├── .github └── workflows │ ├── python-package.yml │ └── testing.yml ├── .gitignore ├── CITATION.cff ├── LICENSE ├── MANIFEST.in ├── README.md ├── benchmarking └── run_dlclive_benchmark.py ├── check_install └── dog_clip.avi ├── dlclive ├── __init__.py ├── benchmark.py ├── check_install │ └── check_install.py ├── display.py ├── dlclive.py ├── exceptions.py ├── graph.py ├── pose.py ├── processor │ ├── README.md │ ├── __init__.py │ ├── kalmanfilter.py │ └── processor.py ├── utils.py └── version.py ├── docs ├── install_desktop.md └── install_jetson.md ├── example_processors ├── DogJumpLED │ ├── __init__.py │ ├── izzy_jump.py │ ├── izzy_jump_offline.py │ └── teensy_leds │ │ └── teensy_leds.ino ├── MouseLickLED │ ├── __init__.py │ ├── lick_led.py │ └── teensy_leds │ │ └── teensy_leds.ino └── TeensyLaser │ ├── __init__.py │ ├── teensy_laser.py │ └── teensy_laser │ └── teensy_laser.ino ├── poetry.lock ├── pyproject.toml └── reinstall.sh /.github/workflows/python-package.yml: -------------------------------------------------------------------------------- 1 | # This workflow will install Python dependencies, run tests and lint with a variety of Python versions 2 | # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions 3 | name: Python package 4 | 5 | on: 6 | push: 7 | branches: [ master ] 8 | pull_request: 9 | branches: [ master ] 10 | 11 | 12 | jobs: 13 | build: 14 | 15 | runs-on: ubuntu-latest 16 | strategy: 17 | # You can use PyPy versions in python-version. For example, pypy2 and pypy3 18 | matrix: 19 | python-version: [3.7, 3.8, 3.9] 20 | 21 | steps: 22 | - uses: actions/checkout@v2 23 | - name: Set up Python ${{ matrix.python-version }} 24 | uses: actions/setup-python@v2 25 | with: 26 | python-version: ${{ matrix.python-version }} 27 | # You can test your matrix by printing the current Python version 28 | - name: Display Python version 29 | run: python -c "import sys; print(sys.version)" 30 | #test installation of DLC-core dependencies: 31 | - name: Install dependencies 32 | run: pip install . 33 | -------------------------------------------------------------------------------- /.github/workflows/testing.yml: -------------------------------------------------------------------------------- 1 | name: Run Tests 2 | 3 | on: 4 | - push 5 | - pull_request 6 | 7 | jobs: 8 | build: 9 | runs-on: ${{ matrix.os }} 10 | 11 | strategy: 12 | fail-fast: false 13 | matrix: 14 | os: [ubuntu-latest, macos-11, windows-latest] 15 | python-version: ["3.7", "3.8", "3.9", "3.10"] #3.9 only failing for tables on macos and windows; mwm 6302021 16 | include: 17 | - os: ubuntu-latest 18 | path: ~/.cache/pip 19 | - os: macos-11 20 | path: ~/Library/Caches/pip 21 | - os: windows-latest 22 | path: ~\AppData\Local\pip\Cache 23 | 24 | steps: 25 | - name: Checkout code 26 | uses: actions/checkout@v2 27 | 28 | - name: Set up Python ${{ matrix.python-version }} 29 | uses: actions/setup-python@v3 30 | with: 31 | python-version: ${{ matrix.python-version }} 32 | - name: Install ffmpeg 33 | run: | 34 | if [ "$RUNNER_OS" == "Linux" ]; then 35 | sudo apt-get update 36 | sudo apt-get install ffmpeg 37 | elif [ "$RUNNER_OS" == "macOS" ]; then 38 | brew install ffmpeg 39 | else 40 | choco install ffmpeg 41 | fi 42 | shell: bash 43 | - name: Install and test 44 | run: | 45 | python -m pip install --upgrade pip wheel poetry 46 | python -m poetry install 47 | python -m poetry run dlc-live-test --nodisplay 48 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # DeepLabCut-live 2 | 3 | # Data related to benchmark! 4 | benchmarking/Data* 5 | benchmarking/results* 6 | 7 | *test* 8 | **DS_Store* 9 | *vscode* 10 | 11 | # Byte-compiled / optimized / DLL files 12 | __pycache__/ 13 | *.py[cod] 14 | *$py.class 15 | 16 | # C extensions 17 | *.so 18 | 19 | # Distribution / packaging 20 | .Python 21 | build/ 22 | develop-eggs/ 23 | dist/ 24 | downloads/ 25 | eggs/ 26 | .eggs/ 27 | lib/ 28 | lib64/ 29 | parts/ 30 | sdist/ 31 | var/ 32 | wheels/ 33 | pip-wheel-metadata/ 34 | share/python-wheels/ 35 | *.egg-info/ 36 | .installed.cfg 37 | *.egg 38 | MANIFEST 39 | 40 | # PyInstaller 41 | # Usually these files are written by a python script from a template 42 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 43 | *.manifest 44 | *.spec 45 | 46 | # Installer logs 47 | pip-log.txt 48 | pip-delete-this-directory.txt 49 | 50 | # Unit test / coverage reports 51 | htmlcov/ 52 | .tox/ 53 | .nox/ 54 | .coverage 55 | .coverage.* 56 | .cache 57 | nosetests.xml 58 | coverage.xml 59 | *.cover 60 | *.py,cover 61 | .hypothesis/ 62 | .pytest_cache/ 63 | 64 | # Translations 65 | *.mo 66 | *.pot 67 | 68 | # Django stuff: 69 | *.log 70 | local_settings.py 71 | db.sqlite3 72 | db.sqlite3-journal 73 | 74 | # Flask stuff: 75 | instance/ 76 | .webassets-cache 77 | 78 | # Scrapy stuff: 79 | .scrapy 80 | 81 | # Sphinx documentation 82 | docs/_build/ 83 | 84 | # PyBuilder 85 | target/ 86 | 87 | # Jupyter Notebook 88 | .ipynb_checkpoints 89 | 90 | # IPython 91 | profile_default/ 92 | ipython_config.py 93 | 94 | # pyenv 95 | .python-version 96 | 97 | # pipenv 98 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 99 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 100 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 101 | # install all needed dependencies. 102 | #Pipfile.lock 103 | 104 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 105 | __pypackages__/ 106 | 107 | # Celery stuff 108 | celerybeat-schedule 109 | celerybeat.pid 110 | 111 | # SageMath parsed files 112 | *.sage.py 113 | 114 | # Environments 115 | .env 116 | .venv 117 | env/ 118 | venv/ 119 | ENV/ 120 | env.bak/ 121 | venv.bak/ 122 | 123 | # Spyder project settings 124 | .spyderproject 125 | .spyproject 126 | 127 | # Rope project settings 128 | .ropeproject 129 | 130 | # mkdocs documentation 131 | /site 132 | 133 | # mypy 134 | .mypy_cache/ 135 | .dmypy.json 136 | dmypy.json 137 | 138 | # Pyre type checker 139 | .pyre/ 140 | -------------------------------------------------------------------------------- /CITATION.cff: -------------------------------------------------------------------------------- 1 | # This CITATION.cff file was generated with cffinit. 2 | # Visit https://bit.ly/cffinit to generate yours today! 3 | 4 | cff-version: 1.2.0 5 | title: >- 6 | Real-time, low-latency closed-loop feedback using 7 | markerless posture tracking 8 | message: >- 9 | If you utilize our tool, please [cite Kane et al, 10 | eLife 11 | 2020](https://elifesciences.org/articles/61909). 12 | The preprint is available here: 13 | https://www.biorxiv.org/content/10.1101/2020.08.04.236422v2 14 | type: article 15 | authors: 16 | - given-names: Gary 17 | name-particle: A 18 | family-names: Kane 19 | affiliation: >- 20 | The Rowland Institute at Harvard, Harvard 21 | University, Cambridge, United States 22 | - given-names: Gonçalo 23 | family-names: Lopes 24 | affiliation: 'NeuroGEARS Ltd, London, United Kingdom' 25 | - given-names: Jonny 26 | name-particle: L 27 | family-names: Saunders 28 | affiliation: >- 29 | Institute of Neuroscience, Department of 30 | Psychology, University of Oregon, Eugene, 31 | United States 32 | - given-names: Alexander 33 | family-names: Mathis 34 | affiliation: >- 35 | The Rowland Institute at Harvard, Harvard 36 | University, Cambridge, United States; Center 37 | for Neuroprosthetics, Center for Intelligent 38 | Systems, & Brain Mind Institute, School of Life 39 | Sciences, Swiss Federal Institute of Technology 40 | (EPFL), Lausanne, Switzerland 41 | - given-names: Mackenzie 42 | name-particle: W 43 | family-names: Mathis 44 | affiliation: >- 45 | The Rowland Institute at Harvard, Harvard 46 | University, Cambridge, United States; Center 47 | for Neuroprosthetics, Center for Intelligent 48 | Systems, & Brain Mind Institute, School of Life 49 | Sciences, Swiss Federal Institute of Technology 50 | (EPFL), Lausanne, Switzerland 51 | email: mackenzie.mathis@epfl.ch 52 | date-released: 2020-08-05 53 | doi: "10.7554/eLife.61909" 54 | license: "AGPL-3.0-or-later" 55 | version: "1.0.3" 56 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright 2020-2022 by Mackenzie Mathis, Gary Kane, Alexander Mathis and contributors. 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SDKDLC LIVE! 2 | 3 | Code style: black 4 | ![PyPI - Python Version](https://img.shields.io/pypi/v/deeplabcut-live) 5 | [![Downloads](https://pepy.tech/badge/deeplabcut-live)](https://pepy.tech/project/deeplabcut-live) 6 | [![Downloads](https://pepy.tech/badge/deeplabcut-live/month)](https://pepy.tech/project/deeplabcut-live) 7 | ![Python package](https://github.com/DeepLabCut/DeepLabCut-live/workflows/Python%20package/badge.svg) 8 | [![GitHub stars](https://img.shields.io/github/stars/DeepLabCut/DeepLabCut-live.svg?style=social&label=Star)](https://github.com/DeepLabCut/DeepLabCut-live) 9 | [![GitHub forks](https://img.shields.io/github/forks/DeepLabCut/DeepLabCut-live.svg?style=social&label=Fork)](https://github.com/DeepLabCut/DeepLabCut-live) 10 | [![Image.sc forum](https://img.shields.io/badge/dynamic/json.svg?label=forum&url=https%3A%2F%2Fforum.image.sc%2Ftags%2Fdeeplabcut.json&query=%24.topic_list.tags.0.topic_count&colorB=brightgreen&&suffix=%20topics&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAOCAYAAAAfSC3RAAABPklEQVR42m3SyyqFURTA8Y2BER0TDyExZ+aSPIKUlPIITFzKeQWXwhBlQrmFgUzMMFLKZeguBu5y+//17dP3nc5vuPdee6299gohUYYaDGOyyACq4JmQVoFujOMR77hNfOAGM+hBOQqB9TjHD36xhAa04RCuuXeKOvwHVWIKL9jCK2bRiV284QgL8MwEjAneeo9VNOEaBhzALGtoRy02cIcWhE34jj5YxgW+E5Z4iTPkMYpPLCNY3hdOYEfNbKYdmNngZ1jyEzw7h7AIb3fRTQ95OAZ6yQpGYHMMtOTgouktYwxuXsHgWLLl+4x++Kx1FJrjLTagA77bTPvYgw1rRqY56e+w7GNYsqX6JfPwi7aR+Y5SA+BXtKIRfkfJAYgj14tpOF6+I46c4/cAM3UhM3JxyKsxiOIhH0IO6SH/A1Kb1WBeUjbkAAAAAElFTkSuQmCC)](https://forum.image.sc/tags/deeplabcut) 11 | [![Gitter](https://badges.gitter.im/DeepLabCut/community.svg)](https://gitter.im/DeepLabCut/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) 12 | [![Twitter Follow](https://img.shields.io/twitter/follow/DeepLabCut.svg?label=DeepLabCut&style=social)](https://twitter.com/DeepLabCut) 13 | 14 | This package contains a [DeepLabCut](http://www.mousemotorlab.org/deeplabcut) inference pipeline for real-time applications that has minimal (software) dependencies. Thus, it is as easy to install as possible (in particular, on atypical systems like [NVIDIA Jetson boards](https://developer.nvidia.com/buy-jetson)). 15 | 16 | **Performance:** If you would like to see estimates on how your model should perform given different video sizes, neural network type, and hardware, please see: https://deeplabcut.github.io/DLC-inferencespeed-benchmark/ 17 | 18 | If you have different hardware, please consider submitting your results too! https://github.com/DeepLabCut/DLC-inferencespeed-benchmark 19 | 20 | **What this SDK provides:** This package provides a `DLCLive` class which enables pose estimation online to provide feedback. This object loads and prepares a DeepLabCut network for inference, and will return the predicted pose for single images. 21 | 22 | To perform processing on poses (such as predicting the future pose of an animal given it's current pose, or to trigger external hardware like send TTL pulses to a laser for optogenetic stimulation), this object takes in a `Processor` object. Processor objects must contain two methods: process and save. 23 | 24 | - The `process` method takes in a pose, performs some processing, and returns processed pose. 25 | - The `save` method saves any valuable data created by or used by the processor 26 | 27 | For more details and examples, see documentation [here](dlclive/processor/README.md). 28 | 29 | ###### 🔥🔥🔥🔥🔥 Note :: alone, this object does not record video or capture images from a camera. This must be done separately, i.e. see our [DeepLabCut-live GUI](https://github.com/gkane26/DeepLabCut-live-GUI).🔥🔥🔥 30 | 31 | ### News! 32 | - March 2022: DeepLabCut-Live! 1.0.2 supports poetry installation `poetry install deeplabcut-live`, thanks to PR #60. 33 | - March 2021: DeepLabCut-Live! [**version 1.0** is released](https://pypi.org/project/deeplabcut-live/), with support for tensorflow 1 and tensorflow 2! 34 | - Feb 2021: DeepLabCut-Live! was featured in **Nature Methods**: ["Real-time behavioral analysis"](https://www.nature.com/articles/s41592-021-01072-z) 35 | - Jan 2021: full **eLife** paper is published: ["Real-time, low-latency closed-loop feedback using markerless posture tracking"](https://elifesciences.org/articles/61909) 36 | - Dec 2020: we talked to **RTS Suisse Radio** about DLC-Live!: ["Capture animal movements in real time"](https://www.rts.ch/play/radio/cqfd/audio/capturer-les-mouvements-des-animaux-en-temps-reel?id=11782529) 37 | 38 | 39 | ### Installation: 40 | 41 | Please see our instruction manual to install on a [Windows or Linux machine](docs/install_desktop.md) or on a [NVIDIA Jetson Development Board](docs/install_jetson.md). Note, this code works with tensorflow (TF) 1 or TF 2 models, but TF requires that whatever version you exported your model with, you must import with the same version (i.e., export with TF1.13, then use TF1.13 with DlC-Live; export with TF2.3, then use TF2.3 with DLC-live). 