Review of the OpenCV library capabilities, checking options, checking algorithms, going through tutorials.
By clicking on the nodes and mixing them, you can quickly see if there is something you want to know about OpenCV.
Instant checking of results, fast and convenient editing, as well as the ability to group functionalities.
Checking existing solutions for various input data and the ability to easily tune them.
You have facial recognition nodes and machine learning functions. At Labolatory, you'll do it faster than you think.
You can use nodes to detect patterns or prepare data for the learning algorithm. The tutorial will guide you all the way.
Something that once was cumbersome and required many attempts now can be done by combining several nodes and quickly tune the parameters.
The nodes have been designed in such a way that you can get not only the result tables, but also see the visualizations directly on the image.
In addition to convenient manipulation of nodes, built-in tools make it easier for you to edit images and code.
Predefined macros will facilitate the use of high-level functions, and grouping nodes will allow you to aggregate simple nodes into more complex functions.
By experimenting on nodes, you do not lose control over the code. Python is available at every stage of work.
For sure you have encountered the problem of installing the OpenCV library on your computer. Compilation errors or dependency errors are common problems for programmers who want to work with OpenCV. Even packages installed from the PyPi repository can cause trouble. At the Laboratory all dependencies are at hand. You do not need to create configuration files anymore and compile different modules. You do not need to look for the right versions to meet the dependencies for the library you need. In OpenCV Lab, all library modules and additional contrib modules are available through normal import.
- OpenCV (core, imgproc, imgcodec, videoio, calib3d, features2d, ...)
- OpenCV Contrib (aruco, bgsegm, ccalib, cvv, dpm, face, ovis, reg, ...)
- numpy, scilab, ipython, pandas
- Seaborn, SciKit-Learn, Keras
- OpenGL, bpy, bgl, blf
- other compatible Python3.5/Python3.6 and system platform
When writing the code and testing on specific examples, it is often necessary to select a critical point, mark the area, or crop the image in the desired size. Sometimes it's enough to guess the coordinates or run an auxiliary application to check the coordinates we're interested in. All kinds of activities related to the editing of a specific image, such as trimming, cropping, checking and finally saving, in the Laboratory can be achieved with a few mouse clicks.
- checking the coordinates and color values of a specific point in the image
- quick selection of a reference point eg for floodFill
- quick selection of the selection area, e.g. for rectangle
- trimming and cropping the ROI image