Some time ago the article “How to create an application on x86 Android with OpenVINO” has been posted. However, most Android devices use ARM-based chips, so we decided to port the instruction to this platform. OpenVINO supports DL network inference on ARM platforms via ARM plugin, so there is no technical limitations to infer networks […]
Until recently OpenCV Python packages were provided for Windows, Linux (x86_64 and ARM), and macOS (formerly known as OSX) for x86_64 and all was right with the world. However, in November 2020, Apple launched its M1 processor and a series of new hardware based on it followed which changed the game- macOS now needs not […]
Undergraduates Southern University of Science and Technology contributed the 1-D barcode recognition algorithm to opencv_contrib. In this blog post, they are introducing the algorithm and telling how to use it.
In computer vision, there are number of general, pretrained models available for deployment to edge devices (such as OpenCV AI Kit). However, the real power in computer vision deployment today lies in custom training your own computer vision model on your own data to apply to your custom solution on your own device. To train […]
Computer vision engineers often face a hard choice when settling on a sensor module: color or grayscale?
This post is the part of the Google Summer of Code 2020 project OpenCV runs on many hardware platforms and makes use of the SIMD (Single Instruction Multiple Data) acceleration on the ones that support it. Today we will describe how OpenCV was ported and accelerated for RISC-V. What is RISC-V and Why RISC-V From […]
How to work with 3D cameras in OpenCV. Illustrated by the example of Orbbec Astra
Are you looking for a fast way to run neural network inferences on Intel platforms? Then OpenVINO toolkit is exactly what you need. It provides a large number of optimizations that allow blazingly fast inference on CPUs, VPUs, integrated graphics, and FPGAs. In the previous post, we’ve learned how to prepare and run DNN models […]