42 | 43 | - available on pypi as: `pip install deeplabcut-live` 44 | 45 | Note, you can then test your installation by running: 46 | 47 | `dlc-live-test` 48 | 49 | If installed properly, this script will i) create a temporary folder ii) download the full_dog model from the [DeepLabCut Model Zoo](http://www.mousemotorlab.org/dlc-modelzoo), iii) download a short video clip of a dog, and iv) run inference while displaying keypoints. v) remove the temporary folder. 50 | 51 | DLC LIVE TEST 52 | 53 | ### Quick Start: instructions for use: 54 | 55 | 1. Initialize `Processor` (if desired) 56 | 2. Initialize the `DLCLive` object 57 | 3. Perform pose estimation! 58 | 59 | ```python 60 | from dlclive import DLCLive, Processor 61 | dlc_proc = Processor() 62 | dlc_live = DLCLive(, processor=dlc_proc) 63 | dlc_live.init_inference() 64 | dlc_live.get_pose() 65 | ``` 66 | 67 | `DLCLive` **parameters:** 68 | 69 | - `path` = string; full path to the exported DLC model directory 70 | - `model_type` = string; the type of model to use for inference. Types include: 71 | - `base` = the base DeepLabCut model 72 | - `tensorrt` = apply [tensor-rt](https://developer.nvidia.com/tensorrt) optimizations to model 73 | - `tflite` = use [tensorflow lite](https://www.tensorflow.org/lite) inference (in progress...) 74 | - `cropping` = list of int, optional; cropping parameters in pixel number: [x1, x2, y1, y2] 75 | - `dynamic` = tuple, optional; defines parameters for dynamic cropping of images 76 | - `index 0` = use dynamic cropping, bool 77 | - `index 1` = detection threshold, float 78 | - `index 2` = margin (in pixels) around identified points, int 79 | - `resize` = float, optional; factor by which to resize image (resize=0.5 downsizes both width and height of image by half). Can be used to downsize large images for faster inference 80 | - `processor` = dlc pose processor object, optional 81 | - `display` = bool, optional; display processed image with DeepLabCut points? Can be used to troubleshoot cropping and resizing parameters, but is very slow 82 | 83 | `DLCLive` **inputs:** 84 | 85 | - `` = path to the folder that has the `.pb` files that you acquire after running `deeplabcut.export_model` 86 | - `` = is a numpy array of each frame 87 | 88 | 89 | ### Benchmarking/Analyzing your exported DeepLabCut models 90 | 91 | DeepLabCut-live offers some analysis tools that allow users to peform the following operations on videos, from python or from the command line: 92 | 93 | 1. Test inference speed across a range of image sizes, downsizing images by specifying the `resize` or `pixels` parameter. Using the `pixels` parameter will resize images to the desired number of `pixels`, without changing the aspect ratio. Results will be saved (along with system info) to a pickle file if you specify an output directory. 94 | ##### python 95 | ```python 96 | dlclive.benchmark_videos('/path/to/exported/model', ['/path/to/video1', '/path/to/video2'], output='/path/to/output', resize=[1.0, 0.75, '0.5']) 97 | ``` 98 | ##### command line 99 | ``` 100 | dlc-live-benchmark /path/to/exported/model /path/to/video1 /path/to/video2 -o /path/to/output -r 1.0 0.75 0.5 101 | ``` 102 | 103 | 2. Display keypoints to visually inspect the accuracy of exported models on different image sizes (note, this is slow and only for testing purposes): 104 | 105 | ##### python 106 | ```python 107 | dlclive.benchmark_videos('/path/to/exported/model', '/path/to/video', resize=0.5, display=True, pcutoff=0.5, display_radius=4, cmap='bmy') 108 | ``` 109 | ##### command line 110 | ``` 111 | dlc-live-benchmark /path/to/exported/model /path/to/video -r 0.5 --display --pcutoff 0.5 --display-radius 4 --cmap bmy 112 | ``` 113 | 114 | 3. Analyze and create a labeled video using the exported model and desired resize parameters. This option functions similar to `deeplabcut.benchmark_videos` and `deeplabcut.create_labeled_video` (note, this is slow and only for testing purposes). 115 | 116 | ##### python 117 | ```python 118 | dlclive.benchmark_videos('/path/to/exported/model', '/path/to/video', resize=[1.0, 0.75, 0.5], pcutoff=0.5, display_radius=4, cmap='bmy', save_poses=True, save_video=True) 119 | ``` 120 | ##### command line 121 | ``` 122 | dlc-live-benchmark /path/to/exported/model /path/to/video -r 0.5 --pcutoff 0.5 --display-radius 4 --cmap bmy --save-poses --save-video 123 | ``` 124 | 125 | ## License: 126 | 127 | This project is licensed under the GNU AGPLv3. Note that the software is provided "as is", without warranty of any kind, express or implied. If you use the code or data, we ask that you please cite us! This software is available for licensing via the EPFL Technology Transfer Office (https://tto.epfl.ch/, info.tto@epfl.ch). 128 | 129 | ## Community Support, Developers, & Help: 130 | 131 | This is an actively developed package and we welcome community development and involvement. 132 | 133 | - If you want to contribute to the code, please read our guide [here](https://github.com/DeepLabCut/DeepLabCut/blob/master/CONTRIBUTING.md), which is provided at the main repository of DeepLabCut. 134 | 135 | - We are a community partner on the [![Image.sc forum](https://img.shields.io/badge/dynamic/json.svg?label=forum&url=https%3A%2F%2Fforum.image.sc%2Ftags%2Fdeeplabcut.json&query=%24.topic_list.tags.0.topic_count&colorB=brightgreen&&suffix=%20topics&logo=data:image/png;base64,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)](https://forum.image.sc/tags/deeplabcut). Please post help and support questions on the forum with the tag DeepLabCut. Check out their mission statement [Scientific Community Image Forum: A discussion forum for scientific image software](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000340). 136 | 137 | - If you encounter a previously unreported bug/code issue, please post here (we encourage you to search issues first): https://github.com/DeepLabCut/DeepLabCut-live/issues 138 | 139 | - For quick discussions here: [![Gitter](https://badges.gitter.im/DeepLabCut/community.svg)](https://gitter.im/DeepLabCut/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) 140 | 141 | ### Reference: 142 | 143 | If you utilize our tool, please [cite Kane et al, eLife 2020](https://elifesciences.org/articles/61909). The preprint is available here: https://www.biorxiv.org/content/10.1101/2020.08.04.236422v2 144 | 145 | ``` 146 | @Article{Kane2020dlclive, 147 | author = {Kane, Gary and Lopes, Gonçalo and Sanders, Jonny and Mathis, Alexander and Mathis, Mackenzie}, 148 | title = {Real-time, low-latency closed-loop feedback using markerless posture tracking}, 149 | journal = {eLife}, 150 | year = {2020}, 151 | } 152 | ``` 153 | 154 | -------------------------------------------------------------------------------- /benchmarking/run_dlclive_benchmark.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | # Script for running the official benchmark from Kane et al, 2020. 9 | # Please share your results at https://github.com/DeepLabCut/DLC-inferencespeed-benchmark 10 | 11 | import os, pathlib 12 | import glob 13 | 14 | from dlclive import benchmark_videos, download_benchmarking_data 15 | 16 | datafolder = os.path.join( 17 | pathlib.Path(__file__).parent.absolute(), "Data-DLC-live-benchmark" 18 | ) 19 | 20 | if not os.path.isdir(datafolder): # only download if data doesn't exist! 21 | # Downloading data.... this takes a while (see terminal) 22 | download_benchmarking_data(datafolder) 23 | 24 | n_frames = 10000 # change to 10000 for testing on a GPU! 25 | pixels = [2500, 10000, 40000, 160000, 320000, 640000] 26 | 27 | dog_models = glob.glob(datafolder + "/dog/*[!avi]") 28 | dog_video = glob.glob(datafolder + "/dog/*.avi")[0] 29 | mouse_models = glob.glob(datafolder + "/mouse_lick/*[!avi]") 30 | mouse_video = glob.glob(datafolder + "/mouse_lick/*.avi")[0] 31 | 32 | this_dir = os.path.dirname(os.path.realpath(__file__)) 33 | # storing results in /benchmarking/results: (for your PR) 34 | out_dir = os.path.normpath(this_dir + "/results") 35 | 36 | if not os.path.isdir(out_dir): 37 | os.mkdir(out_dir) 38 | 39 | for m in dog_models: 40 | benchmark_videos(m, dog_video, output=out_dir, n_frames=n_frames, pixels=pixels) 41 | 42 | for m in mouse_models: 43 | benchmark_videos(m, mouse_video, output=out_dir, n_frames=n_frames, pixels=pixels) 44 | -------------------------------------------------------------------------------- /check_install/dog_clip.avi: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DeepLabCut/DeepLabCut-live/427c12609307ef685e4193c91026567308820cbe/check_install/dog_clip.avi -------------------------------------------------------------------------------- /dlclive/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | from dlclive.version import __version__, VERSION 9 | from dlclive.dlclive import DLCLive 10 | from dlclive.processor import Processor 11 | from dlclive.benchmark import benchmark, benchmark_videos, download_benchmarking_data 12 | -------------------------------------------------------------------------------- /dlclive/benchmark.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import platform 10 | import os 11 | import time 12 | import sys 13 | import warnings 14 | import subprocess 15 | import typing 16 | import pickle 17 | import colorcet as cc 18 | from PIL import ImageColor 19 | import ruamel 20 | 21 | try: 22 | from pip._internal.operations import freeze 23 | except ImportError: 24 | from pip.operations import freeze 25 | 26 | from tqdm import tqdm 27 | import numpy as np 28 | import tensorflow as tf 29 | import cv2 30 | 31 | from dlclive import DLCLive 32 | from dlclive import VERSION 33 | from dlclive import __file__ as dlcfile 34 | 35 | from dlclive.utils import decode_fourcc 36 | 37 | 38 | def download_benchmarking_data( 39 | target_dir=".", 40 | url="http://deeplabcut.rowland.harvard.edu/datasets/dlclivebenchmark.tar.gz", 41 | ): 42 | """ 43 | Downloads a DeepLabCut-Live benchmarking Data (videos & DLC models). 44 | """ 45 | import urllib.request 46 | import tarfile 47 | from tqdm import tqdm 48 | 49 | def show_progress(count, block_size, total_size): 50 | pbar.update(block_size) 51 | 52 | def tarfilenamecutting(tarf): 53 | """' auxfun to extract folder path 54 | ie. /xyz-trainsetxyshufflez/ 55 | """ 56 | for memberid, member in enumerate(tarf.getmembers()): 57 | if memberid == 0: 58 | parent = str(member.path) 59 | l = len(parent) + 1 60 | if member.path.startswith(parent): 61 | member.path = member.path[l:] 62 | yield member 63 | 64 | response = urllib.request.urlopen(url) 65 | print( 66 | "Downloading the benchmarking data from the DeepLabCut server @Harvard -> Go Crimson!!! {}....".format( 67 | url 68 | ) 69 | ) 70 | total_size = int(response.getheader("Content-Length")) 71 | pbar = tqdm(unit="B", total=total_size, position=0) 72 | filename, _ = urllib.request.urlretrieve(url, reporthook=show_progress) 73 | with tarfile.open(filename, mode="r:gz") as tar: 74 | tar.extractall(target_dir, members=tarfilenamecutting(tar)) 75 | 76 | 77 | def get_system_info() -> dict: 78 | """ Return summary info for system running benchmark 79 | Returns 80 | ------- 81 | dict 82 | Dictionary containing the following system information: 83 | * ``host_name`` (str): name of machine 84 | * ``op_sys`` (str): operating system 85 | * ``python`` (str): path to python (which conda/virtual environment) 86 | * ``device`` (tuple): (device type (``'GPU'`` or ``'CPU'```), device information) 87 | * ``freeze`` (list): list of installed packages and versions 88 | * ``python_version`` (str): python version 89 | * ``git_hash`` (str, None): If installed from git repository, hash of HEAD commit 90 | * ``dlclive_version`` (str): dlclive version from :data:`dlclive.VERSION` 91 | """ 92 | 93 | # get os 94 | 95 | op_sys = platform.platform() 96 | host_name = platform.node().replace(" ", "") 97 | 98 | # A string giving the absolute path of the executable binary for the Python interpreter, on systems where this makes sense. 