├── .gitignore ├── .pylintrc ├── README.rst ├── data ├── 3DMOT2015Labels.zip ├── Train.zip ├── data_arxiepiskopi.rar ├── data_university_students.rar ├── data_zara.rar ├── ewap_dataset_light.tgz └── trajnet_original │ ├── biwi │ └── biwi_hotel.txt │ ├── crowds │ ├── arxiepiskopi1.txt │ ├── crowds_zara02.txt │ ├── crowds_zara03.txt │ ├── students001.txt │ └── students003.txt │ ├── mot │ └── PETS09-S2L1.txt │ └── stanford │ ├── bookstore_0.txt │ ├── bookstore_1.txt │ ├── bookstore_2.txt │ ├── bookstore_3.txt │ ├── coupa_3.txt │ ├── deathCircle_0.txt │ ├── deathCircle_1.txt │ ├── deathCircle_2.txt │ ├── deathCircle_3.txt │ ├── deathCircle_4.txt │ ├── gates_0.txt │ ├── gates_1.txt │ ├── gates_3.txt │ ├── gates_4.txt │ ├── gates_5.txt │ ├── gates_6.txt │ ├── gates_7.txt │ ├── gates_8.txt │ ├── hyang_4.txt │ ├── hyang_5.txt │ ├── hyang_6.txt │ ├── hyang_7.txt │ ├── hyang_9.txt │ ├── nexus_0.txt │ ├── nexus_1.txt │ ├── nexus_2.txt │ ├── nexus_3.txt │ ├── nexus_4.txt │ ├── nexus_7.txt │ ├── nexus_8.txt │ └── nexus_9.txt ├── edinburgh_informatics_forum_urls.txt ├── scripts └── convert_original.py ├── setup.py ├── setup_orca.sh ├── setup_social_force.sh └── trajnetdataset ├── __init__.py ├── controlled_data.py ├── convert.py ├── get_type.py ├── orca_helper.py ├── readers.py └── scene.py /.gitignore: -------------------------------------------------------------------------------- 1 | data/raw/ 2 | data/3DMOT2015Labels/ 3 | data/cvpr2015_pedestrianWalkingPathDataset.rar 4 | data/Wildtrack_dataset_full.zip 5 | data/miverva.zip 6 | output* 7 | trajnetdataset/private 8 | 9 | # Python 10 | venv*/ 11 | *.pyc 12 | *.egg-info/ 13 | dist/ 14 | .cache 15 | .idea 16 | 17 | # Editors 18 | .vscode/ 19 | 20 | # OS 21 | .DS_Store 22 | 23 | # backup 24 | backup/ 25 | 26 | -------------------------------------------------------------------------------- /.pylintrc: -------------------------------------------------------------------------------- 1 | [BASIC] 2 | 3 | variable-rgx=[a-z0-9_]{1,30}$ 4 | good-names=n,nx,ny,xy,sc 5 | 6 | [TYPECHECK] 7 | 8 | # List of members which are set dynamically and missed by pylint inference 9 | # system, and so shouldn't trigger E1101 when accessed. Python regular 10 | # expressions are accepted. 11 | generated-members=numpy.*,torch.*,scipy.* 12 | 13 | disable=missing-docstring 14 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | NEW: Converting new external dataset into TrajNet++ format. `Tutorial `_ 2 | 3 | Install 4 | ------- 5 | 6 | .. code-block:: sh 7 | 8 | pip install -e '.[test,plot]' 9 | sh setup_orca.sh 10 | sh setup_social_force.sh 11 | 12 | 13 | Prepare Synthetic Dataset 14 | ------------------------- 15 | 16 | .. code-block:: sh 17 | 18 | python -m trajnetdataset.controlled_data --mode 'trajnet' --num_scenes 1000 19 | 20 | 21 | Converting Synthetic Dataset to TrajNet++ 22 | ----------------------------------------- 23 | 24 | A command to categorize synthetic data into TrajNet++ format will be printed at the end of preparation command above. 25 | 26 | 27 | 28 | Preparing Real World Data 29 | ------------------------- 30 | 31 | Existing real world data: 32 | 33 | .. code-block:: 34 | 35 | data/ 36 | data_arxiepiskopi.rar 37 | data_university_students.rar 38 | data_zara.rar 39 | ewap_dataset_light.tgz 40 | # 3DMOT2015Labels # from: https://motchallenge.net/data/3DMOT2015Labels.zip (video file at http://cs.binghamton.edu/~mrldata/public/PETS2009/S2_L1.tar.bz2) 41 | Train.zip # from trajnet.epfl.ch 42 | cvpr2015_pedestrianWalkingPathDataset.rar # from http://www.ee.cuhk.edu.hk/~syi/ (website not accessible but data are also here: https://www.dropbox.com/s/7y90xsxq0l0yv8d/cvpr2015_pedestrianWalkingPathDataset.rar?dl=0.+63) 43 | cff_dataset.zip # from https://www.dropbox.com/s/cnnk2ofreeoshuz/cff_dataset.zip?dl=0 44 | 45 | Extract: 46 | 47 | .. code-block:: sh 48 | 49 | # biwi 50 | mkdir -p data/raw/biwi 51 | tar -xzf data/ewap_dataset_light.tgz --strip-components=1 -C data/raw/biwi 52 | 53 | # crowds 54 | mkdir -p data/raw/crowds 55 | unrar e data/data_arxiepiskopi.rar data/raw/crowds 56 | unrar e data/data_university_students.rar data/raw/crowds 57 | unrar e data/data_zara.rar data/raw/crowds 58 | 59 | # cff 60 | mkdir -p data/raw/cff_dataset 61 | unzip data/cff_dataset.zip -d data/raw/ 62 | rm -r data/raw/__MACOSX 63 | 64 | # Wildtrack: https://www.epfl.ch/labs/cvlab/data/data-wildtrack/ 65 | mkdir -p data/raw/wildtrack 66 | unzip data/Wildtrack_dataset_full.zip -d data/raw/wildtrack 67 | 68 | # L-CAS: https://drive.google.com/drive/folders/1CPV9XeJsZzvtTxPQ9u1ppLGs_29e-XdQ 69 | mkdir -p data/raw/lcas 70 | cp data/lcas_pedestrian_dataset/minerva/train/data.csv data/raw/lcas 71 | 72 | # pedestrian walking dataset 73 | mkdir -p data/raw/syi 74 | unrar e data/cvpr2015_pedestrianWalkingPathDataset.rar data/raw/syi 75 | 76 | PETS09 S2L1 ground truth -- not used because people behavior is not normal 77 | mkdir -p data/raw/mot 78 | unzip data/3DMOT2015Labels.zip -d data/ 79 | cp data/3DMOT2015Labels/train/PETS09-S2L1/gt/gt.txt data/raw/mot/pets2009_s2l1.txt 80 | 81 | # Edinburgh Informatics Forum tracker -- not used because tracks are not good enough 82 | mkdir -p data/raw/edinburgh 83 | wget -i edinburgh_informatics_forum_urls.txt -P data/raw/edinburgh/ 84 | 85 | 86 | Converting Real World Dataset 87 | ----------------------------- 88 | 89 | .. code-block:: sh 90 | 91 | python -m trajnetdataset.convert 92 | 93 | The above command performs the following operations: 94 | 95 | * Step 1. readers.py: reads the raw data files and converts them to trackrows in .ndjson format 96 | * Step 2. scene.py: prepares different scenes given the obtained trackrows 97 | * Step 3. get_type.py: categorizes each scene based on our defined trajectory categorization 98 | 99 | .. code-block:: sh 100 | 101 | # create plots to check new dataset 102 | python -m trajnetplusplustools.summarize output/train/*.ndjson 103 | 104 | # obtain new dataset statistics 105 | python -m trajnetplusplustools.dataset_stats output/train/*.ndjson 106 | 107 | # visualize sample scenes 108 | python -m trajnetplusplustools.trajectories output/train/*.ndjson 109 | 110 | 111 | Converting Other Real World Datasets 112 | ------------------------------------ 113 | 114 | Refer to this example tutorial: `Tutorial `_ 115 | 116 | 117 | More Synthetic Toy Dataset Examples 118 | ----------------------------------- 119 | 120 | Checkout the `toy `_ branch 121 | 122 | 123 | Difference in generated data in TrajNet++ 124 | ----------------------------------------- 125 | 126 | * partial tracks are now included (for correct occupancy maps) 127 | * pedestrians that appear in multiple chunks had the same id before (might be a problem for some input readers) 128 | * explicit index of scenes with annotation of the primary pedestrian 129 | 130 | # * the primary pedestrian has to move by more than 1 meter 131 | * at one point, the primary pedestrian has to be <3m away from another pedestrian 132 | 133 | Citation 134 | ======== 135 | 136 | If you find this code useful in your research then please cite 137 | 138 | .. code-block:: 139 | 140 | @article{Kothari2020HumanTF, 141 | author={Kothari, Parth and Kreiss, Sven and Alahi, Alexandre}, 142 | journal={IEEE Transactions on Intelligent Transportation Systems}, 143 | title={Human Trajectory Forecasting in Crowds: A Deep Learning Perspective}, 144 | year={2021}, 145 | volume={}, 146 | number={}, 147 | pages={1-15}, 148 | doi={10.1109/TITS.2021.3069362} 149 | } 150 | 151 | 152 | References 153 | ---------- 154 | 155 | * ``eth``: 156 | 157 | .. code-block:: 158 | 159 | @article{Pellegrini2009YoullNW, 160 | title={You'll never walk alone: Modeling social behavior for multi-target tracking}, 161 | author={Stefano Pellegrini and Andreas Ess and Konrad Schindler and Luc Van Gool}, 162 | journal={2009 IEEE 12th International Conference on Computer Vision}, 163 | year={2009}, 164 | pages={261-268} 165 | } 166 | 167 | * ``ucy``: 168 | 169 | .. code-block:: 170 | 171 | @article{Lerner2007CrowdsBE, 172 | title={Crowds by Example}, 173 | author={Alon Lerner and Yiorgos Chrysanthou and Dani Lischinski}, 174 | journal={Comput. Graph. Forum}, 175 | year={2007}, 176 | volume={26}, 177 | pages={655-664} 178 | } 179 | 180 | * ``wildtrack``: 181 | 182 | .. code-block:: 183 | 184 | @inproceedings{chavdarova-et-al-2018, 185 | author = "Chavdarova, T. and Baqué, P. and Bouquet, S. and Maksai, A. and Jose, C. and Bagautdinov, T. and Lettry, L. and Fua, P. and Van Gool, L. and Fleuret, F.", 186 | title = {{WILDTRACK}: A Multi-camera {HD} Dataset for Dense Unscripted Pedestrian Detection}, 187 | journal = "Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR)", 188 | year = 2018, 189 | } 190 | 191 | * ``L-CAS``: 192 | 193 | .. code-block:: 194 | 195 | @article{Sun20173DOFPT, 196 | title={3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data}, 197 | author={Li Sun and Zhi Yan and Sergi Molina Mellado and Marc Hanheide and Tom Duckett}, 198 | journal={2018 IEEE International Conference on Robotics and Automation (ICRA)}, 199 | year={2017}, 200 | pages={1-7} 201 | } 202 | 203 | * ``CFF``: 204 | 205 | .. code-block:: 206 | 207 | @article{Alahi2014SociallyAwareLC, 208 | title={Socially-Aware Large-Scale Crowd Forecasting}, 209 | author={Alexandre Alahi and Vignesh Ramanathan and Fei-Fei Li}, 210 | journal={2014 IEEE Conference on Computer Vision and Pattern Recognition}, 211 | year={2014}, 212 | pages={2211-2218} 213 | } 214 | 215 | * ``syi``: Shuai Yi, Hongsheng Li, and Xiaogang Wang. Understanding Pedestrian Behaviors from Stationary Crowd Groups. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015). 216 | * ``edinburgh``: B. Majecka, "Statistical models of pedestrian behaviour in the Forum", MSc Dissertation, School of Informatics, University of Edinburgh, 2009. 217 | -------------------------------------------------------------------------------- /data/3DMOT2015Labels.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/vita-epfl/trajnetplusplusdataset/75e697d6b2f55c0ade2b2b5f7bbf53a5f34b2d50/data/3DMOT2015Labels.zip -------------------------------------------------------------------------------- /data/Train.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/vita-epfl/trajnetplusplusdataset/75e697d6b2f55c0ade2b2b5f7bbf53a5f34b2d50/data/Train.zip -------------------------------------------------------------------------------- /data/data_arxiepiskopi.rar: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/vita-epfl/trajnetplusplusdataset/75e697d6b2f55c0ade2b2b5f7bbf53a5f34b2d50/data/ewap_dataset_light.tgz -------------------------------------------------------------------------------- /data/trajnet_original/crowds/arxiepiskopi1.txt: -------------------------------------------------------------------------------- 1 | 0 1 -18.56 -3.86 2 | 10 1 -18.06 -3.86 3 | 20 1 -17.56 -3.85 4 | 30 1 -17.05 -3.85 5 | 40 1 -16.55 -3.84 6 | 50 1 -16.05 -3.84 7 | 60 1 -15.55 -3.83 8 | 70 1 -15.05 -3.83 9 | 80 1 -14.55 -3.82 10 | 90 1 -14.11 -3.83 11 | 100 1 -13.69 -3.85 12 | 110 1 -13.28 -3.86 13 | 120 1 -12.86 -3.88 14 | 130 1 -12.44 -3.9 15 | 140 1 -12.03 -3.91 16 | 150 1 -11.61 -3.93 17 | 160 1 -11.21 -3.94 18 | 170 1 -10.85 -3.94 19 | 180 1 -10.49 -3.93 20 | 190 1 -10.13 -3.93 21 | 0 3 -23.16 -6.48 22 | 10 3 -22.49 -6.46 23 | 20 3 -21.82 -6.44 24 | 30 3 -21.16 -6.42 25 | 40 3 -20.49 -6.41 26 | 50 3 -19.82 -6.39 27 | 60 3 -19.23 -6.41 28 | 70 3 -18.68 -6.44 29 | 80 3 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120 22 -18.0 -0.13 194 | 130 22 -17.71 -0.16 195 | 140 22 -17.41 -0.19 196 | 150 22 -17.12 -0.22 197 | 160 22 -16.87 -0.45 198 | 170 22 -16.63 -0.72 199 | 180 22 -16.39 -1.0 200 | 190 22 -16.15 -1.27 201 | 0 25 -22.5 -3.8 202 | 10 25 -21.92 -3.74 203 | 20 25 -21.33 -3.68 204 | 30 25 -20.75 -3.61 205 | 40 25 -20.16 -3.55 206 | 50 25 -19.58 -3.48 207 | 60 25 -19.0 -3.42 208 | 70 25 -18.41 -3.35 209 | 80 25 -17.83 -3.29 210 | 90 25 -17.24 -3.23 211 | 100 25 -16.74 -3.19 212 | 110 25 -16.27 -3.17 213 | 120 25 -15.8 -3.14 214 | 130 25 -15.33 -3.12 215 | 140 25 -14.86 -3.1 216 | 150 25 -14.39 -3.07 217 | 160 25 -13.93 -3.06 218 | 170 25 -13.46 -3.07 219 | 180 25 -13.0 -3.07 220 | 190 25 -12.53 -3.08 221 | 0 27 -22.95 -2.85 222 | 10 27 -22.39 -2.77 223 | 20 27 -21.83 -2.7 224 | 30 27 -21.28 -2.63 225 | 40 27 -20.72 -2.55 226 | 50 27 -20.17 -2.48 227 | 60 27 -19.61 -2.41 228 | 70 27 -19.01 -2.4 229 | 80 27 -18.36 -2.47 230 | 90 27 -17.71 -2.53 231 | 100 27 -17.05 -2.6 232 | 110 27 -16.44 -2.59 233 | 120 27 -15.84 -2.56 234 | 130 27 -15.24 -2.54 235 | 140 27 -14.64 -2.51 236 | 150 27 -14.07 -2.5 237 | 160 27 -13.8 -2.58 238 | 170 27 -13.52 -2.67 239 | 180 27 -13.08 -2.65 240 | 190 27 -12.62 -2.62 241 | 0 29 -19.48 -3.82 242 | 10 29 -19.09 -3.85 243 | 20 29 -18.71 -3.88 244 | 30 29 -18.32 -3.91 245 | 40 29 -17.94 -3.94 246 | 50 29 -17.55 -3.97 247 | 60 29 -17.16 -4.01 248 | 70 29 -16.73 -4.12 249 | 80 29 -16.31 -4.22 250 | 90 29 -15.88 -4.32 251 | 100 29 -15.45 -4.43 252 | 110 29 -15.02 -4.53 253 | 120 29 -14.52 -4.56 254 | 130 29 -14.01 -4.58 255 | 140 29 -13.5 -4.6 256 | 150 29 -12.98 -4.62 257 | 160 29 -12.48 -4.63 258 | 170 29 -12.04 -4.65 259 | 180 29 -11.59 -4.67 260 | 190 29 -11.14 -4.68 261 | 0 30 -23.7 -4.91 262 | 10 30 -23.14 -4.81 263 | 20 30 -22.58 -4.72 264 | 30 30 -22.02 -4.63 265 | 40 30 -21.46 -4.53 266 | 50 30 -20.9 -4.44 267 | 60 30 -20.38 -4.35 268 | 70 30 -19.86 -4.26 269 | 80 30 -19.34 -4.17 270 | 90 30 -18.81 -4.08 271 | 100 30 -18.29 -3.99 272 | 110 30 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| 110 34 -23.62 -3.07 313 | 120 34 -23.14 -3.09 314 | 130 34 -22.69 -3.13 315 | 140 34 -22.23 -3.17 316 | 150 34 -21.78 -3.21 317 | 160 34 -21.32 -3.25 318 | 170 34 -20.87 -3.29 319 | 180 34 -20.41 -3.33 320 | 190 34 -19.96 -3.37 321 | 0 37 -28.59 -4.36 322 | 10 37 -28.03 -4.32 323 | 20 37 -27.47 -4.28 324 | 30 37 -26.91 -4.23 325 | 40 37 -26.35 -4.19 326 | 50 37 -25.79 -4.15 327 | 60 37 -25.23 -4.1 328 | 70 37 -24.67 -4.06 329 | 80 37 -24.11 -4.01 330 | 90 37 -23.55 -3.94 331 | 100 37 -22.99 -3.88 332 | 110 37 -22.44 -3.82 333 | 120 37 -21.88 -3.75 334 | 130 37 -21.32 -3.69 335 | 140 37 -20.76 -3.63 336 | 150 37 -20.2 -3.57 337 | 160 37 -19.64 -3.5 338 | 170 37 -19.16 -3.46 339 | 180 37 -18.68 -3.42 340 | 190 37 -18.21 -3.37 341 | 0 40 -28.45 -5.09 342 | 10 40 -27.92 -5.01 343 | 20 40 -27.38 -4.93 344 | 30 40 -26.85 -4.85 345 | 40 40 -26.32 -4.77 346 | 50 40 -25.78 -4.69 347 | 60 40 -25.25 -4.61 348 | 70 40 -24.71 -4.53 349 | 80 40 -24.19 -4.48 350 | 90 40 -23.67 -4.42 351 | 100 40 -23.15 -4.37 352 | 110 40 -22.63 -4.32 353 | 120 40 -22.11 -4.26 354 | 130 40 -21.59 -4.21 355 | 140 40 -21.07 -4.16 356 | 150 40 -20.55 -4.1 357 | 160 40 -20.04 -4.05 358 | 170 40 -19.52 -4.0 359 | 180 40 -19.0 -3.94 360 | 190 40 -18.48 -3.89 361 | 0 43 -28.57 -5.56 362 | 10 43 -27.92 -5.49 