Узнать версию opencv windows

Verifying whether OpenCV is installed on your system is a crucial step when working on computer vision tasks. OpenCV is one of the most widely used libraries for image processing, object detection, and machine learning in Python and C++. In this guide, we will walk through different methods to check if OpenCV is installed in Python, C++, and across various operating systems. You will also learn how to troubleshoot issues and confirm that OpenCV is functioning properly.

How to Check if OpenCV is Installed in Python

Python is one of the most popular programming languages for OpenCV. Verifying OpenCV installation in Python is straightforward and can be done in multiple ways.

Method 1: Using the cv2 Module

Open a Python interpreter or create a script and run the following code:

import cv2
print("OpenCV version:", cv2.__version__)

If OpenCV is installed correctly, you will see output similar to this:

If you encounter an error like ModuleNotFoundError: No module named 'cv2', OpenCV is not installed. You can install OpenCV using the following command:

pip install opencv-python

Method 2: Checking Installation with pip

You can use pip to check if OpenCV is installed and which version you have:

If OpenCV is installed, you will see information about the installed package, such as its version and location. If nothing is displayed, OpenCV is not installed.

Method 3: Verify Installation with a Simple Program

To test OpenCV in a Python program, write a script to read and display an image:

import cv2

# Read an image
image = cv2.imread('image.jpg')

# Display the image
cv2.imshow('Test Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

If the program runs without errors and displays the image, OpenCV is installed correctly.

How to Check if OpenCV is Installed in C++

Verifying OpenCV installation in C++ involves writing and compiling a simple program.

Step 1: Write a Test Program

Create a file named check_opencv.cpp with the following content:

#include <opencv2/opencv.hpp>
#include <iostream>

int main() {
    std::cout << "OpenCV version: " << CV_VERSION << std::endl;
    return 0;
}

Step 2: Compile the Program

To compile the program, ensure that OpenCV is correctly configured on your system. Use the following command:

g++ check_opencv.cpp -o check_opencv `pkg-config --cflags --libs opencv4`

If OpenCV is installed, the compilation will complete successfully.

Step 3: Run the Program

Execute the compiled program:

If OpenCV is installed, the output will display the version number:

If you encounter errors during compilation, ensure that OpenCV’s include paths and libraries are correctly set up in your environment.

How to Check OpenCV Installation on Linux

Linux users can use system commands to check if OpenCV is installed.

Method 1: Using dpkg (Debian-based Systems)

Run the following command to search for OpenCV packages:

If OpenCV is installed, you will see a list of installed packages with their versions.

Method 2: Using pkg-config

To check OpenCV libraries installed on your system, run:

pkg-config --modversion opencv4

This will display the version of OpenCV installed, such as 4.5.2.

Method 3: Verifying with Python

On Linux systems, use the same Python script as shown earlier to verify OpenCV installation.

How to Check OpenCV Installation on Windows

On Windows systems, checking OpenCV installation can be done in a few ways.

Method 1: Verify Using Python

Use the Python verification script:

import cv2
print("OpenCV version:", cv2.__version__)

Method 2: Check Installed Files

If you installed OpenCV using a pre-built package, check the installation directory (e.g., C:\opencv). Verify that the bin, include, and lib directories are present.

Method 3: Check Environment Variables

Ensure that the OpenCV library paths are added to the system’s PATH variable:

• Go to System Properties > Environment Variables.
• Look for PATH and confirm the OpenCV library directories are included.

How to Check OpenCV Installation on macOS

On macOS, OpenCV installation can be verified using Homebrew or Python.

Method 1: Using Homebrew

If you installed OpenCV using Homebrew, verify the installation with:

If OpenCV is installed, the command will return the installed version.

Method 2: Using Python

Run the Python script:

import cv2
print("OpenCV version:", cv2.__version__)

Troubleshooting OpenCV Installation Issues

If OpenCV is not detected or you encounter errors during verification, consider the following steps:

1. Reinstall OpenCV: Use the appropriate installation command for your environment:pip install --upgrade opencv-python
2. Check Python Path: Ensure that the Python environment you are using has OpenCV installed. Use pip list to check the installed libraries.
3. Verify Compiler and Libraries (C++): Ensure that OpenCV libraries and include paths are correctly set when compiling in C++.
4. Rebuild OpenCV from Source: For advanced users, rebuilding OpenCV from source can resolve compatibility issues.