99 | if platform.system() == "Windows": 100 | host_python = sys.executable.split(os.path.sep)[-2] 101 | else: 102 | host_python = sys.executable.split(os.path.sep)[-3] 103 | 104 | # try to get git hash if possible 105 | dlc_basedir = os.path.dirname(os.path.dirname(dlcfile)) 106 | git_hash = None 107 | try: 108 | git_hash = subprocess.check_output( 109 | ["git", "rev-parse", "HEAD"], cwd=dlc_basedir 110 | ) 111 | git_hash = git_hash.decode("utf-8").rstrip("\n") 112 | except subprocess.CalledProcessError: 113 | # not installed from git repo, eg. pypi 114 | # fine, pass quietly 115 | pass 116 | 117 | # get device info (GPU or CPU) 118 | dev = None 119 | if tf.test.is_gpu_available(): 120 | gpu_name = tf.test.gpu_device_name() 121 | from tensorflow.python.client import device_lib 122 | 123 | dev_desc = [ 124 | d.physical_device_desc 125 | for d in device_lib.list_local_devices() 126 | if d.name == gpu_name 127 | ] 128 | dev = [d.split(",")[1].split(":")[1].strip() for d in dev_desc] 129 | dev_type = "GPU" 130 | else: 131 | from cpuinfo import get_cpu_info 132 | 133 | dev = [get_cpu_info()["brand"]] 134 | dev_type = "CPU" 135 | 136 | return { 137 | "host_name": host_name, 138 | "op_sys": op_sys, 139 | "python": host_python, 140 | "device_type": dev_type, 141 | "device": dev, 142 | # pip freeze to get versions of all packages 143 | "freeze": list(freeze.freeze()), 144 | "python_version": sys.version, 145 | "git_hash": git_hash, 146 | "dlclive_version": VERSION, 147 | } 148 | 149 | 150 | def benchmark( 151 | model_path, 152 | video_path, 153 | tf_config=None, 154 | resize=None, 155 | pixels=None, 156 | cropping=None, 157 | dynamic=(False, 0.5, 10), 158 | n_frames=1000, 159 | print_rate=False, 160 | display=False, 161 | pcutoff=0.0, 162 | display_radius=3, 163 | cmap="bmy", 164 | save_poses=False, 165 | save_video=False, 166 | output=None, 167 | ) -> typing.Tuple[np.ndarray, tuple, bool, dict]: 168 | """ Analyze DeepLabCut-live exported model on a video: 169 | Calculate inference time, 170 | display keypoints, or 171 | get poses/create a labeled video 172 | 173 | Parameters 174 | ---------- 175 | model_path : str 176 | path to exported DeepLabCut model 177 | video_path : str 178 | path to video file 179 | tf_config : :class:`tensorflow.ConfigProto` 180 | tensorflow session configuration 181 | resize : int, optional 182 | resize factor. Can only use one of resize or pixels. If both are provided, will use pixels. by default None 183 | pixels : int, optional 184 | downsize image to this number of pixels, maintaining aspect ratio. Can only use one of resize or pixels. If both are provided, will use pixels. by default None 185 | cropping : list of int 186 | cropping parameters in pixel number: [x1, x2, y1, y2] 187 | dynamic: triple containing (state, detectiontreshold, margin) 188 | If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold), 189 | then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This window is 190 | expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. `, by default "bmy" 205 | save_poses : bool, optional 206 | flag to save poses to an hdf5 file. If True, operates similar to :function:`DeepLabCut.benchmark_videos`, by default False 207 | save_video : bool, optional 208 | flag to save a labeled video. If True, operates similar to :function:`DeepLabCut.create_labeled_video`, by default False 209 | output : str, optional 210 | path to directory to save pose and/or video file. If not specified, will use the directory of video_path, by default None 211 | 212 | Returns 213 | ------- 214 | :class:`numpy.ndarray` 215 | vector of inference times 216 | tuple 217 | (image width, image height) 218 | bool 219 | tensorflow inference flag 220 | dict 221 | metadata for video 222 | 223 | Example 224 | ------- 225 | Return a vector of inference times for 10000 frames: 226 | dlclive.benchmark('/my/exported/model', 'my_video.avi', n_frames=10000) 227 | 228 | Return a vector of inference times, resizing images to half the width and height for inference 229 | dlclive.benchmark('/my/exported/model', 'my_video.avi', n_frames=10000, resize=0.5) 230 | 231 | Display keypoints to check the accuracy of an exported model 232 | dlclive.benchmark('/my/exported/model', 'my_video.avi', display=True) 233 | 234 | Analyze a video (save poses to hdf5) and create a labeled video, similar to :function:`DeepLabCut.benchmark_videos` and :function:`create_labeled_video` 235 | dlclive.benchmark('/my/exported/model', 'my_video.avi', save_poses=True, save_video=True) 236 | """ 237 | 238 | ### load video 239 | 240 | cap = cv2.VideoCapture(video_path) 241 | ret, frame = cap.read() 242 | n_frames = ( 243 | n_frames 244 | if (n_frames > 0) and (n_frames < cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1) 245 | else (cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1) 246 | ) 247 | n_frames = int(n_frames) 248 | im_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) 249 | 250 | ### get resize factor 251 | 252 | if pixels is not None: 253 | resize = np.sqrt(pixels / (im_size[0] * im_size[1])) 254 | if resize is not None: 255 | im_size = (int(im_size[0] * resize), int(im_size[1] * resize)) 256 | 257 | ### create video writer 258 | 259 | if save_video: 260 | colors = None 261 | out_dir = ( 262 | output 263 | if output is not None 264 | else os.path.dirname(os.path.realpath(video_path)) 265 | ) 266 | out_vid_base = os.path.basename(video_path) 267 | out_vid_file = os.path.normpath( 268 | f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_LABELED.avi" 269 | ) 270 | fourcc = cv2.VideoWriter_fourcc(*"DIVX") 271 | fps = cap.get(cv2.CAP_PROP_FPS) 272 | vwriter = cv2.VideoWriter(out_vid_file, fourcc, fps, im_size) 273 | 274 | ### check for pandas installation if using save_poses flag 275 | 276 | if save_poses: 277 | try: 278 | import pandas as pd 279 | 280 | use_pandas = True 281 | except: 282 | use_pandas = False 283 | warnings.warn( 284 | "Could not find installation of pandas; saving poses as a numpy array with the dimensions (n_frames, n_keypoints, [x, y, likelihood])." 285 | ) 286 | 287 | ### initialize DLCLive and perform inference 288 | 289 | inf_times = np.zeros(n_frames) 290 | poses = [] 291 | 292 | live = DLCLive( 293 | model_path, 294 | tf_config=tf_config, 295 | resize=resize, 296 | cropping=cropping, 297 | dynamic=dynamic, 298 | display=display, 299 | pcutoff=pcutoff, 300 | display_radius=display_radius, 301 | display_cmap=cmap, 302 | ) 303 | 304 | poses.append(live.init_inference(frame)) 305 | TFGPUinference = True if len(live.outputs) == 1 else False 306 | 307 | iterator = range(n_frames) if (print_rate) or (display) else tqdm(range(n_frames)) 308 | for i in iterator: 309 | 310 | ret, frame = cap.read() 311 | 312 | if not ret: 313 | warnings.warn( 314 | "Did not complete {:d} frames. There probably were not enough frames in the video {}.".format( 315 | n_frames, video_path 316 | ) 317 | ) 318 | break 319 | 320 | start_pose = time.time() 321 | poses.append(live.get_pose(frame)) 322 | inf_times[i] = time.time() - start_pose 323 | 324 | if save_video: 325 | 326 | if colors is None: 327 | all_colors = getattr(cc, cmap) 328 | colors = [ 329 | ImageColor.getcolor(c, "RGB")[::-1] 330 | for c in all_colors[:: int(len(all_colors) / poses[-1].shape[0])] 331 | ] 332 | 333 | this_pose = poses[-1] 334 | for j in range(this_pose.shape[0]): 335 | if this_pose[j, 2] > pcutoff: 336 | x = int(this_pose[j, 0]) 337 | y = int(this_pose[j, 1]) 338 | frame = cv2.circle( 339 | frame, (x, y), display_radius, colors[j], thickness=-1 340 | ) 341 | 342 | if resize is not None: 343 | frame = cv2.resize(frame, im_size) 344 | vwriter.write(frame) 345 | 346 | if print_rate: 347 | print("pose rate = {:d}".format(int(1 / inf_times[i]))) 348 | 349 | if print_rate: 350 | print("mean pose rate = {:d}".format(int(np.mean(1 / inf_times)))) 351 | 352 | ### gather video and test parameterization 353 | 354 | # dont want to fail here so gracefully failing on exception -- 355 | # eg. some packages of cv2 don't have CAP_PROP_CODEC_PIXEL_FORMAT 356 | try: 357 | fourcc = decode_fourcc(cap.get(cv2.CAP_PROP_FOURCC)) 358 | except: 359 | fourcc = "" 360 | 361 | try: 362 | fps = round(cap.get(cv2.CAP_PROP_FPS)) 363 | except: 364 | fps = None 365 | 366 | try: 367 | pix_fmt = decode_fourcc(cap.get(cv2.CAP_PROP_CODEC_PIXEL_FORMAT)) 368 | except: 369 | pix_fmt = "" 370 | 371 | try: 372 | frame_count = round(cap.get(cv2.CAP_PROP_FRAME_COUNT)) 373 | except: 374 | frame_count = None 375 | 376 | try: 377 | orig_im_size = ( 378 | round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), 379 | round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), 380 | ) 381 | except: 382 | orig_im_size = None 383 | 384 | meta = { 385 | "video_path": video_path, 386 | "video_codec": fourcc, 387 | "video_pixel_format": pix_fmt, 388 | "video_fps": fps, 389 | "video_total_frames": frame_count, 390 | "original_frame_size": orig_im_size, 391 | "dlclive_params": live.parameterization, 392 | } 393 | 394 | ### close video and tensorflow session 395 | 396 | cap.release() 397 | live.close() 398 | 399 | if save_video: 400 | vwriter.release() 401 | 402 | if save_poses: 403 | 404 | cfg_path = os.path.normpath(f"{model_path}/pose_cfg.yaml") 405 | ruamel_file = ruamel.yaml.YAML() 406 | dlc_cfg = ruamel_file.load(open(cfg_path, "r")) 407 | bodyparts = dlc_cfg["all_joints_names"] 408 | poses = np.array(poses) 409 | 410 | if use_pandas: 411 | 412 | poses = poses.reshape((poses.shape[0], poses.shape[1] * poses.shape[2])) 413 | pdindex = pd.MultiIndex.from_product( 414 | [bodyparts, ["x", "y", "likelihood"]], names=["bodyparts", "coords"] 415 | ) 416 | pose_df = pd.DataFrame(poses, columns=pdindex) 417 | 418 | out_dir = ( 419 | output 420 | if output is not None 421 | else os.path.dirname(os.path.realpath(video_path)) 422 | ) 423 | out_vid_base = os.path.basename(video_path) 424 | out_dlc_file = os.path.normpath( 425 | f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_POSES.h5" 426 | ) 427 | pose_df.to_hdf(out_dlc_file, key="df_with_missing", mode="w") 428 | 429 | else: 430 | 431 | out_vid_base = os.path.basename(video_path) 432 | out_dlc_file = os.path.normpath( 433 | f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_POSES.npy" 434 | ) 435 | np.save(out_dlc_file, poses) 436 | 437 | return inf_times, im_size, TFGPUinference, meta 438 | 439 | 440 | def save_inf_times( 441 | sys_info, inf_times, im_size, TFGPUinference, model=None, meta=None, output=None 442 | ): 443 | """ Save inference time data collected using :function:`benchmark` with system information to a pickle file. 444 | This is primarily used through :function:`benchmark_videos` 445 | 446 | 447 | Parameters 448 | ---------- 449 | sys_info : tuple 450 | system information generated by :func:`get_system_info` 451 | inf_times : :class:`numpy.ndarray` 452 | array of inference times generated by :func:`benchmark` 453 | im_size : tuple or :class:`numpy.ndarray` 454 | image size (width, height) for each benchmark run. If an array, each row corresponds to a row in inf_times 455 | TFGPUinference: bool 456 | flag if using tensorflow inference or numpy inference DLC model 457 | model: str, optional 458 | name of model 459 | meta : dict, optional 460 | metadata returned by :func:`benchmark` 461 | output : str, optional 462 | path to directory to save data. If None, uses pwd, by default None 463 | 464 | Returns 465 | ------- 466 | bool 467 | flag indicating successful save 468 | """ 469 | 470 | output = output if output is not None else os.getcwd() 471 | model_type = None 472 | if model is not None: 473 | if "resnet" in model: 474 | model_type = "resnet" 475 | elif "mobilenet" in model: 476 | model_type = "mobilenet" 477 | else: 478 | model_type = None 479 | 480 | fn_ind = 0 481 | base_name = ( 482 | f"benchmark_{sys_info['host_name']}_{sys_info['device_type']}_{fn_ind}.pickle" 483 | ) 484 | out_file = os.path.normpath(f"{output}/{base_name}") 485 | while os.path.isfile(out_file): 486 | fn_ind += 1 487 | base_name = f"benchmark_{sys_info['host_name']}_{sys_info['device_type']}_{fn_ind}.pickle" 488 | out_file = os.path.normpath(f"{output}/{base_name}") 489 | 490 | # summary stats (mean inference time & standard error of mean) 491 | stats = zip( 492 | np.mean(inf_times, 1), 493 | np.std(inf_times, 1) * 1.0 / np.sqrt(np.shape(inf_times)[1]), 494 | ) 495 | 496 | # for stat in stats: 497 | # print("Stats:", stat) 498 | 499 | data = { 500 | "model": model, 501 | "model_type": model_type, 502 | "TFGPUinference": TFGPUinference, 503 | "im_size": im_size, 504 | "inference_times": inf_times, 505 | "stats": stats, 506 | } 507 | 508 | data.update(sys_info) 509 | if meta: 510 | data.update(meta) 511 | 512 | os.makedirs(os.path.normpath(output), exist_ok=True) 513 | pickle.dump(data, open(out_file, "wb")) 514 | 515 | return True 516 | 517 | 518 | def benchmark_videos( 519 | model_path, 520 | video_path, 521 | output=None, 522 | n_frames=1000, 523 | tf_config=None, 524 | resize=None, 525 | pixels=None, 526 | cropping=None, 527 | dynamic=(False, 0.5, 10), 528 | print_rate=False, 529 | display=False, 530 | pcutoff=0.5, 531 | display_radius=3, 532 | cmap="bmy", 533 | save_poses=False, 534 | save_video=False, 535 | ): 536 | """Analyze videos using DeepLabCut-live exported models. 537 | Analyze multiple videos and/or multiple options for the size of the video 538 | by specifying a resizing factor or the number of pixels to use in the image (keeping aspect ratio constant). 539 | Options to record inference times (to examine inference speed), 540 | display keypoints to visually check the accuracy, 541 | or save poses to an hdf5 file as in :function:`deeplabcut.benchmark_videos` and 542 | create a labeled video as in :function:`deeplabcut.create_labeled_video`. 