├── OKS.py ├── LICENSE ├── README.md └── .gitignore /OKS.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from decimal import Decimal, ROUND_HALF_UP 4 | 5 | 6 | def edit_keypoints(kpts): 7 | kpts = np.array(kpts).reshape(-1,3) 8 | vi = kpts[:,2] 9 | kpts = kpts[:,0:2] 10 | return kpts, vi 11 | 12 | 13 | def OKS(kpts1, kpts2, sigma, area): 14 | 15 | kpts1, vi1 = edit_keypoints(kpts1) 16 | kpts2, vi2 = edit_keypoints(kpts2) 17 | 18 | if np.shape(kpts1) != np.shape(kpts2): 19 | print(kpts1, kpts2) 20 | print(np.shape(kpts1), np.shape(kpts2)) 21 | raise ValueError("not same size") 22 | 23 | k = 2*sigma 24 | s = area 25 | 26 | d = np.linalg.norm(kpts1 - kpts2, ord=2, axis=1) 27 | v = np.ones(len(d)) 28 | 29 | for part in range(len(d)): 30 | if vi1[part] == 0 or vi2[part] == 0: 31 | d[part] = 0 32 | v[part] = 0 33 | 34 | if np.sum(v)!=0: 35 | OKS = (np.sum([(np.exp((-d[i]**2)/(2*s*(k[i]**2))))*v[i] for i in range(len(d))])/np.sum(v)) 36 | else: 37 | OKS = 0 38 | 39 | OKS = float(Decimal(str(OKS)).quantize(Decimal('0.000001'), rounding=ROUND_HALF_UP)) 40 | 41 | return OKS -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Yuki Utsuro 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # OKS 2 | 3 | **Calculate OKS(Object Keypoints Similarity) from two sets of keypoints** 4 | 5 | OKS is defined in [here](https://cocodataset.org/#keypoints-eval). 6 | 7 | ## Arguments 8 | 9 | To caluculate OKS, you need four arguments; `kpts1`, `kpts2`, `sigma`, and `area`. 10 | 11 | ### `kpts1` & `kpts2`: Sets of Keypoints 12 | 13 | The `OKS` function caluclates the value of OKS between `kpts1` and `kpts2`. 14 | 15 | `kpts1` and `kpts2` must be same shape and each keypoint must have three parameters; x, y, and v (v is a visibility). 16 | 17 | ### `sigma`: Per-Keypoint Standard Deviation 18 | 19 | `sigma` is a set of parameters of per-keypoint standard deviation. It is determined for each dataset. Detail is [here](https://cocodataset.org/#keypoints-eval). 20 | 21 | Samples of `sigma`: 22 | ```python 23 | # COCO 24 | sigma = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0 25 | 26 | # body_25 27 | sigma = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .8, .8, .8, .89, .89, .89, .89, .89, .89])/10.0 28 | ``` 29 | 30 | If you use your custom dataset, you have to prepare your `sigma`. 31 | 32 | ### `area`: Area of the Object 33 | 34 | `area` is the number of pixels of the object (such as person) in the picture. 35 | 36 | For sake of simplicity, you can use the mean of "area" in your COCO annotation. 37 | 38 | ## How to Use 39 | 40 | You can caluclate the value of OKS between `kpts1` and `kpts2` like below: 41 | 42 | ```python 43 | # This is the sample for COCO keypoints. 44 | sigma = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0 45 | 46 | area = 20000 47 | 48 | kpts1 = [541, 299, 2, 576, 280, 2, 579, 315, 2, 583, 351, 2, 564, 394, 2, 586, 316, 2, 621, 351, 2, 605, 406, 2, 680, 320, 2, 692, 310, 2, 648, 322, 2, 664, 391, 2, 697, 316, 2, 641, 356, 2, 692, 419, 2, 0, 0, 0, 541, 287, 2] 49 | kpts2 = [542, 297, 2, 584, 278, 2, 574, 305, 2, 586, 347, 2, 565, 391, 2, 584, 309, 2, 620, 353, 2, 612, 406, 2, 696, 305, 2, 685, 317, 2, 0, 0, 0, 659, 391, 2, 699, 323, 2, 644, 350, 2, 689, 418, 2, 0, 0, 0, 542, 287, 2] 50 | 51 | # caluclate OKS from two keypoints 52 | oks = OKS(kpts1, kpts2, sigma, area) 53 | 54 | print(oks) 55 | # 0.852633 56 | ``` 57 | 58 | ## Calculate `sigma` for Your Custom Dataset 59 | 60 | You can get your `sigma` from your custom dataset which is annotated redundantly, or caluclate pseudo `sigma` using the diffelence between ground truth and detected keypoints (code is coming soon!). -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; 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