Table of content
- Introduction
- Why Check your OpenCV Version?
- Method to Check OpenCV Version using Command Line
- Step 1: Open Command Prompt or Terminal
- Step 2: Execute a Simple Command
- Step 3: Verify OpenCV Installation & Version
- Practical Code Examples
- Example 1: Checking OpenCV Version in Python
- Example 2: Checking OpenCV Version in C++
- Example 3: Checking OpenCV Version in Java
- Conclusion
- Additional Resources (optional)
Introduction
:
OpenCV is one of the most widely used computer vision libraries, with a wide range of applications in image and video processing. Due to its popularity and versatility, it is important to know the version of OpenCV that you are working with. Checking the version of OpenCV can be useful for several purposes, such as debugging, compatibility with other libraries or frameworks, and keeping track of any updates or new features.
In this article, we will discover a simple yet powerful method to check your OpenCV version using command line with practical code examples. We will explore how to check the version of OpenCV in both C++ and Python programming languages. We will also discuss the importance of keeping your version of OpenCV up-to-date, and how this can help maximize the potential of your computer vision projects. Whether you are new to OpenCV or a seasoned developer, this article will provide you with a practical and useful toolset to manage your OpenCV projects. So let's get started!
Why Check your OpenCV Version?
Checking your OpenCV version is an important step when working with the Open Source Computer Vision Library (OpenCV) for computer vision and image processing. OpenCV is constantly updating and improving its software, with newer versions providing enhanced features and better performance.
By knowing your OpenCV version, you can ensure that your code is compatible with the specific version and that you are utilizing the latest functionalities. Additionally, it allows you to track issues or bugs and implement fixes that are tailored to your version.
The benefits of checking your OpenCV version range from increased productivity to better accuracy and performance, which can be crucial in fields such as robotics, autonomous vehicles, and machine learning. The command line method for checking OpenCV version is simple yet powerful and can provide valuable information for optimizing your computer vision projects.
Method to Check OpenCV Version using Command Line
One simple yet powerful method to check your OpenCV version is to use the Command Line. This method is especially useful for those who are working with OpenCV in a command line interface, or those who want to quickly check the version of OpenCV without having to write any code. In this method, you will use a command line interface such as CMD (Windows) or Terminal (MacOS, Linux) to execute a command that will display your current OpenCV version.
To check your OpenCV version using the Command Line, start by opening your command line interface. Once you have the command line open, type the following command:
pkg-config --modversion opencv
Press Enter to execute the command, and you will see the version number of your current OpenCV installation displayed in the terminal.
This method is straightforward and can be used across multiple platforms such as Windows, MacOS, and Linux. Additionally, it is a quick way to check your OpenCV version without the need of writing any code.
In summary, using the Command Line to check your OpenCV version is a quick and easy method that can be utilized in a variety of contexts. It is a tool that can help OpenCV users maintain their versioning, especially when working with multiple projects that require different versions of OpenCV.
Step 1: Open Command Prompt or Terminal
The first step in checking your OpenCV version using command line is to open your Command Prompt or Terminal. This can vary depending on your operating system, but generally, you can access Command Prompt by clicking the Start menu and typing "Command Prompt" or "cmd" into the search bar. For Mac users, the Terminal can be accessed in the Applications folder, under the Utilities folder.
Once you have opened your Command Prompt or Terminal, you can begin typing in the necessary commands to check your OpenCV version. It is recommended that you have OpenCV installed on your system before attempting to check the version, as this method will not work if OpenCV is not present on your computer.
With the Command Prompt or Terminal open, you can proceed to Step 2 to learn how to check your OpenCV version using command line.
Step 2: Execute a Simple Command
In Step 2 of checking your OpenCV version with a command line, you will execute a simple command that will provide you with the version number of your OpenCV installation. This command is straightforward and easy to follow. Open your command prompt and type in pkg-config --modversion opencv. Press Enter, and the version number of OpenCV you are running should appear on the screen.
This command leverages the pkg-config utility to collect information about OpenCV's version number from the system. You can repeat this command to check the version number of any OpenCV installation on the system.
It is important to note that some systems do not have pkg-config preinstalled, especially on Windows systems. If you are using Windows, you can simply navigate to your OpenCV installation directory, open the cmake subdirectory and look for the OpenCVConfig-version.cmake file. Open this file with a text editor, and the version number should be listed on the first line of the file.
By executing this simple command, you can quickly and easily determine the version number of OpenCV running on your system. This information is essential for ensuring that your code runs correctly and takes advantage of the latest features and bug fixes offered by newer OpenCV versions.
Step 3: Verify OpenCV Installation & Version
Now that we have successfully installed OpenCV on our system, it is important to verify the installation and determine the version of OpenCV that has been installed. This can be done easily using the command line.
To verify the installation, open a terminal window and type the following commands:
$ cd /usr/local/lib/python3.7/site-packages/cv2/python-3.7
$ ls
This will list the contents of the cv2 directory. If the installation was successful, you should see a file named "cv2.cpython-37m-x86_64-linux-gnu.so" in the directory.
To determine the version of OpenCV that has been installed, type the following command:
$ python -c "import cv2; print(cv2.__version__)"
This will display the version number of OpenCV on your system.
By following these steps, you can ensure that OpenCV has been installed correctly on your system and know which version you are working with. This information can be useful in troubleshooting issues and ensuring that your code is compatible with the installed version of OpenCV.
Practical Code Examples
:
Here are some to check your OpenCV version using command line:
Example 1: This example demonstrates how to check the OpenCV version using the pkg-config utility in the command line.
pkg-config --modversion opencv
This command will output the version of OpenCV installed on your system. For example, it could output: 4.5.2.