543 | 544 | Parameters 545 | ---------- 546 | model_path : str 547 | path to exported DeepLabCut model 548 | video_path : str or list 549 | path to video file or list of paths to video files 550 | output : str 551 | path to directory to save results 552 | tf_config : :class:`tensorflow.ConfigProto` 553 | tensorflow session configuration 554 | resize : int, optional 555 | resize factor. Can only use one of resize or pixels. If both are provided, will use pixels. by default None 556 | pixels : int, optional 557 | downsize image to this number of pixels, maintaining aspect ratio. Can only use one of resize or pixels. If both are provided, will use pixels. by default None 558 | cropping : list of int 559 | cropping parameters in pixel number: [x1, x2, y1, y2] 560 | dynamic: triple containing (state, detectiontreshold, margin) 561 | If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold), 562 | then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This window is 563 | expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. `, by default "bmy" 578 | save_poses : bool, optional 579 | flag to save poses to an hdf5 file. If True, operates similar to :function:`DeepLabCut.benchmark_videos`, by default False 580 | save_video : bool, optional 581 | flag to save a labeled video. If True, operates similar to :function:`DeepLabCut.create_labeled_video`, by default False 582 | 583 | Example 584 | ------- 585 | Return a vector of inference times for 10000 frames on one video or two videos: 586 | dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', n_frames=10000) 587 | dlclive.benchmark_videos('/my/exported/model', ['my_video1.avi', 'my_video2.avi'], n_frames=10000) 588 | 589 | Return a vector of inference times, testing full size and resizing images to half the width and height for inference, for two videos 590 | dlclive.benchmark_videos('/my/exported/model', ['my_video1.avi', 'my_video2.avi'], n_frames=10000, resize=[1.0, 0.5]) 591 | 592 | Display keypoints to check the accuracy of an exported model 593 | dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', display=True) 594 | 595 | Analyze a video (save poses to hdf5) and create a labeled video, similar to :function:`DeepLabCut.benchmark_videos` and :function:`create_labeled_video` 596 | dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', save_poses=True, save_video=True) 597 | """ 598 | 599 | # convert video_paths to list 600 | 601 | video_path = video_path if type(video_path) is list else [video_path] 602 | 603 | # fix resize 604 | 605 | if pixels: 606 | pixels = pixels if type(pixels) is list else [pixels] 607 | resize = [None for p in pixels] 608 | elif resize: 609 | resize = resize if type(resize) is list else [resize] 610 | pixels = [None for r in resize] 611 | else: 612 | resize = [None] 613 | pixels = [None] 614 | 615 | # loop over videos 616 | 617 | for v in video_path: 618 | 619 | # initialize full inference times 620 | 621 | inf_times = [] 622 | im_size_out = [] 623 | 624 | for i in range(len(resize)): 625 | 626 | print(f"\nRun {i+1} / {len(resize)}\n") 627 | 628 | this_inf_times, this_im_size, TFGPUinference, meta = benchmark( 629 | model_path, 630 | v, 631 | tf_config=tf_config, 632 | resize=resize[i], 633 | pixels=pixels[i], 634 | cropping=cropping, 635 | dynamic=dynamic, 636 | n_frames=n_frames, 637 | print_rate=print_rate, 638 | display=display, 639 | pcutoff=pcutoff, 640 | display_radius=display_radius, 641 | cmap=cmap, 642 | save_poses=save_poses, 643 | save_video=save_video, 644 | output=output, 645 | ) 646 | 647 | inf_times.append(this_inf_times) 648 | im_size_out.append(this_im_size) 649 | 650 | inf_times = np.array(inf_times) 651 | im_size_out = np.array(im_size_out) 652 | 653 | # save results 654 | 655 | if output is not None: 656 | sys_info = get_system_info() 657 | save_inf_times( 658 | sys_info, 659 | inf_times, 660 | im_size_out, 661 | TFGPUinference, 662 | model=os.path.basename(model_path), 663 | meta=meta, 664 | output=output, 665 | ) 666 | 667 | 668 | def main(): 669 | """Provides a command line interface :function:`benchmark_videos` 670 | """ 671 | 672 | import argparse 673 | 674 | parser = argparse.ArgumentParser() 675 | parser.add_argument("model_path", type=str) 676 | parser.add_argument("video_path", type=str, nargs="+") 677 | parser.add_argument("-o", "--output", type=str, default=None) 678 | parser.add_argument("-n", "--n-frames", type=int, default=1000) 679 | parser.add_argument("-r", "--resize", type=float, nargs="+") 680 | parser.add_argument("-p", "--pixels", type=float, nargs="+") 681 | parser.add_argument("-v", "--print-rate", default=False, action="store_true") 682 | parser.add_argument("-d", "--display", default=False, action="store_true") 683 | parser.add_argument("-l", "--pcutoff", default=0.5, type=float) 684 | parser.add_argument("-s", "--display-radius", default=3, type=int) 685 | parser.add_argument("-c", "--cmap", type=str, default="bmy") 686 | parser.add_argument("--cropping", nargs="+", type=int, default=None) 687 | parser.add_argument("--dynamic", nargs="+", type=float, default=[]) 688 | parser.add_argument("--save-poses", action="store_true") 689 | parser.add_argument("--save-video", action="store_true") 690 | args = parser.parse_args() 691 | 692 | if (args.cropping) and (len(args.cropping) < 4): 693 | raise Exception( 694 | "Cropping not properly specified. Must provide 4 values: x1, x2, y1, y2" 695 | ) 696 | 697 | if not args.dynamic: 698 | args.dynamic = (False, 0.5, 10) 699 | elif len(args.dynamic) < 3: 700 | raise Exception( 701 | "Dynamic cropping not properly specified. Must provide three values: 0 or 1 as boolean flag, pcutoff, and margin" 702 | ) 703 | else: 704 | args.dynamic = (bool(args.dynamic[0]), args.dynamic[1], args.dynamic[2]) 705 | 706 | benchmark_videos( 707 | args.model_path, 708 | args.video_path, 709 | output=args.output, 710 | resize=args.resize, 711 | pixels=args.pixels, 712 | cropping=args.cropping, 713 | dynamic=args.dynamic, 714 | n_frames=args.n_frames, 715 | print_rate=args.print_rate, 716 | display=args.display, 717 | pcutoff=args.pcutoff, 718 | display_radius=args.display_radius, 719 | cmap=args.cmap, 720 | save_poses=args.save_poses, 721 | save_video=args.save_video, 722 | ) 723 | 724 | 725 | if __name__ == "__main__": 726 | main() 727 | -------------------------------------------------------------------------------- /dlclive/check_install/check_install.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import sys 10 | import shutil 11 | import warnings 12 | 13 | from dlclive import benchmark_videos 14 | import urllib.request 15 | import argparse 16 | from pathlib import Path 17 | from dlclibrary.dlcmodelzoo.modelzoo_download import ( 18 | download_huggingface_model, 19 | ) 20 | 21 | 22 | MODEL_NAME = "superanimal_quadruped" 23 | SNAPSHOT_NAME = "snapshot-700000.pb" 24 | 25 | 26 | def urllib_pbar(count, blockSize, totalSize): 27 | percent = int(count * blockSize * 100 / totalSize) 28 | outstr = f"{round(percent)}%" 29 | sys.stdout.write(outstr) 30 | sys.stdout.write("\b"*len(outstr)) 31 | sys.stdout.flush() 32 | 33 | 34 | def main(): 35 | parser = argparse.ArgumentParser( 36 | description="Test DLC-Live installation by downloading and evaluating a demo DLC project!") 37 | parser.add_argument('--nodisplay', action='store_false', help="Run the test without displaying tracking") 38 | args = parser.parse_args() 39 | display = args.nodisplay 40 | 41 | if not display: 42 | print('Running without displaying video') 43 | 44 | # make temporary directory in $HOME 45 | # TODO: why create this temp directory in $HOME? 46 | print("\nCreating temporary directory...\n") 47 | tmp_dir = Path().home() / 'dlc-live-tmp' 48 | tmp_dir.mkdir(mode=0o775,exist_ok=True) 49 | 50 | video_file = str(tmp_dir / 'dog_clip.avi') 51 | model_dir = tmp_dir / 'DLC_Dog_resnet_50_iteration-0_shuffle-0' 52 | 53 | # download dog test video from github: 54 | # TODO: Should check if the video's already there before downloading it (should have been cloned with the files) 55 | print(f"Downloading Video to {video_file}") 56 | url_link = "https://github.com/DeepLabCut/DeepLabCut-live/blob/master/check_install/dog_clip.avi?raw=True" 57 | urllib.request.urlretrieve(url_link, video_file, reporthook=urllib_pbar) 58 | 59 | # download model from the DeepLabCut Model Zoo 60 | if Path(model_dir / SNAPSHOT_NAME).exists(): 61 | print('Model already downloaded, using cached version') 62 | else: 63 | print("Downloading full_dog model from the DeepLabCut Model Zoo...") 64 | download_huggingface_model(MODEL_NAME, model_dir) 65 | 66 | # assert these things exist so we can give informative error messages 67 | assert Path(video_file).exists(), f"Missing video file {video_file}" 68 | assert Path(model_dir / SNAPSHOT_NAME).exists(), f"Missing model file {model_dir / SNAPSHOT_NAME}" 69 | 70 | # run benchmark videos 71 | print("\n Running inference...\n") 72 | benchmark_videos(str(model_dir), video_file, display=display, resize=0.5, pcutoff=0.25) 73 | 74 | # deleting temporary files 75 | print("\n Deleting temporary files...\n") 76 | try: 77 | shutil.rmtree(tmp_dir) 78 | except PermissionError: 79 | warnings.warn(f'Could not delete temporary directory {str(tmp_dir)} due to a permissions error, but otherwise dlc-live seems to be working fine!') 80 | 81 | print("\nDone!\n") 82 | 83 | 84 | if __name__ == "__main__": 85 | main() 86 | -------------------------------------------------------------------------------- /dlclive/display.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | from tkinter import Tk, Label 10 | import colorcet as cc 11 | from PIL import Image, ImageTk, ImageDraw 12 | 13 | 14 | class Display(object): 15 | """ 16 | Simple object to display frames with DLC labels. 17 | 18 | Parameters 19 | ----------- 20 | cmap : string 21 | string indicating the Matoplotlib colormap to use. 22 | pcutoff : float 23 | likelihood threshold to display points 24 | """ 25 | 26 | def __init__(self, cmap="bmy", radius=3, pcutoff=0.5): 27 | """ Constructor method 28 | """ 29 | 30 | self.cmap = cmap 31 | self.colors = None 32 | self.radius = radius 33 | self.pcutoff = pcutoff 34 | self.window = None 35 | 36 | def set_display(self, im_size, bodyparts): 37 | """ Create tkinter window to display image 38 | 39 | Parameters 40 | ---------- 41 | im_size : tuple 42 | (width, height) of image 43 | bodyparts : int 44 | number of bodyparts 45 | """ 46 | 47 | self.window = Tk() 48 | self.window.title("DLC Live") 49 | self.lab = Label(self.window) 50 | self.lab.pack() 51 | 52 | all_colors = getattr(cc, self.cmap) 53 | self.colors = all_colors[:: int(len(all_colors) / bodyparts)] 54 | 55 | def display_frame(self, frame, pose=None): 56 | """ 57 | Display the image with DeepLabCut labels using opencv imshow 58 | 59 | Parameters 60 | ----------- 61 | frame :class:`numpy.ndarray` 62 | an image as a numpy array 63 | 64 | pose :class:`numpy.ndarray` 65 | the pose estimated by DeepLabCut for the image 66 | """ 67 | 68 | im_size = (frame.shape[1], frame.shape[0]) 69 | 70 | if pose is not None: 71 | 72 | if self.window is None: 73 | self.set_display(im_size, pose.shape[0]) 74 | 75 | img = Image.fromarray(frame) 76 | draw = ImageDraw.Draw(img) 77 | 78 | for i in range(pose.shape[0]): 79 | if pose[i, 2] > self.pcutoff: 80 | try: 81 | x0 = ( 82 | pose[i, 0] - self.radius 83 | if pose[i, 0] - self.radius > 0 84 | else 0 85 | ) 86 | x1 = ( 87 | pose[i, 0] + self.radius 88 | if pose[i, 0] + self.radius < im_size[0] 89 | else im_size[1] 90 | ) 91 | y0 = ( 92 | pose[i, 1] - self.radius 93 | if pose[i, 1] - self.radius > 0 94 | else 0 95 | ) 96 | y1 = ( 97 | pose[i, 1] + self.radius 98 | if pose[i, 1] + self.radius < im_size[1] 99 | else im_size[0] 100 | ) 101 | coords = [x0, y0, x1, y1] 102 | draw.ellipse( 103 | coords, fill=self.colors[i], outline=self.colors[i] 104 | ) 105 | except Exception as e: 106 | print(e) 107 | 108 | img_tk = ImageTk.PhotoImage(image=img, master=self.window) 109 | self.lab.configure(image=img_tk) 110 | self.window.update() 111 | 112 | def destroy(self): 113 | """ 114 | Destroys the opencv image window 115 | """ 116 | 117 | self.window.destroy() 118 | -------------------------------------------------------------------------------- /dlclive/dlclive.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | import os 9 | import ruamel.yaml 10 | import glob 11 | import warnings 12 | import numpy as np 13 | import tensorflow as tf 14 | import typing 15 | from pathlib import Path 16 | from typing import Optional, Tuple, List 17 | 18 | try: 19 | TFVER = [int(v) for v in tf.__version__.split(".")] 