OpenCV is an extensive computer vision library that provides a wide range of algorithms. It is used in a variety of industries and applications including facial recognition, object detection, and augmented reality.
In this article, we will walk through a full build-from-source installation of OpenCV for Windows 10. The installation includes the latest CUDA tools to enable high-performance computing for many of the algorithms.
OpenCV is a widely used open-source library that provides state-of-the-art computer vision and image processing algorithms. It is a popular choice among developers for creating applications like face recognition, object detection and tracking, augmented reality, and much more.
Before you can start working with OpenCV, you need to download and install the necessary software. You can use pre-built binaries to get up and running quickly, or you can compile it from the source. The latter option is more flexible but also more complicated.
You should also have Python and Numpy installed on your system (version 2.7.x). You can download them from here and here.
OpenCV is used for a lot of computer vision and machine learning algorithms. This library is capable of things like face detection, object tracking, blob removal, 3D point cloud generation, and more. It also has Python bindings that allow it to be used with Python.
First, we need to download and extract the source files for OpenCV (this will create a folder with the name D:/OpenCV/dep). We also need to download and install a CUDA Toolkit. For this, I recommend using the bundled installer that is provided by Intel.
Finally, we need to download and install CMake. This is a handy tool that will help us to make project files for our chosen IDE as well as compile OpenCV and generate binary files.
Once the build process is complete, you can add the binaries to your Path so that they are available to all Python applications. Then, to test if everything was installed correctly, run Python and import the cv2 module.
OpenCV comes with many state-of-the-art computer vision and machine learning algorithms. It can be used to find faces in images, detect intrusions in security cameras, stitch images together to produce a panorama, identify a person's hand gesture, recognize a scene, track a moving object, remove red eyes from photos, and much more.
Using the pre-built binaries is fine for a lot of use cases however if you want more control over your installation (such as being able to debug) then you will need to build from the source. This blog post walks through a full OpenCV build from a source install on Windows 10.
The first step is to download the OpenCV source code and extract it. Next, you need to set your environmental variables. This can be done by right-clicking on this PC and selecting 'Properties'. In the 'Advanced system settings' pane, in the 'Library dependencies' field add the path to the 'Debug' folder located inside of the 'build' folder (C:
There are many libraries, frameworks, and tools that you can use with OpenCV to add a lot of functionality or enhance the library's capabilities. For example, you might want to make your GUIs using the Qt framework or you might like to add support for CUDA to take advantage of the GPU's processing power.
To build these non-core modules you'll need to install them manually or use a tool that will do this for you, such as CMake. CMake is a handy tool that makes it easy to generate project files for your IDE of choice as well as the native compiler of your system.
By default, the OpenCV build will generate architecture-specific CUDA binaries which will only work on systems that have the same CPU and GPU architecture as the system that was used to compile the code. This can be avoided by passing -DCUDA_ARCH_BIN=6.1 to CMake, which will generate generic CUDA binaries instead.