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Nowadays, augmented reality is one of the top research topic in computer vision and robotics fields. The most elemental problem in augmented reality is the estimation of the camera pose respect of an object in the case of computer vision area to do later some 3D rendering or in the case of robotics obtain an object pose in order to grasp it and do some manipulation. However, this is not a trivial

These steps have been tested for Ubuntu 10.04 but should work with other distros as well. Required Packages GCC 4.4.x or later CMake 2.8.7 or higher Git GTK+2.x or higher, including headers (libgtk2.0-dev) pkg-config Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy) ffmpeg or libav development packages: libavcodec-dev, libavformat-dev, libswscale-dev [op

Goal In this chapter, We will learn to use marker-based image segmentation using watershed algorithm We will see: cv2.watershed() Theory Any grayscale image can be viewed as a topographic surface where high intensity denotes peaks and hills while low intensity denotes valleys. You start filling every isolated valleys (local minima) with different colored water (labels). As the water rises, dependi

Goal In this session, We will learn to create depth map from stereo images. Basics In last session, we saw basic concepts like epipolar constraints and other related terms. We also saw that if we have two images of same scene, we can get depth information from that in an intuitive way. Below is an image and some simple mathematical formulas which proves that intuition. (Image Courtesy : The above

Access pixel values and modify them Access image properties Setting Region of Image (ROI) Splitting and Merging images Almost all the operations in this section is mainly related to Numpy rather than OpenCV. A good knowledge of Numpy is required to write better optimized code with OpenCV. ( Examples will be shown in Python terminal since most of them are just single line codes )

Introduction to OpenCV Learn how to setup OpenCV-Python on your computer! Gui Features in OpenCV Here you will learn how to display and save images and videos, control mouse events and create trackbar. Core Operations In this section you will learn basic operations on image like pixel editing, geometric transformations, code optimization, some mathematical tools etc. Image Processing in OpenCV In

Here you will learn how to display and save images and videos, control mouse events and create trackbar. In this section you will learn basic operations on image like pixel editing, geometric transformations, code optimization, some mathematical tools etc.

Installation in Linux¶ These steps have been tested for Ubuntu 10.04 but should work with other distros as well. GCC 4.4.x or later CMake 2.6 or higher Git GTK+2.x or higher, including headers (libgtk2.0-dev) pkg-config Python 2.6 or later and Numpy 1.5 or later with developer packages (python-dev, python-numpy) ffmpeg or libav development packages: libavcodec-dev, libavformat-dev, libswscale-dev

Introduction to OpenCV Learn how to setup OpenCV-Python on your computer! Gui Features in OpenCV Here you will learn how to display and save images and videos, control mouse events and create trackbar. Core Operations In this section you will learn basic operations on image like pixel editing, geometric transformations, code optimization, some mathematical tools etc. Image Processing in OpenCV In

Camera Calibration and 3D Reconstruction¶ The functions in this section use a so-called pinhole camera model. In this model, a scene view is formed by projecting 3D points into the image plane using a perspective transformation. are the coordinates of a 3D point in the world coordinate space are the coordinates of the projection point in pixels is a camera matrix, or a matrix of intrinsic paramete

Template matching is a technique for finding areas of an image that match (are similar) to a template image (patch). We need two primary components: Source image (I): The image in which we expect to find a match to the template image Template image (T): The patch image which will be compared to the template image our goal is to detect the highest matching area: To identify the matching area, we ha

Introduction¶ The work with a cascade classifier includes two major stages: training and detection. Detection stage is described in a documentation of objdetect module of general OpenCV documentation. Documentation gives some basic information about cascade classifier. Current guide is describing how to train a cascade classifier: preparation of a training data and running the training application

Haar Feature-based Cascade Classifier for Object Detection¶ The object detector described below has been initially proposed by Paul Viola [Viola01] and improved by Rainer Lienhart [Lienhart02]. First, a classifier (namely a cascade of boosted classifiers working with haar-like features) is trained with a few hundred sample views of a particular object (i.e., a face or a car), called positive examp

Camera calibration With OpenCV¶ Cameras have been around for a long-long time. However, with the introduction of the cheap pinhole cameras in the late 20th century, they became a common occurrence in our everyday life. Unfortunately, this cheapness comes with its price: significant distortion. Luckily, these are constants and with a calibration and some remapping we can correct this. Furthermore,

Installation in Windows¶ The description here was tested on Windows 7 SP1. Nevertheless, it should also work on any other relatively modern version of Windows OS. If you encounter errors after following the steps described below, feel free to contact us via our OpenCV Q&A forum. We’ll do our best to help you out. Note To use the OpenCV library you have two options: Installation by Using the Pre-bu

Introduction to Java Development¶ As of OpenCV 2.4.4, OpenCV supports desktop Java development using nearly the same interface as for Android development. This guide will help you to create your first Java (or Scala) application using OpenCV. We will use either Apache Ant or Simple Build Tool (SBT) to build the application. If you want to use Eclipse head to Using OpenCV Java with Eclipse. For fur

Android Development with OpenCV¶ This tutorial has been created to help you use OpenCV library within your Android project. This guide was written with Windows 7 in mind, though it should work with any other OS supported by OpenCV4Android SDK. This tutorial assumes you have the following installed and configured: JDK Android SDK and NDK Eclipse IDE ADT and CDT plugins for Eclipse If you need help

Cross compilation for ARM based Linux systems¶ This steps are tested on Ubuntu Linux 12.04, but should work for other Linux distributions. I case of other distributions package names and names of cross compilation tools may differ. There are several popular EABI versions that are used on ARM platform. This tutorial is written for gnueabi and gnueabihf, but other variants should work with minimal c

What is a Fourier transform and why use it? How to do it in OpenCV? Usage of functions such as: copyMakeBorder(), merge(), dft(), getOptimalDFTSize(), log() and normalize() . Source code¶ You can download this from here or find it in the samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp of the OpenCV source code library. Here’s a sample usage of dft() : 1 2 3