Step-by-Step Guide To Install OpenCV For Python
Tags: opencv, python, aiandml, computervision
Published: 2024-06-11 11:16:12

OpenCV is an open-source **[computer vision](https://www.codetrade.io/blog/essential-value-of-computer-vision-in-a-digital-world/)** library that provides a comprehensive set of tools for image processing, video analysis, and machine learning. 

It is written in C++ and Python, and available for a variety of platforms, including Windows, Linux, and macOS. It is easy to learn and use, making it a popular choice for both beginners and experienced developers.


![Step-by-Step Guide To Install OpenCV For Python-CodeTrade](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1keraj9j2lhokgz671x2.png)

## **Steps to Install OpenCV For Python**

OpenCV for Python is a library of Python bindings designed to solve computer vision problems. It is built on top of the OpenCV C++ library and provides a convenient way to use OpenCV in Python programs.

To install OpenCV-Python (also known as cv2) on your system, you can use the Python package manager pip or conda command in Anaconda.  The installation can be performed in the following steps:  

### **STEP 1. Create Environment**

A virtual environment is an isolated Python environment that has its own Python interpreter, standard library, and pip package manager. 

This makes it easy to isolate different Python projects and avoid conflicts between different versions of **[Python packages](https://www.codetrade.io/blog/choose-python-libraries-modules-packages-and-frameworks-for-your-project/)**. Use the given command to create a new Python virtual environment.

```
$ python3 -m venv your_env_name
```
To create a virtual environment, you can use the ‘python3 -m venv’ command, where ‘your_env_name’ is the name of the virtual environment you want to create. For example, to create a virtual environment named ‘env’, you would use the following command:

```
$ python3 -m venv env
```
This will create a new directory called ‘env’ containing the Python interpreter, standard library, and pip package manager for the virtual environment. To activate the virtual environment, you can use the following command:

```
$ source env/bin/activate // env = your env name
```

![creating_python_env](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/c8jak7o768l3jj5zxvwj.png)

Once the virtual environment is activated, the Python interpreter and pip package manager will use the packages installed in the virtual environment instead of the packages installed in the system Python environment.

### **STEP 2. OpenCV Installation**

To install OpenCV use the following pip command:

```
$ pip install opencv-python
```
The command installs the OpenCV Python module using the Python Package Index (PyPI).

![installing_opencv](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/aa7rztbnbdsu9s5rkgca.png)

By following these steps, you've installed OpenCV-Python using a virtual environment. This approach keeps your project's dependencies organized and avoids conflicts with other Python projects on your system. Now you're ready to explore the vast capabilities of OpenCV for computer vision tasks in your Python programs!

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