learning opencv

Everyone is familiar with Kalman filtering. The basic idea is to get the estimated values of state variables and output signals without considering the effects of input signals and observed noise, after weighting the estimation error of the output signal, the estimation value of the state variable is corrected to minimize the mean variance of the estimation error of the state variable. I don't want to worry about the specific principle and implementation of it here, but this theoretical basis mu

scale of the image x, y direction,Interplolation is the interpolation method when zooming, there are three kinds of interpolation methods:Cv2. Inter_area # uses pixel relationship resampling. This method avoids ripples when the image shrinks. When the image is enlarged, it is similar to the Cv_inter_nn method Cv2. Inter_cubic # cubic interpolation cv2. Inter_linear # Double linear interpolationCv2. Inter_nn # Nearest neighbor interpolationAffine transformation Cv2.warpaffine ()The non-key

Meanshift can be used for image filtering, video tracking, and image segmentation. Generally, the feature points of an image can be extracted at least five dimensions (X, Y, R, G, and B). As we all know, meanshift is often used to find modal points, that is, the point with the highest density. So we can also use it to find the modal points of the Five-dimensional space. Because different points will eventually converge to different peaks, these points form a category, this completes the purpose

This article from http://blog.csdn.net/sangni007/article/details/8112486 In many video tracking or segmentation, you always need to initialize the first frame, that is, to draw a frame on the first frame, or to mark the foreground and background. Today, we will initialize the first frame, I drew a box of code on the first frame to implement it. By the way, I will review the mouse recall event in opencv and paste the code here to share it with you. It

Today we mainly look at the events in OpenCV and the callback function, so it may not be accurate, mainly the following two functions (OPENCV, there are many of these functions, can be in the Http://docs.opencv.org/trunk/modules/highgui/doc /user_interface.html found, did not list each one), and then made a simple drawing programThe functions are as follows:See first function, Cv2.setmousecallback ()ImportC

This small experiment completes the following five functions: 1: Use TrackBar to implement button function (OpenCV without the button, without QT I can only use Trackerbar to achieve visual operation); 2: Calculate and draw the histogram; 3:histogram equalization; 4: Achieve lomography effect; 5: Achieve cartoonize effect. 3 in order to equalize a color image, we can the image of the HSV or YCRCB format for each channel to be balanced, where we have t

The original always feel that the histogram function in OpenCV is very simple, today temporarily need to find the original OpenCV calchist function so powerful, not only can calculate the common 1D Hist, calchist theoretically support 32-dimensional Hist. (32-dimensional Ah, there are wood.) ) void Calchist (const mat* images, int nimages, const int* channels, Inputarray mask, Outputarray hist, int dims, co

First, create a new source program file for. CPP, for example, Gedit Test.cpp Add the following program to the file: #include Save exit. The compilation chain is delivered as an executable file. g++ test.cpp-o test-i/usr/local/include/opencv-i/usr/local/include/opencv2-l/usr/local/lib/*.so Insert Camera, run program ./testDone. cvcreatecameracapture (), initialize get video from camera cvcapture* cvcreatecameracapture (int index); If there is on

will be integrated in the future. Next, I will practice the new version of the sift feature point detection algorithm based on an example in the document, and compare the method of sitf in the previous version. Let's feel the difference in the new structure. # Include Note the initmodule _ In fact, in my opinion, this algorithm class is more important to provide a public interface for developers' algorithms. In the future, the algorithms developed by developers will be added to

glance, I thought it was exactly the same, so I decided to start Ubuntu, but the system that had worked so hard to configure was gone. However, after Ubuntu is installed, we find that Ubuntu is good. The video card is properly installed when there is no problem in the UBUNTU Forum, and the video card information is displayed normally. Then we installed QT + opencv + FFMPEG, which is a variety of smooth procedures. This time, we saved time for Qt to d

// API function usage example of Bayesian classifier in opencv # include "stdafx. H "# include" CV. H "# include" highgui. H "# include" cxcore. H "# include" opencv. HPP "# include" iostream "using namespace CV; using namespace STD; // training sample set with 10 sample feature vector dimensions of 12, double inputarr [10] [13] = {1, 0.708333, 0.320755,-0.105023,-0.419847,-,-1, -0.225806, 0.583333,-1, 0.33

It turns out that the histogram function in opencv is very simple. It is needed temporarily today to find that the calchist function of opencv is so powerful that it can not only compute common 1D Hist, calchist theoretically supports hist of less than 32 dimensions. (32-dimensional !) VoidCalchist(ConstMat * images, int nimages, const int * channels,Inputarray mask, outputarray Hist, int dims,Const int * h

These days, we 've been stuck in the fast, surf, shift, brief, and ORB feature detection algorithms !!! All labs are in the previous blog. Opencv has already implemented them, so we will use opencv to test the effects of various methods, My original intention was match. The feature points detected by fast could not be extracted from descriptor !!! However, it is found that the keypoints detection and match

During the experiment, it is inevitable to read the images in the video for processing. On the contrary, it is also very common to organize and output the processed images into a video file. The following describes how the image output video is implemented in the C ++ version based on opencv. The data for this test is the Swing Tree leaf image, waving trees, its source URL: http://research.microsoft.com/en-us/um/people/jckrumm/WallFlower/TestImages.ht

Author: gnuhpcSource: http://www.cnblogs.com/gnuhpc/ Handvu is the Mathias kolsch of ucsb, an open source computer vision library written by matz@cs.ucsb.edu. It uses C ++ as the basic implementation Language and opencv as the basis for image processing and partial algorithm implementation, the interface is presented in C language to facilitate Linux C programmers. The following is an annotation analysis of this interface:Typedef struct _ hvstate {Int

Int cvcreatetrackbar (Const char * trackbar_name, // The name of the sliderConst char * window_name, // window name. The slider does not block the image.Int * value, // when the slider is dragged, opencv will automatically pass the value represented by the current position to the integer pointed to by the pointerInt count, // maximum value that a slider can reachCvtrackbarcallback on_change // optional callback function. For the callback function, see

1. Read and Write. txt files in opencv format You can use cvsave and cvload to implement the format and. XML /. yml is similar, but if it is dedicated to reading and writing data from opencv, it is still used. XML /. yml file format is better, I prefer it. yml format, good readability. Use cvsave and cvload to read and write. txt files. The implementation method and data format are basically the same as tha

1--We know Mat is an image container class, this data structure consists of two parts: 1--Matrix Header------------------------------------------------- Just created a mat header and did not create a matrix, that is to say, we did not open up the corresponding space for the image to be stored 2--matrix header-Contains: the size of the 1--matrix----such as---class mat----data members in this class Rows,cols ---can specify the size of the image 2--storage method------Corresponding---a variety of m

In this chapter, we will cover: Detecting Image Generation s with the Canny operator Detecting lines in images with the Hough Transform Fitting a line to a set of points Extracting the components' expiration s Computing components 'shape Descriptors Detecting Image Generation s with the Canny operator The Canny algorithm is implemented in opencv by the function CV: canny. As will be explained, this algorithm requires the specification of two

There is an interesting way to use opencv to achieve image fusion on the Internet: 1. Design a mask (half full 1, half full 0) and calculate the gaussion_mask [I] of the level layer; 2. Calculate the Laplacian [I] on each layer of the two images and multiply it with gaussion_mask [I] to synthesize a result_lapacian [I]; 3. continuously calculate the prydown of the two images, store the top layer in gaussion [I], multiply it with gaussion_mask [I], and