Conclusion

Checking if OpenCV is installed is essential before working on computer vision projects. Whether you use Python, C++, or operate on Linux, Windows, or macOS, the methods outlined in this guide will help you confirm your OpenCV installation. By following these steps, you can ensure a smooth start to your OpenCV development journey and avoid common pitfalls. If issues arise, troubleshooting tips are provided to help you quickly resolve them and get back on track.

With each release of OpenCV, new features and various changes are introduced. Also, various bugs are fixed. If the OpenCV code snippet is not working due unavailable function, cause of that problem can be incorrect OpenCV version. This tutorial demonstrates how to get OpenCV version programmatically.

import cv2

version = cv2.__version__
(majorVersion, minorVersion, patchVersion) = cv2.__version__.split('.')

print(version)
print(majorVersion)
print(minorVersion)
print(patchVersion)

#include <opencv2/opencv.hpp>

using namespace cv;

int main()
{
    std::string version = CV_VERSION;
    int majorVersion = CV_MAJOR_VERSION;
    int minorVersion = CV_MINOR_VERSION;
    int patchVersion = CV_SUBMINOR_VERSION;

    std::cout << version << std::endl;
    std::cout << majorVersion << std::endl;
    std::cout << minorVersion << std::endl;
    std::cout << patchVersion << std::endl;

    return 0;
}

package app;

import org.opencv.core.Core;

public class Main
{
    static { System.loadLibrary(Core.NATIVE_LIBRARY_NAME); }

    public static void main(String[] args)
    {
        String version = Core.getVersionString();
        int majorVersion = Core.getVersionMajor();
        int minorVersion = Core.getVersionMinor();
        int patchVersion = Core.getVersionRevision();

        System.out.println(version);
        System.out.println(majorVersion);
        System.out.println(minorVersion);
        System.out.println(patchVersion);

        System.exit(0);
    }
}

Output example:

4.6.0
4
6
0

=====================================

Introduction

OpenCV, or Open Source Computer Vision Library, is a popular library used for computer vision and image processing tasks. It provides a wide range of functions for image and video processing, feature detection, object recognition, and more. However, when working with OpenCV, it’s essential to ensure that you’re using the correct version of the library. In this article, we’ll discuss how to check the version of OpenCV and troubleshoot common issues that may arise when using the library.

Checking the Version of OpenCV

To check the version of OpenCV, you can use the following methods:

Method 1: Using the cv2.__version__ Attribute

You can use the cv2.__version__ attribute to get the version of OpenCV. Here’s an example:

import cv2

print(cv2.__version__)

This will print the version of OpenCV installed on your system.

Method 2: Using the cv2.version Module

Alternatively, you can use the cv2.version module to get the version of OpenCV. Here’s an example:

import cv2

print(cv2.version.version)

This will also print the version of OpenCV installed on your system.

Troubleshooting Common Issues

When working with OpenCV, you may encounter common issues such as errors when reading or writing images. Let’s take a look at how to troubleshoot these issues.

Error: `cv2.error: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\imgcodecs\src\loadsave.cpp:801: error: (-215:Assertion failed) !_img.empty() in function ‘cv::imwrite’

This error occurs when you’re trying to write an image using the cv2.imwrite() function, but the image is empty. Here’s an example of how to troubleshoot this issue:

import cv2

# Create an empty image
img = cv2.imread('image.jpg')

# Try to write the image
cv2.imwrite('output.jpg', img)

In this example, the cv2.imread() function returns an empty image, which causes the cv2.imwrite() function to fail. To troubleshoot this issue, you can check the following:

• Make sure the image file exists and is not corrupted.
• Check the image file format. OpenCV supports a wide range of image formats, but some formats may not be supported.
• Check the image dimensions. OpenCV may not support images with certain dimensions.

To fix this issue, you can create a non-empty image using the cv2.imread() function with a valid image file. Here’s an example:

import cv2

# Create a non-empty image
img = cv2.imread('image.jpg')

# Check if the image is not empty
if img is not None:
    # Try to write the image
    cv2.imwrite('output.jpg', img)
else:
    print("Error: Image is empty.")

In this example, we check if the image is not empty before trying to write it. If the image is empty, we print an error message.

Error: `cv2.error: OpenCV(4..0) D:\a\opencv-python\opencv-python\opencv\modules\imgcodecs\src\loadsave.cpp:801: error: (-215:Assertion failed) !_img.empty() in function ‘cv::imread’

This error occurs when you’re trying to read an image using the cv2.imread() function, but the image is corrupted or does not exist. Here’s an example of how to troubleshoot this issue:

import cv2

# Try to read the image
img = cv2.imread('image.jpg')

In this example, the cv2.imread() function fails to read the image, causing the error. To troubleshoot this issue, you can check the following:

• Make sure the image file exists and is not corrupted.
• Check the image file format. OpenCV supports a wide range of image formats, but some formats may not be supported.
• Check the image dimensions. OpenCV may not support images with certain dimensions.

