Of course! Using OpenCV with Python on Linux is a powerful and common combination for computer vision tasks. This guide will walk you through the entire process, from installation to a simple "Hello, World!" example.

Here's a breakdown of what we'll cover:

1. Prerequisites: What you need before you start.
2. Installation: The best way to install OpenCV and its dependencies.
3. Verification: How to confirm your installation is working.
4. A Simple Example: Reading and displaying an image.
5. Common Issues: Troubleshooting tips.

Prerequisites

Before you install OpenCV, make sure you have the following:

• A Linux Distribution: This guide works for Ubuntu, Debian, Fedora, CentOS, Mint, and other popular distributions.
• Python 3: OpenCV works best with Python 3. You can check if you have it installed by opening your terminal and typing:

  python3 --version

  If it returns a version (e.g., Python 3.8.10), you're good to go. If not, you'll need to install it first. On Ubuntu/Debian, you can use:

  sudo apt update
  sudo apt install python3 python3-pip

• pip (Python's Package Installer): This is used to install Python packages. The command above should install pip for Python 3 as pip3. You can check with:

  pip3 --version

Installation (Recommended Method)

There are two main ways to install OpenCV: using pip or from source. For 99% of users, using pip is the recommended, easiest, and fastest method.

Method A: Using pip (Recommended)

This is the simplest way. It downloads pre-compiled binaries, so you don't have to worry about complex dependencies.

1. Install System Dependencies: OpenCV requires some system-level libraries to build its Python bindings. It's crucial to install these first to avoid errors.

   For Ubuntu/Debian/Mint:

   sudo apt update
   sudo apt install -y build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
   sudo apt install -y libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libdc1394-22-dev

   For Fedora/CentOS/RHEL:

   
   （图片来源网络，侵删）

   sudo dnf update -y
   sudo dnf groupinstall "Development Tools" -y
   sudo dnf install cmake gtk2-devel libdc1394-devel libjpeg-turbo-devel libpng-devel libtiff-devel libv4l-devel
   sudo dnf install tbb-devel gstreamer1-plugins-base-devel

2. Install OpenCV using pip3: Now, install the main opencv-python package. This includes the core OpenCV library. It's a large package, so the first download might take a few minutes.

   pip3 install opencv-python

   Optional but Recommended Packages:

   • opencv-contrib-python: This package includes not only the core modules but also the contrib modules, which contain many extra algorithms and functionalities (like SIFT, SURF, etc.). It's highly recommended to install this instead of the base package.

     pip3 install opencv-contrib-python

   • opencv-python-headless: If you only need to run OpenCV on a server without a display (for processing images/videos in the background), use this lighter version. It doesn't include the GUI (imshow) functionality.

That's it! OpenCV is now installed.

Verification

Let's write a simple Python script to verify that everything is working correctly.

1. Open your favorite code editor (like VS Code, Sublime Text, or even nano in the terminal).

2. Create a file named test_opencv.py.

3. Paste the following code into the file. This script prints the OpenCV version and loads an image.

   import cv2
   import sys
   # Print the OpenCV version
   print("OpenCV Version: " + cv2.__version__)
   # Check if a command-line argument for an image path is provided
   if len(sys.argv) > 1:
       image_path = sys.argv[1]
   else:
       # Use a sample image from the internet if no path is provided
       image_path = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/lena.jpg"
   try:
       # Read the image
       # The 'cv2.IMREAD_COLOR' flag loads the image in BGR format
       image = cv2.imread(image_path, cv2.IMREAD_COLOR)
       # Check if the image was loaded successfully
       if image is None:
           print(f"Error: Could not read image from {image_path}")
       else:
           print(f"Successfully loaded image with shape: {image.shape}")
           # Display the image in a window
           cv2.imshow("Test Image", image)
           # Wait for a key press to close the window
           # The '0' means it will wait indefinitely for any key
           cv2.waitKey(0)
           # Close all OpenCV windows
           cv2.destroyAllWindows()
   except Exception as e:
       print(f"An error occurred: {e}")
   

4. Save the file.

5. Run the script from your terminal. You can either provide a path to your own image or let it use the default Lena image.

   # Option 1: Run with the default image
   python3 test_opencv.py
   # Option 2: Run with your own image
   # python3 test_opencv.py /path/to/your/image.jpg

If everything is installed correctly, you should see a window pop up with the image, and the terminal will print the OpenCV version and the image dimensions. Press any key in the image window to close it.

A Simple Example (Video Capture)

Here's another classic example that accesses your webcam. This is a great way to test the video capture functionality.

1. Create a file named webcam_test.py.

2. Paste this code:

   import cv2
   # Create a VideoCapture object to access the default camera (usually 0)
   cap = cv2.VideoCapture(0)
   # Check if the camera opened successfully
   if not cap.isOpened():
       print("Error: Could not open video device.")
       exit()
   # Loop to continuously get frames from the camera
   while True:
       # Read a frame from the camera
       # 'ret' is a boolean that is True if a frame was read successfully
       # 'frame' is the image itself
       ret, frame = cap.read()
       if not ret:
           print("Error: Failed to capture frame.")
           break
       # Display the frame in a window named "Webcam Feed"
       cv2.imshow("Webcam Feed", frame)
       # Wait for 1 millisecond and check if the 'q' key is pressed
       # If 'q' is pressed, break the loop
       if cv2.waitKey(1) & 0xFF == ord('q'):
           break
   # Release the VideoCapture object and close all OpenCV windows
   cap.release()
   cv2.destroyAllWindows()
   print("Webcam feed stopped.")

3. Run the script:

   python3 webcam_test.py

A window should appear showing your webcam's live feed. Press the 'q' key to stop the script and close the window.

Common Issues and Troubleshooting

• ImportError: libopencv_core.so.4.5.0: cannot open shared object file or similar errors.

  • Cause: The OpenCV shared libraries are not in your system's library path (LD_LIBRARY_PATH).
  • Solution (Recommended): The pip installation should handle this automatically by installing the libraries in a standard location. If you still have issues, you might need to update your library cache:

    sudo ldconfig

  • Alternative Solution: If you installed OpenCV in a non-standard location (like from source), you need to add its lib directory to your LD_LIBRARY_PATH environment variable.

• Error: Couldn't read video stream file or camera not found.

  • Cause: Your application doesn't have permission to access the webcam.

  • Solution: Make sure your user is part of the video group.

    # Check your groups
    groups
    # If 'video' is not listed, add your user to it
    sudo usermod -aG video $USER
    # IMPORTANT: You will need to **log out and log back in** for this change to take effect.

• X11 display not found error when running on a server.

  • Cause: You are trying to use a GUI function like cv2.imshow() on a system that has no graphical display (a headless server).
  • Solution: You have two options:
    1. Use the headless version: Uninstall the full version and install opencv-python-headless.

       pip3 uninstall opencv-python
       pip3 install opencv-python-headless

    2. Use a virtual display: Install a virtual display server like xvfb. This allows applications to think they have a display.

       sudo apt install xvfb
       xvfb-run -a python3 your_script_with imshow.py

You are now set up to start your computer vision journey on Linux with OpenCV and Python! Good luck