Talk was cheap, show you the code:/************************************************************************// * NAME:OPENCV SVM Test * // * DATE:2015/11/7 * // * Author:aban * //************************************************************************///Note:the code is modified from the Internet.#include iostream>#include Cmath>#include string>using namespace Std;#include Opencv2/opencv.Hpp>#include Opencv2/ml.Hpp>Using namespace Cv;bool plotsupportve
with you. Please advise me if something is wrong.
Assume that our company has two types of programmers: VB programmers, which refer to programmers who write programs using VB, and clsVBProgramer programmers. Delphi programmers refer to programmers who write programs using Delphi, it is represented by the clsDelphiProgramer class. Each class has a WriteCode () method. Definition:
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class clsVBProgramer(){....WriteCode(){// Write code in VB;}....}
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c
: VB programmers, which refer to programmers who write programs using VB, and clsvbprogramer programmers. Delphi programmers refer to programmers who write programs using Delphi, it is represented by the clsdelphiprogramer class. Each class has a writecode () method. Definition:
Class clsvbprogramer (){....Writecode (){// Written in VBCode;}....}
Class clsdelphiprogramer (){....Writecode (){// Write the code in Delphi;}....}
the company has a project that requires a programmer to write a p
Reproduced from: http://www.cnblogs.com/MeteorSeed/archive/2012/08/04/2621993.html
A wolf is a real prey in nature, and husky is just a human plaything. The two are indeed very similar, just like the programer and coder in the IT field. If you use Wolf and husky to represent these two occupations, programer is undoubtedly the real wolf in the software industry.
1. Habits of husky
They write code that mach
knowledge of image processing, which I am not going to cover in detail here. In addition, compiling this example requires OPENCV, I use the version is 2.3.1, about OPENCV installation and configuration is not introduced here. We first look at the traditional way of serial programming.1 #include "opencv2/highgui/highgui.hpp"2 #include "opencv2/features2d/features2d.hpp"3 #include 4 #include 5 int main () {6 Cv::surffeaturedetector Detector (400);7
other thread is impersonating in it, and if so, wait for the other threads to execute. This avoids the race condition problem, but it is clear that it will be executed at a lower speed because of possible thread waits. (Personal: This uses the same function as the __syncthreads () fence synchronization function in Cuda)With the above basic knowledge, it is enough for me to do a lot of things. Here we look at a specific application, from the hard disk read into two images, the two images extract
Synchronous OMP algorithm, the main idea is: Similar atoms have the same sparse characteristics. Therefore, when sparse representation of similar atoms, assuming that the sparse atom is in the same position, and its selection in the over-complete dictionary of the same atom, the OMP algorithm is the SOMP algorithm in the original signal is an atomThe special circumstances of the time. The OMP algorithm can be unified into the SOMP algorithm, and its solution process is almost the same as the OM
Basic data structures and examples for OPENCVThe strong mat types in the OpenCV are already familiar to us. Here are some of the basic data types that are commonly used in engineering. Including:VecScalarPointSizeRectRotatedrect 1. Vec class 1.1 Basic Concepts Vec is a template class that is used primarily to store numeric vectors. 1.2 usage (1) Accessing VEC vector members using []myvector[0]=0;(2) Use it to define any type of vectorVec(3) The following predefined types can be used typedef vec
LBPV is a var (variance bar), the Var calculation formula:The above GP is the domain pixel value and then normalized.2. PyramidsA total of three levels of pyramid, i0,i1,i2,i0 for the input image, I0 through the Gaussian lowpass filter (Gaussian low pass filter, LPF), and then the next sample to get I1 (sample size 2), also by I1 to get I2,Finally, from bottom to top, each level is extracted in a unified mode LBP, rotation unchanged LBP, uniform rotation of LBP, in the order of the following co
Operation Steps:1. Load images (grayscale or color) and make them consistent in size;2. If the color map, improve the line color space transformation, from RGB to HSV, if the gray-scale map does not need to transform;3. If the histogram is calculated directly, and the histogram is normalized;4. If the color graph, the color histogram is computed, and the color histogram normalization;5. Use similarity formulas, such as correlation coefficients, chi-square, intersections, or babbitt distances, to
Grabcut
Code from http://www.cnblogs.com/tornadomeet/archive/2012/11/09/2763271.html
# Include
The grabcut method code provided by opencv2 is not easy to implement.
class WatershedSegmenter {private:cv::Mat markers;public:void setMarkers(const cv::Mat markerImage) {// Convert to image of intsmarkerImage.convertTo(markers,CV_32S);}cv::Mat process(const cv::Mat i
} ~Histogram1D(); // Computes the 1D histogram and returns an image of it. cv::Mat getHistogramImage(const cv::Mat image); // Computes the 1D histogram. cv::MatND getHistogram(const cv::Mat image);private: int histSize[1]; // number of bins float hranges[2]; // min and max pixel value const
, we calculate the probability of each pixel and display it. The output result generated at the appropriate threshold gives us a separate result.
Numpy Algorithm
1. First, we need to calculate the color histogram of the object we need to find (Make It 'M') and the image we will search for (Make It 'I ').
Import numpy as npimport cv2 as cvfrom matplotlib import pyplot as plt # roi is the object we need to find or region roi = cv.imread('rose_red.png ') hsv =
The following is the implementation of the project when a dynamic array, successively added several after the project, under the sun below the code.
Header file:
# ifndef __cvector_h__ # define __CVECTOR_H__ # define Min_len 256 # define CVEFAILED-1 # define cvesuccess 0 # def INE Cvepushback 1 # define Cvepopback 2 # define Cveinsert 3 # define CVERM 4 # define Expaned_val 1 # define RED
Used_val 2 typedef void *citerator;
typedef struct _CVECTOR *cvector; # ifdef _cplusplus # define
reprint Please specify: @ Xiao Wu Yi Http://www.cnblogs.com/xiaowuyi qq Group: 64770604If the robot's face rotates with the face in front of you, you will not find this interaction interesting. Years ago, learning a bit of OPENCV, through the OPENCV can be simple to achieve the face of the follow. Plus a few servos to control the head rotation, the robot can interact. OhHere do not do video demonstration, just put out the code, interested friends can experiment with their own. The basic principl
Long time no update, forgive yourself put a fake recently in a hurry, so ... The updated content is very close to the eighth chapter, because the most recent work to jump first, the following will update the note number ... Come on, come on!Finding outlines in a binary imagevoid cv::findcontours ( inputoutputarray image, outputarrayofarrays contours, outputarray hierarchy,
], the approximate expression is:
where M is the 2*2 matrix, which can be obtained by the derivative of the image:
E (u,v) of the oval-like diagram below:
Define the corner response function R as:
Harris Corner detection algorithm is the diagonal point response function R for threshold processing: R > Threshold, that is to extract the local maxima of R.
Second, OPENCV code implementation
Harris class
[cpp] View plain copy #ifndef HARRIS_H #define HARRIS_H #include " Opencv2/opencv.hpp "
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