 | result[key][key1] += item[key][key1] 221 | 222 | for key in result.keys(): 223 | result[key]["ee"] = result[key]["ee"] / result[key]["noPoints"] 224 | result[key]["R1"] = result[key]["R1"] / result[key]["noPoints"] 225 | result[key]["R2"] = result[key]["R2"] / result[key]["noPoints"] 226 | result[key]["R3"] = result[key]["R3"] / result[key]["noPoints"] 227 | sequence_result[seq_keys] = copy.deepcopy(result) 228 | return sequence_result 229 | 230 | def get_sequence_measures(result_list): 231 | 232 | sequence_list = dict() 233 | for item in result_list: 234 | parameter_str = parameter_to_string(item[0]["parameter"]) 235 | if "dir" in item[0]["files"]: 236 | seq_name = item[0]["files"]["dir"] 237 | else: 238 | seq_name = "None" 239 | 240 | if seq_name.find("_dyn") >= 0 : 241 | continue 242 | 243 | 244 | 245 | if parameter_str not in sequence_list: 246 | sequence_list[parameter_str] = dict() 247 | 248 | if seq_name not in sequence_list[parameter_str]: 249 | sequence_list[parameter_str][seq_name] = list() 250 | 251 | sequence_list[parameter_str][seq_name].append(item[1]) 252 | 253 | 254 | return sequence_list 255 | 256 | def avg_measures(src): 257 | 258 | total_result = dict() 259 | for seq_keys in src.keys(): 260 | result = dict() 261 | for item in src[seq_keys]: 262 | for key in item.keys(): 263 | if key == "time": 264 | continue 265 | if key not in result: 266 | result[key] = item[key] 267 | else: 268 | for key1 in item[key].keys(): 269 | result[key][key1] += int(item[key][key1]) 270 | 271 | for key in result.keys(): 272 | result[key]["ee"] = result[key]["ee"] / result[key]["noPoints"] 273 | result[key]["R1"] = result[key]["R1"] / result[key]["noPoints"] 274 | result[key]["R2"] = result[key]["R2"] / result[key]["noPoints"] 275 | result[key]["R3"] = result[key]["R3"] / result[key]["noPoints"] 276 | 277 | if len(total_result) == 0: 278 | for key in result.keys(): 279 | total_result[key] = dict() 280 | total_result[key]["ee"] = result[key]["ee"] / len(src) 281 | total_result[key]["R1"] = result[key]["R1"] / len(src) 282 | total_result[key]["R2"] = result[key]["R2"] / len(src) 283 | total_result[key]["R3"] = result[key]["R3"] / len(src) 284 | else: 285 | for key in result.keys(): 286 | total_result[key]["ee"] += result[key]["ee"] / len(src) 287 | total_result[key]["R1"] += result[key]["R1"] / len(src) 288 | total_result[key]["R2"] += result[key]["R2"] / len(src) 289 | total_result[key]["R3"] += result[key]["R3"] / len(src) 290 | return total_result 291 | 292 | def avg_measures_no_dict(src): 293 | total_result = dict() 294 | for seq_keys in src.keys(): 295 | result = dict() 296 | for item in src[seq_keys]: 297 | for key in item.keys(): 298 | if key not in result: 299 | result[key] = item[key] 300 | else: 301 | result[key] += item[key] 302 | 303 | 304 | result["ee"] = result["ee"] / result["no_points"] 305 | result["R1"] = result["R1"] / result["no_points"] 306 | result["R2"] = result["R2"] / result["no_points"] 307 | result["R3"] = result["R3"] / result["no_points"] 308 | 309 | if len(total_result) == 0: 310 | for key in result.keys(): 311 | total_result = dict() 312 | total_result["ee"] = result["ee"] / len(src) 313 | total_result["R1"] = result["R1"] / len(src) 314 | total_result["R2"] = result["R2"] / len(src) 315 | total_result["R3"] = result["R3"] / len(src) 316 | else: 317 | for key in result.keys(): 318 | total_result["ee"] += result["ee"] / len(src) 319 | total_result["R1"] += result["R1"] / len(src) 320 | total_result["R2"] += result["R2"] / len(src) 321 | total_result["R3"] += result["R3"] / len(src) 322 | return total_result 323 | 324 | def avg_sequences(sequence_list, use_type): 325 | res_FG_ee = [] 326 | res_FG_R2 = [] 327 | res_BG_ee = [] 328 | res_BG_R2 = [] 329 | res_total_ee = [] 330 | res_total_R2 = [] 331 | for seq_name in sequence_list.keys(): 332 | if use_type == 1 and seq_name.find("_hDyn") == -1: 333 | continue 334 | if use_type == 0 and seq_name.find("_hDyn") >= 0 : 335 | continue 336 | res_FG_ee.append(sequence_list[seq_name]["FG"]["ee"]) 337 | res_FG_R2.append(sequence_list[seq_name]["FG"]["R2"]) 338 | res_BG_ee.append(sequence_list[seq_name]["BG"]["ee"]) 339 | res_BG_R2.append(sequence_list[seq_name]["BG"]["R2"]) 340 | res_total_ee.append(sequence_list[seq_name]["Total"]["ee"]) 341 | res_total_R2.append(sequence_list[seq_name]["Total"]["R2"]) 342 | 343 | return np.mean(res_FG_ee), 100 * np.mean(res_FG_R2), \ 344 | np.mean(res_BG_ee), 100 * np.mean(res_BG_R2), \ 345 | np.mean(res_total_ee), 100 * np.mean(res_total_R2) 346 | 347 | def getLatexTable(filename): 348 | 349 | data = pickle.load(open(filename, "rb")) 350 | 351 | str_result = "\\begin{table} \n \\centering " \ 352 | "\\begin{tabular}{l|crcr|crcr|crcrcr|r} \n" \ 353 | "\\hline \n " \ 354 | "\\multicolumn{1}{c|}{} & \\multicolumn{2}{|c}{FG (Static) } & \\multicolumn{2}{c|} { BG (Static)} & " \ 355 | "\\multicolumn{2}{|c}{FG (Dynamic)} & \\multicolumn{2}{c|}{ BG (Dynamic)} & " \ 356 | "\\multicolumn{2}{c}{FG($\\varnothing$)}&\multicolumn{2}{c}{BG ($\\varnothing$)} & " \ 357 | "\\multicolumn{2}{c|}{$\\varnothing$} \\\\ \n " \ 358 | "\\multicolumn{1}{c|}{}& EPE & R2[\\%] & EPE & R2[\\%]& EPE & R2[\\%]& EPE & R2[\\%]" \ 359 | "& EPE & R2[\\%]& EPE & R2[\\%]& EPE & R2[\\%] \\\\ \n " 360 | result_list = data["result"] 361 | method_result_list = get_sequence_measures(result_list) 362 | for method_key in method_result_list.keys(): 363 | sequence_result = avg_sequence(method_result_list[method_key]) 364 | # print(sequence_result) 365 | ret_static = avg_sequences(sequence_result, 0) 366 | ret_dynamic = avg_sequences(sequence_result, 1) 367 | ret_total = avg_sequences(sequence_result, 2) 368 | name = method_key.replace("/","") 369 | name = name.replace("_", "") 370 | str_out = method_key \ 371 | + " & {:.3f}".format(ret_static[0]) \ 372 | + " & {:.2f}".format(ret_static[1]) \ 373 | + " & {:.3f}".format(ret_static[2]) \ 374 | + " & {:.2f}".format(ret_static[3]) \ 