Introduction to Computer Vision Laboratories

Transcription

1 Introduction to Computer Vision Laboratories Antonino Furnari

2 Computer Vision Laboratories Format: practical session + questions and homeworks. Material available at my web page:

3 Python Python is an object oriented high level programming language. Advantages of Python: 1. Popular; 2. Multi-paradigm; 3. Portable; 4. Easy to learn; 5. Provided with an interactive mode.

4 Python Hello World If it walks like a duck and it quacks like a duck, then it must be a duck Duck Typing Mandatory indentation List of strings For loop Comments

5 Python Versions Two official branches: Python 2.7: older, but more standard (so far). Python 3.6: modern, but less standard. We will use Python 2.7.

6 OpenCV Overview Initially launched in 1999 as an Intel Research initiative to advance CPU-intensive applications, later supported by Willow Garage and now supported by Itseez; Cross platform, free for use and released under the BDS licence (releasing the application with an open source licence is not mandatory!);

7 OpenCV Goals Advance vision research by providing open and optimized cod for basic vision. No more reinventing the wheel! Provide a common infrastructure for developers; Advance vision-based commercial applications by making portable, performance-optimized code available for free.

8 OpenCV Platforms Supports Windows, Linux, macos, FreeBSD, NetBSD, OpenBSD, Android, ios, Maemo, BlackBerry 10; Written in C++ with bindings for Python, Java, MATLAB/OCTAVE, C#, Perl, Ch, Haskell and Ruby; CUDA and OpenCL based GPU interfaces are in progress.

9 OpenCV Main Modules core imgproc imgcodecs videoio highgui video calib3d features2d objdetect ml superres stitching superres videostab basic data structures and basic functions used by all other modules image filtering, geometrical transformations, color space conversion Image file reading and writing Media Input/Output high level user interface video analysis, motion estimation, background subtraction, tracking single and stereo camera calibration, 3D reconstruction feature extraction, descrition and matching object detection machine learning and pattern recognition (e.g., k-means, SVM, knn) super resolution images stitching super resolution video stabilization

10 Prototyping vs Developing In the past, Computer Vision researchers used MATLAB for prototyping and C++/OpenCV for deployment. MATLAB is excellent to rapidly test new ideas: fast to code and debug; extensive library for (practically) everything; X slow to execute; X makes it hard to organize your code and is not great for big projects. C++/OpenCV is great to build optimize software: offers optimized implementation for many CV algorithms; allows to easily scale to big projects; X slow to code X makes debugging very hard

11 Python + OpenCV (one ring to rule them all) We will use Python + OpenCV to get the best of both worlds. Python is good for both prototyping and development: easy to learn, easy to write and read code; with IPython and scipy, it is powerful for scientific calculus (like MATLAB!); can be used for scripting, prototyping, scientific computation and development in a very natural way; Libraries (practically) for everything;

12 Note about versions You can install different versions of Python and OpenCV. You are free to experiment, but we will consider the following setup: Python 2.7: is the most «mature» branch of Python; OpenCV : is the OpenCV version with the best documentation: You will also need the scipy stack including: numpy for scientific calculus (matrix operations etc.); matplotlib to plot data and show images; IPython provides an interactive shell for prototyping.

13 Scipy + OpenCV installation The easiest way to install the scipy stack is to choose a Python distribution. We will consider Anaconda, provided by continuum analytics: Download Anaconda (Python 2.7 version) from Install OpenCV from anaconda prompt: conda install -c menpo opencv= This will install a number of tools, including: Anaconda prompt; IPython console; Jupyter notebooks; Spyder;

14 IPython The core of a scipy distribution is IPython. It provides a powerful interactive shell in which you can do everything Python can do. However, it is usually convenient to use more sophisticated user interfaces such as an IDE.

15 Python: two paths you can go by (but in the long run there s still time to change the road you re on) full featured IDE Jupyter notebooks

16 Full featured IDE An IDE, similar to MATLAB, such as Spyder (installed with Anaconda by default). Other choices are also available. The workflow is similar to those of all interpreted languages: write your.py file, execute, debug etc. Good for development.

17 Jupyter Notebooks A web interface which allows you to write «notebooks», i.e., files containing both the code, the obtained results and formatted text. Powerful and flexible. Very good for learning and experimenting.

18 IDE vs Notebook IDEs are good for: big projects with many different modules and classes; interactive software (e.g., processing a video stream in realtime); Notebooks are good for: experimenting new ideas; documenting them; practical sessions; assigments;

19 Getting Started with Python + OpenCV

20 Resources IPython documentation: Numpy quickstart tutorial: html Matplotlib documentation: OpenCV documentation: