We needed to execute some pretrained Tensorflow models in C++ on our robot, and found this task to be pretty difficult to get it right. For Python inference, it's just `<depend>python-tensorflow-pip</depend>` and that's it.

So we wrote a support package [tensorflow_ros](https://github.com/tradr-project/tensorflow_ros) which is a facade hiding the complicated stuff and allowing you to "just" `<depend>tensorflow_ros</depend>` and that's it. An example of use is shown in package [tensorflow_ros_test](https://github.com/tradr-project/tensorflow_ros_test).

The package supports several ways of Tensorflow installation:

* It can "steal" from the files installed by Python's pip, so just installing Tensorflow via pip is enough to get the C++ API! (though it has [some problems](https://github.com/tradr-project/tensorflow_ros#c-abi-difference-problems) on newer systems).
* It supports [tensorflow_catkin](https://github.com/Skydes/tensorflow_catkin)
* It supports custom builds using bazel

The good thing is that by depending on `tensorflow_ros` you don't force users of your package into any specific kind of Tensorflow installation, he can freely choose, and your code stays the same (again, except for the [systems with C++ ABI problems](https://github.com/tradr-project/tensorflow_ros#c-abi-difference-problems)).

By nature of this package, it will never be distributed as a binary package, it always needs to be compiled from source.

Unfortunately, I found out there's [another package with the same name](https://github.com/tue-robotics/image_recognition/tree/master/tensorflow_ros), but they have nothing in common (except Tensorflow).

Let us know if you find it useful!





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