20 | if TFVER[1] < 14: 21 | from tensorflow.contrib.tensorrt import trt_convert as trt 22 | else: 23 | from tensorflow.python.compiler.tensorrt import trt_convert as trt 24 | except Exception: 25 | pass 26 | 27 | from dlclive.graph import ( 28 | read_graph, 29 | finalize_graph, 30 | get_output_nodes, 31 | get_output_tensors, 32 | extract_graph, 33 | ) 34 | from dlclive.pose import extract_cnn_output, argmax_pose_predict, multi_pose_predict 35 | from dlclive.display import Display 36 | from dlclive import utils 37 | from dlclive.exceptions import DLCLiveError, DLCLiveWarning 38 | if typing.TYPE_CHECKING: 39 | from dlclive.processor import Processor 40 | 41 | class DLCLive(object): 42 | """ 43 | Object that loads a DLC network and performs inference on single images (e.g. images captured from a camera feed) 44 | 45 | Parameters 46 | ----------- 47 | 48 | path : string 49 | Full path to exported model directory 50 | 51 | model_type: string, optional 52 | which model to use: 'base', 'tensorrt' for tensorrt optimized graph, 'lite' for tensorflow lite optimized graph 53 | 54 | precision : string, optional 55 | precision of model weights, only for model_type='tensorrt'. Can be 'FP16' (default), 'FP32', or 'INT8' 56 | 57 | cropping : list of int 58 | cropping parameters in pixel number: [x1, x2, y1, y2] 59 | 60 | dynamic: triple containing (state, detectiontreshold, margin) 61 | If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold), 62 | then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This window is 63 | expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. dict: 177 | """ 178 | Return 179 | Returns 180 | ------- 181 | """ 182 | return {param: getattr(self, param) for param in self.PARAMETERS} 183 | 184 | def process_frame(self, frame): 185 | """ 186 | Crops an image according to the object's cropping and dynamic properties. 187 | 188 | Parameters 189 | ----------- 190 | frame :class:`numpy.ndarray` 191 | image as a numpy array 192 | 193 | Returns 194 | ---------- 195 | frame :class:`numpy.ndarray` 196 | processed frame: convert type, crop, convert color 197 | """ 198 | 199 | if frame.dtype != np.uint8: 200 | 201 | frame = utils.convert_to_ubyte(frame) 202 | 203 | if self.cropping: 204 | 205 | frame = frame[ 206 | self.cropping[2] : self.cropping[3], self.cropping[0] : self.cropping[1] 207 | ] 208 | 209 | if self.dynamic[0]: 210 | 211 | if self.pose is not None: 212 | 213 | detected = self.pose[:, 2] > self.dynamic[1] 214 | 215 | if np.any(detected): 216 | 217 | x = self.pose[detected, 0] 218 | y = self.pose[detected, 1] 219 | 220 | x1 = int(max([0, int(np.amin(x)) - self.dynamic[2]])) 221 | x2 = int(min([frame.shape[1], int(np.amax(x)) + self.dynamic[2]])) 222 | y1 = int(max([0, int(np.amin(y)) - self.dynamic[2]])) 223 | y2 = int(min([frame.shape[0], int(np.amax(y)) + self.dynamic[2]])) 224 | self.dynamic_cropping = [x1, x2, y1, y2] 225 | 226 | frame = frame[y1:y2, x1:x2] 227 | 228 | else: 229 | 230 | self.dynamic_cropping = None 231 | 232 | if self.resize != 1: 233 | frame = utils.resize_frame(frame, self.resize) 234 | 235 | if self.convert2rgb: 236 | frame = utils.img_to_rgb(frame) 237 | 238 | return frame 239 | 240 | def init_inference(self, frame=None, **kwargs): 241 | """ 242 | Load model and perform inference on first frame -- the first inference is usually very slow. 243 | 244 | Parameters 245 | ----------- 246 | frame :class:`numpy.ndarray` 247 | image as a numpy array 248 | 249 | Returns 250 | -------- 251 | pose :class:`numpy.ndarray` 252 | the pose estimated by DeepLabCut for the input image 253 | """ 254 | 255 | # get model file 256 | 257 | model_file = glob.glob(os.path.normpath(self.path + "/*.pb"))[0] 258 | if not os.path.isfile(model_file): 259 | raise FileNotFoundError( 260 | "The model file {} does not exist.".format(model_file) 261 | ) 262 | 263 | # process frame 264 | 265 | if frame is None and (self.model_type == "tflite"): 266 | raise DLCLiveError( 267 | "No image was passed to initialize inference. An image must be passed to the init_inference method" 268 | ) 269 | 270 | if frame is not None: 271 | if frame.ndim == 2: 272 | self.convert2rgb = True 273 | processed_frame = self.process_frame(frame) 274 | 275 | # load model 276 | 277 | if self.model_type == "base": 278 | 279 | graph_def = read_graph(model_file) 280 | graph = finalize_graph(graph_def) 281 | self.sess, self.inputs, self.outputs = extract_graph( 282 | graph, tf_config=self.tf_config 283 | ) 284 | 285 | elif self.model_type == "tflite": 286 | 287 | ### 288 | # the frame size needed to initialize the tflite model as 289 | # tflite does not support saving a model with dynamic input size 290 | ### 291 | 292 | # get input and output tensor names from graph_def 293 | graph_def = read_graph(model_file) 294 | graph = finalize_graph(graph_def) 295 | output_nodes = get_output_nodes(graph) 296 | output_nodes = [on.replace("DLC/", "") for on in output_nodes] 297 | 298 | tf_version_2 = tf.__version__[0] == '2' 299 | 300 | if tf_version_2: 301 | converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph( 302 | model_file, 303 | ["Placeholder"], 304 | output_nodes, 305 | input_shapes={"Placeholder": [1, processed_frame.shape[0], processed_frame.shape[1], 3]}, 306 | ) 307 | else: 308 | converter = tf.lite.TFLiteConverter.from_frozen_graph( 309 | model_file, 310 | ["Placeholder"], 311 | output_nodes, 312 | input_shapes={"Placeholder": [1, processed_frame.shape[0], processed_frame.shape[1], 3]}, 313 | ) 314 | 315 | try: 316 | tflite_model = converter.convert() 317 | except Exception: 318 | raise DLCLiveError( 319 | ( 320 | "This model cannot be converted to tensorflow lite format. " 321 | "To use tensorflow lite for live inference, " 322 | "make sure to set TFGPUinference=False " 323 | "when exporting the model from DeepLabCut" 324 | ) 325 | ) 326 | 327 | self.tflite_interpreter = tf.lite.Interpreter(model_content=tflite_model) 328 | self.tflite_interpreter.allocate_tensors() 329 | self.inputs = self.tflite_interpreter.get_input_details() 330 | self.outputs = self.tflite_interpreter.get_output_details() 331 | 332 | elif self.model_type == "tensorrt": 333 | 334 | graph_def = read_graph(model_file) 335 | graph = finalize_graph(graph_def) 336 | output_tensors = get_output_tensors(graph) 337 | output_tensors = [ot.replace("DLC/", "") for ot in output_tensors] 338 | 339 | if (TFVER[0] > 1) | (TFVER[0] == 1 & TFVER[1] >= 14): 340 | converter = trt.TrtGraphConverter( 341 | input_graph_def=graph_def, 342 | nodes_blacklist=output_tensors, 343 | is_dynamic_op=True, 344 | ) 345 | graph_def = converter.convert() 346 | else: 347 | graph_def = trt.create_inference_graph( 348 | input_graph_def=graph_def, 349 | outputs=output_tensors, 350 | max_batch_size=1, 351 | precision_mode=self.precision, 352 | is_dynamic_op=True, 353 | ) 354 | 355 | graph = finalize_graph(graph_def) 356 | self.sess, self.inputs, self.outputs = extract_graph( 357 | graph, tf_config=self.tf_config 358 | ) 359 | 360 | else: 361 | 362 | raise DLCLiveError( 363 | "model_type = {} is not supported. model_type must be 'base', 'tflite', or 'tensorrt'".format( 364 | self.model_type 365 | ) 366 | ) 367 | 368 | # get pose of first frame (first inference is often very slow) 369 | 370 | if frame is not None: 371 | pose = self.get_pose(frame, **kwargs) 372 | else: 373 | pose = None 374 | 375 | self.is_initialized = True 376 | 377 | return pose 378 | 379 | def get_pose(self, frame=None, **kwargs): 380 | """ 381 | Get the pose of an image 382 | 383 | Parameters 384 | ----------- 385 | frame :class:`numpy.ndarray` 386 | image as a numpy array 387 | 388 | Returns 389 | -------- 390 | pose :class:`numpy.ndarray` 391 | the pose estimated by DeepLabCut for the input image 392 | """ 393 | 394 | if frame is None: 395 | raise DLCLiveError("No frame provided for live pose estimation") 396 | 397 | frame = self.process_frame(frame) 398 | 399 | if self.model_type in ["base", "tensorrt"]: 400 | 401 | pose_output = self.sess.run( 402 | self.outputs, feed_dict={self.inputs: np.expand_dims(frame, axis=0)} 403 | ) 404 | 405 | elif self.model_type == "tflite": 406 | 407 | self.tflite_interpreter.set_tensor( 408 | self.inputs[0]["index"], 409 | np.expand_dims(frame, axis=0).astype(np.float32), 410 | ) 411 | self.tflite_interpreter.invoke() 412 | 413 | if len(self.outputs) > 1: 414 | pose_output = [ 415 | self.tflite_interpreter.get_tensor(self.outputs[0]["index"]), 416 | self.tflite_interpreter.get_tensor(self.outputs[1]["index"]), 417 | ] 418 | else: 419 | pose_output = self.tflite_interpreter.get_tensor( 420 | self.outputs[0]["index"] 421 | ) 422 | 423 | else: 424 | 425 | raise DLCLiveError( 426 | "model_type = {} is not supported. model_type must be 'base', 'tflite', or 'tensorrt'".format( 427 | self.model_type 428 | ) 429 | ) 430 | 431 | # check if using TFGPUinference flag 432 | # if not, get pose from network output 433 | 434 | if len(pose_output) > 1: 435 | scmap, locref = extract_cnn_output(pose_output, self.cfg) 436 | num_outputs = self.cfg.get("num_outputs", 1) 437 | if num_outputs > 1: 438 | self.pose = multi_pose_predict( 439 | scmap, locref, self.cfg["stride"], num_outputs 440 | ) 441 | else: 442 | self.pose = argmax_pose_predict(scmap, locref, self.cfg["stride"]) 443 | else: 444 | pose = np.array(pose_output[0]) 445 | self.pose = pose[:, [1, 0, 2]] 446 | 447 | # display image if display=True before correcting pose for cropping/resizing 448 | 449 | if self.display is not None: 450 | self.display.display_frame(frame, self.pose) 451 | 452 | # if frame is cropped, convert pose coordinates to original frame coordinates 453 | 454 | if self.resize is not None: 455 | self.pose[:, :2] *= 1 / self.resize 456 | 457 | if self.cropping is not None: 458 | self.pose[:, 0] += self.cropping[0] 459 | self.pose[:, 1] += self.cropping[2] 460 | 461 | if self.dynamic_cropping is not None: 462 | self.pose[:, 0] += self.dynamic_cropping[0] 463 | self.pose[:, 1] += self.dynamic_cropping[2] 464 | 465 | # process the pose 466 | 467 | if self.processor: 468 | self.pose = self.processor.process(self.pose, **kwargs) 469 | 470 | return self.pose 471 | 472 | def close(self): 473 | """ Close tensorflow session 474 | """ 475 | 476 | self.sess.close() 477 | self.sess = None 478 | self.is_initialized = False 479 | if self.display is not None: 480 | self.display.destroy() 481 | -------------------------------------------------------------------------------- /dlclive/exceptions.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | class DLCLiveError(Exception): 10 | """ Generic error type for incorrect use of the DLCLive class """ 11 | 12 | pass 13 | 14 | 15 | class DLCLiveWarning(Warning): 16 | """ Generic warning for incorrect use of the DLCLive class """ 17 | 18 | pass 19 | -------------------------------------------------------------------------------- /dlclive/graph.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import tensorflow as tf 10 | 11 | vers = (tf.__version__).split(".") 12 | if int(vers[0]) == 2 or int(vers[0]) == 1 and int(vers[1]) > 12: 13 | tf = tf.compat.v1 14 | else: 15 | tf = tf 16 | 17 | 18 | def read_graph(file): 19 | """ 20 | Loads the graph from a protobuf file 21 | 22 | Parameters 23 | ----------- 24 | file : string 25 | path to the protobuf file 26 | 27 | Returns 28 | -------- 29 | graph_def :class:`tensorflow.tf.compat.v1.GraphDef` 30 | The graph definition of the DeepLabCut model found at the object's path 31 | """ 32 | 33 | with tf.io.gfile.GFile(file, "rb") as f: 34 | graph_def = tf.GraphDef() 35 | graph_def.ParseFromString(f.read()) 36 | return graph_def 37 | 38 | 39 | def finalize_graph(graph_def): 40 | """ 41 | Finalize the graph and get inputs to model 42 | 43 | Parameters 44 | ----------- 45 | graph_def :class:`tensorflow.compat.v1.GraphDef` 46 | The graph of the DeepLabCut model, read using the :func:`read_graph` method 47 | 48 | Returns 49 | -------- 50 | graph :class:`tensorflow.compat.v1.GraphDef` 51 | The finalized graph of the DeepLabCut model 52 | inputs :class:`tensorflow.Tensor` 53 | Input tensor(s) for the model 54 | """ 55 | 56 | graph = tf.Graph() 57 | with graph.as_default(): 58 | tf.import_graph_def(graph_def, name="DLC") 59 | graph.finalize() 60 | 61 | return graph 62 | 63 | 64 | def get_output_nodes(graph): 65 | """ 66 | Get the output node names from a graph 67 | 68 | Parameters 69 | ----------- 70 | graph :class:`tensorflow.Graph` 71 | The graph of the DeepLabCut model 72 | 73 | Returns 74 | -------- 75 | output : list 76 | the output node names as a list of strings 77 | """ 78 | 79 | op_names = [str(op.name) for op in graph.get_operations()] 80 | if "concat_1" in op_names[-1]: 81 | output = [op_names[-1]] 82 | else: 83 | output = [op_names[-1], op_names[-2]] 84 | 85 | return output 86 | 87 | 88 | def get_output_tensors(graph): 89 | """ 90 | Get the names of the output tensors from a graph 91 | 92 | Parameters 93 | ----------- 94 | graph :class:`tensorflow.Graph` 95 | The graph of the DeepLabCut model 96 | 97 | Returns 98 | -------- 99 | output : list 100 | the output tensor names as a list of strings 101 | """ 102 | 103 | output_nodes = get_output_nodes(graph) 104 | output_tensor = [out + ":0" for out in output_nodes] 105 | return output_tensor 106 | 107 | 108 | def get_input_tensor(graph): 109 | 110 | input_tensor = str(graph.get_operations()[0].name) + ":0" 111 | return input_tensor 112 | 113 | 114 | def extract_graph(graph, tf_config=None): 115 | """ 116 | Initializes a tensorflow session with the specified graph and extracts the model's inputs and outputs 117 | 118 | Parameters 119 | ----------- 120 | graph :class:`tensorflow.Graph` 121 | a tensorflow graph containing the desired model 122 | tf_config :class:`tensorflow.ConfigProto` 123 | 124 | Returns 125 | -------- 126 | sess :class:`tensorflow.Session` 127 | a tensorflow session with the specified graph definition 128 | outputs :class:`tensorflow.Tensor` 129 | the output tensor(s) for the model 130 | """ 131 | 132 | input_tensor = get_input_tensor(graph) 133 | output_tensor = get_output_tensors(graph) 134 | sess = tf.Session(graph=graph, config=tf_config) 135 | inputs = graph.get_tensor_by_name(input_tensor) 136 | outputs = [graph.get_tensor_by_name(out) for out in output_tensor] 137 | 138 | return sess, inputs, outputs 139 | -------------------------------------------------------------------------------- /dlclive/pose.