363 | 20 43 -27.27 -5.43 364 | 30 43 -26.62 -5.36 365 | 40 43 -25.97 -5.29 366 | 50 43 -25.32 -5.23 367 | 60 43 -24.67 -5.16 368 | 70 43 -24.09 -5.1 369 | 80 43 -23.59 -5.04 370 | 90 43 -23.08 -4.99 371 | 100 43 -22.57 -4.93 372 | 110 43 -22.06 -4.87 373 | 120 43 -21.56 -4.82 374 | 130 43 -21.05 -4.76 375 | 140 43 -20.54 -4.7 376 | 150 43 -20.04 -4.65 377 | 160 43 -19.53 -4.59 378 | 170 43 -19.05 -4.53 379 | 180 43 -18.59 -4.46 380 | 190 43 -18.12 -4.39 381 | 0 46 -30.1 -7.03 382 | 10 46 -29.43 -6.91 383 | 20 46 -28.75 -6.79 384 | 30 46 -28.08 -6.67 385 | 40 46 -27.4 -6.55 386 | 50 46 -26.72 -6.43 387 | 60 46 -26.05 -6.31 388 | 70 46 -25.37 -6.19 389 | 80 46 -24.71 -6.07 390 | 90 46 -24.1 -5.96 391 | 100 46 -23.49 -5.85 392 | 110 46 -22.87 -5.73 393 | 120 46 -22.26 -5.62 394 | 130 46 -21.65 -5.51 395 | 140 46 -21.04 -5.4 396 | 150 46 -20.46 -5.29 397 | 160 46 -19.93 -5.2 398 | 170 46 -19.39 -5.11 399 | 180 46 -18.86 -5.02 400 | 190 46 -18.32 -4.93 401 | 0 49 -31.92 -6.81 402 | 10 49 -31.42 -6.83 403 | 20 49 -30.92 -6.85 404 | 30 49 -30.42 -6.87 405 | 40 49 -29.91 -6.89 406 | 50 49 -29.41 -6.91 407 | 60 49 -28.91 -6.94 408 | 70 49 -28.41 -6.96 409 | 80 49 -27.91 -6.98 410 | 90 49 -27.41 -7.0 411 | 100 49 -26.9 -7.02 412 | 110 49 -26.31 -6.96 413 | 120 49 -25.73 -6.91 414 | 130 49 -25.14 -6.86 415 | 140 49 -24.56 -6.8 416 | 150 49 -23.97 -6.75 417 | 160 49 -23.38 -6.7 418 | 170 49 -22.8 -6.64 419 | 180 49 -22.21 -6.59 420 | 190 49 -21.64 -6.52 421 | 0 55 -35.21 -5.59 422 | 10 55 -35.14 -5.58 423 | 20 55 -35.07 -5.57 424 | 30 55 -35.0 -5.56 425 | 40 55 -34.93 -5.54 426 | 50 55 -34.86 -5.53 427 | 60 55 -34.79 -5.52 428 | 70 55 -34.72 -5.51 429 | 80 55 -34.65 -5.49 430 | 90 55 -34.58 -5.48 431 | 100 55 -34.52 -5.47 432 | 110 55 -34.45 -5.46 433 | 120 55 -34.38 -5.44 434 | 130 55 -34.31 -5.43 435 | 140 55 -34.16 -5.47 436 | 150 55 -33.98 -5.53 437 | 160 55 -33.8 -5.58 438 | 170 55 -33.62 -5.64 439 | 180 55 -33.44 -5.7 440 | 190 55 -33.19 -5.74 441 | 0 58 -34.13 -4.87 442 | 10 58 -34.15 -4.89 443 | 20 58 -34.18 -4.91 444 | 30 58 -34.2 -4.94 445 | 40 58 -34.23 -4.96 446 | 50 58 -34.25 -4.98 447 | 60 58 -34.28 -5.0 448 | 70 58 -34.3 -5.02 449 | 80 58 -34.32 -5.05 450 | 90 58 -34.35 -5.07 451 | 100 58 -34.37 -5.09 452 | 110 58 -34.4 -5.11 453 | 120 58 -34.42 -5.13 454 | 130 58 -34.45 -5.16 455 | 140 58 -34.47 -5.18 456 | 150 58 -34.5 -5.2 457 | 160 58 -34.12 -5.19 458 | 170 58 -33.63 -5.16 459 | 180 58 -33.15 -5.14 460 | 190 58 -32.66 -5.11 461 | 30 52 -34.17 -6.3 462 | 40 52 -32.98 -6.06 463 | 50 52 -32.51 -6.09 464 | 60 52 -32.04 -6.13 465 | 70 52 -31.57 -6.16 466 | 80 52 -31.1 -6.2 467 | 90 52 -30.64 -6.23 468 | 100 52 -30.14 -6.26 469 | 110 52 -29.37 -6.25 470 | 120 52 -28.59 -6.24 471 | 130 52 -27.82 -6.23 472 | 140 52 -27.05 -6.22 473 | 150 52 -26.28 -6.21 474 | 160 52 -25.51 -6.2 475 | 170 52 -25.0 -6.21 476 | 180 52 -24.66 -6.25 477 | 190 52 -24.31 -6.28 478 | 200 52 -23.97 -6.31 479 | 210 52 -23.63 -6.34 480 | 220 52 -23.6 -6.22 481 | 200 2 -9.77 -3.92 482 | 210 2 -9.41 -3.92 483 | 220 2 -9.05 -3.91 484 | 230 2 -8.68 -4.0 485 | 240 2 -8.32 -4.08 486 | 250 2 -7.95 -4.17 487 | 260 2 -7.59 -4.25 488 | 270 2 -7.22 -4.33 489 | 280 2 -6.86 -4.42 490 | 290 2 -6.6 -4.59 491 | 300 2 -6.35 -4.77 492 | 310 2 -6.1 -4.94 493 | 320 2 -5.85 -5.12 494 | 330 2 -5.6 -5.3 495 | 340 2 -5.34 -5.48 496 | 350 2 -5.07 -5.64 497 | 360 2 -4.8 -5.79 498 | 370 2 -4.6 -5.98 499 | 380 2 -4.49 -6.21 500 | 390 2 -4.38 -6.44 501 | 200 4 -16.68 -6.01 502 | 210 4 -16.32 -5.96 503 | 220 4 -15.96 -5.91 504 | 230 4 -15.59 -5.86 505 | 240 4 -15.23 -5.81 506 | 250 4 -14.87 -5.77 507 | 260 4 -14.51 -5.72 508 | 270 4 -14.14 -5.67 509 | 280 4 -13.78 -5.62 510 | 290 4 -13.42 -5.57 511 | 300 4 -13.06 -5.52 512 | 310 4 -12.7 -5.47 513 | 320 4 -12.33 -5.42 514 | 330 4 -11.98 -5.39 515 | 340 4 -11.7 -5.48 516 | 350 4 -11.42 -5.57 517 | 360 4 -11.14 -5.66 518 | 370 4 -10.85 -5.76 519 | 380 4 -10.57 -5.85 520 | 390 4 -10.22 -5.91 521 | 200 7 -17.8 -5.62 522 | 210 7 -17.35 -5.54 523 | 220 7 -16.88 -5.45 524 | 230 7 -16.41 -5.37 525 | 240 7 -15.95 -5.28 526 | 250 7 -15.48 -5.2 527 | 260 7 -15.01 -5.11 528 | 270 7 -14.55 -5.03 529 | 280 7 -14.08 -4.94 530 | 290 7 -13.61 -4.86 531 | 300 7 -13.16 -4.78 532 | 310 7 -12.75 -4.74 533 | 320 7 -12.34 -4.7 534 | 330 7 -11.93 -4.65 535 | 340 7 -11.52 -4.61 536 | 350 7 -11.11 -4.57 537 | 360 7 -10.71 -4.52 538 | 370 7 -10.3 -4.48 539 | 380 7 -9.95 -4.48 540 | 390 7 -9.62 -4.48 541 | 200 11 -9.03 -3.13 542 | 210 11 -8.58 -3.15 543 | 220 11 -8.12 -3.17 544 | 230 11 -7.71 -3.19 545 | 240 11 -7.38 -3.22 546 | 250 11 -7.06 -3.25 547 | 260 11 -6.73 -3.29 548 | 270 11 -6.4 -3.32 549 | 280 11 -6.07 -3.35 550 | 290 11 -5.74 -3.38 551 | 300 11 -5.35 -3.41 552 | 310 11 -4.95 -3.43 553 | 320 11 -4.54 -3.46 554 | 330 11 -4.13 -3.48 555 | 340 11 -3.76 -3.53 556 | 350 11 -3.4 -3.58 557 | 360 11 -3.04 -3.64 558 | 370 11 -2.76 -3.73 559 | 380 11 -2.53 -3.84 560 | 390 11 -2.29 -3.95 561 | 200 13 -9.73 -1.71 562 | 210 13 -9.32 -1.76 563 | 220 13 -8.9 -1.82 564 | 230 13 -8.49 -1.87 565 | 240 13 -8.07 -1.93 566 | 250 13 -7.69 -1.99 567 | 260 13 -7.35 -2.05 568 | 270 13 -7.0 -2.12 569 | 280 13 -6.66 -2.19 570 | 290 13 -6.31 -2.25 571 | 300 13 -5.97 -2.32 572 | 310 13 -5.65 -2.4 573 | 320 13 -5.35 -2.5 574 | 330 13 -5.04 -2.59 575 | 340 13 -4.74 -2.69 576 | 350 13 -4.44 -2.78 577 | 360 13 -4.13 -2.87 578 | 370 13 -3.83 -2.95 579 | 380 13 -3.53 -3.02 580 | 390 13 -3.22 -3.1 581 | 200 15 -11.46 -1.44 582 | 210 15 -11.09 -1.46 583 | 220 15 -10.72 -1.47 584 | 230 15 -10.35 -1.48 585 | 240 15 -9.98 -1.49 586 | 250 15 -9.61 -1.51 587 | 260 15 -9.24 -1.52 588 | 270 15 -8.89 -1.54 589 | 280 15 -8.55 -1.56 590 | 290 15 -8.21 -1.57 591 | 300 15 -7.88 -1.59 592 | 310 15 -7.54 -1.61 593 | 320 15 -7.21 -1.63 594 | 330 15 -6.79 -1.72 595 | 340 15 -6.38 -1.81 596 | 350 15 -5.97 -1.9 597 | 360 15 -5.58 -1.98 598 | 370 15 -5.24 -2.02 599 | 380 15 -4.89 -2.07 600 | 390 15 -4.55 -2.11 601 | 200 17 -15.17 -0.36 602 | 210 17 -15.03 -0.42 603 | 220 17 -14.89 -0.49 604 | 230 17 -14.67 -0.7 605 | 240 17 -14.42 -0.98 606 | 250 17 -14.17 -1.27 607 | 260 17 -13.92 -1.55 608 | 270 17 -13.67 -1.83 609 | 280 17 -13.31 -2.09 610 | 290 17 -12.95 -2.35 611 | 300 17 -12.59 -2.61 612 | 310 17 -12.23 -2.85 613 | 320 17 -11.87 -3.04 614 | 330 17 -11.51 -3.23 615 | 340 17 -11.16 -3.42 616 | 350 17 -10.8 -3.61 617 | 360 17 -10.45 -3.62 618 | 370 17 -10.1 -3.61 619 | 380 17 -9.75 -3.61 620 | 390 17 -9.41 -3.6 621 | 200 20 -14.06 0.01 622 | 210 20 -13.81 -0.23 623 | 220 20 -13.57 -0.47 624 | 230 20 -13.32 -0.72 625 | 240 20 -13.08 -0.96 626 | 250 20 -12.83 -1.2 627 | 260 20 -12.61 -1.45 628 | 270 20 -12.46 -1.74 629 | 280 20 -12.32 -2.02 630 | 290 20 -12.05 -2.28 631 | 300 20 -11.76 -2.53 632 | 310 20 -11.46 -2.78 633 | 320 20 -11.08 -2.88 634 | 330 20 -10.7 -2.98 635 | 340 20 -10.32 -3.08 636 | 350 20 -9.95 -3.18 637 | 360 20 -9.55 -3.16 638 | 370 20 -9.16 -3.13 639 | 380 20 -8.77 -3.1 640 | 390 20 -8.43 -3.1 641 | 200 23 -15.92 -1.6 642 | 210 23 -15.68 -1.93 643 | 220 23 -15.44 -2.27 644 | 230 23 -15.21 -2.61 645 | 240 23 -14.95 -2.93 646 | 250 23 -14.49 -3.11 647 | 260 23 -14.02 -3.29 648 | 270 23 -13.56 -3.47 649 | 280 23 -13.27 -3.63 650 | 290 23 -12.98 -3.8 651 | 300 23 -12.69 -3.96 652 | 310 23 -12.4 -4.12 653 | 320 23 -11.99 -4.09 654 | 330 23 -11.58 -4.05 655 | 340 23 -11.17 -4.01 656 | 350 23 -10.76 -3.97 657 | 360 23 -10.41 -3.96 658 | 370 23 -10.1 -3.97 659 | 380 23 -9.79 -3.98 660 | 390 23 -9.49 -3.99 661 | 200 26 -12.07 -3.08 662 | 210 26 -11.6 -3.09 663 | 220 26 -11.13 -3.09 664 | 230 26 -10.73 -3.12 665 | 240 26 -10.35 -3.14 666 | 250 26 -9.97 -3.17 667 | 260 26 -9.59 -3.2 668 | 270 26 -9.21 -3.22 669 | 280 26 -8.82 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-11.74 -4.05 710 | 290 31 -11.47 -4.06 711 | 300 31 -11.2 -4.07 712 | 310 31 -10.93 -4.08 713 | 320 31 -10.66 -4.09 714 | 330 31 -10.38 -4.1 715 | 340 31 -10.07 -4.16 716 | 350 31 -9.76 -4.22 717 | 360 31 -9.39 -4.25 718 | 370 31 -8.99 -4.26 719 | 380 31 -8.59 -4.26 720 | 390 31 -8.19 -4.26 721 | 200 33 -14.35 -4.75 722 | 210 33 -14.05 -4.78 723 | 220 33 -13.74 -4.8 724 | 230 33 -13.45 -4.82 725 | 240 33 -13.21 -4.81 726 | 250 33 -12.97 -4.8 727 | 260 33 -12.73 -4.79 728 | 270 33 -12.49 -4.78 729 | 280 33 -12.24 -4.77 730 | 290 33 -11.92 -4.73 731 | 300 33 -11.59 -4.69 732 | 310 33 -11.26 -4.65 733 | 320 33 -10.93 -4.61 734 | 330 33 -10.61 -4.57 735 | 340 33 -10.26 -4.59 736 | 350 33 -9.91 -4.63 737 | 360 33 -9.56 -4.67 738 | 370 33 -9.2 -4.7 739 | 380 33 -8.85 -4.74 740 | 390 33 -8.5 -4.76 741 | 200 35 -19.56 -3.38 742 | 210 35 -19.15 -3.4 743 | 220 35 -18.74 -3.42 744 | 230 35 -18.33 -3.44 745 | 240 35 -17.92 -3.46 746 | 250 35 -17.52 -3.48 747 | 260 35 -17.11 -3.5 748 | 270 35 -16.7 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70 | 108 12 -1.628 13.771 71 | 120 12 -1.628 13.771 72 | 132 12 -1.628 13.771 73 | 144 12 -1.628 13.771 74 | 156 12 -1.628 13.771 75 | 168 12 -1.628 13.771 76 | 180 12 -1.628 13.771 77 | 192 12 -1.628 13.771 78 | 204 12 -1.628 13.771 79 | 216 12 -1.628 13.771 80 | 228 12 -1.628 13.771 81 | 0 14 -1.735 14.709 82 | 12 14 -1.735 14.709 83 | 24 14 -1.735 14.709 84 | 36 14 -1.735 14.709 85 | 48 14 -1.735 14.709 86 | 60 14 -1.735 14.709 87 | 72 14 -1.735 14.709 88 | 84 14 -1.735 14.709 89 | 96 14 -1.735 14.709 90 | 108 14 -1.735 14.709 91 | 120 14 -1.735 14.709 92 | 132 14 -1.735 14.709 93 | 144 14 -1.735 14.709 94 | 156 14 -1.735 14.709 95 | 168 14 -1.735 14.709 96 | 180 14 -1.735 14.709 97 | 192 14 -1.735 14.709 98 | 204 14 -1.735 14.709 99 | 216 14 -1.735 14.709 100 | 228 14 -1.735 14.709 101 | 0 16 23.683 -5.399 102 | 12 16 23.825 -5.682 103 | 24 16 24.002 -5.824 104 | 36 16 24.179 -5.965 105 | 48 16 24.356 -6.248 106 | 60 16 24.533 -6.39 107 | 72 16 24.71 -6.532 108 | 84 16 24.852 -6.673 109 | 96 16 25.064 -6.956 110 | 108 16 25.241 -7.098 111 | 120 16 25.312 -7.098 112 | 132 16 25.312 -7.098 113 | 144 16 25.312 -7.098 114 | 156 16 25.312 -6.956 115 | 168 16 25.312 -6.956 116 | 180 16 25.312 -6.815 117 | 192 16 25.312 -6.815 118 | 204 16 25.312 -6.815 119 | 216 16 25.312 -6.673 120 | 228 16 25.312 -6.673 121 | 0 17 0.336 5.257 122 | 12 17 0.195 4.407 123 | 24 17 0.124 3.699 124 | 36 17 -0.018 2.903 125 | 48 17 -0.089 2.106 126 | 60 17 -0.159 1.328 127 | 72 17 -0.23 0.62 128 | 84 17 -0.23 -0.23 129 | 96 17 -0.159 -1.115 130 | 108 17 -0.089 -1.823 131 | 120 17 -0.089 -2.673 132 | 132 17 -0.018 -3.522 133 | 144 17 0.053 -4.266 134 | 156 17 0.195 -4.974 135 | 168 17 0.336 -5.682 136 | 180 17 0.478 -6.248 137 | 192 17 0.62 -6.956 138 | 204 17 0.62 -7.7 139 | 216 17 0.62 -8.549 140 | 228 17 0.62 -9.257 141 | 0 19 8.284 11.488 142 | 12 19 8.284 11.488 143 | 24 19 8.284 11.488 144 | 36 19 8.284 11.488 145 | 48 19 8.284 11.488 146 | 60 19 8.284 11.488 147 | 72 19 8.284 11.488 148 | 84 19 8.284 11.488 149 | 96 19 8.284 11.488 150 | 108 19 8.284 11.488 151 | 120 19 8.284 11.488 152 | 132 19 8.284 11.488 153 | 144 19 8.284 11.488 154 | 156 19 8.284 11.488 155 | 168 19 8.284 11.488 156 | 180 19 8.284 11.488 157 | 192 19 8.284 11.488 158 | 204 19 8.284 11.488 159 | 216 19 8.284 11.488 160 | 228 19 8.284 11.488 161 | 0 21 -1.451 9.257 162 | 12 21 -1.522 9.7 163 | 24 21 -1.593 10.142 164 | 36 21 -1.664 10.638 165 | 48 21 -1.664 11.134 166 | 60 21 -1.735 11.629 167 | 72 21 -1.735 12.054 168 | 84 21 -1.522 12.196 169 | 96 21 -1.239 12.479 170 | 108 21 -1.027 12.762 171 | 120 21 -0.956 13.346 172 | 132 21 -0.814 13.93 173 | 144 21 -0.814 14.497 174 | 156 21 -0.885 14.78 175 | 168 21 -1.097 15.346 176 | 180 21 -1.31 15.771 177 | 192 21 -1.451 16.338 178 | 204 21 -1.664 16.939 179 | 216 21 -1.876 17.506 180 | 228 21 -1.947 17.931 181 | 0 24 17.807 9.912 182 | 12 24 17.311 9.771 183 | 24 24 16.816 9.771 184 | 36 24 16.373 9.771 185 | 48 24 15.877 9.771 186 | 60 24 15.382 9.771 187 | 72 24 14.939 9.771 188 | 84 24 14.444 9.771 189 | 96 24 13.948 9.629 190 | 108 24 13.523 9.629 191 | 120 24 13.028 9.629 192 | 132 24 12.532 9.629 193 | 144 24 12.107 9.629 194 | 156 24 11.612 9.629 195 | 168 24 11.169 9.629 196 | 180 24 10.673 9.629 197 | 192 24 10.178 9.488 198 | 204 24 9.735 9.488 199 | 216 24 9.24 9.488 200 | 228 24 8.744 9.488 201 | 0 26 17.701 8.62 202 | 12 26 17.205 8.62 203 | 24 26 16.709 8.62 204 | 36 26 16.196 8.62 205 | 48 26 15.771 8.62 206 | 60 26 15.276 8.62 207 | 72 26 14.762 8.62 208 | 84 26 14.338 8.62 209 | 96 26 13.842 8.62 210 | 108 26 13.346 8.62 211 | 120 26 12.921 8.62 212 | 132 26 12.426 8.62 213 | 144 26 11.93 8.62 214 | 156 26 11.505 8.62 215 | 168 26 10.992 8.62 216 | 180 26 10.496 8.62 217 | 192 26 10.072 8.62 218 | 204 26 9.558 8.62 219 | 216 26 9.063 8.62 220 | 228 26 8.638 8.62 221 | 0 28 -20.338 10.266 222 | 12 28 -19.842 10.266 223 | 24 28 -19.258 10.266 224 | 36 28 -18.692 10.355 225 | 48 28 -18.125 10.426 226 | 60 28 -17.559 10.496 227 | 72 28 -16.993 10.496 228 | 84 28 -16.426 10.496 229 | 96 28 -15.86 10.638 230 | 108 28 -15.276 10.638 231 | 120 28 -14.709 10.638 232 | 132 28 -14.125 10.638 233 | 144 28 -13.559 10.638 234 | 156 28 -12.921 10.638 235 | 168 28 -12.355 10.638 236 | 180 28 -11.718 10.638 237 | 192 28 -11.151 10.78 238 | 204 28 -10.585 10.78 239 | 216 28 -9.93 10.78 240 | 228 28 -9.434 10.921 241 | 0 30 -20.409 9.346 242 | 12 30 -19.913 9.346 243 | 24 30 -19.329 9.346 244 | 36 30 -18.763 9.346 245 | 48 30 -18.196 9.488 246 | 60 30 -17.63 9.488 247 | 72 30 -17.134 9.488 248 | 84 30 -16.568 9.629 249 | 96 30 -16.001 9.629 250 | 108 30 -15.417 9.629 251 | 120 30 -14.886 9.771 252 | 132 30 -14.302 9.771 253 | 144 30 -13.771 9.771 254 | 156 30 -13.205 9.912 255 | 168 30 -12.674 9.912 256 | 180 30 -12.107 9.912 257 | 192 30 -11.541 10.054 258 | 204 30 -11.045 10.054 259 | 216 30 -10.62 10.054 260 | 228 30 -10.107 10.054 261 | 0 32 9.629 10.921 262 | 12 32 9.629 10.921 263 | 24 32 9.629 10.921 264 | 36 32 9.629 10.921 265 | 48 32 9.629 10.921 266 | 60 32 9.629 10.921 267 | 72 32 9.629 10.921 268 | 84 32 9.629 10.921 269 | 96 32 9.629 10.921 270 | 108 32 9.629 10.921 271 | 120 32 9.629 10.921 272 | 132 32 9.629 10.921 273 | 144 32 9.629 10.921 274 | 156 32 9.629 10.921 275 | 168 32 9.629 10.921 276 | 180 32 9.629 10.921 277 | 192 32 9.629 10.921 278 | 204 32 9.629 10.921 279 | 216 32 9.629 10.921 280 | 228 32 9.629 10.921 281 | 0 34 17.347 -3.239 282 | 12 34 16.993 -3.381 283 | 24 34 16.709 -3.381 284 | 36 34 16.408 -3.522 285 | 48 34 16.054 -3.522 286 | 60 34 15.771 -3.522 287 | 72 34 15.488 -3.682 288 | 84 34 15.205 -3.682 289 | 96 34 14.833 -3.682 290 | 108 34 14.55 -3.823 291 | 120 34 14.267 -3.823 292 | 132 34 13.983 -3.823 293 | 144 34 13.629 -3.965 294 | 156 34 13.346 -3.965 295 | 168 34 13.063 -3.965 296 | 180 34 12.78 -4.107 297 | 192 34 12.426 -4.107 298 | 204 34 12.072 -4.107 299 | 216 34 11.718 -4.107 300 | 228 34 11.435 -4.195 301 | 0 36 1.735 -1.186 302 | 12 36 1.735 -1.186 303 | 24 36 