Using the ARM SDK
The purpose of the SDK is to detect facial expressions and their underlying emotions, appearance and emojis from facial images. The SDK is distributed as a tarball. Its included assemblies enable integration with C++ applications and the data folder required by API in runtime:
affdex-native: C++ Namespace headers and library files.
data: The classifier data files required by both affdex-native and Affdex libraries runtime.
1. Download and extract the SDK archive
Click on the download link from the table for the desired architecture:
||3.1-40-beta (release notes)
Download the SDK here.
Extract the archive:
tar -xzvf affdex-cpp-sdk-3.1-40-linux-arm7.tar.gz -C $HOME/affdex-sdk
2. Import an affdex API into your application
The tarball includes the header and library files that will be needed by your application. Amend your application compilation process to define the location of the affdex-sdk “include” and “lib” directories.
For example, main.cpp initializes an instance of affdex::FrameDetector
int main(int argc, char ** argsv)
To compile main.cpp file.
g++ main.cpp -o main -std=c++11 -I$HOME/affdex-sdk/include -L$HOME/affdex-sdk/lib -l affdex-native
For more complex applications, you might want to use CMake to generate the makefiles for compiling your application. An example of how to configure CMake can be found here.
In addition to libaffdex-native.so, the SDK lib folder contains FFmpeg libraries which are required by the VideoDetector [c++] in runtime for the video decoding. FFmpeg is an open source library for video decoding licensed under LGPL.
3. Capture and analyze faces
Facial images can be captured from different sources. For each of the different sources, the SDK defines a detector class that can handle processing images acquired from that source:
4. Check out sample applications on GitHub
Sample applications for processing videos and connecting to the camera are available for cloning on our GitHub repository.
C++ Namespace: [HTML]
Requirements & Dependencies
Tracking multiple faces
As of v3.1, the SDK exposes a parameter max_faces in the detectors constructor to specify the maximum number of faces to look for in an image. For the realtime use cases, to achieve a high accuracy and processing throughput (20+ processed frames per second), the SDK requires a CPU thread per face.
On a recent dual core machine, we can track up to 3 people in parallel with all the facial expressions, emotions and appearance metrics enabled.
If the number of faces tracked is greater than the number of available CPU threads on the machine, they will all be tracked, but at a cost of the processing frame rate. Therefore, make sure to plan for providing enough hardware power for the number of faces they are expecting to track with each camera.
Supported operating systems
SDK-1202: VideoDetector is unable to decode videos