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import numpy as np 10 | 11 | 12 | def extract_cnn_output(outputs, cfg): 13 | """ 14 | Extract location refinement and score map from DeepLabCut network 15 | 16 | Parameters 17 | ----------- 18 | outputs : list 19 | List of outputs from DeepLabCut network. 20 | Requires 2 entries: 21 | index 0 is output from Sigmoid 22 | index 1 is output from pose/locref_pred/block4/BiasAdd 23 | 24 | cfg : dict 25 | Dictionary read from the pose_cfg.yaml file for the network. 26 | 27 | Returns 28 | -------- 29 | scmap : ? 30 | score map 31 | 32 | locref : ? 33 | location refinement 34 | """ 35 | 36 | scmap = outputs[0] 37 | scmap = np.squeeze(scmap) 38 | locref = None 39 | if cfg["location_refinement"]: 40 | locref = np.squeeze(outputs[1]) 41 | shape = locref.shape 42 | locref = np.reshape(locref, (shape[0], shape[1], -1, 2)) 43 | locref *= cfg["locref_stdev"] 44 | if len(scmap.shape) == 2: # for single body part! 45 | scmap = np.expand_dims(scmap, axis=2) 46 | return scmap, locref 47 | 48 | 49 | def argmax_pose_predict(scmap, offmat, stride): 50 | """ 51 | Combines score map and offsets to the final pose 52 | 53 | Parameters 54 | ----------- 55 | scmap : ? 56 | score map 57 | 58 | offmat : ? 59 | offsets 60 | 61 | stride : ? 62 | ? 63 | 64 | Returns 65 | -------- 66 | pose :class:`numpy.ndarray` 67 | pose as a numpy array 68 | """ 69 | 70 | num_joints = scmap.shape[2] 71 | pose = [] 72 | for joint_idx in range(num_joints): 73 | maxloc = np.unravel_index( 74 | np.argmax(scmap[:, :, joint_idx]), scmap[:, :, joint_idx].shape 75 | ) 76 | offset = np.array(offmat[maxloc][joint_idx])[::-1] 77 | pos_f8 = np.array(maxloc).astype("float") * stride + 0.5 * stride + offset 78 | pose.append(np.hstack((pos_f8[::-1], [scmap[maxloc][joint_idx]]))) 79 | return np.array(pose) 80 | 81 | 82 | def get_top_values(scmap, n_top=5): 83 | batchsize, ny, nx, num_joints = scmap.shape 84 | scmap_flat = scmap.reshape(batchsize, nx * ny, num_joints) 85 | if n_top == 1: 86 | scmap_top = np.argmax(scmap_flat, axis=1)[None] 87 | else: 88 | scmap_top = np.argpartition(scmap_flat, -n_top, axis=1)[:, -n_top:] 89 | for ix in range(batchsize): 90 | vals = scmap_flat[ix, scmap_top[ix], np.arange(num_joints)] 91 | arg = np.argsort(-vals, axis=0) 92 | scmap_top[ix] = scmap_top[ix, arg, np.arange(num_joints)] 93 | scmap_top = scmap_top.swapaxes(0, 1) 94 | 95 | Y, X = np.unravel_index(scmap_top, (ny, nx)) 96 | return Y, X 97 | 98 | 99 | def multi_pose_predict(scmap, locref, stride, num_outputs): 100 | Y, X = get_top_values(scmap[None], num_outputs) 101 | Y, X = Y[:, 0], X[:, 0] 102 | num_joints = scmap.shape[2] 103 | DZ = np.zeros((num_outputs, num_joints, 3)) 104 | for m in range(num_outputs): 105 | for k in range(num_joints): 106 | x = X[m, k] 107 | y = Y[m, k] 108 | DZ[m, k, :2] = locref[y, x, k, :] 109 | DZ[m, k, 2] = scmap[y, x, k] 110 | 111 | X = X.astype("float32") * stride + 0.5 * stride + DZ[:, :, 0] 112 | Y = Y.astype("float32") * stride + 0.5 * stride + DZ[:, :, 1] 113 | P = DZ[:, :, 2] 114 | 115 | pose = np.empty((num_joints, num_outputs * 3), dtype="float32") 116 | pose[:, 0::3] = X.T 117 | pose[:, 1::3] = Y.T 118 | pose[:, 2::3] = P.T 119 | 120 | return pose 121 | -------------------------------------------------------------------------------- /dlclive/processor/README.md: -------------------------------------------------------------------------------- 1 | ### DeepLabCut-live Processors 2 | 3 | The `Processor` class allows users to implement processing or computation steps after DeepLabCut pose estimation. For example, a `Processor` can detect certain features of a pose and turn on an LED or an optogenetics laser, a `Processor` can implement a forward-prediction model that predicts animal's pose ~10-100 ms into the future to apply feedback with zero latency, or a `Processor` can do both. 4 | 5 | The `Processor` is designed to be extremely flexible: it must only contain two methods: `Processor.process`, whose input and output is a pose as a numpy array, and `Processor.save`, which allows users to implement a method that saves any data the `Processor` acquires, such as the time that desired behavior occured or the times an LED or laser was turned on/off. The save method must be written by the user, so users can choose whether this data is saved as a text/csv, numpy, pickle, or pandas file to provide a few examples. 6 | 7 | To write your own custom `Processor`, your class must inherit the base `Processor` class (see [here](./processor.py)): 8 | ``` 9 | from dlclive import Processor 10 | class MyCustomProcessor(Processor): 11 | ... 12 | ``` 13 | 14 | To implement your processing steps, overwrite the `Processor.process` method: 15 | ``` 16 | def process(pose, **kwargs): 17 | # my processing steps go here 18 | return pose 19 | ``` 20 | 21 | For example `Processor` objects that communicate with Teensy microcontrollers to [control an optogenetics laser](../../example_processors/TeensyLaser), [turn on an LED when upond detecting a mouse licking](../../example_processors/MouseLickLED), or [turn on an LED upon detecting a dog's rearing movement](../../example_processors/DogJumpLED), see our [example_teensy](../../example_processors) directory. 22 | -------------------------------------------------------------------------------- /dlclive/processor/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | from dlclive.processor.processor import Processor 9 | from dlclive.processor.kalmanfilter import KalmanFilterPredictor 10 | -------------------------------------------------------------------------------- /dlclive/processor/kalmanfilter.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import time 10 | import numpy as np 11 | from dlclive.processor import Processor 12 | 13 | 14 | class KalmanFilterPredictor(Processor): 15 | def __init__( 16 | self, 17 | adapt=True, 18 | forward=0.002, 19 | fps=30, 20 | nderiv=2, 21 | priors=[10, 10], 22 | initial_var=5, 23 | process_var=5, 24 | dlc_var=20, 25 | lik_thresh=0, 26 | **kwargs, 27 | ): 28 | 29 | super().__init__(**kwargs) 30 | 31 | self.adapt = adapt 32 | self.forward = forward 33 | self.dt = 1.0 / fps 34 | self.nderiv = nderiv 35 | self.priors = np.hstack(([1e5], priors)) 36 | self.initial_var = initial_var 37 | self.process_var = process_var 38 | self.dlc_var = dlc_var 39 | self.lik_thresh = lik_thresh 40 | self.is_initialized = False 41 | self.last_pose_time = 0 42 | 43 | def _get_forward_model(self, dt): 44 | 45 | F = np.zeros((self.n_states, self.n_states)) 46 | for d in range(self.nderiv + 1): 47 | for i in range(self.n_states - (d * self.bp * 2)): 48 | F[i, i + (2 * self.bp * d)] = (dt ** d) / max(1, d) 49 | 50 | return F 51 | 52 | def _init_kf(self, pose): 53 | 54 | # get number of body parts 55 | self.bp = pose.shape[0] 56 | self.n_states = self.bp * 2 * (self.nderiv + 1) 57 | 58 | # initialize state matrix, set position to first pose 59 | self.X = np.zeros((self.n_states, 1)) 60 | self.X[: (self.bp * 2)] = pose[:, :2].reshape(self.bp * 2, 1) 61 | 62 | # initialize covariance matrix, measurement noise and process noise 63 | self.P = np.eye(self.n_states) * self.initial_var 64 | self.R = np.eye(self.n_states) * self.dlc_var 65 | self.Q = np.eye(self.n_states) * self.process_var 66 | 67 | self.H = np.eye(self.n_states) 68 | self.K = np.zeros((self.n_states, self.n_states)) 69 | self.I = np.eye(self.n_states) 70 | 71 | # initialize priors for forward prediction step only 72 | B = np.repeat(self.priors, self.bp * 2) 73 | self.B = B.reshape(B.size, 1) 74 | 75 | self.is_initialized = True 76 | 77 | def _predict(self): 78 | 79 | F = self._get_forward_model(time.time() - self.last_pose_time) 80 | 81 | Pd = np.diag(self.P).reshape(self.P.shape[0], 1) 82 | X = (1 / ((1 / Pd) + (1 / self.B))) * (self.X / Pd) 83 | 84 | self.Xp = np.dot(F, X) 85 | self.Pp = np.dot(np.dot(F, self.P), F.T) + self.Q 86 | 87 | def _get_residuals(self, pose): 88 | 89 | z = np.zeros((self.n_states, 1)) 90 | z[: (self.bp * 2)] = pose[: self.bp, :2].reshape(self.bp * 2, 1) 91 | for i in range(self.bp * 2, self.n_states): 92 | z[i] = (z[i - (self.bp * 2)] - self.X[i - (self.bp * 2)]) / self.dt 93 | self.y = z - np.dot(self.H, self.Xp) 94 | 95 | def _update(self, liks): 96 | 97 | S = np.dot(self.H, np.dot(self.Pp, self.H.T)) + self.R 98 | K = np.dot(np.dot(self.Pp, self.H.T), np.linalg.inv(S)) 99 | self.X = self.Xp + np.dot(K, self.y) 100 | self.X[liks < self.lik_thresh] = self.Xp[liks < self.lik_thresh] 101 | self.P = np.dot(self.I - np.dot(K, self.H), self.Pp) 102 | 103 | def _get_future_pose(self, dt): 104 | 105 | Ff = self._get_forward_model(dt) 106 | Xf = np.dot(Ff, self.X) 107 | future_pose = Xf[: (self.bp * 2)].reshape(self.bp, 2) 108 | 109 | return future_pose 110 | 111 | def _get_state_likelihood(self, pose): 112 | 113 | liks = pose[:, 2] 114 | liks_xy = np.repeat(liks, 2) 115 | liks_xy_deriv = np.tile(liks_xy, self.nderiv + 1) 116 | liks_state = liks_xy_deriv.reshape(liks_xy_deriv.shape[0], 1) 117 | return liks_state 118 | 119 | def process(self, pose, **kwargs): 120 | 121 | if not self.is_initialized: 122 | 123 | self._init_kf(pose) 124 | self.last_pose_time = time.time() 125 | return pose 126 | 127 | else: 128 | 129 | self._predict() 130 | self._get_residuals(pose) 131 | liks = self._get_state_likelihood(pose) 132 | self._update(liks) 133 | 134 | forward_time = ( 135 | (time.time() - kwargs["frame_time"] + self.forward) 136 | if self.adapt 137 | else self.forward 138 | ) 139 | 140 | future_pose = self._get_future_pose(forward_time) 141 | future_pose = np.hstack((future_pose, pose[:, 2].reshape(self.bp, 1))) 142 | 143 | self.last_pose_time = time.time() 144 | return future_pose 145 | -------------------------------------------------------------------------------- /dlclive/processor/processor.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | """ 9 | Default processor class. Processors must contain two methods: 10 | i) process: takes in a pose, performs operations, and returns a pose 11 | ii) save: saves any internal data generated by the processor (such as timestamps for commands to external hardware) 12 | """ 13 | 14 | 15 | class Processor(object): 16 | def __init__(self, **kwargs): 17 | pass 18 | 19 | def process(self, pose, **kwargs): 20 | return pose 21 | 22 | def save(self, file=""): 23 | return 0 24 | -------------------------------------------------------------------------------- /dlclive/utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import numpy as np 10 | import warnings 11 | from dlclive.exceptions import DLCLiveWarning 12 | 13 | try: 14 | import skimage 15 | 16 | SK_IM = True 17 | except Exception: 18 | SK_IM = False 19 | 20 | try: 21 | import cv2 22 | 23 | OPEN_CV = True 24 | except Exception: 25 | from PIL import Image 26 | 27 | OPEN_CV = False 28 | warnings.warn( 29 | "OpenCV is not installed. Using pillow for image processing, which is slower.", 30 | DLCLiveWarning, 31 | ) 32 | 33 | 34 | def convert_to_ubyte(frame): 35 | """ Converts an image to unsigned 8-bit integer numpy array. 36 | If scikit-image is installed, uses skimage.img_as_ubyte, otherwise, uses a similar custom function. 37 | 38 | Parameters 39 | ---------- 40 | image : :class:`numpy.ndarray` 41 | an image as a numpy array 42 | 43 | Returns 44 | ------- 45 | :class:`numpy.ndarray` 46 | image converted to uint8 47 | """ 48 | 49 | if SK_IM: 50 | return skimage.img_as_ubyte(frame) 51 | else: 52 | return _img_as_ubyte_np(frame) 53 | 54 | 55 | def resize_frame(frame, resize=None): 56 | """ Resizes an image. Uses OpenCV if installed, otherwise, uses pillow 57 | 58 | Parameters 59 | ---------- 60 | image : :class:`numpy.ndarray` 61 | an image as a numpy array 62 | """ 63 | 64 | if (resize is not None) and (resize != 1): 65 | 66 | if OPEN_CV: 67 | 68 | new_x = int(frame.shape[0] * resize) 69 | new_y = int(frame.shape[1] * resize) 70 | return cv2.resize(frame, (new_y, new_x)) 71 | 72 | else: 73 | 74 | img = Image.fromarray(frame) 75 | img = img.resize((new_y, new_x)) 76 | return np.asarray(img) 77 | 78 | else: 79 | 80 | return frame 81 | 82 | 83 | def img_to_rgb(frame): 84 | """ Convert an image to RGB. Uses OpenCV is installed, otherwise uses pillow. 