Trueface SDK Reference - Stable¶
Welcome to the Trueface SDK. The SDK allows you to integrate Trueface’s AI models directly into your application while ensuring maximum performance and flexibility. Start by downloading the correct version of the SDK for your target platform and desired language. Next, refer to the General section for guidance on how to initialize the SDK.
Downloads¶
Stable release version 0.27.10876
Choosing Your Release Type¶
Alpha contains the absolute latest features (updated daily), but may also contain bugs. Beta contains fewer bugs while still having relatively new features. Stable will contain the fewest bugs but will take the longest to get new features. You will generally want to choose the Beta or Stable releases.
Note, if downloading the SDK in an automated manner (ex. building docker images) and you always require the latest SDK version, then replace the postfix in the download link (SDK version) with _latest.zip instead.
So for example, https://reference.trueface.ai/cpp/staging/latest/uploads/truefaceSDK_v0.10.5483.zip would become https://reference.trueface.ai/cpp/staging/latest/uploads/truefaceSDK_latest.zip.
If you require a previous version of the documentation or SDK downloads, refer to the Previous Stable Releases tab on the left.
x86-64 C++¶
Target platform |
SHA256 |
Notes |
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Compiled with gcc 7.5.0, GLIBC 2.27, tested on Ubuntu 18.04 and CentOS 8. |
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Compiled with gcc 9.3.0, GLIBC 2.31, tested on Ubuntu 20.04. |
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Compiled with gcc 7.5.0, GLIBC 2.27, Cuda 10.1, tested on Ubuntu 18.04 and CentOS 8. Must install GPU dependencies. |
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Compiled with gcc 9.3.0, GLIBC 2.31, Cuda 11.2, tested on Ubuntu 20.04. Must install GPU dependencies. |
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Compiled with AppleClang 11. |
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Compiled with MSVC 19.28.29913.0 in Release mode. More info. |
x86-64 Python bindings¶
Target platform |
SHA256 |
Notes |
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Tested on Ubuntu 18.04, Ubuntu 20.04, and CentOS 8. For CentOS, must run |
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Tested on Ubuntu 18.04, Ubuntu 20.04, and CentOS 8. Must install GPU dependencies. |
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Tested on Ubuntu 18.04, Ubuntu 20.04, and CentOS 8. For CentOS, must run |
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Tested on Ubuntu 18.04, Ubuntu 20.04, and CentOS 8. Must install GPU dependencies. |
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Tested on Ubuntu 18.04, Ubuntu 20.04, and CentOS 8. For CentOS, must run |
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Tested on Ubuntu 18.04, Ubuntu 20.04, and CentOS 8. Must install GPU dependencies. |
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Tested on Ubuntu 20.04. Must install GPU dependencies. |
ARM C++¶
Target platform |
SHA256 |
Notes |
|---|---|---|
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Compiled with aarch64-linux-gnu-g++ 7.5.0, GLIBC 2.27, tested on Ubuntu 18.04. |
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Compiled with aarch64-linux-gnu-g++ 7.5.0, GLIBC 2.27, CUDA-10.2, tested on Ubuntu 18.04. Must install GPU dependencies |
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Compiled with arm-linux-gnueabihf-g++ 7.5.0, GLIBC 2.27, tested on Ubuntu 18.04. |
ARM Python Bindings¶
Target platform |
SHA256 |
Notes |
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Tested on Ubuntu 18.04. |
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Tested on Ubuntu 18.04. Must install GPU dependencies. |
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Tested on Ubuntu 18.04. |
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Tested on Ubuntu 18.04. |
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Tested on Ubuntu 18.04. |
GPU SDK Dependencies¶
While the CPU SDK is dependency free (only requires OpenMP for Linux and Windows), the GPU SDK does have a few dependencies which must be installed. As of right now, the CUDA 10.1 SDK supports Ubuntu and CentOS, while the CUDA 11.2 SDK only supports Ubuntu (this is due to GLIBC versions). We will assume you already have the nvidia drivers installed on your machine.
apt-get install -y libomp-dev libopenblas-dev software-properties-common libgomp1
add-apt-repository ppa:graphics-drivers
apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
bash -c 'echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/cuda.list'
bash -c 'echo "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/cuda_learn.list'
apt update
apt install cuda-10-1
apt install libcudnn7
export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
If using docker, we advise using the nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 docker image.
If using Ubuntu 20.04 (for python bindings library), then you may have to run sudo ln -s /usr/lib/x86_64-linux-gnu/libomp.so.5 /usr/lib/x86_64-linux-gnu/libomp.so.
nvidia/cuda:10.1-cudnn7-runtime-centos8 docker image, run the following commands:dnf --enablerepo=powertools install -y openblas-devel
dnf install -y libgomp libomp
nvidia/cuda:11.2.0-cudnn8-runtime-ubuntu20.04 docker image, run the following commands:apt-get install -y libomp-dev libopenblas-dev libgomp1
ln -s /usr/lib/x86_64-linux-gnu/libomp.so.5 /usr/lib/x86_64-linux-gnu/libomp.so
apt-get install -y libomp-dev libopenblas-dev libgomp1
Windows SDK¶
Our team does the majority of development and testing in a Linux environment. The Windows SDK may therefore contain more bugs than the other platform releases and may be lacking in a few features. The current known issues and limitations are as follows:
Any file including
winerror.hmust have#undef NO_ERRORas theNO_ERRORdefined inwinerror.hconflicts withTrueface::ErrorCodes::NO_ERROR.TFV5 inference is slower than FULL model inference and is under investigation.
