Python 3.5 + OpenCV 3.2

Python3
12
1.0.0
by PLON

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Readme.md

Base project for OpenCV with Python 3.5

OpenCV (Open Source Computer Vision) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel®, it was later supported by Willow Garage and is now maintained by Itseez. The library is cross-platform and free for use under the open-source BSD license.

This is a project with pre-compiled OpenCV 3.2 libraries ready for use in Python 3.5 workspace.

To start using the library simply add

import cv2

at the beginning of your file or straight into console.

The image is based on Python 3.5 + SciPy, so every package included there can be used in this one as well.

Basic images drawing

The easiest way to do it is by using pyplot function from matplotlib library.

from matplotlib import pyplot as plt
img = cv2.imread('opencv-logo.png',0)
img = cv2.medianBlur(img,5)
plt.imshow(img)
plt.show()

Getting to know the library

There is plenty of well written tutorials and examples available regarding the OpenCV library.

Those are the places worth to begin with:

Libraries installed (inherited from Python 3.5 + SciPy):

  • SciPy - is a set of open-source libraries for mathematics, science, and engineering
  • NumPy - is the fundamental package for scientific computing with Python. With NumPy you can use:
    • N-dimensional array object
    • sophisticated (broadcasting) functions
    • useful linear algebra, Fourier transform, and random number capabilities
  • Pandas - is an open source library providing high-performance, easy-to-use data structures for data analysis
  • Matplotlib - is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
  • Sympy - is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system.
  • Numba -Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters.

Image is based on Ubuntu 16.04

Changelog

  • 1.0.0 Initial commit