TensorFlow integration with Qt & Felgo
Artificial intelligence and smart applications are steadily becoming more popular. Companies strongly rely on AI systems and machine learning to make faster and more accurate decisions based on their data. This example shows how to create apps that take advantage of both QML and Qt C++.
TensorFlow is Google’s open machine learning framework. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and architectures (desktops, clusters of servers, mobile and edge devices).
This example integrates TensorFlow with Felgo and provides Image Classification and Object Detection features.
For the full integration guide how to use TensorFlow with Qt and Felgo, see: https://felgo.com/cross-platform-development/machine-learning-add-image-classification-for-ios-and-android-with-qt-and-tensorflow
For more information about creating Apps with Felgo, see here: https://felgo.com/apps/.
This app was made with Felgo. You need the SDK which is freely available on https://felgo.com/ for all desktop platforms.
- Go to https://felgo.com/, register, download and install the free Felgo SDK.
- Clone the repository.
- Open the project file .pro in QtCreator (comes with Felgo SDK).
- Hit run to start the app.
If you need any help feel free to ask in the Felgo Forums https://felgo.com/developers/forums/ or have a look at our online documentation https://felgo.com/doc/.
Currently there are no features open, but if you like to contribute use the code standards coming with the Felgo SDK.
Many thanks to the project developers for sharing this example and preparing the guide:
Javier Bonilla, Ph.D. in Computer Science doing research about modeling, optimization and automatic control of concentrating solar thermal facilities and power plants at CIEMAT - Plataforma Solar de Almería (PSA), one of the largest concentrating solar technology research, development and test centers in Europe.
Jose Antonio Carballo, Mechanical Engenieer and Ph.D. student from University of Almería working on his doctoral thesis on modeling, optimization and automatic control for an efficient use of water and energy resources in concentrating solar thermal facilities and power plants at CIEMAT - Plataforma Solar de Almería (PSA).
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The app sourcecode is released under the MIT license.
Permission is NOT granted to merge, publish, distribute, sublicense and/or sell the provided image, audio and video files of this software.
If You have any questions about those Agreements, please write to [email protected] or visit https://felgo.com/.
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