Dependencies which must be installed. They can easily be installed by installing Git for Windows:
libintl-8.dll
libcrypto-1_1-x64.dll
libsll-1_1-x64.dll
As of this time, we are only supporting a release configured version of the library (and not a debug version).
The library is built as a dynamic library; therefore, you must link against libtf.lib when compiling your application and must ensure that libtf.dll is in the same directory as your executable.
Getting Started Tutorials¶
Every SDK download package comes bundled with sample code demonstrating proper usage of the SDK for various tasks such as face detection, face recognition, object detection, mask detection, and more. Start by understanding how the sample code works by reading the comments in the code.
Sample Apps¶
Sample Apps demonstrate full working applications. These extend the scope of the sample code which comes shipped with the SDK.
Reporting SDK Bugs and Documentation Errors¶
If you encounter a bug in the SDK, please send an email to support@trueface.ai. Please include at minimum the following:
SDK version
SDK target (ex. Python 3.7 64Bit CPU Linux)
Expected behaviour
Observed behaviour
A minimal reproducible code example showing how to replicate the bug
Any input images used
The more information you provide, the faster we can diagnose the issue and push out a fix.
If you happen to find a mistake (spelling, syntax error, etc.) in the latest Alpha, Beta, or Stable documentation, please email support@trueface.ai with a screenshot of the mistake, documentation version number (same as SDK version), and the release type (Alpha, Beta, or Stable). We will push out a fix as soon as we can.
C++ (Stable) - Contents:
- General
- License Validation
- Input Image
- Face Detection
- 1 to 1 Face Recognition
- 1 to N Identification
- Object Detection
- Spoof Detection
- Body Pose Estimation
- Liveness
- Mask Detection
- Eye glasses Detection
- Environment Variables
- Frequently Asked Questions
- How many threads does the SDK use for inference?
- How can I reduce the number of threads used by the SDK?
- How can I run inference with multiple instances of the SDK on a single CPU?
- How can I increase throughput?
- Is the SDK threadsafe?
- What architecture should I use when I have multiple camera streams producing lots of data?
- What is the difference between the static library and the dynamic library?
- What hardware does the GPU library support?
- Why is my license key not working with the GPU library?
- Why does the first call to an inference function take much longer than the subsequent calls?
- How do I use the python bindings for the SDK?
- How do I choose a similarity threshold for face recognition?
- What are the differences between the face recognition models?
- Are Faceprints compatible between models?
- How can I upgrade my collection if is filled with Faceprints from a deprecated model?
- What is the difference between similarity score and match probability?
- There are many face detection and recognition functions. Which should I use?
- How do createDatabaseConnection and createLoadCollection work?
- Why are no faces being detected in my large images?
- How can I speed up face detection?
- What does the frVectorCompression flag do? When should I use it?
- Changelog
- v0.27: August 2, 2021
- v0.26: July 6, 2021
- v0.25: June 22, 2021
- v0.24: June 8, 2021
- v0.23: May 27, 2021
- v0.22: May 12, 2021
- v0.21: May 4, 2021
- v0.20: April 26, 2021
- v0.19: April 14, 2021
- v0.18: March 29, 2021
- v0.17: March 11, 2021
- v0.16: February 25, 2021
- v0.15: February 16, 2021
- v0.14: January 25, 2021
- v0.13: January 15, 2021
- v0.12: January 4, 2021
- v0.11: December 23, 2020
- v0.10: December 11, 2020
- v0.9: November 20, 2020
- v0.8: November 9, 2020
- v0.7: August 14, 2020
- v0.6: July 7, 2020
- Previous Stable Releases
- 0.26.10390
- 0.25.10099
- 0.24.9786
- 0.24.9740
- 0.23.9508
- 0.23.9476
- 0.22.9292
- 0.22.9196
- 0.21.8922
- 0.20.8553
- 0.19.8416
- 0.18.7976
- 0.17.7745
- 0.17.7722
- 0.16.7561
- 0.16.7542
- 0.15.7233
- 0.15.7198
- 0.15.7184
- 0.14.6743
- 0.14.6732
- 0.14.6709
- 0.14.6699
- 0.13.6676
- 0.13.6670
- 0.13.6500
- 0.12.6330
- 0.12.6300
- 0.11.6229
- 0.10.5855
- 0.10.5732
- 0.9.5131
- 0.8.4786
- 0.7.3005