85 | 86 | Parameters 87 | ---------- 88 | frame : :class:`numpy.ndarray 89 | an image as a numpy array 90 | """ 91 | 92 | if frame.ndim == 2: 93 | 94 | return gray_to_rgb(frame) 95 | 96 | elif frame.ndim == 3: 97 | 98 | return bgr_to_rgb(frame) 99 | 100 | else: 101 | 102 | warnings.warn( 103 | f"Image has {frame.ndim} dimensions. Must be 2 or 3 dimensions to convert to RGB", 104 | DLCLiveWarning, 105 | ) 106 | return frame 107 | 108 | 109 | def gray_to_rgb(frame): 110 | """ Convert an image from grayscale to RGB. Uses OpenCV is installed, otherwise uses pillow. 111 | 112 | Parameters 113 | ---------- 114 | frame : :class:`numpy.ndarray 115 | an image as a numpy array 116 | """ 117 | 118 | if OPEN_CV: 119 | 120 | return cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB) 121 | 122 | else: 123 | 124 | img = Image.fromarray(frame) 125 | img = img.convert("RGB") 126 | return np.asarray(img) 127 | 128 | 129 | def bgr_to_rgb(frame): 130 | """ Convert an image from BGR to RGB. Uses OpenCV is installed, otherwise uses pillow. 131 | 132 | Parameters 133 | ---------- 134 | frame : :class:`numpy.ndarray 135 | an image as a numpy array 136 | """ 137 | 138 | if OPEN_CV: 139 | 140 | return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) 141 | 142 | else: 143 | 144 | img = Image.fromarray(frame) 145 | img = img.convert("RGB") 146 | return np.asarray(img) 147 | 148 | 149 | def _img_as_ubyte_np(frame): 150 | """ Converts an image as a numpy array to unsinged 8-bit integer. 151 | As in scikit-image img_as_ubyte, converts negative pixels to 0 and converts range to [0, 255] 152 | 153 | Parameters 154 | ---------- 155 | image : :class:`numpy.ndarray` 156 | an image as a numpy array 157 | 158 | Returns 159 | ------- 160 | :class:`numpy.ndarray` 161 | image converted to uint8 162 | """ 163 | 164 | frame = np.array(frame) 165 | im_type = frame.dtype.type 166 | 167 | # check if already ubyte 168 | if np.issubdtype(im_type, np.uint8): 169 | 170 | return frame 171 | 172 | # if floating 173 | elif np.issubdtype(im_type, np.floating): 174 | 175 | if (np.min(frame) < -1) or (np.max(frame) > 1): 176 | raise ValueError("Images of type float must be between -1 and 1.") 177 | 178 | frame *= 255 179 | frame = np.rint(frame) 180 | frame = np.clip(frame, 0, 255) 181 | return frame.astype(np.uint8) 182 | 183 | # if integer 184 | elif np.issubdtype(im_type, np.integer): 185 | 186 | im_type_info = np.iinfo(im_type) 187 | frame *= 255 / im_type_info.max 188 | frame[frame < 0] = 0 189 | return frame.astype(np.uint8) 190 | 191 | else: 192 | 193 | raise TypeError( 194 | "image of type {} could not be converted to ubyte".format(im_type) 195 | ) 196 | 197 | 198 | def decode_fourcc(cc): 199 | """ 200 | Convert float fourcc code from opencv to characters. 201 | If decode fails, returns empty string. 202 | https://stackoverflow.com/a/49138893 203 | Arguments: 204 | cc (float, int): fourcc code from opencv 205 | Returns: 206 | str: Character format of fourcc code 207 | 208 | Examples: 209 | >>> vid = cv2.VideoCapture('/some/video/path.avi') 210 | >>> decode_fourcc(vid.get(cv2.CAP_PROP_FOURCC)) 211 | 'DIVX' 212 | """ 213 | try: 214 | decoded = "".join([chr((int(cc) >> 8 * i) & 0xFF) for i in range(4)]) 215 | except: 216 | decoded = "" 217 | 218 | return decoded 219 | -------------------------------------------------------------------------------- /dlclive/version.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Live Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | admin@deeplabcut.org 5 | 6 | Licensed under GNU Lesser General Public License v3.0 7 | """ 8 | 9 | 10 | __version__ = "1.0.4" 11 | VERSION = __version__ 12 | -------------------------------------------------------------------------------- /docs/install_desktop.md: -------------------------------------------------------------------------------- 1 | ### Install DeepLabCut-live on a desktop (Windows/Ubuntu) 2 | 3 | We recommend that you install DeepLabCut-live in a conda environment (It is a standard python package though, and other distributions will also likely work). In this case, please install Anaconda: 4 | 5 | - [Windows](https://docs.anaconda.com/anaconda/install/windows/) 6 | - [Linux](https://docs.anaconda.com/anaconda/install/linux/) 7 | 8 | Create a conda environment with python 3.7 and tensorflow: 9 | 10 | ``` 11 | conda create -n dlc-live python=3.7 tensorflow-gpu==1.13.1 # if using GPU 12 | conda create -n dlc-live python=3.7 tensorflow==1.13.1 # if not using GPU 13 | ``` 14 | 15 | Activate the conda environment, install the DeepLabCut-live package, then test the installation: 16 | 17 | ``` 18 | conda activate dlc-live 19 | pip install deeplabcut-live 20 | dlc-live-test 21 | ``` 22 | 23 | Note, you can also just run the test: 24 | 25 | `dlc-live-test` 26 | 27 | If installed properly, this script will i) create a temporary folder ii) download the full_dog model from the [DeepLabCut Model Zoo](http://www.mousemotorlab.org/dlc-modelzoo), iii) download a short video clip of a dog, and iv) run inference while displaying keypoints. v) remove the temporary folder. 28 | 29 | Please note, you also should have curl installed on your computer (typically this is already installed on your system), but just in case, just run `sudo apt install curl` 30 | -------------------------------------------------------------------------------- /docs/install_jetson.md: -------------------------------------------------------------------------------- 1 | ### Install DeepLabCut-live on a NVIDIA Jetson Development Kit 2 | 3 | First, please follow NVIDIA's specific instructions to setup your Jetson Development Kit (see [Jetson Development Kit User Guides](https://developer.nvidia.com/embedded/learn/getting-started-jetson)). Once you have installed the NVIDIA Jetpack on your Jetson Development Kit, make sure all system libraries are up-to-date. In a terminal, run: 4 | 5 | ``` 6 | sudo apt-get update 7 | sudo apt-get upgrade 8 | ``` 9 | 10 | Lastly, please test that CUDA is installed properly by running: `nvcc --version`. The output should say the version of CUDA installed on your Jetson. 11 | 12 | #### Install python, virtualenv, and tensorflow 13 | 14 | We highly recommend installing DeepLabCut-live in a virtual environment. Please run the following command to install system dependencies needed to run python, to create virtual environments, and to run tensorflow: 15 | 16 | ``` 17 | sudo apt-get update 18 | sudo apt-get install libhdf5-serial-dev \ 19 | hdf5-tools \ 20 | libhdf5-dev \ 21 | zlib1g-dev \ 22 | zip \ 23 | libjpeg8-dev \ 24 | liblapack-dev \ 25 | libblas-dev \ 26 | gfortran \ 27 | python3-pip \ 28 | python3-venv \ 29 | python3-tk \ 30 | curl 31 | ``` 32 | 33 | #### Create a virtual environment 34 | 35 | Next, create a virtual environment called `dlc-live`, activate the `dlc-live` environment, and update it's package manger: 36 | 37 | ``` 38 | python3 -m venv dlc-live 39 | source dlc-live/bin/activate 40 | pip install -U pip testresources setuptools 41 | ``` 42 | 43 | #### Install DeepLabCut-live dependencies 44 | 45 | First, install python dependencies to run tensorflow (from [NVIDIA instructions to install tensorflow on Jetson platforms](https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html)). _This may take ~15-30 minutes._ 46 | 47 | ``` 48 | pip3 install numpy==1.16.1 \ 49 | future==0.17.1 \ 50 | mock==3.0.5 \ 51 | h5py==2.9.0 \ 52 | keras_preprocessing==1.0.5 \ 53 | keras_applications==1.0.8 \ 54 | gast==0.2.2 \ 55 | futures \ 56 | protobuf \ 57 | pybind11 58 | ``` 59 | 60 | Next, install tensorflow 1.x. This command will depend on the version of Jetpack you are using. If you are uncertain, please refer to [NVIDIA's instructions](https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html#install). To install tensorflow 1.x on the latest version of NVIDIA Jetpack (version 4.4 as of 8/2/2020), please the command below. _This step will also take 15-30 mins_. 61 | 62 | ``` 63 | pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 'tensorflow<2' 64 | ``` 65 | 66 | Lastly, copy the opencv-python bindings into your virtual environment: 67 | 68 | ``` 69 | cp -r /usr/lib/python3.6/dist-packages ~/dlc-live/lib/python3.6/dist-packages 70 | ``` 71 | 72 | #### Install the DeepLabCut-live package 73 | 74 | Finally, please install DeepLabCut-live from PyPi (_this will take 3-5 mins_), then test the installation: 75 | 76 | ``` 77 | pip install deeplabcut-live 78 | dlc-live-test 79 | ``` 80 | 81 | If installed properly, this script will i) download the full_dog model from the DeepLabCut Model Zoo, ii) download a short video clip of a dog, and iii) run inference while displaying keypoints. 82 | -------------------------------------------------------------------------------- /example_processors/DogJumpLED/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | from .izzy_jump import IzzyJump, IzzyJumpKF 9 | from .izzy_jump import IzzyJumpOffline, IzzyJumpKFOffline 10 | -------------------------------------------------------------------------------- /example_processors/DogJumpLED/izzy_jump.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import serial 10 | import struct 11 | import time 12 | import numpy as np 13 | 14 | from dlclive.processor import Processor, KalmanFilterPredictor 15 | 16 | 17 | class IzzyJump(Processor): 18 | def __init__(self, com="", lik_thresh=0.5, baudrate=int(9600), **kwargs): 19 | 20 | super().__init__() 21 | self.ser = serial.Serial(com, baudrate, timeout=0) 22 | self.lik_thresh = lik_thresh 23 | self.led_times = [] 24 | self.last_light = 0 25 | 26 | def close_serial(self): 27 | 28 | self.ser.close() 29 | 30 | def switch_led(self, val, frame_time): 31 | 32 | ### check status of led ### 33 | 34 | self.ser.write(b"R") 35 | 36 | led_byte = b"" 37 | led_status = None 38 | while (len(led_byte) != 0) or (led_status is None): 39 | led_byte = self.ser.read() 40 | if len(led_byte) > 0: 41 | led_status = ord(led_byte) 42 | 43 | if led_status != val: 44 | ctime = time.time() 45 | if ctime - self.last_light > 0.25: 46 | self.ser.write(b"L") 47 | self.last_light = ctime 48 | self.led_times.append((val, frame_time, ctime)) 49 | 50 | def process(self, pose, **kwargs): 51 | 52 | ### bodyparts 53 | # 0. nose 54 | # 1. L-eye 55 | # 2. R-eye 56 | # 3. L-ear 57 | # 4. R-ear 58 | # 5. Throat 59 | # 6. Withers 60 | # 7. Tailset 61 | # 8. L-front-paw 62 | # 9. R-front-paw 63 | # 10. L-front-wrist 64 | # 11. R-front-wrist 65 | # 12. L-front-elbow 66 | # 13. R-front-elbow 67 | # ... 68 | 69 | l_elbow = pose[12, 1] if pose[12, 2] > self.lik_thresh else None 70 | r_elbow = pose[13, 1] if pose[13, 2] > self.lik_thresh else None 71 | elbows = [l_elbow, r_elbow] 72 | this_elbow = ( 73 | min([e for e in elbows if e is not None]) 74 | if any([e is not None for e in elbows]) 75 | else None 76 | ) 77 | 78 | withers = pose[6, 1] if pose[6, 2] > self.lik_thresh else None 79 | 80 | if kwargs["record"]: 81 | if withers is not None and this_elbow is not None: 82 | if this_elbow < withers: 83 | self.switch_led(True, kwargs["frame_time"]) 84 | else: 85 | self.switch_led(False, kwargs["frame_time"]) 86 | 87 | return pose 88 | 89 | def save(self, filename): 90 | 91 | ### save stim on and stim off times 92 | 93 | if filename[-4:] != ".npy": 94 | filename += ".npy" 95 | arr = np.array(self.led_times, dtype=float) 96 | try: 97 | np.save(filename, arr) 98 | save_code = True 99 | except Exception: 100 | save_code = False 101 | 102 | return save_code 103 | 104 | 105 | class IzzyJumpKF(KalmanFilterPredictor, IzzyJump): 106 | def __init__( 107 | self, 108 | com="", 109 | lik_thresh=0.5, 110 | baudrate=int(9600), 111 | adapt=True, 112 | forward=0.003, 113 | fps=30, 114 | nderiv=2, 115 | priors=[1, 1], 116 | initial_var=1, 117 | process_var=1, 118 | dlc_var=4, 119 | ): 120 | 121 | super().__init__( 122 | adapt=adapt, 123 | forward=forward, 124 | fps=fps, 125 | nderiv=nderiv, 126 | priors=priors, 127 | initial_var=initial_var, 128 | process_var=process_var, 129 | dlc_var=dlc_var, 130 | com=com, 131 | lik_thresh=lik_thresh, 132 | baudrate=baudrate, 133 | ) 134 | 135 | def process(self, pose, **kwargs): 136 | 137 | future_pose = KalmanFilterPredictor.process(self, pose, **kwargs) 138 | final_pose = IzzyJump.process(self, future_pose, **kwargs) 139 | return final_pose 140 | 141 | def save(self, filename): 142 | 143 | return IzzyJump.save(self, filename) 144 | -------------------------------------------------------------------------------- /example_processors/DogJumpLED/izzy_jump_offline.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import struct 10 | import time 11 | import numpy as np 12 | 13 | from dlclive.processor import Processor, KalmanFilterPredictor 14 | 15 | 16 | class IzzyJumpOffline(Processor): 17 | def __init__(self, lik_thresh=0.5, **kwargs): 18 | 19 | super().