Opencv_version_patch = int(patch, base=10) Opencv_version_minor = int(minor, base=10) Opencv_version_major = int(major, base=10) (major, minor, patch) = cv2._version_.split(".") The version of OpenCV can be read with cv2._version_: The first thing to do in a Python / OpenCV demo is to import OpenCV: The goal is to check that OpenCV is installed and to display the version of OpenCV: The boring task of installing OpenCV for Python is now behind us.įrom now on, I supposed that Python 3 and OpenCV are ready to be used by GeeXLab. OpenCV for Python should be now installed. To install pip, run the following command: This script can be also downloaded from this page. I shipped the get-pip.py script in the Python 3 demopack. If you don’t have pip, don’t worry, you can easily install it.
If you want to use your own Python 3 installation, let’s see how to install OpenCV for Python.īefore installing OpenCV for Python, be sure to have the pip utility. It uses the Python 3 that comes with the system.
On Linux and Raspberry Pi, GeeXLab is not shipped with a Python 3 installation. GeeXLab /python3_home="C:/Users/YOUR_NAME/AppData/Local/Programs/Python/Python38/"Ģ/ with the python3_home attribute in the init0.xml (in GeeXLab root folder) file: On Windows you should have something like: There are two ways:ġ/ with a command line option: /python3_home=".". So if you don’t have Python 3 on your system, you can still run Python 3 demos.īut if you want to use your own Python 3 installation, you can tell GeeXLab to use it. The latest GeeXlab 0.29.17.0 for Windows 64-bit comes with Python 3.8.2 and OpenCV 4.2.0. The nice thing with OpenCV is that it comes with a complete Python 3 library. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. Here is the first article of a series a four small tutorials about OpenCV and Python 3 with GeeXlab. Demo: geexlab-demopack-python3/opencv/01-version/main.xml
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