1.735 -1.186 304 | 36 36 1.735 -1.186 305 | 48 36 1.735 -1.186 306 | 60 36 1.735 -1.186 307 | 72 36 1.735 -1.186 308 | 84 36 1.735 -1.186 309 | 96 36 1.805 -1.186 310 | 108 36 1.805 -1.186 311 | 120 36 1.805 -1.186 312 | 132 36 1.805 -1.186 313 | 144 36 1.805 -1.186 314 | 156 36 1.805 -1.186 315 | 168 36 1.805 -1.186 316 | 180 36 1.805 -1.186 317 | 192 36 1.876 -1.186 318 | 204 36 1.876 -1.186 319 | 216 36 1.876 -1.186 320 | 228 36 1.876 -1.186 321 | 0 38 1.204 12.479 322 | 12 38 1.133 11.913 323 | 24 38 1.133 11.346 324 | 36 38 1.062 10.78 325 | 48 38 1.062 10.196 326 | 60 38 1.009 9.629 327 | 72 38 0.974 9.045 328 | 84 38 0.938 8.479 329 | 96 38 0.938 7.912 330 | 108 38 0.903 7.487 331 | 120 38 0.867 6.903 332 | 132 38 0.867 6.337 333 | 144 38 0.726 5.77 334 | 156 38 0.549 5.045 335 | 168 38 0.372 4.478 336 | 180 38 0.195 3.912 337 | 192 38 0.124 3.328 338 | 204 38 0.089 2.62 339 | 216 38 0.089 2.036 340 | 228 38 0.018 1.469 341 | 0 40 0.549 12.638 342 | 12 40 0.478 12.054 343 | 24 40 0.407 11.488 344 | 36 40 0.336 10.921 345 | 48 40 0.266 10.337 346 | 60 40 0.195 9.771 347 | 72 40 0.124 9.204 348 | 84 40 0.053 8.62 349 | 96 40 -0.018 8.054 350 | 108 40 -0.089 7.487 351 | 120 40 -0.177 6.903 352 | 132 40 -0.319 6.337 353 | 144 40 -0.389 5.77 354 | 156 40 -0.46 5.186 355 | 168 40 -0.602 4.62 356 | 180 40 -0.673 4.053 357 | 192 40 -0.743 3.469 358 | 204 40 -0.885 2.903 359 | 216 40 -0.956 2.336 360 | 228 40 -1.027 1.752 361 | 0 42 1.08 26.498 362 | 12 42 1.044 25.931 363 | 24 42 1.044 25.365 364 | 36 42 1.044 24.94 365 | 48 42 1.044 24.374 366 | 60 42 1.044 23.949 367 | 72 42 1.044 23.347 368 | 84 42 1.044 22.922 369 | 96 42 1.044 22.356 370 | 108 42 1.044 21.931 371 | 120 42 1.044 21.365 372 | 132 42 1.044 20.94 373 | 144 42 1.044 20.373 374 | 156 42 1.044 19.913 375 | 168 42 1.044 19.347 376 | 180 42 1.044 18.922 377 | 192 42 1.044 18.356 378 | 204 42 1.044 17.931 379 | 216 42 1.044 17.364 380 | 228 42 1.009 16.939 381 | 0 44 -22.374 5.469 382 | 12 44 -21.807 5.469 383 | 24 44 -21.241 5.469 384 | 36 44 -20.657 5.469 385 | 48 44 -20.019 5.469 386 | 60 44 -19.453 5.469 387 | 72 44 -18.869 5.469 388 | 84 44 -18.232 5.469 389 | 96 44 -17.665 5.469 390 | 108 44 -17.099 5.469 391 | 120 44 -16.532 5.469 392 | 132 44 -15.895 5.469 393 | 144 44 -15.311 5.469 394 | 156 44 -14.745 5.469 395 | 168 44 -14.09 5.469 396 | 180 44 -13.523 5.469 397 | 192 44 -12.957 5.469 398 | 204 44 -12.32 5.469 399 | 216 44 -11.753 5.469 400 | 228 44 -11.187 5.469 401 | 0 46 3.894 -3.522 402 | 12 46 3.894 -3.522 403 | 24 46 3.894 -3.522 404 | 36 46 3.894 -3.522 405 | 48 46 3.894 -3.522 406 | 60 46 3.894 -3.522 407 | 72 46 3.894 -3.522 408 | 84 46 3.894 -3.522 409 | 96 46 3.894 -3.522 410 | 108 46 3.894 -3.522 411 | 120 46 3.894 -3.522 412 | 132 46 3.894 -3.522 413 | 144 46 3.894 -3.522 414 | 156 46 3.894 -3.522 415 | 168 46 3.894 -3.522 416 | 180 46 3.894 -3.522 417 | 192 46 3.894 -3.522 418 | 204 46 3.894 -3.522 419 | 216 46 3.894 -3.522 420 | 228 46 3.894 -3.522 421 | 12 6 19.541 46.96 422 | 24 6 19.825 46.818 423 | 36 6 20.055 46.677 424 | 48 6 20.338 46.535 425 | 60 6 20.621 46.252 426 | 72 6 20.904 45.827 427 | 84 6 21.258 45.402 428 | 96 6 21.542 44.977 429 | 108 6 21.896 44.694 430 | 120 6 22.338 44.552 431 | 132 6 22.763 44.411 432 | 144 6 23.188 44.252 433 | 156 6 23.542 44.11 434 | 168 6 23.967 43.951 435 | 180 6 24.391 43.809 436 | 192 6 24.745 43.809 437 | 204 6 24.958 43.809 438 | 216 6 25.17 43.809 439 | 228 6 25.56 43.809 440 | 240 6 25.424 43.888 441 | 180 5 -9.151 49.243 442 | 192 5 -8.992 48.323 443 | 204 5 -8.709 48.181 444 | 216 5 -8.425 48.11 445 | 228 5 -8.213 47.898 446 | 240 5 -7.93 47.756 447 | 252 5 -7.576 47.544 448 | 264 5 -7.293 47.261 449 | 276 5 -6.939 47.101 450 | 288 5 -6.585 46.818 451 | 300 5 -6.408 46.677 452 | 312 5 -6.337 46.535 453 | 324 5 -6.16 46.252 454 | 336 5 -6.018 45.969 455 | 348 5 -5.877 45.685 456 | 360 5 -5.806 45.402 457 | 372 5 -5.806 45.402 458 | 384 5 -5.806 45.402 459 | 396 5 -5.806 45.402 460 | 408 5 -5.806 45.402 461 | 204 23 2.797 48.535 462 | 216 23 2.726 48.181 463 | 228 23 2.69 47.898 464 | 240 23 2.655 47.615 465 | 252 23 2.655 47.331 466 | 264 23 2.655 46.889 467 | 276 23 2.655 46.464 468 | 288 23 2.655 46.039 469 | 300 23 2.655 45.615 470 | 312 23 2.655 45.19 471 | 324 23 2.655 44.623 472 | 336 23 2.655 44.181 473 | 348 23 2.655 43.738 474 | 360 23 2.655 43.313 475 | 372 23 2.655 42.889 476 | 384 23 2.655 42.464 477 | 396 23 2.655 42.039 478 | 408 23 2.655 41.614 479 | 420 23 2.655 41.03 480 | 432 23 2.655 40.605 481 | 240 1 7.363 -5.399 482 | 252 1 7.788 -5.399 483 | 264 1 8.284 -5.54 484 | 276 1 8.709 -5.54 485 | 288 1 9.134 -5.54 486 | 300 1 9.558 -5.682 487 | 312 1 10.072 -5.682 488 | 324 1 10.496 -5.682 489 | 336 1 10.921 -5.824 490 | 348 1 11.417 -5.824 491 | 360 1 11.859 -5.824 492 | 372 1 12.284 -5.824 493 | 384 1 12.638 -5.824 494 | 396 1 12.992 -5.824 495 | 408 1 13.417 -5.824 496 | 420 1 13.771 -5.824 497 | 432 1 14.125 -5.824 498 | 444 1 14.55 -5.824 499 | 456 1 14.904 -5.824 500 | 468 1 15.276 -5.824 501 | 240 9 -3.434 13.488 502 | 252 9 -3.434 13.488 503 | 264 9 -3.434 13.488 504 | 276 9 -3.434 13.488 505 | 288 9 -3.434 13.488 506 | 300 9 -3.434 13.488 507 | 312 9 -3.434 13.488 508 | 324 9 -3.434 13.488 509 | 336 9 -3.434 13.488 510 | 348 9 -3.434 13.488 511 | 360 9 -3.434 13.488 512 | 372 9 -3.434 13.488 513 | 384 9 -3.434 13.488 514 | 396 9 -3.434 13.488 515 | 408 9 -3.434 13.488 516 | 420 9 -3.434 13.488 517 | 432 9 -3.434 13.488 518 | 444 9 -3.434 13.488 519 | 456 9 -3.434 13.488 520 | 468 9 -3.434 13.488 521 | 240 11 -2.584 13.488 522 | 252 11 -2.584 13.488 523 | 264 11 -2.584 13.488 524 | 276 11 -2.584 13.488 525 | 288 11 -2.584 13.488 526 | 300 11 -2.584 13.488 527 | 312 11 -2.584 13.488 528 | 324 11 -2.584 13.488 529 | 336 11 -2.584 13.488 530 | 348 11 -2.584 13.488 531 | 360 11 -2.584 13.488 532 | 372 11 -2.584 13.488 533 | 384 11 -2.584 13.488 534 | 396 11 -2.584 13.488 535 | 408 11 -2.584 13.488 536 | 420 11 -2.584 13.488 537 | 432 11 -2.584 13.488 538 | 444 11 -2.584 13.488 539 | 456 11 -2.584 13.488 540 | 468 11 -2.584 13.488 541 | 240 13 -1.628 13.771 542 | 252 13 -1.628 13.771 543 | 264 13 -1.628 13.771 544 | 276 13 -1.628 13.771 545 | 288 13 -1.628 13.771 546 | 300 13 -1.628 13.771 547 | 312 13 -1.628 13.771 548 | 324 13 -1.628 13.771 549 | 336 13 -1.628 13.771 550 | 348 13 -1.628 13.771 551 | 360 13 -1.628 13.771 552 | 372 13 -1.628 13.771 553 | 384 13 -1.628 13.771 554 | 396 13 -1.628 13.771 555 | 408 13 -1.628 13.771 556 | 420 13 -1.628 13.771 557 | 432 13 -1.628 13.771 558 | 444 13 -1.628 13.771 559 | 456 13 -1.628 13.771 560 | 468 13 -1.628 13.771 561 | 240 15 -1.735 14.709 562 | 252 15 -1.735 14.709 563 | 264 15 -1.735 14.709 564 | 276 15 -1.735 14.709 565 | 288 15 -1.735 14.709 566 | 300 15 -1.735 14.709 567 | 312 15 -1.735 14.709 568 | 324 15 -1.735 14.709 569 | 336 15 -1.735 14.709 570 | 348 15 -1.735 14.709 571 | 360 15 -1.735 14.709 572 | 372 15 -1.735 14.709 573 | 384 15 -1.735 14.709 574 | 396 15 -1.735 14.709 575 | 408 15 -1.735 14.709 576 | 420 15 -1.735 14.709 577 | 432 15 -1.735 14.709 578 | 444 15 -1.735 14.709 579 | 456 15 -1.735 14.709 580 | 468 15 -1.735 14.709 581 | 240 18 0.69 -9.965 582 | 252 18 0.761 -10.691 583 | 264 18 0.832 -11.559 584 | 276 18 0.903 -12.267 585 | 288 18 0.974 -12.975 586 | 300 18 1.115 -13.683 587 | 312 18 1.115 -14.568 588 | 324 18 1.204 -15.417 589 | 336 18 1.274 -16.267 590 | 348 18 1.345 -17.117 591 | 360 18 1.416 -17.63 592 | 372 18 1.416 -18.267 593 | 384 18 1.362 -17.983 594 | 396 18 1.232 -16.453 595 | 408 18 1.102 -14.923 596 | 420 18 0.972 -13.393 597 | 432 18 0.842 -11.863 598 | 444 18 0.712 -10.333 599 | 456 18 0.582 -8.803 600 | 468 18 0.452 -7.272 601 | 240 20 8.284 11.488 602 | 252 20 8.284 11.488 603 | 264 20 8.284 11.488 604 | 276 20 8.284 11.488 605 | 288 20 8.284 11.488 606 | 300 20 8.284 11.488 607 | 312 20 8.284 11.488 608 | 324 20 8.284 11.488 609 | 336 20 8.284 11.488 610 | 348 20 8.284 11.488 611 | 360 20 8.284 11.488 612 | 372 20 8.284 11.488 613 | 384 20 8.284 11.488 614 | 396 20 8.284 11.488 615 | 408 20 8.284 11.488 616 | 420 20 8.284 11.488 617 | 432 20 8.284 11.488 618 | 444 20 8.284 11.488 619 | 456 20 8.284 11.488 620 | 468 20 8.284 11.488 621 | 240 22 -1.947 18.356 622 | 252 22 -2.018 18.78 623 | 264 22 -2.018 19.347 624 | 276 22 -2.089 19.772 625 | 288 22 -2.089 20.214 626 | 300 22 -2.159 20.657 627 | 312 22 -2.159 21.081 628 | 324 22 -2.23 21.648 629 | 336 22 -2.301 22.073 630 | 348 22 -2.301 22.497 631 | 360 22 -2.301 22.922 632 | 372 22 -2.301 23.506 633 | 384 22 -2.301 23.949 634 | 396 22 -2.301 24.515 635 | 408 22 -2.301 24.94 636 | 420 22 -2.301 25.507 637 | 432 22 -2.23 25.931 638 | 444 22 -2.23 26.356 639 | 456 22 -2.23 26.94 640 | 468 22 -2.23 27.383 641 | 240 25 8.319 9.488 642 | 252 25 7.824 9.488 643 | 264 25 7.328 9.488 644 | 276 25 6.903 9.488 645 | 288 25 6.478 9.488 646 | 300 25 6.107 9.629 647 | 312 25 5.682 9.771 648 | 324 25 5.328 9.912 649 | 336 25 4.974 10.054 650 | 348 25 4.531 10.196 651 | 360 25 4.177 10.355 652 | 372 25 3.823 10.496 653 | 384 25 3.399 10.638 654 | 396 25 3.115 10.921 655 | 408 25 2.832 11.346 656 | 420 25 2.62 11.913 657 | 432 25 2.336 12.337 658 | 444 25 2.266 12.762 659 | 456 25 2.195 13.063 660 | 468 25 2.124 13.488 661 | 240 27 8.142 8.62 662 | 252 27 7.647 8.62 663 | 264 27 7.222 8.62 664 | 276 27 6.726 8.62 665 | 288 27 6.231 8.62 666 | 300 27 5.717 8.762 667 | 312 27 5.222 8.903 668 | 324 27 4.638 9.045 669 | 336 27 4.142 9.187 670 | 348 27 3.717 9.488 671 | 360 27 3.434 9.771 672 | 372 27 3.151 10.196 673 | 384 27 2.868 10.496 674 | 396 27 2.584 10.921 675 | 408 27 2.23 11.204 676 | 420 27 1.947 11.629 677 | 432 27 1.664 11.913 678 | 444 27 1.381 12.337 679 | 456 27 1.31 12.78 680 | 468 27 1.239 13.205 681 | 240 29 -8.992 10.921 682 | 252 29 -8.496 11.063 683 | 264 29 -8.071 11.063 684 | 276 29 -7.647 11.063 685 | 288 29 -7.293 11.204 686 | 300 29 -7.08 11.204 687 | 312 29 -6.868 11.346 688 | 324 29 -6.62 11.346 689 | 336 29 -6.337 11.488 690 | 348 29 -5.983 11.488 691 | 360 29 -5.558 11.488 692 | 372 29 -5.186 11.488 693 | 384 29 -4.832 11.488 694 | 396 29 -4.478 11.629 695 | 408 29 -4.124 11.771 696 | 420 29 -3.753 12.054 697 | 432 29 -3.469 12.196 698 | 444 29 -3.115 12.337 699 | 456 29 -2.761 12.479 700 | 468 29 -2.62 12.621 701 | 240 31 -9.611 10.196 702 | 252 31 -9.098 10.196 703 | 264 31 -8.603 10.196 704 | 276 31 -8.178 10.337 705 | 288 31 -7.894 10.337 706 | 300 31 -7.54 10.479 707 | 312 31 -7.257 10.638 708 | 324 31 -6.903 10.638 709 | 336 31 -6.62 10.78 710 | 348 31 -6.266 10.78 711 | 360 31 -5.983 10.921 712 | 372 31 -5.7 11.063 713 | 384 31 -5.257 11.063 714 | 396 31 -4.903 11.063 715 | 408 31 -4.478 11.063 716 | 420 31 -4.124 11.204 717 | 432 31 -3.753 11.346 718 | 444 31 -3.399 11.488 719 | 456 31 -3.045 11.629 720 | 468 31 -2.832 11.629 721 | 240 33 9.629 10.921 722 | 252 33 9.629 10.921 723 | 264 33 9.629 10.921 724 | 276 33 9.629 10.921 725 | 288 33 9.629 10.921 726 | 300 33 9.629 10.921 727 | 312 33 9.629 10.921 728 | 324 33 9.629 10.921 729 | 336 33 9.629 10.921 730 | 348 33 9.629 10.921 731 | 360 33 9.629 10.921 732 | 372 33 9.629 10.921 733 | 384 33 9.629 10.921 734 | 396 33 9.629 10.921 735 | 408 33 9.629 10.921 736 | 420 33 9.629 10.921 737 | 432 33 9.629 10.921 738 | 444 33 9.629 10.921 739 | 456 33 9.629 10.921 740 | 468 33 9.629 10.921 741 | 240 35 11.063 -4.195 742 | 252 35 10.709 -4.195 743 | 264 35 10.426 -4.195 744 | 276 35 9.983 -4.337 745 | 288 35 9.558 -4.337 746 | 300 35 9.134 -4.478 747 | 312 35 8.709 -4.478 748 | 324 35 8.284 -4.62 749 | 336 35 7.788 -4.62 750 | 348 35 7.222 -4.62 751 | 360 35 6.726 -4.62 752 | 372 35 6.142 -4.62 753 | 384 35 5.646 -4.62 754 | 396 35 5.08 -4.549 755 | 408 35 4.638 -4.549 756 | 420 35 4.142 -4.407 757 | 432 35 3.646 -4.266 758 | 444 35 3.222 -4.266 759 | 456 35 2.726 -4.124 760 | 468 35 2.23 -3.965 761 | 240 37 1.876 -1.186 762 | 252 37 1.876 -1.186 763 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396 | 408 12 -25.259 20.418 397 | 420 12 -24.984 20.097 398 | 432 12 -24.617 19.73 399 | 444 12 -24.227 19.73 400 | 456 12 -23.745 19.73 401 | 228 50 -31.545 20.808 402 | 240 50 -31.201 20.074 403 | 252 50 -30.742 20.074 404 | 264 50 -30.237 20.074 405 | 276 50 -29.664 20.074 406 | 288 50 -29.182 19.914 407 | 300 50 -28.677 19.914 408 | 312 50 -28.173 19.914 409 | 324 50 -27.714 19.914 410 | 336 50 -27.209 19.914 411 | 348 50 -26.704 19.753 412 | 360 50 -26.154 19.753 413 | 372 50 -25.649 19.753 414 | 384 50 -25.144 19.753 415 | 396 50 -24.915 19.569 416 | 408 50 -25.144 19.248 417 | 420 50 -25.007 19.065 418 | 432 50 -24.686 18.904 419 | 444 50 -24.364 18.744 420 | 456 50 -23.951 18.56 421 | 276 59 25.534 17.941 422 | 288 59 24.869 16.977 423 | 300 59 24.387 16.793 424 | 312 59 23.814 16.472 425 | 324 59 23.309 16.289 426 | 336 59 22.827 16.289 427 | 348 59 22.345 16.289 428 | 360 59 21.772 16.289 429 | 372 59 21.267 16.289 430 | 384 59 20.785 16.197 431 | 396 59 20.212 16.059 432 | 408 59 19.547 15.876 433 | 420 59 18.973 15.876 434 | 432 59 18.331 15.876 435 | 444 59 17.711 15.876 436 | 456 59 17.046 15.876 437 | 468 59 16.403 15.784 438 | 480 59 15.715 15.623 439 | 492 59 15.417 15.555 440 | 504 59 14.912 15.463 441 | 276 61 4.657 -19.34 442 | 288 61 4.336 -19.845 443 | 300 61 4.107 -20.212 444 | 312 61 3.762 -20.717 445 | 324 61 3.51 -20.946 446 | 336 61 3.189 -21.29 447 | 348 61 2.868 -21.634 448 | 360 61 2.961 -21.503 449 | 372 61 3.072 -21.346 450 | 384 61 3.184 -21.188 451 | 396 61 3.296 -21.03 452 | 408 61 3.407 -20.873 453 | 420 61 3.519 -20.715 454 | 432 61 3.631 -20.558 455 | 444 61 3.742 -20.4 456 | 456 61 3.854 -20.243 457 | 468 61 3.966 -20.085 458 | 480 61 4.077 -19.928 459 | 492 61 4.189 -19.77 460 | 504 61 4.301 -19.613 461 | 300 16 18.606 -8.511 462 | 312 16 18.606 -8.007 463 | 324 16 18.606 -7.318 464 | 336 16 18.606 -6.814 465 | 348 16 18.606 -6.309 466 | 360 16 18.606 -5.804 467 | 372 16 18.606 -5.139 468 | 384 16 18.606 -4.634 469 | 396 16 18.606 -4.13 470 | 408 16 18.606 -3.625 471 | 420 16 18.606 -2.96 472 | 432 16 18.606 -2.455 473 | 444 16 18.606 -1.95 474 | 456 16 18.606 -1.262 475 | 468 16 18.606 -0.757 476 | 480 16 18.606 -0.252 477 | 492 16 18.606 0.229 478 | 504 16 18.56 0.826 479 | 516 16 18.606 1.17 480 | 528 16 18.629 1.583 481 | 300 40 18.399 12.343 482 | 312 40 18.399 12.343 483 | 324 40 18.399 12.343 484 | 336 40 18.399 12.343 485 | 348 40 18.399 12.343 486 | 360 40 18.399 12.343 487 | 372 40 18.399 12.343 488 | 384 40 18.399 12.182 489 | 396 40 18.331 11.838 490 | 408 40 18.239 11.677 491 | 420 40 18.17 11.333 492 | 432 40 18.032 11.173 493 | 444 40 18.032 11.173 494 | 456 40 18.032 11.333 495 | 468 40 17.941 11.333 496 | 480 40 17.941 11.333 497 | 492 40 17.895 11.173 498 | 504 40 17.895 10.989 499 | 516 40 17.895 10.989 500 | 528 40 17.895 10.989 501 | 