This quick-start shows the recommended way for most users to get OpenCV in Python: install from PyPI with pip. It also explains virtual environments, platform notes, and common troubleshooting. If you need OS‑specific alternatives (system packages or source builds), see the OS pages linked below, but those are not required for typical Python use.

Note: : OpenCV team maintains PyPI packages only. Conda distributions and platform specific builds are community builds and hardware vendor builds and may differ from the official one.

Quick start

# 1) Create and activate a virtual environment (recommended)
python -m venv .venv
# Windows:
.venv\Scripts\activate
# Linux/macOS:
source .venv/bin/activate
 
# 2) Upgrade pip tooling
python -m pip install --upgrade pip setuptools wheel
 
# 3) Install OpenCV from PyPI (choose ONE)
pip install opencv-python          # main package (most users)
# or
pip install opencv-contrib-python  # + extra modules (contrib)
# or
pip install opencv-python-headless # no GUI/backends (servers/CI)
# or
pip install opencv-contrib-python-headless # no GUI/backends with extra modules (servers/CI)

Tiny hello‑world

import cv2 as cv
import numpy as np
 
print("OpenCV:", cv.__version__)
img = np.zeros((120, 400, 3), dtype=np.uint8)
cv.putText(img, "OpenCV OK", (10, 80), cv.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 3)
# If you installed a non-headless build, you can display a window:
# cv.imshow("hello", img); cv.waitKey(0)
# Always safe (headless or not): save to file
cv.imwrite("hello.png", img)

Virtual environments and IDEs

Using a virtual environment keeps project dependencies isolated. Tools that create or activate envs include:

venv (built-in) and virtualenv
Conda environments
IDEs (VS Code, PyCharm) that may auto-create and auto-activate an env per workspace

If imports fail inside an IDE, verify the interpreter selected by the IDE matches the environment where you installed OpenCV.

OS notes

Linux: Your default Python may be python3. Use python3 -m venv .venv and python3 -m pip .... If you cannot use a virtual env, pip --user installs to your home directory: python3 -m pip install --user opencv-python.
Windows: Install Python from [python.org] or via winget install Python.Python.3. Make sure “Add python to PATH” is enabled or use the “Open in terminal” from your IDE, which selects the right interpreter automatically.
macOS: Use the system python3 or a managed one (Homebrew or Python.org). Always prefer a virtual environment.
Raspberry Pi / ARM boards: Prebuilt wheels may not exist for some Pi OS / Python combinations. See Troubleshooting below.

Choosing a PyPI variant

opencv-python: core OpenCV modules with GUI/backends
opencv-contrib-python: includes contrib modules in addition to the core
opencv-python-headless: no GUI/backends (ideal for servers/containers/CI)
opencv-contrib-python-headless: contrib + headless

Install exactly one of these per environment.

Troubleshooting

Please start with opencv-python project README

Pip is trying to build from source Symptoms: very long build step, CMake errors, compiler errors. Fixes:

Upgrade build tooling: python -m pip install --upgrade pip setuptools wheel
Ensure your Python version is supported by the chosen package.
If you are on an uncommon platform or Python build, switch to a supported Python or try a different variant (headless vs non‑headless).

“No matching distribution found” or “Unsupported wheel”

Confirm your Python version (e.g., python -V). Choose a wheel that supports that version (manylinux/macOS/Windows wheels on PyPI target specific Python versions).
Create a fresh virtual environment with a mainstream Python (e.g., 3.10–3.12 for now) and reinstall.

Raspberry Pi / ARM

Wheels may lag behind new Python/Pi OS releases. Try opencv-python-headless first. If unavailable, consider system packages for camera/GUI pieces, or build from source following the OS page linked below.

Import works in terminal but fails in IDE

The IDE is using a different interpreter. Select the same environment inside your IDE’s interpreter settings.

What about system packages or building from source?

For beginners using Python, PyPI is recommended. Native distribution packages and full source builds are better suited to advanced users with platform‑specific needs. You can still find them on the OS‑specific pages, moved under “Alternatives.”