learning opencv

#include #includeintMainintargcChar**argv) {Cvnamedwindow ("Example2", cv_window_autosize); Cvcapture* Capture = Cvcreatefilecapture ("/users/apple/pictures/opencv/video/andyou.mp4");//This is the equivalent of creating an objectiplimage*frame; while(1) {Frame= Cvqueryframe (Capture);//frame points to a piece of memory that has already been allocated, so it does not use release if(!frame) Break; Cvshowimage ("Example2", frame); Charc = Cvwaitk

then calculate the offset, and then control the read and write to the pixel, we are lazy here, with the array way of access, in fact, and the starting address plus offset is the same ha.It can be found that we did not use data[i*img->widthstep+j] = 0;, but instead used Cv_image_elem (IMG, unsigned char, I, j) = 0; Cv_image_elem is a macro, it helps us to encapsulate the next, it is very simple to use, so it is used, its definition in the types_c.h inside, its macro definition is as follows:1 /*

value is greater than threshold, the line segment returned by the function. Param1 Parameters related to the first method: Do not use (0) for traditional Hough transformations ). This is the minimum line segment length. For the multi-scale hough transformation, it is the denominator of the Distance Precision, and the approximate distance precision is, and the precise precision should be ). Param2 Parameters related to the second method: Do not

] Onchange --The function called when the position of the trackbar is changed. The prototype of the function must conform to the following prototype: void XXXXX (INT); you can take the function name as needed.Experience: Onchange is just a function placeholder. You can change the name to any function name you have defined !! 3. In short: 1. This function creates a slide entry named trackbarname in the window named windowname. The sliding range of the slide bar is [0, Count]. As for whether the

I wrote a blog about learning surf algorithms: http://blog.csdn.net/sangni007/article/details/7482960. However, the Code is troublesome and involves the FLANN algorithm (random kdtree + KNN). Although it can be understood, it is difficult to find a simplified version in the document today: 1. surffeaturedetector detector (minhessian); construct a surf detector; Detector. Detect (img_1, keypoints_1); detector. Detect (img_2, keypoints_2); Detection 2.

The camshift algorithm, Kalman filtering algorithm and condensation Algorithm in opencv need to be used for a Multi-target tracking problem recently. The first two algorithms have a relatively large amount of data, in the book "Learning opencv", I have explained it quite clearly, but there is little information about the condensation algorithm. I have not provide

method generates a row pointer when traversing each image element. Later, we will analyze the differences between methods (1) and methods (3.Tutotials also provides the time comparison of the above three methods. Efficient Way Iterator On-the-fly Ra 79.4717 milliseconds 83.7201 milliseconds 93.7878 milliseconds Therefore, when traversing a matrix, the preferred method is method 1.Why is there so much difference between method 1 and method 3? Next, we will ans

The parameter format is as follows: Cv_hough_probabilistic. The specific implementation code is: #include "stdafx.h"#include Result: References: 1. Learning opencv, Yu Shiqi, Liu Rui, published by Tsinghua University, pp.175-179 2. http://www.opencv.org.cn/index.php/Cv%E5%9B%BE%E5%83%8F%E5%A4%84%E7%90%86#HoughLines 3. opencv document, path: "opencv2.2 \ doc \ o

First of all, has been considering such a great opencv should change some of the outdated things, such as: detectors, recognizers and so on, sure enough, openv the big guys or secretly changed. Direct load Caffe Depth learning (SSD face detection) model has been OPENCV: (a powerful one) Here's the Python code: Use Picture: Python detect_faces.py--image rooster.jp

From: http://blog.sciencenet.cn/blog-297739-251363.html Explanation of iplimage structure: Typedef struct _ iplimage{Int nsize;/* Iplimage size */Int ID;/* Version (= 0 )*/Int nchannels;/* Most opencv Functions Support 1, 2, 3, or 4 channels */Int alphachannel;/* Ignored by opencv */Int depth;/* Pixel bit depth: ipl_depth_8u, ipl_depth_8s, ipl_depth_16u,Ipl_depth_16s, ipl_depth_32s, ipl_depth_32f and ipl

OPENCV provides several classifiers, which are described by character recognition in routines. 1, Support vector Machine (SVM): Given the training samples, support vector machines to establish a hyperplane as a decision plane, so that the positive and inverse of the isolation between the edge is maximized. Function prototype: Training prototype CV2. Svm.train (Traindata, responses[, varidx[, sampleidx[, params]]) Where Traindata is the training data,

system's environment variables, open your opencv3.0 extracted folder, find the following path:... \opencv\build\x64\vc12\bin... \opencv\build\x86\vc12\binThe x64 and x86 respectively refer to 64-bit systems and 32-bit systems, where they are added to the system environment variable to facilitate switching compiler Win32 and x64 in vs2015. VC12 refers to the use of VS2013 compiled, newer.Open the System env

Machine Vision Learning Notes (8)--Bouguet stereo correction based on OPENCV In Machine Vision Learning notes (7)--based on OPENCV's binocular camera calibration, we have calculated the Matrix R and T, which describes the relationship of two {camera} coordinate systems, and the stereo correction is mainly the function of these two parameters. Binocular camera

:10.0 (not used this time) gamma:8.0 coef0:1.0 (not used this time) c:10.0 nu:0.5 (not used this time) p:0.1 (not used this time) then normalizes the training data and places it in an array of cvmat types. */ //☆☆☆☆☆☆☆☆☆ (5) SVM Learning ☆☆☆☆☆☆☆☆☆☆☆☆Svm.train (Data_mat, res_mat, null, NULL, param); //★ ☆ Using training data and determined learning