312 56 13.215 17.734 502 | 324 56 12.802 17.551 503 | 336 56 12.389 17.551 504 | 348 56 11.976 17.39 505 | 360 56 11.563 17.229 506 | 372 56 11.15 17.229 507 | 384 56 10.691 17.046 508 | 396 56 10.278 16.885 509 | 408 56 9.865 16.885 510 | 420 56 9.452 16.725 511 | 432 56 9.039 16.541 512 | 444 56 8.718 16.381 513 | 456 56 8.305 16.22 514 | 468 56 7.892 16.22 515 | 480 56 7.479 16.036 516 | 492 56 7.066 15.876 517 | 504 56 6.699 15.532 518 | 516 56 6.47 15.119 519 | 528 56 6.263 14.683 520 | 540 56 6.034 14.27 521 | 312 58 13.765 15.623 522 | 324 58 13.375 15.44 523 | 336 58 12.985 15.44 524 | 348 58 12.572 15.44 525 | 360 58 12.159 15.302 526 | 372 58 11.746 15.302 527 | 384 58 11.333 15.119 528 | 396 58 10.92 15.119 529 | 408 58 10.507 15.119 530 | 420 58 10.094 14.935 531 | 432 58 9.773 14.798 532 | 444 58 9.36 14.614 533 | 456 58 8.947 14.43 534 | 468 58 8.626 14.362 535 | 480 58 8.213 14.201 536 | 492 58 7.8 14.018 537 | 504 58 7.479 13.857 538 | 516 58 7.296 13.696 539 | 528 58 7.112 13.444 540 | 540 58 6.906 13.26 541 | 324 64 16.197 24.135 542 | 336 64 16.289 24.456 543 | 348 64 16.358 24.777 544 | 360 64 16.449 25.144 545 | 372 64 16.518 25.466 546 | 384 64 16.61 25.787 547 | 396 64 16.61 25.97 548 | 408 64 16.61 25.97 549 | 420 64 16.518 25.97 550 | 432 64 16.518 25.97 551 | 444 64 16.449 25.97 552 | 456 64 16.449 25.787 553 | 468 64 16.358 25.787 554 | 480 64 16.289 25.787 555 | 492 64 16.289 25.787 556 | 504 64 16.289 25.649 557 | 516 64 16.289 25.649 558 | 528 64 16.289 25.649 559 | 540 64 16.289 25.649 560 | 552 64 16.289 25.649 561 | 336 7 -6.676 35.721 562 | 348 7 -6.424 35.721 563 | 360 7 -6.194 35.721 564 | 372 7 -5.942 35.721 565 | 384 7 -6.103 35.721 566 | 396 7 -6.837 35.216 567 | 408 7 -7.617 34.803 568 | 420 7 -7.708 34.803 569 | 432 7 -7.708 34.803 570 | 444 7 -7.708 34.803 571 | 456 7 -7.708 34.803 572 | 468 7 -7.708 34.803 573 | 480 7 -7.708 34.803 574 | 492 7 -7.708 34.803 575 | 504 7 -7.708 34.619 576 | 516 7 -7.433 34.619 577 | 528 7 -7.296 34.619 578 | 540 7 -7.112 34.619 579 | 552 7 -6.928 34.619 580 | 564 7 -6.699 34.619 581 | 348 42 -2.684 35.721 582 | 360 42 -2.638 35.537 583 | 372 42 -2.455 35.537 584 | 384 42 -2.363 35.376 585 | 396 42 -2.638 35.216 586 | 408 42 -3.281 35.032 587 | 420 42 -3.923 34.872 588 | 432 42 -4.015 34.872 589 | 444 42 -4.107 34.872 590 | 456 42 -4.175 34.872 591 | 468 42 -4.244 34.872 592 | 480 42 -4.244 35.032 593 | 492 42 -4.244 35.032 594 | 504 42 -4.244 34.964 595 | 516 42 -4.061 34.964 596 | 528 42 -3.831 34.964 597 | 540 42 -3.694 34.964 598 | 552 42 -3.51 34.872 599 | 564 42 -3.327 34.872 600 | 576 42 -3.143 34.872 601 | 360 25 -11.655 21.52 602 | 372 25 -11.287 21.336 603 | 384 25 -10.92 21.336 604 | 396 25 -10.553 21.336 605 | 408 25 -10.14 21.175 606 | 420 25 -9.819 21.175 607 | 432 25 -9.406 21.175 608 | 444 25 -9.039 21.015 609 | 456 25 -8.672 21.015 610 | 468 25 -8.305 20.831 611 | 480 25 -7.892 20.831 612 | 492 25 -7.571 20.831 613 | 504 25 -7.135 20.671 614 | 516 25 -6.63 20.671 615 | 528 25 -6.125 20.602 616 | 540 25 -5.644 20.51 617 | 552 25 -5.162 20.418 618 | 564 25 -4.657 20.418 619 | 576 25 -4.175 20.235 620 | 588 25 -3.694 20.235 621 | 360 28 -11.677 22.69 622 | 372 28 -11.356 22.529 623 | 384 28 -10.943 22.529 624 | 396 28 -10.599 22.529 625 | 408 28 -10.255 22.345 626 | 420 28 -9.842 22.345 627 | 432 28 -9.521 22.345 628 | 444 28 -9.108 22.185 629 | 456 28 -8.787 22.185 630 | 468 28 -8.374 22.024 631 | 480 28 -8.053 22.024 632 | 492 28 -7.64 22.024 633 | 504 28 -7.273 21.841 634 | 516 28 -6.791 21.841 635 | 528 28 -6.309 21.68 636 | 540 28 -5.804 21.68 637 | 552 28 -5.323 21.588 638 | 564 28 -4.795 21.428 639 | 576 28 -4.29 21.428 640 | 588 28 -3.808 21.267 641 | 360 62 12.457 -2.592 642 | 372 62 12.32 -3.281 643 | 384 62 12.228 -3.969 644 | 396 62 12.159 -4.474 645 | 408 62 12.067 -4.978 646 | 420 62 12.067 -5.483 647 | 432 62 12.067 -5.988 648 | 444 62 12.067 -6.653 649 | 456 62 12.067 -7.158 650 | 468 62 12.067 -7.663 651 | 480 62 12.067 -8.167 652 | 492 62 12.067 -8.672 653 | 504 62 12.067 -9.337 654 | 516 62 12.067 -9.842 655 | 528 62 12.113 -10.439 656 | 540 62 12.159 -10.943 657 | 552 62 12.205 -11.54 658 | 564 62 12.251 -12.113 659 | 576 62 12.251 -12.71 660 | 588 62 12.274 -13.306 661 | 360 69 -11.264 23.63 662 | 372 69 -10.943 23.63 663 | 384 69 -10.53 23.63 664 | 396 69 -10.209 23.447 665 | 408 69 -9.842 23.447 666 | 420 69 -9.475 23.263 667 | 432 69 -9.108 23.263 668 | 444 69 -8.695 23.125 669 | 456 69 -8.374 23.125 670 | 468 69 -7.961 23.125 671 | 480 69 -7.64 22.942 672 | 492 69 -7.227 22.942 673 | 504 69 -6.906 22.758 674 | 516 69 -6.447 22.758 675 | 528 69 -5.942 22.69 676 | 540 69 -5.437 22.69 677 | 552 69 -4.978 22.621 678 | 564 69 -4.565 22.529 679 | 576 69 -4.061 22.529 680 | 588 69 -3.602 22.345 681 | 384 22 -16.013 22.942 682 | 396 22 -15.738 22.781 683 | 408 22 -15.509 22.437 684 | 420 22 -15.325 22.093 685 | 432 22 -15.096 21.932 686 | 444 22 -14.866 21.588 687 | 456 22 -14.545 21.428 688 | 468 22 -13.949 21.428 689 | 480 22 -13.398 21.428 690 | 492 22 -12.802 21.428 691 | 504 22 -12.251 21.428 692 | 516 22 -11.655 21.428 693 | 528 22 -11.104 21.428 694 | 540 22 -10.507 21.428 695 | 552 22 -9.957 21.428 696 | 564 22 -9.36 21.428 697 | 576 22 -8.81 21.428 698 | 588 22 -8.213 21.428 699 | 600 22 -7.663 21.428 700 | 612 22 -7.066 21.428 701 | 420 54 18.652 43.521 702 | 432 54 18.881 44.026 703 | 444 54 19.157 44.53 704 | 456 54 19.386 45.035 705 | 468 54 19.569 45.54 706 | 480 54 19.799 46.044 707 | 492 54 20.028 46.549 708 | 504 54 20.372 46.733 709 | 516 54 20.74 47.146 710 | 528 54 21.107 47.582 711 | 540 54 21.52 47.995 712 | 552 54 21.887 48.407 713 | 564 54 22.254 48.752 714 | 576 54 22.621 49.188 715 | 588 54 23.034 49.6 716 | 600 54 23.401 49.922 717 | 612 54 23.768 50.358 718 | 624 54 24.135 50.679 719 | 636 54 24.548 51.183 720 | 648 54 24.892 51.596 721 | 456 46 1.835 30.329 722 | 468 46 1.354 30.008 723 | 480 46 0.849 29.664 724 | 492 46 0.367 29.159 725 | 504 46 -0.206 28.815 726 | 516 46 -0.711 28.654 727 | 528 46 -1.193 28.494 728 | 540 46 -1.721 28.31 729 | 552 46 -2.202 28.15 730 | 564 46 -2.707 27.989 731 | 576 46 -3.281 27.829 732 | 588 46 -3.762 27.645 733 | 600 46 -4.267 27.484 734 | 612 46 -4.795 27.324 735 | 624 46 -5.277 27.14 736 | 636 46 -5.781 26.98 737 | 648 46 -6.103 26.819 738 | 660 46 -6.263 26.636 739 | 672 46 -6.424 26.636 740 | 684 46 -6.584 26.475 741 | 468 13 -23.332 19.73 742 | 480 13 -22.827 19.73 743 | 492 13 -22.414 19.661 744 | 504 13 -21.932 19.592 745 | 516 13 -21.451 19.592 746 | 528 13 -20.946 19.592 747 | 540 13 -20.441 19.592 748 | 552 13 -19.937 19.592 749 | 564 13 -19.432 19.501 750 | 576 13 -18.904 19.501 751 | 588 13 -18.422 19.501 752 | 600 13 -17.872 19.501 753 | 612 13 -17.39 19.409 754 | 624 13 -16.725 19.409 755 | 636 13 -16.174 19.409 756 | 648 13 -15.509 19.409 757 | 660 13 -14.935 19.409 758 | 672 13 -14.293 19.409 759 | 684 13 -13.696 19.409 760 | 696 13 -13.054 19.409 761 | 468 51 -23.493 18.56 762 | 480 51 -22.988 18.468 763 | 492 51 -22.506 18.468 764 | 504 51 -22.024 18.468 765 | 516 51 -21.52 18.468 766 | 528 51 -20.992 18.468 767 | 540 51 -20.51 18.468 768 | 552 51 -19.982 18.468 769 | 564 51 -19.478 18.56 770 | 576 51 -18.927 18.56 771 | 588 51 -18.468 18.56 772 | 600 51 -17.918 18.56 773 | 612 51 -17.413 18.744 774 | 624 51 -16.771 18.744 775 | 636 51 -16.128 18.744 776 | 648 51 -15.509 18.744 777 | 660 51 -14.889 18.904 778 | 672 51 -14.247 18.904 779 | 684 51 -13.605 18.904 780 | 696 51 -12.962 18.904 781 | 516 60 14.201 15.463 782 | 528 60 13.605 15.463 783 | 540 60 13.054 15.463 784 | 552 60 12.435 15.279 785 | 564 60 11.953 15.279 786 | 576 60 11.448 15.188 787 | 588 60 10.943 15.119 788 | 600 60 10.439 15.05 789 | 612 60 9.934 14.866 790 | 624 60 9.429 14.866 791 | 636 60 8.97 14.775 792 | 648 60 8.466 14.683 793 | 660 60 7.961 14.614 794 | 672 60 7.502 14.614 795 | 684 60 7.158 14.614 796 | 696 60 6.906 14.683 797 | 708 60 6.676 14.775 798 | 720 60 6.401 14.775 799 | 732 60 6.08 14.775 800 | 744 60 5.85 14.683 801 | 540 17 18.606 1.927 802 | 552 17 18.56 2.409 803 | 564 17 18.422 2.914 804 | 576 17 18.147 3.418 805 | 588 17 17.918 3.923 806 | 600 17 17.688 4.267 807 | 612 17 17.275 4.611 808 | 624 17 16.771 4.933 809 | 636 17 16.358 5.277 810 | 648 17 15.945 5.621 811 | 660 17 15.623 6.125 812 | 672 17 15.394 6.791 813 | 684 17 15.05 7.296 814 | 696 17 14.798 7.961 815 | 708 17 14.522 8.466 816 | 720 17 14.201 9.154 817 | 732 17 13.88 9.819 818 | 744 17 13.559 10.324 819 | 756 17 13.467 10.989 820 | 768 17 13.329 11.494 821 | 564 65 16.289 25.557 822 | 576 65 16.289 25.557 823 | 588 65 16.289 25.557 824 | 600 65 16.358 25.466 825 | 612 65 16.358 25.374 826 | 624 65 16.358 25.374 827 | 636 65 16.358 25.374 828 | 648 65 16.358 25.374 829 | 660 65 16.358 25.374 830 | 672 65 16.358 25.305 831 | 684 65 16.358 25.305 832 | 696 65 16.449 25.213 833 | 708 65 16.449 25.213 834 | 720 65 16.449 25.121 835 | 732 65 16.449 25.121 836 | 744 65 16.449 25.121 837 | 756 65 16.449 25.121 838 | 768 65 16.449 25.03 839 | 780 65 16.449 25.03 840 | 792 65 16.518 24.961 841 | 576 8 -6.561 34.619 842 | 588 8 -6.332 34.619 843 | 600 8 -6.148 34.619 844 | 612 8 -5.965 34.619 845 | 624 8 -5.781 34.528 846 | 636 8 -5.552 34.528 847 | 648 8 -5.414 34.528 848 | 660 8 -5.185 34.528 849 | 672 8 -5.047 34.528 850 | 684 8 -4.795 34.528 851 | 696 8 -4.634 34.528 852 | 708 8 -4.451 34.528 853 | 720 8 -4.221 34.528 854 | 732 8 -4.038 34.528 855 | 744 8 -3.808 34.528 856 | 756 8 -3.487 34.528 857 | 768 8 -3.074 34.528 858 | 780 8 -2.982 34.528 859 | 792 8 -2.914 34.528 860 | 804 8 -2.822 34.528 861 | 588 43 -3.005 34.78 862 | 600 43 -2.822 34.78 863 | 612 43 -2.592 34.78 864 | 624 43 -2.409 34.711 865 | 636 43 -2.248 34.711 866 | 648 43 -2.088 34.711 867 | 660 43 -1.881 34.711 868 | 672 43 -1.721 34.619 869 | 684 43 -1.56 34.619 870 | 696 43 -1.354 34.619 871 | 708 43 -1.147 34.528 872 | 720 43 -0.987 34.528 873 | 732 43 -0.826 34.528 874 | 744 43 -0.574 34.459 875 | 756 43 -0.252 34.459 876 | 768 43 0.161 34.459 877 | 780 43 0.252 34.367 878 | 792 43 0.321 34.367 879 | 804 43 0.321 34.367 880 | 816 43 0.413 34.367 881 | 600 26 -3.189 20.097 882 | 612 26 -2.707 20.097 883 | 624 26 -2.202 20.005 884 | 636 26 -1.721 19.914 885 | 648 26 -1.239 19.822 886 | 660 26 -0.688 19.822 887 | 672 26 -0.138 20.005 888 | 684 26 0.459 20.327 889 | 696 26 1.193 20.602 890 | 708 26 2.157 20.923 891 | 720 26 3.166 21.336 892 | 732 26 3.625 21.336 893 | 744 26 4.13 21.336 894 | 756 26 4.634 21.428 895 | 768 26 5.093 21.428 896 | 780 26 5.69 21.588 897 | 792 26 6.194 21.428 898 | 804 26 6.653 21.267 899 | 816 26 7.158 21.267 900 | 828 26 7.663 21.084 901 | 600 29 -3.327 21.267 902 | 612 29 -2.776 21.084 903 | 624 29 -2.294 20.992 904 | 636 29 -1.789 20.923 905 | 648 29 -1.308 20.854 906 | 660 29 -0.78 20.854 907 | 672 29 -0.275 20.992 908 | 684 29 0.275 21.359 909 | 696 29 1.055 21.68 910 | 708 29 2.042 22.001 911 | 720 29 3.074 22.368 912 | 732 29 3.556 22.368 913 | 744 29 4.107 22.368 914 | 756 29 4.657 22.506 915 | 768 29 5.208 22.506 916 | 780 29 5.758 22.69 917 | 792 29 6.263 22.506 918 | 804 29 6.768 22.368 919 | 816 29 7.227 22.185 920 | 828 29 7.731 22.001 921 | 600 70 -3.097 22.345 922 | 612 70 -2.661 22.277 923 | 624 70 -2.225 22.185 924 | 636 70 -1.721 22.093 925 | 648 70 -1.216 22.093 926 | 660 70 -0.734 22.093 927 | 672 70 -0.161 22.345 928 | 684 70 0.39 22.69 929 | 696 70 1.216 22.942 930 | 708 70 2.202 23.125 931 | 720 70 3.189 23.263 932 | 732 70 3.694 23.263 933 | 744 70 4.175 23.263 934 | 756 70 4.657 23.447 935 | 768 70 5.162 23.447 936 | 780 70 5.713 23.63 937 | 792 70 6.217 23.447 938 | 804 70 6.722 23.355 939 | 816 70 7.204 23.194 940 | 828 70 7.686 23.103 941 | 624 23 -6.516 21.428 942 | 636 23 -5.919 21.428 943 | 648 23 -5.368 21.428 944 | 660 23 -4.772 21.428 945 | 672 23 -4.175 21.588 946 | 684 23 -3.189 21.772 947 | 696 23 -2.179 21.932 948 | 708 23 -1.193 22.093 949 | 720 23 -0.367 22.277 950 | 732 23 0.436 22.437 951 | 744 23 1.239 22.598 952 | 756 23 1.927 22.598 953 | 768 23 2.432 22.598 954 | 780 23 2.891 22.437 955 | 792 23 3.395 22.277 956 | 804 23 3.9 22.277 957 | 816 23 4.359 22.093 958 | 828 23 4.955 22.093 959 | 840 23 5.437 21.932 960 | 852 23 5.919 21.772 961 | 660 11 -32.027 16.725 962 | 672 11 -31.637 16.816 963 | 684 11 -30.903 16.954 964 | 696 11 -29.205 17.459 965 | 708 11 -27.507 17.964 966 | 720 11 -25.764 18.399 967 | 732 11 -24.594 18.399 968 | 744 11 -23.447 18.399 969 | 756 11 -22.529 18.239 970 | 768 11 -21.726 18.239 971 | 780 11 -20.923 18.055 972 | 792 11 -20.189 18.055 973 | 804 11 -19.363 17.895 974 | 816 11 -18.537 17.895 975 | 828 11 -17.711 17.895 976 | 840 11 -17.069 17.734 977 | 852 11 -16.518 17.551 978 | 864 11 -15.968 17.551 979 | 876 11 -15.371 17.39 980 | 888 11 -14.866 17.39 981 | 672 19 25.488 -3.028 982 | 684 19 24.984 -3.212 983 | 696 19 24.433 -3.533 984 | 708 19 23.928 -3.785 985 | 720 19 23.424 -4.13 986 | 732 19 22.965 -4.474 987 | 744 19 22.46 -4.978 988 | 756 19 22.047 -5.3 989 | 768 19 21.634 -5.804 990 | 780 19 21.13 -6.148 991 | 792 19 20.762 -6.653 992 | 804 19 20.349 -7.158 993 | 816 19 20.074 -7.823 994 | 828 19 19.845 -8.511 995 | 840 19 19.524 -9.177 996 | 852 19 19.248 -9.842 997 | 864 19 19.019 -10.53 998 | 876 19 18.927 -11.196 999 | 888 19 18.927 -11.861 1000 | 900 19 18.927 -12.366 1001 | 672 20 12.113 5.781 1002 | 684 20 12.779 5.781 1003 | 696 20 13.421 5.781 1004 | 708 20 14.063 5.781 1005 | 720 20 14.568 5.781 1006 | 732 20 15.05 5.781 1007 | 744 20 15.532 5.781 1008 | 756 20 16.036 5.713 1009 | 768 20 16.541 5.713 1010 | 780 20 17.023 5.713 1011 | 792 20 17.551 5.713 1012 | 804 20 17.918 5.208 1013 | 816 20 18.331 4.772 1014 | 828 20 18.56 4.107 1015 | 840 20 18.652 3.418 1016 | 852 20 18.789 2.776 1017 | 864 20 18.858 2.088 1018 | 876 20 18.927 1.399 1019 | 888 20 18.927 0.757 1020 | 900 20 18.927 0.069 1021 | 696 10 -32.532 17.229 1022 | 708 10 -31.499 17.39 1023 | 720 10 -30.26 17.734 1024 | 732 10 -29.067 18.239 1025 | 744 10 -27.92 18.491 1026 | 756 10 -26.957 18.491 1027 | 768 10 -26.062 18.491 1028 | 780 10 -25.121 18.491 1029 | 792 10 -24.387 18.308 1030 | 804 10 -23.768 18.308 1031 | 816 10 -23.194 18.308 1032 | 828 10 -22.552 18.147 1033 | 840 10 -21.978 18.147 1034 | 852 10 -21.359 18.147 1035 | 864 10 -20.717 18.147 1036 | 876 10 -20.12 18.147 1037 | 888 10 -19.638 18.147 1038 | 900 10 -19.065 18.308 1039 | 912 10 -18.491 18.308 1040 | 924 10 -18.009 18.491 1041 | 696 47 -6.516 26.314 1042 | 708 47 -6.011 26.131 1043 | 720 47 -5.529 26.131 1044 | 732 47 -5.598 25.81 1045 | 744 47 -5.69 25.466 1046 | 756 47 -5.781 25.121 1047 | 768 47 -5.85 24.8 1048 | 780 47 -5.896 24.617 1049 | 792 47 -6.286 24.456 1050 | 804 47 -6.63 24.296 1051 | 816 47 -6.974 24.112 1052 | 828 47 -7.296 23.951 1053 | 840 47 -7.617 23.791 1054 | 852 47 -7.938 23.607 1055 | 864 47 -8.351 23.607 1056 | 876 47 -8.856 23.607 1057 | 888 47 -9.314 23.607 1058 | 900 47 -9.819 23.791 1059 | 912 47 -10.324 23.791 1060 | 924 47 -10.806 23.791 1061 | 696 71 25.167 16.381 1062 | 708 71 24.479 16.541 1063 | 720 71 23.768 16.725 1064 | 732 71 23.057 16.885 1065 | 744 71 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-------------------------------------------------------------------------------- /scripts/convert_original.py: -------------------------------------------------------------------------------- 1 | """Trying to reproduce original Trajnet dataset.""" 