OpenCV SVM 训练数据
OpenCV SVM Training Data
我想通过使用 C++ 和 Visual Studio 2013 中的 opencv 3.00 库来学习 svm 实现。
我的代码:
#include<stdio.h>
#include<math.h>
#include<opencv\cv.h>
#include<opencv\highgui.h>
#include<opencv2\objdetect\objdetect.hpp>
#include<opencv2\highgui\highgui.hpp>
#include<opencv2\imgproc\imgproc.hpp>
#include<vector>
#include <windows.h>
#include <atlstr.h>
#include <iostream>
#include <sstream>
#include <iomanip>
#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\core\core.hpp>
#include <opencv2\highgui\highgui.hpp>
#include <opencv\cvaux.hpp>
using namespace cv;
using namespace std;
#include <opencv2\ml.hpp>
using namespace cv;
int main()
{
// Data for visual representation
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);
// Set up training data
float labels[4] = { 1.0, -1.0, -1.0, -1.0 };
Mat labelsMat(4, 1, CV_32FC1, labels);
float trainingData[4][2] = { { 501, 10 }, { 255, 10 }, { 501, 255 }, { 10, 501 } };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
// Set up SVM's parameters
Ptr<ml::SVM> svm = ml::SVM::create();
// edit: the params struct got removed,
// we use setter/getter now:
svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM::LINEAR);
svm->setGamma(3);
svm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);
Mat res; // output
Vec3b green(0, 255, 0), blue(255, 0, 0);
// Show the decision regions given by the SVM
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1, 2) << j, i);
float response = svm->predict(sampleMat, res);
if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}
// Show the training data
int thickness = -1;
int lineType = 8;
circle(image, Point(501, 10), 5, Scalar(0, 0, 0), thickness, lineType);
circle(image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);
circle(image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
circle(image, Point(10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
// Show support vectors
thickness = 2;
lineType = 8;
Mat sv = svm->getSupportVectors();
for (int i = 0; i < sv.rows; ++i)
{
const float* v = sv.ptr<float>(i);
circle(image, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
}
imwrite("result.png", image); // save the image
imshow("SVM Simple Example", image); // show it to the user
waitKey(0);
}
在 运行 这段代码之后,我得到了那个错误:
OpenCV Error: Bad argument < in the case of classification problem the responses must be categorical;
either specify varType when creating TrainData, or pass integer responses > in cv::ml::SVMImpl::train,
file C:\builds\master_PackSlave-win64-vc12-shared\opencv\modules\ml\src\svm.cpp, line 1610
我调试了那个代码。调试器停在这一行:svm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);
它说:
First-chance exception at 0x000007FEFDA5AAAD in train.exe: Microsoft C++ exception: cv::Exception at memory location 0x00000000001CEE50.
Unhandled exception at 0x000007FEFDA5AAAD in train.exe: Microsoft C++ exception: cv::Exception at memory location 0x00000000001CEE50.
此外,上面写着:
(Win32): Loaded 'C:\OpenCV3.0.0\opencv\build\x64\vc12\bin\opencv_world300d.dll'. Cannot find or open the PDB file.