Example 2: This example demonstrates how to use the cv2.__version__ attribute in Python to check the OpenCV version.
import cv2
print(cv2.__version__)
This will output the version of OpenCV installed on your system. For example, it could output: 4.5.2.
Example 3: This example demonstrates how to check the OpenCV version by compiling and running a small C++ program.
Create a file named opencv_version.cpp with the following code:
#include <opencv2/core/version.hpp>
#include <iostream>
int main() {
std::cout << "OpenCV version : " << CV_VERSION << std::endl;
return 0;
}
Then compile and run the program using the following commands:
g++ opencv_version.cpp `pkg-config --cflags --libs opencv` -o opencv_version
./opencv_version
This will output the version of OpenCV installed on your system. For example, it could output: 4.5.2.
With these , you should be able to easily check your OpenCV version using the command line. Use whichever method works best for you and ensure you have the correct OpenCV version for any projects you may be working on.
Example 1: Checking OpenCV Version in Python
If you're working with OpenCV in Python, it's important to know what version of OpenCV you're using. Fortunately, checking your OpenCV version in Python is easy to accomplish.
To check your OpenCV version, you'll need to open a Python shell or a Python script in your preferred code editor. Then, start by importing OpenCV:
import cv2
Once you've imported the CV2 library, you can use the following code to check your OpenCV version:
print(cv2.__version__)
This will print the version of OpenCV you're using in your Python code. For example, if you're using OpenCV version 4.5.1, the output of the above code will be:
4.5.1
Knowing your OpenCV version is important because it can have an impact on your code. Different versions of OpenCV may have different features, so it's important to know what version you're using to ensure compatibility with your code.
In summary, checking the OpenCV version in Python is a simple task that can be accomplished with just a few lines of code. Once you know your OpenCV version, you'll be better equipped to work with the library and ensure compatibility with your code.
Example 2: Checking OpenCV Version in C++
In Example 2, we will explore how to check OpenCV version in C++. OpenCV provides a special function called "cv::getVersion()" that allows you to check the version of OpenCV library installed on your system.
Here's the C++ code to check OpenCV version using "cv::getVersion()" function:
#include <opencv2/core/version.hpp>
#include <iostream>
int main()
{
std::cout << "OpenCV version : " << CV_VERSION << std::endl;
return 0;
}
In this code, we first include the header file "<opencv2/core/version.hpp>" that contains the "cv::getVersion()" function. Then we use this function to get the OpenCV version and print it using the standard output stream "std::cout".
One important thing to note here is that, unlike the previous example, we don't need to link any additional libraries or compile the code with any additional flags. This is because we are using the "cv::getVersion()" function provided by the OpenCV library itself.
In conclusion, checking OpenCV version in C++ is a straightforward process, thanks to the "cv::getVersion()" function provided by the OpenCV library. By following the code example above, you can easily check the OpenCV version installed on your system and make sure that your programs run smoothly with the correct version of OpenCV library.
Example 3: Checking OpenCV Version in Java
For developers working with OpenCV in Java, checking the library's version is just as important as it is in other languages. Fortunately, the process is quite similar. To check the version of OpenCV installed on your system in Java, you'll need to use a similar approach as the one used in Python.
First, include the necessary OpenCV libraries in your Java project. Then, in your code, call the Core.getVersionString method which returns the version information as a string. Here's a simple example:
import org.opencv.core.Core;
public class Main {
public static void main(String[] args) {
System.out.println(Core.getVersionString());
}
}
When you run this code, it will return the version of OpenCV that is currently installed on your system as a string.
One thing to keep in mind is that, as with other languages, the version information returned by OpenCV may differ from platform to platform. Also, just like with Python, you may need administrative or root privileges to access the OpenCV library and its version information.
In conclusion, using Java to check your OpenCV version is a simple and straightforward process. By following the example above, you can easily check the version of OpenCV installed on your system and ensure that you're using the correct version for your project.
Conclusion
In summary, OpenCV is a powerful computer vision library that is widely used in various industries, such as robotics, healthcare, and automotive. By knowing your OpenCV version, you can easily troubleshoot any issues and ensure compatibility with other libraries and tools.
Checking your OpenCV version using command line is a simple and convenient way to obtain this information. The process involves using the pkg-config command in the terminal and extracting the version number from the output. This method can be used in various operating systems, including Windows, Linux, and macOS.
In this article, we have explored the step-by-step process of checking your OpenCV version using command line, as well as provided practical code examples for each operating system. We hope that this guide has been helpful and that you are now able to easily check your OpenCV version.
Remember, keeping your OpenCV version up-to-date can lead to better performance, bug fixes, and new features. So, make sure to regularly check and update your OpenCV version for optimal results.
Additional Resources (optional)
Additional Resources:
If you want to learn more about OpenCV, there are many resources available online that can help you get started. Here are a few resources that you might find useful:
- The OpenCV website: The official website for OpenCV offers documentation, tutorials, and code samples for using the library.
- The OpenCV GitHub repository: The source code for the OpenCV library is available on GitHub, and you can contribute to the project or report issues through this repository.
- The OpenCV forum: The OpenCV community forum is a great place to ask questions, share code, and get help from other users of the library.
- OpenCV Python Tutorials: If you prefer to use Python, there are many tutorials available online that demonstrate how to use OpenCV in Python. Some popular resources include PyImageSearch, OpenCV Python Tutorials, OpenCV-Python Documentation, and more.
- Udemy Courses: To learn OpenCV, you can also take online courses such as OpenCV Complete Dummies Guide to Computer Vision with Python, Computer Vision and OpenCV Fundamentals using Python and more.
With these resources, you should be well-equipped to start working with OpenCV and begin exploring the possibilities of computer vision.