2 | 3 | import pysparkling 4 | import trajnetdataset 5 | import trajnetplusplustools 6 | 7 | 8 | def main(): 9 | sc = pysparkling.Context() 10 | 11 | biwi_train = (sc 12 | .textFile('data/raw/biwi/seq_hotel/obsmat.txt') 13 | .map(trajnetdataset.readers.biwi) 14 | .cache()) 15 | 16 | good_start_frames = set(biwi_train 17 | .groupBy(lambda r: r.pedestrian) 18 | .filter(lambda kv: len(kv[1]) >= 20) 19 | .values() 20 | .map(lambda rs: rs[0].frame) 21 | .collect()) 22 | 23 | # good_start_frames_filtered = [] 24 | # for f in sorted(good_start_frames): 25 | # if good_start_frames_filtered and \ 26 | # f <= good_start_frames_filtered[-1] + 20: 27 | # continue 28 | # good_start_frames_filtered.append(f) 29 | # print(len(good_start_frames), len(good_start_frames_filtered)) 30 | # print(good_start_frames_filtered) 31 | good_start_frames_filtered = good_start_frames 32 | 33 | good_frames = {f 34 | for s in good_start_frames_filtered 35 | for f in range(s, s + 200, 10)} 36 | print(sorted(good_frames)) 37 | 38 | (biwi_train 39 | .filter(lambda r: r.frame in good_frames) 40 | 41 | # filter out short pedestrian paths 42 | .groupBy(lambda r: r.pedestrian) 43 | .filter(lambda kv: len(kv[1]) >= 20) 44 | .mapValues(lambda rs: rs[:20]) 45 | .values() 46 | .flatMap(lambda v: v) 47 | 48 | # write output 49 | .sortBy(lambda r: (r.pedestrian, r.frame)) 50 | .map(trajnetplusplustools.writers.trajnet_tracks) 51 | .saveAsTextFile('data/train/biwi/biwi_hotel.ndjson')) 52 | 53 | 54 | if __name__ == '__main__': 55 | main() 56 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | """setup trajnetplusplusdataset""" 2 | 3 | from setuptools import setup 4 | 5 | # extract version from __init__.py 6 | with open('trajnetdataset/__init__.py', 'r') as f: 7 | VERSION_LINE = [l for l in f if l.startswith('__version__')][0] 8 | VERSION = VERSION_LINE.split('=')[1].strip()[1:-1] 9 | 10 | 11 | setup( 12 | name='trajnetdataset', 13 | version=VERSION, 14 | packages=[ 15 | 'trajnetdataset', 16 | ], 17 | license='MIT', 18 | description='Trajnet++ dataset.', 19 | long_description=open('README.rst').read(), 20 | author='Sven Kreiss', 21 | author_email='me@svenkreiss.com', 22 | url='https://github.com/vita-epfl/trajnetplusplusdataset', 23 | 24 | install_requires=[ 25 | 'pysparkling', 26 | 'scipy', 27 | 'trajnetplusplustools', 28 | ], 29 | extras_require={ 30 | 'test': [ 31 | 'pylint', 32 | 'pytest', 33 | ], 34 | 'plot': [ 35 | 'matplotlib', 36 | ] 37 | }, 38 | ) 39 | -------------------------------------------------------------------------------- /setup_orca.sh: -------------------------------------------------------------------------------- 1 | ## Additional Requirements (ORCA) 2 | wget https://github.com/sybrenstuvel/Python-RVO2/archive/master.zip 3 | unzip master.zip 4 | rm master.zip 5 | 6 | ## Setting up ORCA (steps provided in the Python-RVO2 repo) 7 | cd Python-RVO2-main/ 8 | pip install cmake 9 | pip install cython 10 | python setup.py build 11 | python setup.py install 12 | cd ../ 13 | rm -rf Python-RVO2-main/ 14 | -------------------------------------------------------------------------------- /setup_social_force.sh: -------------------------------------------------------------------------------- 1 | ## Additional Requirements (Social Force) 2 | wget https://github.com/svenkreiss/socialforce/archive/refs/heads/main.zip 3 | unzip main.zip 4 | rm main.zip 5 | 6 | ## Setting up Social Force 7 | cd socialforce-main/ 8 | pip install -e . 9 | cd ../ 10 | -------------------------------------------------------------------------------- /trajnetdataset/__init__.py: -------------------------------------------------------------------------------- 1 | __version__ = '0.1.0' 2 | 3 | from . import readers 4 | -------------------------------------------------------------------------------- /trajnetdataset/controlled_data.py: -------------------------------------------------------------------------------- 1 | """ Generating Controlled data for pretraining collision avoidance """ 2 | 3 | import random 4 | import argparse 5 | import os 6 | import itertools 7 | 8 | import numpy as np 9 | from numpy.linalg import norm 10 | import matplotlib.pyplot as plt 11 | 12 | import rvo2 13 | import pickle 14 | import socialforce 15 | from socialforce.potentials import PedPedPotential 16 | from socialforce.field_of_view import FieldOfView 17 | 18 | def generate_circle_crossing(num_ped, sim=None, radius=4, mode=None): 19 | positions = [] 20 | goals = [] 21 | speed = [] 22 | agent_list = [] 23 | if mode == 'trajnet': 24 | radius = 10 ## 10 (TrajNet++) 25 | for _ in range(num_ped): 26 | while True: 27 | angle = random.uniform(0, 1) * np.pi * 2 28 | # add some noise to simulate all the possible cases robot could meet with human 29 | px_noise = (random.uniform(0, 1) - 0.5) ## human.v_pref 30 | py_noise = (random.uniform(0, 1) - 0.5) ## human.v_pref 31 | px = radius * np.cos(angle) + px_noise 32 | py = radius * np.sin(angle) + py_noise 33 | collide = False 34 | for agent in agent_list: 35 | min_dist = 0.8 36 | if mode == 'trajnet': 37 | min_dist = 2 ## min_dist ~ 2*human.radius + discomfort_dist ## 2 (TrajNet++) 38 | if norm((px - agent[0], py - agent[1])) < min_dist or \ 39 | norm((px - agent[2], py - agent[3])) < min_dist: 40 | collide = True 41 | break 42 | if not collide: 43 | break 44 | 45 | positions.append((px, py)) 46 | goals.append((-px, -py)) 47 | if sim is not None: 48 | sim.addAgent((px, py)) 49 | velocity = np.array([-2 * px, -2 * py]) 50 | magnitude = np.linalg.norm(velocity) 51 | init_vel = 1 * velocity / magnitude if magnitude > 1 else velocity 52 | speed.append([init_vel[0], init_vel[1]]) 53 | agent_list.append([px, py, -px, -py]) 54 | trajectories = [[positions[i]] for i in range(num_ped)] 55 | return trajectories, positions, goals, speed 56 | 57 | def generate_orca_trajectory(sim_scene, num_ped, min_dist=3, react_time=1.5, end_range=1.0, mode=None): 58 | """ Simulating Scenario using ORCA """ 59 | ## Default: (1 / 60., 1.5, 5, 1.5, 2, 0.4, 2) 60 | sampling_rate = 1 61 | 62 | ## Circle Crossing 63 | if sim_scene == 'circle_crossing': 64 | fps = 100 65 | sampling_rate = fps / 2.5 66 | sim = rvo2.PyRVOSimulator(1/fps, 10, 10, 5, 5, 0.3, 1) 67 | if mode == 'trajnet': 68 | sim = rvo2.PyRVOSimulator(1/fps, 4, 10, 4, 5, 0.6, 1.5) ## (TrajNet++) 69 | trajectories, _, goals, speed = generate_circle_crossing(num_ped, sim, mode=mode) 70 | else: 71 | raise NotImplementedError 72 | 73 | # run 74 | done = False 75 | reaching_goal_by_ped = [False] * num_ped 76 | count = 0 77 | valid = True 78 | ##Simulate a scene 79 | while not done and count < 6000: 80 | count += 1 81 | sim.doStep() 82 | reaching_goal = [] 83 | for i in range(num_ped): 84 | if count == 1: 85 | trajectories[i].pop(0) 86 | position = sim.getAgentPosition(i) 87 | 88 | ## Append only if Goal not reached 89 | if not reaching_goal_by_ped[i]: 90 | if count % sampling_rate == 0: 91 | trajectories[i].append(position) 92 | 93 | # check if this agent reaches the goal 94 | if np.linalg.norm(np.array(position) - np.array(goals[i])) < end_range: 95 | reaching_goal.append(True) 96 | sim.setAgentPrefVelocity(i, (0, 0)) 97 | reaching_goal_by_ped[i] = True 98 | else: 99 | reaching_goal.append(False) 100 | velocity = np.array((goals[i][0] - position[0], goals[i][1] - position[1])) 101 | speed = np.linalg.norm(velocity) 102 | pref_vel = 1 * velocity / speed if speed > 1 else velocity 103 | sim.setAgentPrefVelocity(i, tuple(pref_vel.tolist())) 104 | done = all(reaching_goal) 105 | 106 | if not done or not are_smoothes(trajectories): 107 | valid = False 108 | 109 | return trajectories, valid, goals 110 | 111 | def generate_sf_trajectory(sim_scene, num_ped, sf_params=[0.5, 2.1, 0.3], end_range=0.2): 112 | """ Simulating Scenario using SF """ 113 | ## Default: (0.5, 2.1, 0.3) 114 | sampling_rate = 1 115 | 116 | ## Circle Crossing 117 | if sim_scene == 'circle_crossing': 118 | fps = 10 119 | sampling_rate = fps / 2.5 120 | trajectories, positions, goals, speed = generate_circle_crossing(num_ped) 121 | else: 122 | raise NotImplementedError 123 | 124 | initial_state = np.array([[positions[i][0], positions[i][1], speed[i][0], speed[i][1], 125 | goals[i][0], goals[i][1]] for i in range(num_ped)]) 126 | 127 | ped_ped = PedPedPotential(1./fps, v0=sf_params[1], sigma=sf_params[2]) 128 | field_of_view = FieldOfView() 129 | s = socialforce.Simulator(initial_state, ped_ped=ped_ped, field_of_view=field_of_view, 130 | delta_t=1./fps, tau=sf_params[0]) 131 | 132 | # run 133 | reaching_goal = [False] * num_ped 134 | done = False 135 | count = 0 136 | 137 | #Simulate a scene 138 | while not done and count < 500: 139 | count += 1 140 | position = np.stack(s.step().state.copy()) 141 | for i in range(len(initial_state)): 142 | if count % sampling_rate == 0: 143 | trajectories[i].append((position[i, 0], position[i, 1])) 144 | # check if this agent reaches the goal 145 | if np.linalg.norm(position[i, :2] - np.array(goals[i])) < end_range: 146 | reaching_goal[i] = True 147 | else: 148 | s.state[i, :4] = position[i, :4] 149 | done = all(reaching_goal) 150 | 151 | return trajectories, count 152 | 153 | 154 | def getAngle(a, b, c): 155 | """ 156 | Return angle formed by 3 points 157 | """ 158 | ba = a - b 159 | bc = c - b 160 | cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc)) 161 | angle = np.arccos(cosine_angle) 162 | return angle 163 | 164 | def are_smoothes(trajectories): 165 | """ 166 | Check if there is no sharp turns in the trajectories 167 | """ 168 | is_smooth = True 169 | for i, _ in enumerate(trajectories): 170 | trajectory = np.array(trajectories[i]) 171 | for j in range(0, len(trajectory[:, 0]) - 3): 172 | p1 = np.array([trajectory[j, 0], trajectory[j, 1]]) 173 | p2 = np.array([trajectory[j+1, 0], trajectory[j+1, 1]]) 174 | p3 = np.array([trajectory[j+2, 0], trajectory[j+2, 1]]) 175 | 176 | angle = getAngle(p1, p2, p3) 177 | if angle <= np.pi / 2: 178 | is_smooth = False 179 | # plt.scatter(p1[0], p1[1], color='red', marker='X') 180 | return is_smooth 181 | 182 | def find_collisions(trajectories, max_steps): 183 | """ 184 | Look for collisions in the trajectories 185 | """ 186 | for timestep in range(max_steps): 187 | positions = [] 188 | for ped, _ in enumerate(trajectories): 189 | traj = np.array(trajectories[ped]) 190 | if timestep < len(traj): 191 | positions.append(traj[timestep]) 192 | 193 | # Check if distance between 2 points is smaller than 0.1m 194 | # If yes -> collision detected 195 | for combi in itertools.combinations(positions, 2): 196 | distance = (np.linalg.norm(combi[0]-combi[1])) 197 | if distance < 0.2: 198 | return True 199 | 200 | return False 201 | 202 | def write_to_txt(trajectories, path, count, frame, dict_dest=None, goals=None): 203 | """ Write Trajectories to the text file """ 204 | 205 | last_frame = 0 206 | with open(path, 'a') as fo: 207 | track_data = [] 208 | for i, _ in enumerate(trajectories): 209 | for t, _ in enumerate(trajectories[i]): 210 | 211 | track_data.append('{}, {}, {}, {}'.format(t+frame, count+i, 212 | trajectories[i][t][0], 213 | trajectories[i][t][1])) 214 | 215 | if t == len(trajectories[i])-1 and t+frame > last_frame: 216 | last_frame = t+frame 217 | if goals: 218 | dict_dest[count+i] = goals[i] 219 | 220 | for track in track_data: 221 | fo.write(track) 222 | fo.write('\n') 223 | 224 | return last_frame 225 | 226 | def viz(trajectories, mode=None): 227 | """ Visualize Trajectories """ 228 | for i, _ in enumerate(trajectories): 229 | trajectory = np.array(trajectories[i]) 230 | plt.plot(trajectory[:, 0], trajectory[:, 1]) 231 | 232 | plt.xlim(-5, 5) 233 | plt.ylim(-5, 5) 234 | if mode == 'trajnet': 235 | plt.xlim(-15, 15) ## TrajNet++ 236 | plt.ylim(-15, 15) ## TrajNet++ 237 | plt.show() 238 | plt.close() 239 | 240 | def predict_all(input_paths, goals, n_predict=12): 241 | 242 | pred_length = n_predict 243 | 244 | fps = 100 245 | sampling_rate = fps / 2.5 246 | 247 | sim = rvo2.PyRVOSimulator(1/fps, 4, 10, 4, 5, 0.6, 1.5) ## (TrajNet++) 248 | trajectories = [[input_paths[i][-1]] for i, _ in enumerate(input_paths)] 249 | [sim.addAgent((p[-1][0],p[-1][1])) for p in input_paths] 250 | 251 | num_ped = len(trajectories) 252 | reaching_goal_by_ped = [False] * num_ped 253 | count = 0 254 | end_range = 1.0 255 | done = False 256 | 257 | for i in range(num_ped): 258 | velocity = np.array((input_paths[i][-1][0] - input_paths[i][-3][0], input_paths[i][-1][1] - input_paths[i][-3][1])) 259 | velocity = velocity/0.8 260 | sim.setAgentVelocity(i, tuple(velocity.tolist())) 261 | 262 | velocity = np.array((goals[i][0] - input_paths[i][-1][0], goals[i][1] - input_paths[i][-1][1])) 263 | speed = np.linalg.norm(velocity) 264 | pref_vel = 1 * velocity / speed if speed > 1 else velocity 265 | sim.setAgentPrefVelocity(i, tuple(pref_vel.tolist())) 266 | 267 | ##Simulate a scene 268 | while (not done) and count < sampling_rate * pred_length + 1: 269 | # print("Count: ", count) 270 | count += 1 271 | sim.doStep() 272 | reaching_goal = [] 273 | for i in range(num_ped): 274 | if count == 1: 275 | trajectories[i].pop(0) 276 | position = sim.getAgentPosition(i) 277 | 278 | ## Append only if Goal not reached 279 | if not reaching_goal_by_ped[i]: 280 | if count % sampling_rate == 0: 281 | trajectories[i].append(position) 282 | 283 | # check if this agent reaches the goal 284 | if np.linalg.norm(np.array(position) - np.array(goals[i])) < end_range: 285 | reaching_goal.append(True) 286 | sim.setAgentPrefVelocity(i, (0, 0)) 287 | reaching_goal_by_ped[i] = True 288 | else: 289 | reaching_goal.append(False) 290 | velocity = np.array((goals[i][0] - position[0], goals[i][1] - position[1])) 291 | speed = np.linalg.norm(velocity) 292 | pref_vel = 1 * velocity / speed if speed > 1 else velocity 293 | sim.setAgentPrefVelocity(i, tuple(pref_vel.tolist())) 294 | 295 | done = all(reaching_goal) 296 | 297 | return trajectories 298 | 299 | def evaluate_sensitivity(trajectories, goals, mode=None, ade_thresh=0.11, fde_thresh=0.2, iters=20): 300 | observation = np.array([trajectory[10:15] for trajectory in trajectories]) 301 | observation = np.round(observation, 2) 302 | goals = np.array(goals) 303 | 304 | trajectories_re_list = [] 305 | for k in range(iters): 306 | observation_re = add_noise(observation.copy()) 