__init__() 20 | self.lik_thresh = lik_thresh 21 | self.led_times = [] 22 | self.last_light = 0 23 | self.led_status = False 24 | 25 | def switch_led(self, val, frame_time): 26 | 27 | if self.led_status != val: 28 | ctime = frame_time 29 | if ctime - self.last_light > 0.25: 30 | self.led_status = val 31 | self.last_light = ctime 32 | self.led_times.append((val, frame_time, ctime)) 33 | 34 | def process(self, pose, **kwargs): 35 | 36 | ### bodyparts 37 | # 0. nose 38 | # 1. L-eye 39 | # 2. R-eye 40 | # 3. L-ear 41 | # 4. R-ear 42 | # 5. Throat 43 | # 6. Withers 44 | # 7. Tailset 45 | # 8. L-front-paw 46 | # 9. R-front-paw 47 | # 10. L-front-wrist 48 | # 11. R-front-wrist 49 | # 12. L-front-elbow 50 | # 13. R-front-elbow 51 | # ... 52 | 53 | l_elbow = pose[12, 1] if pose[12, 2] > self.lik_thresh else None 54 | r_elbow = pose[13, 1] if pose[13, 2] > self.lik_thresh else None 55 | elbows = [l_elbow, r_elbow] 56 | this_elbow = ( 57 | min([e for e in elbows if e is not None]) 58 | if any([e is not None for e in elbows]) 59 | else None 60 | ) 61 | 62 | withers = pose[6, 1] if pose[6, 2] > self.lik_thresh else None 63 | 64 | if kwargs["record"]: 65 | if withers is not None and this_elbow is not None: 66 | if this_elbow < withers: 67 | self.switch_led(True, kwargs["frame_time"]) 68 | else: 69 | self.switch_led(False, kwargs["frame_time"]) 70 | 71 | return pose 72 | 73 | def save(self, filename): 74 | 75 | ### save stim on and stim off times 76 | 77 | if filename[-4:] != ".npy": 78 | filename += ".npy" 79 | arr = np.array(self.led_times, dtype=float) 80 | try: 81 | np.save(filename, arr) 82 | save_code = True 83 | except Exception: 84 | save_code = False 85 | 86 | return save_code 87 | 88 | 89 | class IzzyJumpKFOffline(KalmanFilterPredictor, IzzyJumpOffline): 90 | def __init__( 91 | self, 92 | lik_thresh=0.5, 93 | adapt=True, 94 | forward=0.003, 95 | fps=30, 96 | nderiv=2, 97 | priors=[1, 1], 98 | initial_var=1, 99 | process_var=1, 100 | dlc_var=4, 101 | ): 102 | 103 | super().__init__( 104 | adapt=adapt, 105 | forward=forward, 106 | fps=fps, 107 | nderiv=nderiv, 108 | priors=priors, 109 | initial_var=initial_var, 110 | process_var=process_var, 111 | dlc_var=dlc_var, 112 | lik_thresh=lik_thresh, 113 | ) 114 | 115 | def process(self, pose, **kwargs): 116 | 117 | future_pose = KalmanFilterPredictor.process(self, pose, **kwargs) 118 | final_pose = IzzyJumpOffline.process(self, future_pose, **kwargs) 119 | return final_pose 120 | 121 | def save(self, filename): 122 | 123 | return IzzyJumpOffline.save(self, filename) 124 | -------------------------------------------------------------------------------- /example_processors/DogJumpLED/teensy_leds/teensy_leds.ino: -------------------------------------------------------------------------------- 1 | const int LED = 0; 2 | const int IR = 1; 3 | const int REC = 2; 4 | 5 | void blink() { 6 | 7 | Serial.write(!digitalRead(REC)); 8 | Serial.flush(); 9 | noTone(IR); 10 | while (digitalRead(REC) == 0) {} 11 | 12 | } 13 | 14 | void setup() { 15 | 16 | pinMode(LED, OUTPUT); 17 | pinMode(IR, OUTPUT); 18 | pinMode(REC, INPUT); 19 | attachInterrupt(digitalPinToInterrupt(REC), blink, FALLING); 20 | 21 | Serial.begin(9600); 22 | } 23 | 24 | void loop() { 25 | 26 | unsigned int ser_avail = Serial.available(); 27 | 28 | while (ser_avail > 0) { 29 | 30 | unsigned int cmd = Serial.read(); 31 | 32 | if (cmd == 'L') { 33 | 34 | digitalWrite(LED, !digitalRead(LED)); 35 | 36 | } else if (cmd == 'R') { 37 | 38 | Serial.write(digitalRead(LED)); 39 | Serial.flush(); 40 | 41 | } else if (cmd == 'I') { 42 | 43 | tone(IR, 38000); 44 | 45 | } 46 | 47 | } 48 | 49 | } 50 | -------------------------------------------------------------------------------- /example_processors/MouseLickLED/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | from .lick_led import MouseLickLED 9 | -------------------------------------------------------------------------------- /example_processors/MouseLickLED/lick_led.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | import serial 10 | import struct 11 | import time 12 | import numpy as np 13 | 14 | from dlclive import Processor 15 | 16 | 17 | class MouseLickLED(Processor): 18 | def __init__(self, com, lik_thresh=0.5, baudrate=int(9600)): 19 | 20 | super().__init__() 21 | self.ser = serial.Serial(com, baudrate, timeout=0) 22 | self.lik_thresh = lik_thresh 23 | self.lick_frame_time = [] 24 | self.out_time = [] 25 | self.in_time = [] 26 | 27 | def close_serial(self): 28 | 29 | self.ser.close() 30 | 31 | def switch_led(self): 32 | 33 | ### flush input buffer ### 34 | 35 | self.ser.reset_input_buffer() 36 | 37 | ### turn on IR LED ### 38 | 39 | self.out_time.append(time.time()) 40 | self.ser.write(b"I") 41 | 42 | ### wait for receiver ### 43 | 44 | while True: 45 | led_byte = self.ser.read() 46 | if len(led_byte) > 0: 47 | break 48 | self.in_time.append(time.time()) 49 | 50 | def process(self, pose, **kwargs): 51 | 52 | ### bodyparts 53 | # 0. pupil-top 54 | # 1. pupil-left 55 | # 2. pupil-bottom 56 | # 3. pupil-right 57 | # 4. lip-upper 58 | # 5. lip-lower 59 | # 6. tongue 60 | # 7. tube 61 | 62 | if kwargs["record"]: 63 | if pose[6, 2] > self.lik_thresh: 64 | self.lick_frame_time.append(kwargs["frame_time"]) 65 | self.switch_led() 66 | 67 | return pose 68 | 69 | def save(self, filename): 70 | 71 | ### save stim on and stim off times 72 | 73 | filename += ".npy" 74 | out_time = np.array(self.out_time) 75 | in_time = np.array(self.in_time) 76 | frame_time = np.array(self.lick_frame_time) 77 | try: 78 | np.savez( 79 | filename, out_time=out_time, in_time=in_time, frame_time=frame_time 80 | ) 81 | save_code = True 82 | except Exception: 83 | save_code = False 84 | 85 | return save_code 86 | -------------------------------------------------------------------------------- /example_processors/MouseLickLED/teensy_leds/teensy_leds.ino: -------------------------------------------------------------------------------- 1 | const int LED = 0; 2 | const int IR = 1; 3 | const int REC = 2; 4 | 5 | void blink() { 6 | 7 | Serial.write(!digitalRead(REC)); 8 | Serial.flush(); 9 | noTone(IR); 10 | while (digitalRead(REC) == 0) {} 11 | 12 | } 13 | 14 | void setup() { 15 | 16 | pinMode(LED, OUTPUT); 17 | pinMode(IR, OUTPUT); 18 | pinMode(REC, INPUT); 19 | attachInterrupt(digitalPinToInterrupt(REC), blink, FALLING); 20 | 21 | Serial.begin(9600); 22 | } 23 | 24 | void loop() { 25 | 26 | unsigned int ser_avail = Serial.available(); 27 | 28 | while (ser_avail > 0) { 29 | 30 | unsigned int cmd = Serial.read(); 31 | 32 | if (cmd == 'L') { 33 | 34 | digitalWrite(LED, !digitalRead(LED)); 35 | 36 | } else if (cmd == 'R') { 37 | 38 | Serial.write(digitalRead(LED)); 39 | Serial.flush(); 40 | 41 | } else if (cmd == 'I') { 42 | 43 | tone(IR, 38000); 44 | 45 | } 46 | 47 | } 48 | 49 | } 50 | -------------------------------------------------------------------------------- /example_processors/TeensyLaser/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | from .teensy_laser import * 9 | -------------------------------------------------------------------------------- /example_processors/TeensyLaser/teensy_laser.py: -------------------------------------------------------------------------------- 1 | """ 2 | DeepLabCut Toolbox (deeplabcut.org) 3 | © A. & M. Mathis Labs 4 | 5 | Licensed under GNU Lesser General Public License v3.0 6 | """ 7 | 8 | 9 | from dlclive.processor.processor import Processor 10 | import serial 11 | import struct 12 | import pickle 13 | import time 14 | 15 | 16 | class TeensyLaser(Processor): 17 | def __init__( 18 | self, com, baudrate=115200, pulse_freq=50, pulse_width=5, max_stim_dur=0 19 | ): 20 | 21 | super().__init__() 22 | self.ser = serial.Serial(com, baudrate) 23 | self.pulse_freq = pulse_freq 24 | self.pulse_width = pulse_width 25 | self.max_stim_dur = ( 26 | max_stim_dur if (max_stim_dur >= 0) and (max_stim_dur < 65356) else 0 27 | ) 28 | self.stim_on = False 29 | self.stim_on_time = [] 30 | self.stim_off_time = [] 31 | 32 | def close_serial(self): 33 | 34 | self.ser.close() 35 | 36 | def turn_stim_on(self): 37 | 38 | # command to activate PWM signal to laser is the letter 'O' followed by three 16 bit integers -- pulse frequency, pulse width, and max stim duration 39 | if not self.stim_on: 40 | self.ser.write( 41 | b"O" 42 | + struct.pack( 43 | "HHH", self.pulse_freq, self.pulse_width, self.max_stim_dur 44 | ) 45 | ) 46 | self.stim_on = True 47 | self.stim_on_time.append(time.time()) 48 | 49 | def turn_stim_off(self): 50 | 51 | # command to turn off PWM signal to laser is the letter 'X' 52 | if self.stim_on: 53 | self.ser.write(b"X") 54 | self.stim_on = False 55 | self.stim_off_time.append(time.time()) 56 | 57 | def process(self, pose, **kwargs): 58 | 59 | # define criteria to stimulate (e.g. if first point is in a corner of the video) 60 | box = [[0, 100], [0, 100]] 61 | if ( 62 | (pose[0][0] > box[0][0]) 63 | and (pose[0][0] < box[0][1]) 64 | and (pose[0][1] > box[1][0]) 65 | and (pose[0][1] < box[1][1]) 66 | ): 67 | self.turn_stim_on() 68 | else: 69 | self.turn_stim_off() 70 | 71 | return pose 72 | 73 | def save(self, file=None): 74 | 75 | ### save stim on and stim off times 76 | save_code = 0 77 | if file: 78 | try: 79 | pickle.dump( 80 | {"stim_on": self.stim_on_time, "stim_off": self.stim_off_time}, 81 | open(file, "wb"), 82 | ) 83 | save_code = 1 84 | except Exception: 85 | save_code = -1 86 | return save_code 87 | -------------------------------------------------------------------------------- /example_processors/TeensyLaser/teensy_laser/teensy_laser.ino: -------------------------------------------------------------------------------- 1 | /* 2 | * Commands: 3 | * O = opto on; command = O, frequency, width, duration 4 | * X = opto off 5 | * R = reboot 6 | */ 7 | 8 | 9 | const int opto_pin = 0; 10 | unsigned int opto_start = 0, 11 | opto_duty_cycle = 0, 12 | opto_freq = 0, 13 | opto_width = 0, 14 | opto_dur = 0; 15 | 16 | unsigned int read_int16() { 17 | union u_tag { 18 | byte b[2]; 19 | unsigned int val; 20 | } par; 21 | for (int i=0; i<2; i++){ 22 | if ((Serial.available() > 0)) 23 | par.b[i] = Serial.read(); 24 | else 25 | par.b[i] = 0; 26 | } 27 | return par.val; 28 | } 29 | 30 | void setup() { 31 | Serial.begin(115200); 32 | pinMode(opto_pin, OUTPUT); 33 | } 34 | 35 | void loop() { 36 | 37 | unsigned int curr_time = millis(); 38 | 39 | while (Serial.available() > 0) { 40 | 41 | unsigned int cmd = Serial.read(); 42 | 43 | if(cmd == 'O') { 44 | 45 | opto_start = curr_time; 46 | opto_freq = read_int16(); 47 | opto_width = read_int16(); 48 | opto_dur = read_int16(); 49 | if (opto_dur == 0) 50 | opto_dur = 65355; 51 | opto_duty_cycle = opto_width * opto_freq * 4096 / 1000; 52 | analogWriteFrequency(opto_pin, opto_freq); 53 | analogWrite(opto_pin, opto_duty_cycle); 54 | 55 | Serial.print(opto_freq); 56 | Serial.print(','); 57 | Serial.print(opto_width); 58 | Serial.print(','); 59 | Serial.print(opto_dur); 60 | Serial.print('\n'); 61 | Serial.flush(); 62 | 63 | } else if(cmd == 'X') { 64 | 65 | analogWrite(opto_pin, 0); 66 | 67 | } else if(cmd == 'R') { 68 | 69 | _reboot_Teensyduino_(); 70 | 71 | } 72 | } 73 | 74 | if (curr_time > opto_start + opto_dur) 75 | analogWrite(opto_pin, 0); 76 | 77 | } 78 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "deeplabcut-live" 3 | version = "1.0.4" 4 | description = "Class to load exported DeepLabCut networks and perform pose estimation on single frames (from a camera feed)" 5 | authors = ["A. & M. Mathis Labs "] 6 | license = "AGPL-3.0-or-later" 7 | readme = "README.md" 8 | homepage = "https://github.com/DeepLabCut/DeepLabCut-live" 9 | repository = "https://github.com/DeepLabCut/DeepLabCut-live" 10 | classifiers = [ 11 | "Programming Language :: Python :: 3", 12 | "Programming Language :: Python :: 3.7", 13 | "Programming Language :: Python :: 3.8", 14 | "Programming Language :: Python :: 3.9", 15 | "Programming Language :: Python :: 3.10", 16 | "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", 17 | "Operating System :: OS Independent" 18 | ] 19 | packages = [ 20 | { include = "dlclive" } 21 | ] 22 | include = ["dlclive/check_install/*"] 23 | 24 | [tool.poetry.scripts] 25 | dlc-live-test = "dlclive.check_install.check_install:main" 26 | dlc-live-benchmark = "dlclive.benchmark:main" 27 | 28 | [tool.poetry.dependencies] 29 | python = ">=3.7.1,<3.11" 30 | numpy = "^1.20" 31 | "ruamel.yaml" = "^0.17.20" 32 | colorcet = "^3.0.0" 33 | Pillow = ">=8.0.0" 34 | py-cpuinfo = ">=5.0.0" 35 | tqdm = "^4.62.3" 36 | tensorflow = "^2.7.0,<=2.12" 37 | pandas = "^1.3" 38 | tables = "^3.6" 39 | opencv-python-headless = "^4.5" 40 | dlclibrary = ">=0.0.2" 41 | 42 | [tool.poetry.dev-dependencies] 43 | 44 | [build-system] 45 | requires = ["poetry-core>=1.0.0"] 46 | build-backend = "poetry.core.masonry.api" 47 | -------------------------------------------------------------------------------- /reinstall.sh: -------------------------------------------------------------------------------- 1 | poetry shell # activating current environment 2 | poetry install # creating and installing current project 3 | poetry build # creating the tarball 4 | poetry publish # uploading to pypi 5 | #poetry publish --username= --password= --------------------------------------------------------------------------------