Introduction
cv3 is a Pythonic wrapper for OpenCV that simplifies computer vision tasks by providing more intuitive interfaces and eliminating common boilerplate code.
Principles
Solve more important tasks than writing extra code
You are programming in Python, not C++
OpenCV is not scary
Why cv3?
OpenCV is a powerful computer vision library, but its API can be verbose and unintuitive, especially for Python developers. cv3 addresses these issues by:
Providing sensible defaults for common parameters
Accepting more flexible input types (e.g., float coordinates, pathlib.Path objects)
Eliminating repetitive code patterns
Adding Pythonic features like context managers and iterators
Handling common error cases with better error messages
Key Benefits
Simplified API: Common operations require fewer parameters
Type Flexibility: Accepts various input types where OpenCV is strict
Error Handling: Better error messages and automatic handling of common issues
Pythonic Design: Context managers, iterators, and other Python idioms
RGB by Default: Works with RGB color format by default (configurable)
Installation
You can install cv3 using either of the following methods:
From PyPI (recommended):
pip install cv3
From GitHub (latest development version):
pip install git+https://github.com/gorodion/cv3.git
Quick Start
import cv3
# Read an image (automatically handles RGB conversion)
img = cv3.imread('image.jpg')
# Draw a rectangle without specifying color or thickness
cv3.rectangle(img, 100, 50, 150, 100)
# Save the image (automatically creates directories if needed)
cv3.imwrite('output/image_result.jpg', img, mkdir=True)
# Display in a window with context manager
with cv3.Window('Result') as window:
window.imshow(img)
window.wait_key(0)
This is just a small example of what cv3 can do. Check out the Core Concepts and Package Features page for a comprehensive overview of all the improvements and additions that cv3 provides over raw OpenCV.
Requirements
Python 3.6+
opencv-python (or variants)