其实我的理解是内存的问题。
responses
的类型不能是float
或double
。
改变
float labels[4] = { 1.0, -1.0, -1.0, -1.0 };
Mat labelsMat(4, 1, CV_32FC1, labels);
到
int labels[4] = { 1, -1, -1, -1 };
Mat labelsMat(4, 1, CV_32S, labels);
顺便说一句,如果你使用线性内核,唯一的参数是C
,所以你不需要setGamma
。
另一个问题是获得预测响应的方式。由于每次只有一个样本要预测,如果要使用 return 值作为响应,则不应将 res
传递给 predict
。
你可以改变
float response = svm->predict(sampleMat, res);
到
float response = svm->predict(sampleMat);
否则,如果要使用res
,那么return值就不再是响应值了。但是您可以从 res
获得响应。
你可以改变
if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}
到
if (res.at<float>(0) == 1)
image.at<Vec3b>(i, j) = green;
else if (res.at<float>(0) == -1)
image.at<Vec3b>(i, j) = blue;
}
我想通过使用 C++ 和 Visual Studio 2013 中的 opencv 3.00 库来学习 svm 实现。 我的代码:
#include<stdio.h>
#include<math.h>
#include<opencv\cv.h>
#include<opencv\highgui.h>
#include<opencv2\objdetect\objdetect.hpp>
#include<opencv2\highgui\highgui.hpp>
#include<opencv2\imgproc\imgproc.hpp>
#include<vector>
#include <windows.h>
#include <atlstr.h>
#include <iostream>
#include <sstream>
#include <iomanip>
#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\core\core.hpp>
#include <opencv2\highgui\highgui.hpp>
#include <opencv\cvaux.hpp>
using namespace cv;
using namespace std;
#include <opencv2\ml.hpp>
using namespace cv;
int main()
{
// Data for visual representation
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);
// Set up training data
float labels[4] = { 1.0, -1.0, -1.0, -1.0 };
Mat labelsMat(4, 1, CV_32FC1, labels);
float trainingData[4][2] = { { 501, 10 }, { 255, 10 }, { 501, 255 }, { 10, 501 } };
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
// Set up SVM's parameters
Ptr<ml::SVM> svm = ml::SVM::create();
// edit: the params struct got removed,
// we use setter/getter now:
svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM::LINEAR);
svm->setGamma(3);
svm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);
Mat res; // output
Vec3b green(0, 255, 0), blue(255, 0, 0);
// Show the decision regions given by the SVM
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1, 2) << j, i);
float response = svm->predict(sampleMat, res);
if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}
// Show the training data
int thickness = -1;
int lineType = 8;
circle(image, Point(501, 10), 5, Scalar(0, 0, 0), thickness, lineType);
circle(image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);
circle(image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
circle(image, Point(10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
// Show support vectors
thickness = 2;
lineType = 8;
Mat sv = svm->getSupportVectors();
for (int i = 0; i < sv.rows; ++i)
{
const float* v = sv.ptr<float>(i);
circle(image, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
}
imwrite("result.png", image); // save the image
imshow("SVM Simple Example", image); // show it to the user
waitKey(0);
}
在 运行 这段代码之后,我得到了那个错误:
OpenCV Error: Bad argument < in the case of classification problem the responses must be categorical;
either specify varType when creating TrainData, or pass integer responses > in cv::ml::SVMImpl::train,
file C:\builds\master_PackSlave-win64-vc12-shared\opencv\modules\ml\src\svm.cpp, line 1610
我调试了那个代码。调试器停在这一行:svm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);
它说:
First-chance exception at 0x000007FEFDA5AAAD in train.exe: Microsoft C++ exception: cv::Exception at memory location 0x00000000001CEE50.
Unhandled exception at 0x000007FEFDA5AAAD in train.exe: Microsoft C++ exception: cv::Exception at memory location 0x00000000001CEE50.
此外,上面写着:
(Win32): Loaded 'C:\OpenCV3.0.0\opencv\build\x64\vc12\bin\opencv_world300d.dll'. Cannot find or open the PDB file.
其实我的理解是内存的问题。
responses
的类型不能是float
或double
。
改变
float labels[4] = { 1.0, -1.0, -1.0, -1.0 };
Mat labelsMat(4, 1, CV_32FC1, labels);
到
int labels[4] = { 1, -1, -1, -1 };
Mat labelsMat(4, 1, CV_32S, labels);
顺便说一句,如果你使用线性内核,唯一的参数是C
,所以你不需要setGamma
。
另一个问题是获得预测响应的方式。由于每次只有一个样本要预测,如果要使用 return 值作为响应,则不应将 res
传递给 predict
。
你可以改变
float response = svm->predict(sampleMat, res);
到
float response = svm->predict(sampleMat);
否则,如果要使用res
,那么return值就不再是响应值了。但是您可以从 res
获得响应。
你可以改变
if (response == 1)
image.at<Vec3b>(i, j) = green;
else if (response == -1)
image.at<Vec3b>(i, j) = blue;
}
到
if (res.at<float>(0) == 1)
image.at<Vec3b>(i, j) = green;
else if (res.at<float>(0) == -1)
image.at<Vec3b>(i, j) = blue;
}