# Python 3.5 + OpenCV 3.2

### Files

### 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