375 | + " & {:.3f}".format(ret_dynamic[0]) \ 376 | + " & {:.2f}".format(ret_dynamic[1]) \ 377 | + " & {:.3f}".format(ret_dynamic[2]) \ 378 | + " & {:.2f}".format(ret_dynamic[3]) \ 379 | + " & {:.3f}".format(ret_total[0]) \ 380 | + " & {:.3f}".format(ret_total[1]) \ 381 | + " & {:.3f}".format(ret_total[2]) \ 382 | + " & {:.2f}".format(ret_total[3]) \ 383 | + " & {:.3f}".format(ret_total[4]) \ 384 | + " & {:.2f}".format(ret_total[5]) \ 385 | + " \\\ " 386 | str_result = str_result + str_out + "\n" 387 | 388 | str_result = str_result + "\end{tabular} \n " \ 389 | "\\vspace{0.1cm} \n" \ 390 | "\\caption{Evaluation results common optical flow metrics. " \ 391 | "Dynamic comprised sequences with and static without camera motion, " \ 392 | "BG - background motion vectors and FG - motion vectors located at persons of the crowd.} \n" \ 393 | "\\end{table}" 394 | return str_result 395 | 396 | def genTrajectoryLatexTable(filename, item_key = "dense_person"): 397 | 398 | str_result = "\n \\begin{table} \n " \ 399 | "\\scriptsize \n " \ 400 | "\\setlength{\\tabcolsep}{2.4pt} \n " \ 401 | "\\centering \n " \ 402 | "\\begin{tabular}{l|cc|cc|cc|cc|cc|c} \n " \ 403 | " & \\multicolumn{2}{c|}{IM01 (Dyn)} & \\multicolumn{2}{c|}{IM02 (Dyn)} " \ 404 | " & \\multicolumn{2}{c|}{IM03 (Dyn)} & \\multicolumn{2}{c|}{IM04 (Dyn)} " \ 405 | " & \\multicolumn{2}{c|}{IM05 (Dyn)} & $\\varnothing$ \\\\ \n" \ 406 | "\\hline \n" 407 | methods = pickle.load(open(filename, "rb")) 408 | 409 | for ret in methods: 410 | result = ret["name"] \ 411 | + " & " + "{:2.2f}".format(100 * ret["IM01"][item_key][14]) \ 412 | + " & " + "{:2.2f}".format(100 * ret["IM01_hDyn"][item_key][14]) \ 413 | + " & " + "{:2.2f}".format(100 * ret["IM02"][item_key][14]) \ 414 | + " & " + "{:2.2f}".format(100 * ret["IM02_hDyn"][item_key][14]) \ 415 | + " & " + "{:2.2f}".format(100 * ret["IM03"][item_key][14]) \ 416 | + " & " + "{:2.2f}".format(100 * ret["IM03_hDyn"][item_key][14]) \ 417 | + " & " + "{:2.2f}".format(100 * ret["IM04"][item_key][14]) \ 418 | + " & " + "{:2.2f}".format(100 * ret["IM04_hDyn"][item_key][14]) \ 419 | + " & " + "{:2.2f}".format(100 * ret["IM05"][item_key][14]) \ 420 | + " & " + "{:2.2f}".format(100 * ret["IM05_hDyn"][item_key][14]) \ 421 | + " & " + "{:2.2f}".format(100 * ret["all"][item_key][14]) + "\\\ " 422 | 423 | str_result = str_result + result + "\n" 424 | 425 | if item_key == "person": 426 | name_str = "person trajectories" 427 | else: 428 | name_str = "dense person trajectories" 429 | str_result = str_result + "\\hline \n " \ 430 | "\\end{tabular} \n " \ 431 | "\\vspace{0.01cm} \n " \ 432 | "\\caption{Evaluation results with long-term motion metric (" + name_str + ")" \ 433 | "The \\textbf{tracking accuracy} in percentage for the threshold set to 15 pixels. " \ 434 | "Higher values denote more accurate results.} \n " \ 435 | "\\end{table} \n" 436 | return str_result 437 | --------------------------------------------------------------------------------