For a number of different reasons, you might want/need to build OpenCV from scratch instead of using the pre-built and official libraries provided by OpenCV. Since you’ve ended up reading this post, there’s a good chance that you already know why you might need to do this, but if you don’t, you can check out my similar post from a couple of years ago for some answers on this. That post was based on OpenCV 3.3 which is considered out-of-date these days, especially with OpenCV 4 out in the market (OpenCV 4.0.1 at the time of writing this article), so I decided to write a new tutorial to address some of the differences. So without without further ado, here we go.
Over the years I have written quite a few guides and tutorials that describe building OpenCV from sources for Windows, however they were mostly done using the GUI. It means you’d have to be using CMake GUI to set some parameters and then a few other actions and you’d end up building OpenCV using Visual Studio like any other VS solution. Even though it’s quite simple to build OpenCV like this, you can’t automate it and you need to perform all the required actions manually. In this post though, I’ll be sharing a few simple commands that can be put inside a batch script that will allow you to build OpenCV for any platform you like, simply by executing (or double clicking) that script!
As simple as it sounds, most of the time it’s a hassle to add the required libraries to your Qt projects. You have to add the include path, the libraries and if you are aiming to have a cross-platform project, you need to account for Windows, macOS and Linux operating systems separately. Well, there are a couple a methods to simplify this a bit which I’ll describe in this tutorial.
In this post I’m going to describe how you can use FFmpeg library to convert videos in your Qt applications, or even write a Video Conversion program that uses FFmpeg as its underlying powerful conversion engine.
Before I come up with the current title of this post, I thought of many other titles including:
- How to send mouse clicks and events to a Qt widget behind another widget
- How to make a Qt widget ignore mouse events
- and so on …
Although the answer was very straightforward and easy, I struggled a lot with it since I wasn’t asking the right question.
In this post I will describe the process of of reading, performing any arbitrary image processing algorithm and displaying an image read from a video file, camera or RTSP feed using OpenCV , and the same time keeping the user interface (created using Qt) responsive.
Here is what you need to add to your Qt qmake projects to be able to add and use OpenCV 3.4.1 default set of libraries. I usually add them into a separate *.pri file and include that in my *.pro files to avoid repetition, but that’s up to you. Well, here it is:
In this post we’re going to learn how to create an image classifier application with a proper GUI that allows the users to choose a camera or a video file as the input and classify the incoming images (video or camera frames) in real time. We’ll be using the power of Qt for cross-platform GUI creation and anything related to visualizing the output and allowing a seamless experience for the user. We’ll also be using TensorFlow and OpenCV to handle the actual classification task, along with accessing cameras and video files.
Following a number of questions that appear before me every now and then, I decided to write a post about using CMake to create and build projects using Qt5 and OpenCV 3. For those of you who are qmake fans, using CMake doesn’t mean you can’t use Qt Creator to create and build your projects. In fact, after the release of more recent versions of Qt Creator, CMake support is getting better and better, and it’s fair to say that using CMake with Qt Creator 4.5.1 is almost as easy as using qmake.
In this post I’ll share an example project that describes how to load and view 3D models using Qt (such as Blender files and so on). The source codes here are tested with Qt 5.9.4 which was released a few days ago.