307 | trajectories_re = predict_all(observation_re, goals) 308 | for m, _ in enumerate(trajectories_re): 309 | diff_ade = np.mean(np.linalg.norm(np.array(trajectories[m][15:27]) - np.array(trajectories_re[m]), axis=1)) 310 | diff_fde = np.linalg.norm(np.array(trajectories[m][26]) - np.array(trajectories_re[m][-1])) 311 | if diff_ade > ade_thresh or diff_fde > fde_thresh: 312 | print("INVALID", diff_ade, diff_fde) 313 | trajectories_re_list.append(np.array(trajectories_re)) 314 | 315 | visualize_sensitivity(trajectories, trajectories_re_list, mode=mode) 316 | 317 | def visualize_sensitivity(trajectories, trajectories_pred_scenes, mode=None): 318 | """ Visualize Trajectories """ 319 | plt.grid(linestyle='dotted') 320 | for i, _ in enumerate(trajectories): 321 | trajectory = np.array(trajectories[i]) 322 | if i == 0: 323 | plt.plot(trajectory[:, 0], trajectory[:, 1], linestyle='solid', 324 | color='black', marker='o', markersize=1.0, zorder=1.9) 325 | else: 326 | plt.plot(trajectory[:, 0], trajectory[:, 1], linestyle='None', 327 | color='black', marker='o', markersize=1.0, zorder=0.9) 328 | 329 | for i, _ in enumerate(trajectories_pred_scenes): 330 | trajectory_set = np.array(trajectories_pred_scenes[i]) 331 | for j, _ in enumerate(trajectory_set): 332 | trajectory = trajectory_set[j] 333 | plt.plot(trajectory[:, 0], trajectory[:, 1], linestyle='solid', 334 | color='blue', alpha=0.4, linewidth=2) 335 | 336 | plt.xlim(-5, 5) 337 | plt.ylim(-5, 5) 338 | if mode == 'trajnet': 339 | plt.xlim(-7, 7) ## TrajNet++ 340 | plt.ylim(-7, 7) ## TrajNet++ 341 | plt.show() 342 | plt.close() 343 | 344 | def add_noise(observation): 345 | ## Last Position Noise 346 | # observation[0][-1] += np.random.uniform(0, 0.04, (2,)) 347 | 348 | ## Last Position Noise 349 | thresh = 0.005 350 | observation += np.random.uniform(-thresh, thresh, observation.shape) 351 | return observation 352 | 353 | def write_goals(filename, dict_dest): 354 | # Make goal folders and write save goals (.pkl files) 355 | if not os.path.isdir('./goal_files'): 356 | os.makedirs('./goal_files') 357 | 358 | if not os.path.isdir('./goal_files/train'): 359 | os.makedirs('./goal_files/train') 360 | with open('goal_files/train/' + filename + '.pkl', 'wb') as f: 361 | pickle.dump(dict_dest, f) 362 | 363 | if not os.path.isdir('./goal_files/val'): 364 | os.makedirs('./goal_files/val') 365 | with open('goal_files/val/' + filename + '.pkl', 'wb') as f: 366 | pickle.dump(dict_dest, f) 367 | 368 | if not os.path.isdir('./goal_files/test_private'): 369 | os.makedirs('./goal_files/test_private') 370 | with open('goal_files/test_private/' + filename + '.pkl', 'wb') as f: 371 | pickle.dump(dict_dest, f) 372 | 373 | def main(): 374 | parser = argparse.ArgumentParser() 375 | parser.add_argument('--simulator', default='orca', 376 | choices=('orca', 'social_force')) 377 | parser.add_argument('--simulation_scene', default='circle_crossing', 378 | choices=('circle_crossing')) 379 | parser.add_argument('--mode', default=None, 380 | help='Set to trajnet for trajnet-based dataset generation') 381 | parser.add_argument('--num_ped', type=int, default=6, 382 | help='Number of ped in scene, if mode=trajnet then num_ped is randomly chosen from (4, 5, 6)') 383 | parser.add_argument('--num_scenes', type=int, default=100, 384 | help='Number of scenes') 385 | parser.add_argument('--seed', type=int, default=42) 386 | args = parser.parse_args() 387 | 388 | np.seterr('ignore') 389 | # Set Seed 390 | random.seed(args.seed) 391 | np.random.seed(args.seed) 392 | 393 | ##Decide the number of scenes & agents per scene 394 | num_scenes = args.num_scenes 395 | num_ped = args.num_ped 396 | mode = args.mode 397 | min_dist, react_time = 1.5, 1.5 398 | 399 | if not os.path.isdir('./data'): 400 | os.makedirs('./data') 401 | if not os.path.isdir('./data/raw'): 402 | os.makedirs('./data/raw') 403 | if not os.path.isdir('./data/raw/controlled'): 404 | os.makedirs('./data/raw/controlled') 405 | 406 | ## Text File To Write the Scene 407 | output_file = 'data/raw/controlled/' 408 | output_file = output_file \ 409 | + args.simulator + '_' \ 410 | + args.simulation_scene + '_' \ 411 | + str(num_ped) + 'ped_' \ 412 | + str(num_scenes) + 'scenes_' \ 413 | + '.txt' 414 | 415 | goal_filename = args.simulator + '_' \ 416 | + args.simulation_scene + '_' \ 417 | + str(num_ped) + 'ped_' \ 418 | + str(num_scenes) + 'scenes_' 419 | 420 | ## removes the file, if previously generated 421 | if os.path.isfile(output_file): 422 | os.remove(output_file) 423 | 424 | count = 0 425 | last_frame = -5 426 | 427 | dict_dest = {} 428 | 429 | for i in range(num_scenes): 430 | if mode == 'trajnet': 431 | num_ped = random.choice([4, 5, 6]) ## TrajNet++ 432 | ## Print every 10th scene 433 | if (i+1) % 10 == 0: 434 | print(i) 435 | 436 | ##Generate scenes 437 | if args.simulator == 'orca': 438 | trajectories, valid, goals = generate_orca_trajectory(sim_scene=args.simulation_scene, 439 | num_ped=num_ped, 440 | min_dist=min_dist, 441 | react_time=react_time, 442 | mode=mode) 443 | ## To evaluate sensitivity of ORCA 444 | # evaluate_sensitivity(trajectories, goals, mode) 445 | 446 | elif args.simulator == 'social_force': 447 | trajectories, valid = generate_sf_trajectory(sim_scene=args.simulation_scene, 448 | num_ped=num_ped, 449 | sf_params=[0.5, 1.0, 0.1]) 450 | else: 451 | raise NotImplementedError 452 | 453 | ## Visualizing scenes 454 | # viz(trajectories, mode=mode) 455 | 456 | ## Write if the scene is valid 457 | if valid: 458 | last_frame = write_to_txt(trajectories, output_file, 459 | count=count, frame=last_frame+5, 460 | dict_dest=dict_dest, 461 | goals=goals) 462 | count += num_ped 463 | 464 | ## Write Goal Dict of ORCA 465 | write_goals(goal_filename, dict_dest) 466 | 467 | print(f'ORCA trajectories stored at: {output_file}') 468 | print(f'Goal information stored at: goal_files/train/{goal_filename}.pkl \n \n') 469 | 470 | print(f'You can convert this trajectories into TrajNet++ format using the following command \n') 471 | print(f'python -m trajnetdataset.convert --direct --synthetic --mode trajnet --linear_threshold 0.3 --acceptance 0.0 0.0 1.0 0.0 \ 472 | --orca_file {output_file} --goal_file goal_files/train/{goal_filename}.pkl --output_filename orca_synthetic') 473 | 474 | if __name__ == '__main__': 475 | main() 476 | -------------------------------------------------------------------------------- /trajnetdataset/convert.py: -------------------------------------------------------------------------------- 1 | """Create Trajnet data from original datasets.""" 2 | import argparse 3 | import shutil 4 | import numpy as np 5 | import random 6 | 7 | import pysparkling 8 | import scipy.io 9 | 10 | from . import readers 11 | from .scene import Scenes 12 | from .get_type import trajectory_type 13 | 14 | import warnings 15 | warnings.filterwarnings("ignore") 16 | 17 | def biwi(sc, input_file): 18 | print('processing ' + input_file) 19 | return (sc 20 | .textFile(input_file) 21 | .map(readers.biwi) 22 | .cache()) 23 | 24 | 25 | def crowds(sc, input_file): 26 | print('processing ' + input_file) 27 | return (sc 28 | .wholeTextFiles(input_file) 29 | .values() 30 | .flatMap(readers.crowds) 31 | .cache()) 32 | 33 | 34 | def mot(sc, input_file): 35 | """Was 7 frames per second in original recording.""" 36 | print('processing ' + input_file) 37 | return (sc 38 | .textFile(input_file) 39 | .map(readers.mot) 40 | .filter(lambda r: r.frame % 2 == 0) 41 | .cache()) 42 | 43 | 44 | def edinburgh(sc, input_file): 45 | print('processing ' + input_file) 46 | return (sc 47 | .wholeTextFiles(input_file) 48 | .zipWithIndex() 49 | .flatMap(readers.edinburgh) 50 | .cache()) 51 | 52 | 53 | def syi(sc, input_file): 54 | print('processing ' + input_file) 55 | return (sc 56 | .wholeTextFiles(input_file) 57 | .flatMap(readers.syi) 58 | .cache()) 59 | 60 | 61 | def dukemtmc(sc, input_file): 62 | print('processing ' + input_file) 63 | contents = scipy.io.loadmat(input_file)['trainData'] 64 | return (sc 65 | .parallelize(readers.dukemtmc(contents)) 66 | .cache()) 67 | 68 | 69 | def wildtrack(sc, input_file): 70 | print('processing ' + input_file) 71 | return (sc 72 | .wholeTextFiles(input_file) 73 | .flatMap(readers.wildtrack) 74 | .cache()) 75 | 76 | def cff(sc, input_file): 77 | print('processing ' + input_file) 78 | return (sc 79 | .textFile(input_file) 80 | .map(readers.cff) 81 | .filter(lambda r: r is not None) 82 | .cache()) 83 | 84 | def lcas(sc, input_file): 85 | print('processing ' + input_file) 86 | return (sc 87 | .textFile(input_file) 88 | .map(readers.lcas) 89 | .cache()) 90 | 91 | def controlled(sc, input_file): 92 | print('processing ' + input_file) 93 | return (sc 94 | .textFile(input_file) 95 | .map(readers.controlled) 96 | .cache()) 97 | 98 | def get_trackrows(sc, input_file): 99 | print('processing ' + input_file) 100 | return (sc 101 | .textFile(input_file) 102 | .map(readers.get_trackrows) 103 | .filter(lambda r: r is not None) 104 | .cache()) 105 | 106 | def standard(sc, input_file): 107 | print('processing ' + input_file) 108 | return (sc 109 | .textFile(input_file) 110 | .map(readers.standard) 111 | .cache()) 112 | 113 | def car_data(sc, input_file): 114 | print('processing ' + input_file) 115 | return (sc 116 | .wholeTextFiles(input_file) 117 | .flatMap(readers.car_data) 118 | .cache()) 119 | 120 | def write(input_rows, output_file, args): 121 | """ Write Valid Scenes without categorization """ 122 | 123 | print(" Entering Writing ") 124 | ## To handle two different time stamps 7:00 and 17:00 of cff 125 | if args.order_frames: 126 | frames = sorted(set(input_rows.map(lambda r: r.frame).toLocalIterator()), 127 | key=lambda frame: frame % 100000) 128 | else: 129 | frames = sorted(set(input_rows.map(lambda r: r.frame).toLocalIterator())) 130 | 131 | # split 132 | train_split_index = int(len(frames) * args.train_fraction) 133 | val_split_index = train_split_index + int(len(frames) * args.val_fraction) 134 | train_frames = set(frames[:train_split_index]) 135 | val_frames = set(frames[train_split_index:val_split_index]) 136 | test_frames = set(frames[val_split_index:]) 137 | 138 | # train dataset 139 | train_rows = input_rows.filter(lambda r: r.frame in train_frames) 140 | train_output = output_file.format(split='train') 141 | train_scenes = Scenes(fps=args.fps, start_scene_id=0, args=args).rows_to_file(train_rows, train_output) 142 | 143 | # validation dataset 144 | val_rows = input_rows.filter(lambda r: r.frame in val_frames) 145 | val_output = output_file.format(split='val') 146 | val_scenes = Scenes(fps=args.fps, start_scene_id=train_scenes.scene_id, args=args).rows_to_file(val_rows, val_output) 147 | 148 | # public test dataset 149 | test_rows = input_rows.filter(lambda r: r.frame in test_frames) 150 | test_output = output_file.format(split='test') 151 | test_scenes = Scenes(fps=args.fps, start_scene_id=val_scenes.scene_id, args=args) # !!! Chunk Stride 152 | test_scenes.rows_to_file(test_rows, test_output) 153 | 154 | # private test dataset 155 | private_test_output = output_file.format(split='test_private') 156 | private_test_scenes = Scenes(fps=args.fps, start_scene_id=val_scenes.scene_id, args=args) 157 | private_test_scenes.rows_to_file(test_rows, private_test_output) 158 | 159 | def categorize(sc, input_file, args): 160 | """ Categorize the Scenes """ 161 | 162 | print(" Entering Categorizing ") 163 | test_fraction = 1 - args.train_fraction - args.val_fraction 164 | 165 | train_id = 0 166 | if args.train_fraction: 167 | print("Categorizing Training Set") 168 | train_rows = get_trackrows(sc, input_file.replace('split', '').format('train')) 169 | train_id = trajectory_type(train_rows, input_file.replace('split', '').format('train'), 170 | fps=args.fps, track_id=0, args=args) 171 | 172 | val_id = train_id 173 | if args.val_fraction: 174 | print("Categorizing Validation Set") 175 | val_rows = get_trackrows(sc, input_file.replace('split', '').format('val')) 176 | val_id = trajectory_type(val_rows, input_file.replace('split', '').format('val'), 177 | fps=args.fps, track_id=train_id, args=args) 178 | 179 | 180 | if test_fraction: 181 | print("Categorizing Test Set") 182 | test_rows = get_trackrows(sc, input_file.replace('split', '').format('test_private')) 183 | _ = trajectory_type(test_rows, input_file.replace('split', '').format('test_private'), 184 | fps=args.fps, track_id=val_id, args=args) 185 | 186 | def edit_goal_file(old_filename, new_filename): 187 | """ Rename goal files. 188 | The name of goal files should be identical to the data files 189 | """ 190 | 191 | shutil.copy("goal_files/train/" + old_filename, "goal_files/train/" + new_filename) 192 | shutil.copy("goal_files/val/" + old_filename, "goal_files/val/" + new_filename) 193 | shutil.copy("goal_files/test_private/" + old_filename, "goal_files/test_private/" + new_filename) 194 | 195 | def main(): 196 | parser = argparse.ArgumentParser() 197 | parser.add_argument('--obs_len', type=int, default=9, 198 | help='Length of observation') 199 | parser.add_argument('--pred_len', type=int, default=12, 200 | help='Length of prediction') 201 | parser.add_argument('--train_fraction', default=0.6, type=float, 202 | help='Training set fraction') 203 | parser.add_argument('--val_fraction', default=0.2, type=float, 204 | help='Validation set fraction') 205 | parser.add_argument('--fps', default=2.5, type=float, 206 | help='fps') 207 | parser.add_argument('--order_frames', action='store_true', 208 | help='For CFF') 209 | parser.add_argument('--chunk_stride', type=int, default=2, 210 | help='Sampling Stride') 211 | parser.add_argument('--min_length', default=0.0, type=float, 212 | help='Min Length of Primary Trajectory') 213 | parser.add_argument('--synthetic', action='store_true', 214 | help='convert synthetic datasets (if false, convert real)') 215 | parser.add_argument('--direct', action='store_true', 216 | help='directy convert synthetic datasets using commandline') 217 | parser.add_argument('--all_present', action='store_true', 218 | help='filter scenes where all pedestrians present at all times') 219 | parser.add_argument('--orca_file', default=None, 220 | help='Txt file for ORCA trajectories, required in direct mode') 221 | parser.add_argument('--goal_file', default=None, 222 | help='Pkl file for goals (required for ORCA sensitive scene filtering)') 223 | parser.add_argument('--output_filename', default=None, 224 | help='name of the output dataset filename constructed in .ndjson format, required in direct mode') 225 | parser.add_argument('--mode', default='default', choices=('default', 'trajnet'), 226 | help='mode of ORCA scene generation (required for ORCA sensitive scene filtering)') 227 | 228 | ## For Trajectory categorizing and filtering 229 | categorizers = parser.add_argument_group('categorizers') 230 | categorizers.add_argument('--static_threshold', type=float, default=1.0, 231 | help='Type I static threshold') 232 | categorizers.add_argument('--linear_threshold', type=float, default=0.5, 233 | help='Type II linear threshold (0.3 for Synthetic)') 234 | categorizers.add_argument('--inter_dist_thresh', type=float, default=5, 235 | help='Type IIId distance threshold for cone') 236 | categorizers.add_argument('--inter_pos_range', type=float, default=15, 237 | help='Type IIId angle threshold for cone (degrees)') 238 | categorizers.add_argument('--grp_dist_thresh', type=float, default=0.8, 239 | help='Type IIIc distance threshold for group') 240 | categorizers.add_argument('--grp_std_thresh', type=float, default=0.2, 241 | help='Type IIIc std deviation for group') 242 | categorizers.add_argument('--acceptance', nargs='+', type=float, default=[0.1, 1, 1, 1], 243 | help='acceptance ratio of different trajectory (I, II, III, IV) types') 244 | 245 | args = parser.parse_args() 246 | # Set Seed 247 | random.seed(42) 248 | np.random.seed(42) 249 | 250 | sc = pysparkling.Context() 251 | 252 | # Real datasets conversion 253 | if not args.synthetic: 254 | write(biwi(sc, 'data/raw/biwi/seq_hotel/obsmat.txt'), 255 | 'output_pre/{split}/biwi_hotel.ndjson', args) 256 | categorize(sc, 'output_pre/{split}/biwi_hotel.ndjson', args) 257 | write(crowds(sc, 'data/raw/crowds/crowds_zara01.vsp'), 258 | 'output_pre/{split}/crowds_zara01.ndjson', args) 259 | categorize(sc, 'output_pre/{split}/crowds_zara01.ndjson', args) 260 | write(crowds(sc, 'data/raw/crowds/crowds_zara03.vsp'), 261 | 'output_pre/{split}/crowds_zara03.ndjson', args) 262 | categorize(sc, 'output_pre/{split}/crowds_zara03.ndjson', args) 263 | write(crowds(sc, 'data/raw/crowds/students001.vsp'), 264 | 'output_pre/{split}/crowds_students001.ndjson', args) 265 | categorize(sc, 'output_pre/{split}/crowds_students001.ndjson', args) 266 | write(crowds(sc, 'data/raw/crowds/students003.vsp'), 267 | 'output_pre/{split}/crowds_students003.ndjson', args) 268 | categorize(sc, 'output_pre/{split}/crowds_students003.ndjson', args) 269 | 270 | # # # new datasets 271 | # write(lcas(sc, 'data/raw/lcas/test/data.csv'), 272 | # 'output_pre/{split}/lcas.ndjson', args) 273 | # categorize(sc, 'output_pre/{split}/lcas.ndjson', args) 274 | 275 | # args.fps = 2 276 | # write(wildtrack(sc, 'data/raw/wildtrack/Wildtrack_dataset/annotations_positions/*.json'), 277 | # 'output_pre/{split}/wildtrack.ndjson', args) 278 | # categorize(sc, 'output_pre/{split}/wildtrack.ndjson', args) 279 | # args.fps = 2.5 # (Default) 280 | 281 | # # CFF: More trajectories 282 | # # Chunk_stride > 20 preferred & order_frames. 283 | # args.chunk_stride = 20 284 | # args.order_frames = True 285 | # write(cff(sc, 'data/raw/cff_dataset/al_position2013-02-06.csv'), 286 | # 'output_pre/{split}/cff_06.ndjson', args) 287 | # categorize(sc, 'output_pre/{split}/cff_06.ndjson', args) 288 | # args.chunk_stride = 2 # (Default) 289 | # args.order_frames = False # (Default) 290 | 291 | # Direct synthetic datasets conversion 292 | elif args.direct: 293 | # Note: Generate Trajectories First! See command below 294 | ## 'python -m trajnetdataset.controlled_data ' 295 | print("Direct Synthetic Data Converion") 296 | assert args.orca_file is not None 297 | assert args.goal_file is not None 298 | assert args.output_filename is not None 299 | write(controlled(sc, args.orca_file), 'output_pre/{split}/' + f'{args.output_filename}.ndjson', args) 300 | categorize(sc, 'output_pre/{split}/' + f'{args.output_filename}.ndjson', args) 301 | edit_goal_file(args.goal_file.split('/')[-1], f'{args.output_filename}.pkl') 302 | 303 | # Manual synthetic datasets conversion 304 | else: 305 | # Note: Generate Trajectories First! See command below 306 | ## 'python -m trajnetdataset.controlled_data ' 307 | print("Manual Synthetic Data Converion") 308 | write(controlled(sc, 'data/raw/controlled/orca_circle_crossing_5ped_1000scenes_.txt'), 309 | 'output_pre/{split}/orca_five_synth.ndjson', args) 310 | categorize(sc, 'output_pre/{split}/orca_five_synth.ndjson', args) 311 | edit_goal_file('orca_circle_crossing_5ped_1000scenes_.pkl', 'orca_five_synth.pkl') 312 | 313 | if __name__ == '__main__': 314 | main() 315 | -------------------------------------------------------------------------------- /trajnetdataset/get_type.py: -------------------------------------------------------------------------------- 1 | """ Categorization of Primary Pedestrian """ 2 | 3 | import numpy as np 4 | import pysparkling 5 | 6 | import trajnetplusplustools 7 | from trajnetplusplustools.kalman import predict as kalman_predict 8 | from trajnetplusplustools.interactions import check_interaction, group 9 | from trajnetplusplustools.interactions import get_interaction_type 10 | 11 | import pickle 12 | from .orca_helper import predict_all 13 | 14 | def get_type(scene, args): 15 | ''' 16 | Categorization of Single Scene 17 | :param scene: All trajectories as TrackRows, args 18 | :return: The type of the traj 19 | ''' 20 | 21 | ## Get xy-coordinates from trackRows 22 | scene_xy = trajnetplusplustools.Reader.paths_to_xy(scene) 23 | 24 | ## Type 1 25 | def euclidean_distance(row1, row2): 26 | """Euclidean distance squared between two rows.""" 27 | return np.sqrt((row1.x - row2.x) ** 2 + (row1.y - row2.y) ** 2) 28 | 29 | ## Type 2 30 | def linear_system(scene, obs_len, pred_len): 31 | ''' 32 | return: True if the traj is linear according to Kalman 33 | ''' 34 | kalman_prediction, _ = kalman_predict(scene, obs_len, pred_len)[0] 35 | return trajnetplusplustools.metrics.final_l2(scene[0], kalman_prediction) 36 | 37 | ## Type 3 38 | def interaction(rows, pos_range, dist_thresh, obs_len): 39 | ''' 40 | :return: Determine if interaction exists and type (optionally) 41 | ''' 42 | interaction_matrix = check_interaction(rows, pos_range=pos_range, \ 43 | dist_thresh=dist_thresh, obs_len=obs_len) 44 | return np.any(interaction_matrix) 45 | 46 | ## Category Tags 47 | mult_tag = [] 48 | sub_tag = [] 49 | 50 | # Static 51 | if euclidean_distance(scene[0][0], scene[0][-1]) < args.static_threshold: 52 | mult_tag.append(1) 53 | 54 | # Linear 55 | elif linear_system(scene, args.obs_len, args.pred_len) < args.linear_threshold: 56 | mult_tag.append(2) 57 | 58 | # Interactions 59 | elif interaction(scene_xy, args.inter_pos_range, args.inter_dist_thresh, args.obs_len) \ 60 | or np.any(group(scene_xy, args.grp_dist_thresh, args.grp_std_thresh, args.obs_len)): 61 | mult_tag.append(3) 62 | 63 | # Non-Linear (No explainable reason) 64 | else: 65 | mult_tag.append(4) 66 | 67 | # Interaction Types 68 | if mult_tag[0] == 3: 69 | sub_tag = get_interaction_type(scene_xy, args.inter_pos_range, 70 | args.inter_dist_thresh, args.obs_len) 71 | else: 72 | sub_tag = [] 73 | 74 | return mult_tag[0], mult_tag, sub_tag 75 | 76 | def check_collision(scene, n_predictions): 77 | ''' 78 | Skip the track if collision occurs between primanry and others 79 | return: True if collision occurs 80 | ''' 81 | ped_interest = scene[0] 82 | for ped_other in scene[1:]: 83 | if trajnetplusplustools.metrics.collision(ped_interest, ped_other, n_predictions): 84 | return True 85 | return False 86 | 87 | def add_noise(observation): 88 | ## Last Position Noise 89 | # observation[0][-1] += np.random.uniform(0, 0.04, (2,)) 90 | 91 | ## Last Position Noise 92 | thresh = 0.005 ## 0.01 for num_ped 3 93 | observation += np.random.uniform(-thresh, thresh, observation.shape) 94 | return observation 95 | 96 | def orca_validity(scene, goals, pred_len=12, obs_len=9, mode='trajnet', iters=5): #iters 15 for original 97 | ''' 98 | Check ORCA can reconstruct scene on rounding (To clean in future) 99 | ''' 100 | scene_xy = trajnetplusplustools.Reader.paths_to_xy(scene) 101 | for _ in range(iters): 102 | observation = add_noise(scene_xy[:obs_len].copy()) 103 | orca_pred = predict_all(observation, goals, mode, pred_len) 104 | if len(orca_pred[0]) != pred_len: 105 | # print("Length Invalid") 106 | return True 107 | for m, _ in enumerate(orca_pred): 108 | if len(orca_pred[m]) != pred_len: 109 | continue 110 | diff_ade = np.mean(np.linalg.norm(np.array(scene_xy[-pred_len:, m]) - np.array(orca_pred[m]), axis=1)) 111 | diff_fde = np.linalg.norm(np.array(scene_xy[-1, m]) - np.array(orca_pred[m][-1])) 112 | if diff_ade > 0.11 or diff_fde > 0.2: ## (0.08, 0.1) for num_ped 3 113 | # print("ORCA Invalid") 114 | return True 115 | return False 116 | 117 | def all_ped_present(scene): 118 | """ 119 | Consider only those scenes where all pedestrians are present 120 | Note: Different from removing incomplete trajectories 121 | Useful for generating dataset for fast_parallel code: https://github.com/vita-epfl/trajnetplusplusbaselines/tree/fast_parallel 122 | """ 123 | scene_xy = trajnetplusplustools.Reader.paths_to_xy(scene) 124 | return (not np.isnan(scene_xy).any()) 125 | 126 | def write(rows, path, new_scenes, new_frames): 127 | """ Writing scenes with categories """ 128 | output_path = path.replace('output_pre', 'output') 129 | pysp_tracks = rows.filter(lambda r: r.frame in new_frames).map(trajnetplusplustools.writers.trajnet) 130 | pysp_scenes = pysparkling.Context().parallelize(new_scenes).map(trajnetplusplustools.writers.trajnet) 131 | pysp_scenes.union(pysp_tracks).saveAsTextFile(output_path) 132 | 133 | def trajectory_type(rows, path, fps, track_id=0, args=None): 134 | """ Categorization of all scenes """ 135 | 136 | ## Read 137 | reader = trajnetplusplustools.Reader(path, scene_type='paths') 138 | scenes = [s for _, s in reader.scenes()] 139 | ## Filtered Frames and Scenes 140 | new_frames = set() 141 | new_scenes = [] 142 | 143 | start_frames = set() 144 | ########################################################################### 145 | # scenes_test helps to handle both test and test_private simultaneously 146 | # scenes_test correspond to Test 147 | ########################################################################### 148 | test = 'test' in path 149 | if test: 150 | path_test = path.replace('test_private', 'test') 151 | reader_test = trajnetplusplustools.Reader(path_test, scene_type='paths') 152 | scenes_test = [s for _, s in reader_test.scenes()] 153 | ## Filtered Test Frames and Test Scenes 154 | new_frames_test = set() 155 | new_scenes_test = [] 156 | 157 | ## For ORCA (Sensitivity) 158 | orca_sensitivity = False 159 | if args.goal_file is not None: 160 | goal_dict = pickle.load(open(args.goal_file, "rb")) 161 | orca_sensitivity = True 162 | print("Checking sensitivity to initial conditions") 163 | 164 | ## Initialize Tag Stats to be collected 165 | tags = {1: [], 2: [], 3: [], 4: []} 166 | mult_tags = {1: [], 2: [], 3: [], 4: []} 167 | sub_tags = {1: [], 2: [], 3: [], 4: []} 168 | col_count = 0 169 | 170 | if not scenes: 171 | raise Exception('No scenes found') 172 | 173 | for index, scene in enumerate(scenes): 174 | if (index+1) % 50 == 0: 175 | print(index) 176 | 177 | ## Primary Path 178 | ped_interest = scene[0] 179 | 180 | # if ped_interest[0].frame in start_frames: 181 | # # print("Got common start") 182 | # continue 183 | 184 | # Assert Test Scene length 185 | if test: 186 | assert len(scenes_test[index][0]) >= args.obs_len, \ 187 | 'Scene Test not adequate length' 188 | 189 | ## Check Collision 190 | ## Used in CFF Datasets to account for imperfect tracking 191 | # if check_collision(scene, args.pred_len): 192 | # col_count += 1 193 | # continue 194 | 195 | # ## Consider only those scenes where all pedestrians are present 196 | # # Note: Different from removing incomplete trajectories 197 | if args.all_present and (not all_ped_present(scene)): 198 | continue 199 | 200 | ## Get Tag 201 | tag, mult_tag, sub_tag = get_type(scene, args) 202 | 203 | if np.random.uniform() < args.acceptance[tag - 1]: 204 | ## Check Validity 205 | ## Used in ORCA Datasets to account for rounding sensitivity 206 | if orca_sensitivity: 207 | goals = [goal_dict[path[0].pedestrian] for path in scene] 208 | # print('Type III') 209 | if orca_validity(scene, goals, args.pred_len, args.obs_len, args.mode): 210 | col_count += 1 211 | continue 212 | 213 | ## Update Tags 214 | tags[tag].append(track_id) 215 | for tt in mult_tag: 216 | mult_tags[tt].append(track_id) 217 | for st in sub_tag: 218 | sub_tags[st].append(track_id) 219 | 220 | ## Define Scene_Tag 221 | scene_tag = [] 222 | scene_tag.append(tag) 223 | scene_tag.append(sub_tag) 224 | 225 | ## Filtered scenes and Frames 226 | # start_frames |= set(ped_interest[i].frame for i in range(len(ped_interest[0:1]))) 227 | # print(start_frames) 228 | new_frames |= set(ped_interest[i].frame for i in range(len(ped_interest))) 229 | new_scenes.append( 230 | trajnetplusplustools.data.SceneRow(track_id, ped_interest[0].pedestrian, 231 | ped_interest[0].frame, ped_interest[-1].frame, 232 | fps, scene_tag)) 233 | 234 | ## Append to list of scenes_test as well if Test Set 235 | if test: 236 | new_frames_test |= set(ped_interest[i].frame for i in range(args.obs_len)) 237 | new_scenes_test.append( 238 | trajnetplusplustools.data.SceneRow(track_id, ped_interest[0].pedestrian, 239 | ped_interest[0].frame, ped_interest[-1].frame, 240 | fps, 0)) 241 | 242 | track_id += 1 243 | 244 | 245 | # Writes the Final Scenes and Frames 246 | write(rows, path, new_scenes, new_frames) 247 | if test: 248 | write(rows, path_test, new_scenes_test, new_frames_test) 249 | 250 | ## Stats 251 | 252 | # Number of collisions found 253 | print("Col Count: ", col_count) 254 | 255 | if scenes: 256 | print("Total Scenes: ", index) 257 | 258 | # Types: 259 | print("Main Tags") 260 | print("Type 1: ", len(tags[1]), "Type 2: ", len(tags[2]), 261 | "Type 3: ", len(tags[3]), "Type 4: ", len(tags[4])) 262 | print("Sub Tags") 263 | print("LF: ", len(sub_tags[1]), "CA: ", len(sub_tags[2]), 264 | "Group: ", len(sub_tags[3]), "Others: ", len(sub_tags[4])) 265 | 266 | return track_id 267 | -------------------------------------------------------------------------------- /trajnetdataset/orca_helper.py: -------------------------------------------------------------------------------- 1 | import rvo2 2 | import numpy as np 3 | 4 | def predict_all(input_paths, goals, mode, pred_length): 5 | fps = 100 6 | sampling_rate = fps / 2.5 7 | if mode == 'trajnet': 8 | sim = rvo2.PyRVOSimulator(1/fps, 4, 10, 4, 5, 0.6, 1.5) ## (TrajNet++) 9 | else: 10 | sim = rvo2.PyRVOSimulator(1/fps, 10, 10, 5, 5, 0.3, 1) ## Default 11 | 12 | # initialize 13 | trajectories = [[(p[0], p[1])] for p in input_paths[-1]] 14 | [sim.addAgent((p[0], p[1])) for p in input_paths[-1]] 15 | num_ped = len(trajectories) 16 | 17 | for i in range(num_ped): 18 | velocity = np.array((input_paths[-1][i][0] - input_paths[-3][i][0], input_paths[-1][i][1] - input_paths[-3][i][1])) 19 | velocity = velocity/0.8 20 | sim.setAgentVelocity(i, tuple(velocity.tolist())) 21 | velocity = np.array((goals[i][0] - input_paths[-1][i][0], goals[i][1] - input_paths[-1][i][1])) 22 | speed = np.linalg.norm(velocity) 23 | pref_vel = 1 * velocity / speed if speed > 1 else velocity 24 | sim.setAgentPrefVelocity(i, tuple(pref_vel.tolist())) 25 | 26 | reaching_goal_by_ped = [False] * num_ped 27 | count = 0 28 | end_range = 1.0 29 | done = False 30 | ##Simulate a scene 31 | while count < sampling_rate * pred_length + 1: 32 | count += 1 33 | sim.doStep() 34 | reaching_goal = [] 35 | for i in range(num_ped): 36 | if count == 1: 37 | trajectories[i].pop(0) 38 | position = sim.getAgentPosition(i) 39 | 40 | ## Append only if Goal not reached 41 | if not reaching_goal_by_ped[i]: 42 | if count % sampling_rate == 0: 43 | trajectories[i].append(position) 44 | 45 | # check if this agent reaches the goal 46 | if np.linalg.norm(np.array(position) - np.array(goals[i])) < end_range: 47 | reaching_goal.append(True) 48 | sim.setAgentPrefVelocity(i, (0, 0)) 49 | reaching_goal_by_ped[i] = True 50 | else: 51 | reaching_goal.append(False) 52 | velocity = np.array((goals[i][0] - position[0], goals[i][1] - position[1])) 53 | speed = np.linalg.norm(velocity) 54 | pref_vel = 1 * velocity / speed if speed > 1 else velocity 55 | sim.setAgentPrefVelocity(i, tuple(pref_vel.tolist())) 56 | 57 | # states = np.array(trajectories[0]) 58 | # return states 59 | return trajectories -------------------------------------------------------------------------------- /trajnetdataset/readers.py: -------------------------------------------------------------------------------- 1 | """ Read Raw files as TrackRows """ 2 | 3 | import json 4 | import os 5 | import xml.etree.ElementTree 6 | 7 | import numpy as np 8 | import scipy.interpolate 9 | 10 | from trajnetplusplustools import TrackRow 11 | 12 | 13 | def biwi(line): 14 | line = [e for e in line.split(' ') if e != ''] 15 | return TrackRow(int(float(line[0]) - 1), # shift from 1-index to 0-index 16 | int(float(line[1])), 17 | float(line[2]), 18 | float(line[4])) 19 | 20 | def crowds_interpolate_person(ped_id, person_xyf): 21 | ## Earlier 22 | # xs = np.array([x for x, _, _ in person_xyf]) / 720 * 12 # 0.0167 23 | # ys = np.array([y for _, y, _ in person_xyf]) / 576 * 12 # 0.0208 24 | 25 | ## Pixel-to-meter scale conversion according to 26 | ## https://github.com/agrimgupta92/sgan/issues/5 27 | xs = np.array([x for x, _, _ in person_xyf]) * 0.0210 28 | ys = np.array([y for _, y, _ in person_xyf]) * 0.0239 29 | 30 | fs = np.array([f for _, _, f in person_xyf]) 31 | 32 | kind = 'linear' 33 | if len(fs) > 5: 34 | kind = 'cubic' 35 | 36 | x_fn = scipy.interpolate.interp1d(fs, xs, kind=kind) 37 | y_fn = scipy.interpolate.interp1d(fs, ys, kind=kind) 38 | 39 | frames = np.arange(min(fs) // 10 * 10 + 10, max(fs), 10) 40 | return [TrackRow(int(f), ped_id, x, y) 41 | for x, y, f in np.stack([x_fn(frames), y_fn(frames), frames]).T] 42 | 43 | 44 | def crowds(whole_file): 45 | pedestrians = [] 46 | current_pedestrian = [] 47 | for line in whole_file.split('\n'): 48 | if '- Num of control points' in line or \ 49 | '- the number of splines' in line: 50 | if current_pedestrian: 51 | pedestrians.append(current_pedestrian) 52 | current_pedestrian = [] 53 | continue 54 | 55 | # strip comments 56 | if ' - ' in line: 57 | line = line[:line.find(' - ')] 58 | 59 | # tokenize 60 | entries = [e for e in line.split(' ') if e] 61 | if len(entries) != 4: 62 | continue 63 | 64 | x, y, f, _ = entries 65 | current_pedestrian.append([float(x), float(y), int(f)]) 66 | 67 | if current_pedestrian: 68 | pedestrians.append(current_pedestrian) 69 | 70 | return [row 71 | for i, p in enumerate(pedestrians) 72 | for row in crowds_interpolate_person(i, p)] 73 | 74 | 75 | def mot_xml(file_name): 76 | """PETS2009 dataset. 77 | 78 | Original frame rate is 7 frames / sec. 79 | """ 80 | tree = xml.etree.ElementTree.parse(file_name) 81 | root = tree.getroot() 82 | for frame in root: 83 | f = int(frame.attrib['number']) 84 | if f % 2 != 0: # reduce to 3.5 rows / sec 85 | continue 86 | 87 | for ped in frame.find('objectlist'): 88 | p = ped.attrib['id'] 89 | box = ped.find('box') 90 | x = box.attrib['xc'] 91 | y = box.attrib['yc'] 92 | 93 | yield TrackRow(f, int(p), float(x) / 100.0, float(y) / 100.0) 94 | 95 | 96 | def mot(line): 97 | """Line reader for MOT files. 98 | 99 | MOT format: 100 | , , , , , , , , , 101 | """ 102 | line = [e for e in line.split(',') if e != ''] 103 | return TrackRow(int(float(line[0])), 104 | int(float(line[1])), 105 | float(line[7]), 106 | float(line[8])) 107 | 108 | 109 | def edinburgh(filename_content_index): 110 | """Edinburgh Informatics Forum data reader. 111 | 112 | Original frame rate is 9fps. 113 | Every pixel corresponds to 24.7mm. 114 | http://homepages.inf.ed.ac.uk/rbf/FORUMTRACKING/ 115 | """ 116 | (_, whole_file), index = filename_content_index 117 | 118 | for line in whole_file.splitlines(): 119 | line = line.strip() 120 | if not line.startswith('TRACK.R'): 121 | continue 122 | 123 | # get to track id 124 | line = line[7:] 125 | track_id, _, coordinates = line.partition('=') 126 | track_id = int(track_id) + index * 1000000 127 | 128 | # parse track 129 | for coordinates in coordinates.split(';'): 130 | if not coordinates: 131 | continue 132 | x, y, frame = coordinates.strip('[] ').split(' ') 133 | frame = int(frame) + index * 1000000 134 | if frame % 3 != 0: # downsample frame rate 135 | continue 136 | yield TrackRow(frame, track_id, float(x) * 0.0247, float(y) * 0.0247) 137 | 138 | 139 | def syi(filename_content): 140 | """Tracking dataset in Grand Central. 141 | 142 | Yi_Pedestrian_Travel_Time_ICCV_2015_paper.pdf states that original 143 | frame rate is 25fps. 144 | 145 | Input rows are sampled every 20 frames. Assuming 25fps at recording, 146 | need to interpolate an additional row to get to 2.5 rows per second. 147 | """ 148 | filename, whole_file = filename_content 149 | track_id = int(os.path.basename(filename).replace('.txt', '')) 150 | 151 | chunk = [] 152 | last_row = None 153 | for line in whole_file.split('\n'): 154 | if not line: 155 | continue 156 | chunk.append(int(line)) 157 | if len(chunk) < 3: 158 | continue 159 | 160 | # rough approximation of mapping to world coordinates (main concourse is 37m x 84m) 161 | new_row = TrackRow(chunk[2], track_id, chunk[0] * 30.0 / 1920, chunk[1] * 70.0 / 1080) 162 | 163 | # interpolate one row to increase frame rate 164 | if last_row is not None: 165 | interpolated_row = TrackRow( 166 | int((last_row.frame + new_row.frame) / 2), 167 | track_id, 168 | (last_row.x + new_row.x) / 2, 169 | (last_row.y + new_row.y) / 2, 170 | ) 171 | yield interpolated_row 172 | 173 | yield new_row 174 | chunk = [] 175 | last_row = new_row 176 | 177 | 178 | def dukemtmc(input_array, query_camera=5): 179 | """DukeMTMC dataset. 180 | 181 | Recorded at 59.940059 fps. 182 | 183 | Line format: 184 | [camera, ID, frame, left, top, width, height, worldX, worldY, feetX, feetyY] 185 | """ 186 | for line in input_array: 187 | camera, person, frame, _, _, _, _, world_x, world_y, _, _ = line 188 | 189 | camera = int(camera) 190 | if camera != query_camera: 191 | continue 192 | 193 | frame = int(frame) 194 | if frame % 24 != 0: 195 | continue 196 | 197 | yield TrackRow(frame, int(person), world_x, world_y) 198 | 199 | 200 | def wildtrack(filename_content): 201 | filename, content = filename_content 202 | 203 | frame = int(os.path.basename(filename).replace('.json', '')) 204 | for entry in json.loads(content): 205 | ped_id = entry['personID'] 206 | position_id = entry['positionID'] 207 | 208 | x = -3.0 + 0.025 * (position_id % 480) 209 | y = -9.0 + 0.025 * (position_id / 480) 210 | 211 | yield TrackRow(frame, ped_id, x, y) 212 | 213 | 214 | def trajnet_original(line): 215 | line = [e for e in line.split(' ') if e != ''] 216 | return TrackRow(int(float(line[0])), 217 | int(float(line[1])), 218 | float(line[2]), 219 | float(line[3])) 220 | 221 | def cff(line): 222 | line = [e for e in line.split(';') if e != ''] 223 | 224 | ## Time Stamp 225 | time = [t for t in line[0].split(':') if t != ''] 226 | 227 | ## Check Line Entry Valid 228 | if len(line) != 5: 229 | return None 230 | 231 | ## Check Time Entry Valid 232 | if len(time) != 4: 233 | return None 234 | 235 | ## Check Location 236 | if line[1] != 'PIW': 237 | return None 238 | 239 | ## Check Time Format 240 | if time[0][-3:] == 'T07': 241 | ped_id = int(line[4]) 242 | f = 0 243 | elif time[0][-3:] == 'T17': 244 | ped_id = 100000 + int(line[4]) 245 | f = 100000 246 | else: 247 | # "Time Format Incorrect" 248 | return None 249 | 250 | ## Extract Frame 251 | f += int(time[-3])*1000 + int(time[-2])*10 + int(time[-1][0]) 252 | 253 | if f % 4 == 0: 254 | return TrackRow(f, # shift from 1-index to 0-index 255 | ped_id, 256 | float(line[2])/1000, 257 | float(line[3])/1000) 258 | return None 259 | 260 | 261 | def lcas(line): 262 | line = [e for e in line.split(',') if e != ''] 263 | return TrackRow(int(float(line[0])), 264 | int(float(line[1])), 265 | float(line[2]), 266 | float(line[3])) 267 | 268 | def controlled(line): 269 | line = [e for e in line.split(', ') if e != ''] 270 | return TrackRow(int(float(line[0])), 271 | int(float(line[1])), 272 | float(line[2]), 273 | float(line[3])) 274 | 275 | def get_trackrows(line): 276 | line = json.loads(line) 277 | track = line.get('track') 278 | if track is not None: 279 | return TrackRow(track['f'], track['p'], track['x'], track['y'], 280 | track.get('prediction_number')) 281 | return None 282 | 283 | def standard(line): 284 | line = [e for e in line.split('\t') if e != ''] 285 | return TrackRow(int(float(line[0])), 286 | int(float(line[1])), 287 | float(line[2]), 288 | float(line[3])) 289 | 290 | def car_data(filename_content): 291 | frame_id = int(filename_content[0].split('.')[0].split('/')[-1]) 292 | ratio = 5.0 / 162 ## 162 pix = 5 m 293 | lines = filename_content[1].split('\n') 294 | ## First Line: ID, Front1x, Front1y, Front2x, Front2y, Back1x, Back1y, Back2x, Back2y, Type, Occlusion 295 | assert lines[0] == 'ID,Front1x,Front1y,Front2x,Front2y,Back1x,Back1y,Back2x,Back2y,Type,Occlusion' 296 | ## Last Line: "" 297 | assert lines[-1] == '' 298 | 299 | for line in lines[1:-1]: 300 | id_, F1x, F1y, F2x, F2y, B1x, B1y, B2x, B2y, type_, occ = line.split(',') 301 | 302 | if int(type_) != 2: 303 | continue 304 | 305 | if int(frame_id) % 12 != 0: 306 | continue 307 | 308 | yield TrackRow(frame_id, int(id_), ratio * float(F1x), ratio * float(F1y)) 309 | -------------------------------------------------------------------------------- /trajnetdataset/scene.py: -------------------------------------------------------------------------------- 1 | """ Preparng Scenes for TrajNet """ 2 | import os 3 | from collections import defaultdict 4 | 5 | import trajnetplusplustools 6 | from trajnetplusplustools import SceneRow 7 | 8 | 9 | class Scenes(object): 10 | def __init__(self, fps, start_scene_id=0, args=None): 11 | self.scene_id = start_scene_id 12 | self.chunk_size = args.obs_len + args.pred_len 13 | self.chunk_stride = args.chunk_stride 14 | self.obs_len = args.obs_len 15 | self.visible_chunk = None 16 | self.frames = set() 17 | self.fps = fps 18 | self.min_length = args.min_length 19 | 20 | @staticmethod 21 | def euclidean_distance_2(row1, row2): 22 | """Euclidean distance squared between two rows.""" 23 | return (row1.x - row2.x)**2 + (row1.y - row2.y)**2 24 | 25 | @staticmethod 26 | def close_pedestrians(rows, cell_size=10): 27 | """Fast computation of spatially close pedestrians. 28 | 29 | By frame, get the list of pedestrian ids that or close to other 30 | pedestrians. Approximate with multi-occupancy of discrete grid cells. 31 | """ 32 | sparse_occupancy = defaultdict(list) 33 | for row in rows: 34 | x = int(row.x // cell_size * cell_size) 35 | y = int(row.y // cell_size * cell_size) 36 | sparse_occupancy[(x, y)].append(row.pedestrian) 37 | return {ped_id 38 | for cell in sparse_occupancy.values() if len(cell) > 1 39 | for ped_id in cell} 40 | 41 | @staticmethod 42 | def continuous_frames(frames, tolerance=1.5): 43 | increments = [f2 - f1 for f1, f2 in zip(frames[:-1], frames[1:])] 44 | median_increment = sorted(increments)[int(len(increments) / 2)] 45 | ok = median_increment * tolerance > max(increments) 46 | 47 | # if not ok: 48 | # print('!!!!!!!!! DETECTED GAP IN FRAMES') 49 | # print(increments) 50 | 51 | return ok 52 | 53 | def from_rows(self, rows): 54 | count_by_frame = rows.groupBy(lambda r: r.frame).mapValues(len).collectAsMap() 55 | occupancy_by_frame = (rows 56 | .groupBy(lambda r: r.frame) 57 | .mapValues(self.close_pedestrians) 58 | .collectAsMap()) 59 | 60 | def to_scene_row(ped_frames): 61 | ped_id, scene_frames = ped_frames 62 | row = SceneRow(self.scene_id, ped_id, scene_frames[0], scene_frames[-1], self.fps, 0) 63 | self.scene_id += 1 64 | return row 65 | 66 | # scenes: pedestrian of interest, [frames] 67 | scenes = ( 68 | rows 69 | .groupBy(lambda r: r.pedestrian) 70 | .filter(lambda p_path: len(p_path[1]) >= self.chunk_size) 71 | .mapValues(lambda path: sorted(path, key=lambda p: p.frame)) 72 | .flatMapValues(lambda path: [ 73 | [path[ii].frame for ii in range(i, i + self.chunk_size)] 74 | for i in range(0, len(path) - self.chunk_size + 1, self.chunk_stride) 75 | # filter for pedestrians moving by more than min_length meter 76 | if self.euclidean_distance_2(path[i], path[i+self.chunk_size-1]) > self.min_length 77 | ]) 78 | 79 | # filter out scenes with large gaps in frame numbers 80 | .filter(lambda ped_frames: self.continuous_frames(ped_frames[1])) 81 | 82 | # filter for scenes that have some activity 83 | .filter(lambda ped_frames: 84 | sum(count_by_frame[f] for f in ped_frames[1]) >= 2.0 * self.chunk_size) 85 | 86 | # require some proximity to other pedestrians 87 | .filter(lambda ped_frames: 88 | ped_frames[0] in {p 89 | for frame in ped_frames[1] 90 | for p in occupancy_by_frame[frame]}) 91 | 92 | .cache() 93 | ) 94 | 95 | self.frames |= set(scenes 96 | .flatMap(lambda ped_frames: 97 | ped_frames[1] 98 | if self.visible_chunk is None 99 | else ped_frames[1][:self.visible_chunk]) 100 | .toLocalIterator()) 101 | 102 | return scenes.map(to_scene_row) 103 | 104 | 105 | def rows_to_file(self, rows, output_file): 106 | if '/test/' in output_file: 107 | print('Output File: ', output_file) 108 | self.visible_chunk = self.obs_len 109 | else: 110 | self.visible_chunk = None 111 | scenes = self.from_rows(rows) 112 | tracks = rows.filter(lambda r: r.frame in self.frames) 113 | all_data = rows.context.union((scenes, tracks)) 114 | 115 | ## removes the file, if previously generated 116 | if os.path.isfile(output_file): 117 | os.remove(output_file) 118 | 119 | ## write scenes and tracks 120 | all_data.map(trajnetplusplustools.writers.trajnet).saveAsTextFile(output_file) 121 | 122 | return self 123 | --------------------------------------------------------------------------------