- ๐ซ How to reach me https://www.linkedin.com/in/luiz-correia-78a346168/
A face recognition server written in C++ [Under development]
Currently our messaging schema it's pretty straightforward. We just sent a message within an image through the server and expect a vector with the face's positions.
However, once the project becomes more solid we going to have several flavors of imaging that could be exchanged once via interprocess communication or even via a network.
So it would be good to have a place with the intended design schema.
When the client does the face recognition request, the server will respond to it with a descriptor set.
The client must search the database and get the corresponding match.
In the future, I will create a match request to the server, in case the client is not powerful enough to do it,
but for now, I don't see the motivation for it.
Currently, we are logging into the default std output using std::cout. Ideally would be good if we log into a file.
For that, I'm proposing the creation of a class to do this job.
Currently, the repository has 2 executables: A client and a server that performs face detection.
In the future, we going to have more modules that could be running on the same machine and for it. It would be good to configure it via the command line to set up ports and whatever else.
We need a class to report runtime errors over the wire or even in IPC communications. This class must derive from ApplicationMessage
.
Hey!, I find your project to be very interesting. I''d like to join and help! I'm a beginner level with C++ but if you need help with anything i'd be happy to assist!
Right now the client just sends one single message. Although the server can handle multiple messages, the client just receives one single type of input which is images. This issue should be addressed after the argument parser implementation #19 .
Refactor the serializer module would involve 3 milestones:
class Image : public Serializable
{
public:
//...
// (whatever we have here)
//...
cv::Mat m_image;
}
Right now we have some code reports that have a wrong regex expression using the flag `-ignore-filename-regex=boost``.
The right expression should exclude mocks, tests, and all the .cpp and .h files that are not present in the target library/executable.
Code coverage must be working in all platforms that support llvm compilers.
In order to visualize a code coverage report, we need to host it as an artifact.
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Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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China tencent open source team.