To fix this issue, you can use the cv2.error attribute to get the error message and troubleshoot the issue. Here’s an example:

import cv2

try:
    # Try to read the image
    img = cv2.imread('image.jpg')
except cv2.error as e:
    print("Error:", e)

In this example, we use a try-except block to catch the cv2.error exception and print the error message.

Conclusion

In this article, we discussed how to check the version of OpenCV and troubleshoot common issues that may arise when using the library. We covered how to use the cv2.__version__ attribute and the cv2.version module to get the version of OpenCV, as well as how to troubleshoot issues such as errors when reading or writing images. By following the tips and examples provided in this article, you should be able to troubleshoot common issues with OpenCV and get the most out of this powerful library.

Additional Resources

For more information on OpenCV, you can check out the following resources:

• OpenCV Official Website
• OpenCV Documentation
• OpenCV Tutorials

By following these resources, you should be able to learn more about OpenCV and get started with using this powerful library for your computer vision and image processing tasks.

=============================================

Introduction

OpenCV is a powerful library used for computer vision and image processing tasks. However, with its vast range of features and functions, it can be overwhelming for beginners to navigate. In this article, we’ll answer some of the most frequently asked questions about OpenCV, covering topics such as installation, usage, and troubleshooting.

Q: What is OpenCV?

A: OpenCV, or Open Source Computer Vision Library, is a library of programming functions for computer vision and image processing tasks. It provides a wide range of functions for image and video processing, feature detection, object recognition, and more.

Q: How do I install OpenCV?

A: To install OpenCV, you can use pip, the Python package manager. Here’s an example:

pip install opencv-python

Alternatively, you can install OpenCV using a package manager such as conda or apt-get.

Q: What is the difference between OpenCV 3 and OpenCV 4?

A: OpenCV 3 and OpenCV 4 are two different versions of the OpenCV library. OpenCV 3 is an older version that is still supported, while OpenCV 4 is the latest version with new features and improvements. If you’re starting a new project, it’s recommended to use OpenCV 4.

Q: How do I read an image using OpenCV?

A: To read an image using OpenCV, you can use the cv2.imread() function. Here’s an example:

import cv2

img = cv2.imread('image.jpg')

This will read the image file ‘image.jpg’ and store it in the img variable.

Q: How do I write an image using OpenCV?

A: To write an image using OpenCV, you can use the cv2.imwrite() function. Here’s an example:

import cv2

img = cv2.imread('image.jpg')
cv2.imwrite('output.jpg', img)

This will write the image stored in the img variable to a new file called ‘output.jpg’.

Q: What is the difference between cv2.imread() and cv2.load()?

A: cv2.imread() and cv2.load() are two different functions used to read images in OpenCV. cv2.imread() is used to read images from a file, while cv2.load() is used to read images from a memory buffer.

Q: How do I convert an image from BGR to RGB?

A: To convert an image from BGR to RGB, you can use the cv2.cvtColor() function. Here’s an example:

import cv2

img = cv2.imread('image.jpg')
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

This will convert the image stored in the img variable from BGR to RGB and store it in the img_rgb variable.

Q: How do I detect faces in an image using OpenCV?

A: To detect faces in an image using OpenCV, you can use the cv2.CascadeClassifier() function. Here’s an:

import cv2

img = cv2.imread('image.jpg')
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(img)

This will detect faces in the image stored in the img variable and store the coordinates of the detected faces in the faces variable.

Q: How do I track objects in a video using OpenCV?

A: To track objects in a video using OpenCV, you can use the cv2.Tracker() function. Here’s an example:

import cv2

cap = cv2.VideoCapture('video.mp4')
tracker = cv2.TrackerKCF_create()
ret, frame = cap.read()
bbox = (100, 100, 300, 300)
tracker.init(frame, bbox)
while True:
    ret, frame = cap.read()
    if not ret:
        break
    tracker.update(frame)
    bbox = tracker.get_position()
    cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])), (0, 255, 0), 2)
    cv2.imshow('frame', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

This will track the object in the video stored in the ‘video.mp4’ file and display the tracked object in a window.

Conclusion

In this article, we’ve answered some of the most frequently asked questions about OpenCV, covering topics such as installation, usage, and troubleshooting. We hope this article has been helpful in answering your questions and providing you with a better understanding of OpenCV. If you have any further questions, feel free to ask!