我正在使用一个程序来训练svm分类器，以便使用hog描述符进行对象检测。而且我无法获得主程序的可执行文件。我的代码如下：
#include <stdio.h>
#include <dirent.h>
#include <ios>
#include <fstream>
#include <stdexcept>
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#define SVMLIGHT 1
#define LIBSVM 2
#define TRAINHOG_USEDSVM SVMLIGHT
#if TRAINHOG_USEDSVM == SVMLIGHT
#include "svm_light/svmlight.h"
#define TRAINHOG_SVM_TO_TRAIN SVMlight
#elif TRAINHOG_USEDSVM == LIBSVM
#include "libsvm/libsvm.h"
#define TRAINHOG_SVM_TO_TRAIN libSVM
#endif
using namespace std;
using namespace cv;
static string posSamplesDir = "pos/";
static string negSamplesDir = "neg/";
static string featuresFile = "genfiles/features.dat";
static string svmModelFile = "genfiles/svmlightmodel.dat";
static string descriptorVectorFile = "genfiles/descriptorvector.dat";
static const Size trainingPadding = Size(0,0);
static const Size winStride = Size(8,8);
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Helper functions">
/* Helper functions */
static string toLowerCase(const string& in) {
string t;
for (string::const_iterator i = in.begin(); i != in.end(); ++i) {
t += tolower(*i);
}
return t;
}
static void storeCursor(void) {
printf("\033[s");
}
static void resetCursor(void) {
printf("\033[u");
}
/**
* Saves the given descriptor vector to a file
* @param descriptorVector the descriptor vector to save
* @param _vectorIndices contains indices for the corresponding vector values (e.g. descriptorVector(0)=3.5f may have index 1)
* @param fileName
* @TODO Use _vectorIndices to write correct indices
*/
static void saveDescriptorVectorToFile(vector<float>& descriptorVector, vector<unsigned int>& _vectorIndices, string fileName) {
printf("Saving descriptor vector to file '%s'\n", fileName.c_str());
string separator = " "; // Use blank as default separator between single features
fstream File;
float percent;
File.open(fileName.c_str(), ios::out);
if (File.good() && File.is_open()) {
printf("Saving %lu descriptor vector features:\t", descriptorVector.size());
storeCursor();
for (int feature = 0; feature < descriptorVector.size(); ++feature) {
if ((feature % 10 == 0) || (feature == (descriptorVector.size()-1)) ) {
percent = ((1 + feature) * 100 / descriptorVector.size());
printf("%4u (%3.0f%%)", feature, percent);
fflush(stdout);
resetCursor();
}
File << descriptorVector.at(feature) << separator;
}
printf("\n");
File << endl;
File.flush();
File.close();
}
}
/**
* For unixoid systems only: Lists all files in a given directory and returns a vector of path+name in string format
* @param dirName
* @param fileNames found file names in specified directory
* @param validExtensions containing the valid file extensions for collection in lower case
*/
static void getFilesInDirectory(const string& dirName, vector<string>& fileNames, const vector<string>& validExtensions) {
printf("Opening directory %s\n", dirName.c_str());
struct dirent* ep;
size_t extensionLocation;
DIR* dp = opendir(dirName.c_str());
if (dp != NULL) {
while ((ep = readdir(dp))) {
// Ignore (sub-)directories like . , .. , .svn, etc.
if (ep->d_type & DT_DIR) {
continue;
}
extensionLocation = string(ep->d_name).find_last_of("."); // Assume the last point marks beginning of extension like file.ext
// Check if extension is matching the wanted ones
string tempExt = toLowerCase(string(ep->d_name).substr(extensionLocation + 1));
if (find(validExtensions.begin(), validExtensions.end(), tempExt) != validExtensions.end()) {
printf("Found matching data file '%s'\n", ep->d_name);
fileNames.push_back((string) dirName + ep->d_name);
} else {
printf("Found file does not match required file type, skipping: '%s'\n", ep->d_name);
}
}
(void) closedir(dp);
} else {
printf("Error opening directory '%s'!\n", dirName.c_str());
}
return;
}
/**
* This is the actual calculation from the (input) image data to the HOG descriptor/feature vector using the hog.compute() function
* @param imageFilename file path of the image file to read and calculate feature vector from
* @param descriptorVector the returned calculated feature vector<float> ,
* I can't comprehend why openCV implementation returns std::vector<float> instead of cv::MatExpr_<float> (e.g. Mat<float>)
* @param hog HOGDescriptor containin HOG settings
*/
static void calculateFeaturesFromInput(const string& imageFilename, vector<float>& featureVector, HOGDescriptor& hog) {
/** for imread flags from openCV documentation,
* @see http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html?highlight=imread#Mat imread(const string& filename, int flags)
* @note If you get a compile-time error complaining about following line (esp. imread),
* you either do not have a current openCV version (>2.0)
* or the linking order is incorrect, try g++ -o openCVHogTrainer main.cpp `pkg-config --cflags --libs opencv`
*/
Mat imageData = imread(imageFilename, 0);
if (imageData.empty()) {
featureVector.clear();
printf("Error: HOG image '%s' is empty, features calculation skipped!\n", imageFilename.c_str());
return;
}
// Check for mismatching dimensions
if (imageData.cols != hog.winSize.width || imageData.rows != hog.winSize.height) {
featureVector.clear();
printf("Error: Image '%s' dimensions (%u x %u) do not match HOG window size (%u x %u)!\n", imageFilename.c_str(), imageData.cols, imageData.rows, hog.winSize.width, hog.winSize.height);
return;
}
vector<Point> locations;
hog.compute(imageData, featureVector, winStride, trainingPadding, locations);
imageData.release(); // Release the image again after features are extracted
}
/**
* Shows the detections in the image
* @param found vector containing valid detection rectangles
* @param imageData the image in which the detections are drawn
*/
static void showDetections(const vector<Point>& found, Mat& imageData) {
size_t i, j;
for (i = 0; i < found.size(); ++i) {
Point r = found[i];
// Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height);
rectangle(imageData, Rect(r.x-16, r.y-32, 32, 64), Scalar(64, 255, 64), 3);
}
}
/**
* Shows the detections in the image
* @param found vector containing valid detection rectangles
* @param imageData the image in which the detections are drawn
*/
static void showDetections(const vector<Rect>& found, Mat& imageData) {
vector<Rect> found_filtered;
size_t i, j;
for (i = 0; i < found.size(); ++i) {
Rect r = found[i];
for (j = 0; j < found.size(); ++j)
if (j != i && (r & found[j]) == r)
break;
if (j == found.size())
found_filtered.push_back(r);
}
for (i = 0; i < found_filtered.size(); i++) {
Rect r = found_filtered[i];
rectangle(imageData, r.tl(), r.br(), Scalar(64, 255, 64), 3);
}
}
/**
* Test the trained detector against the same training set to get an approximate idea of the detector.
* Warning: This does not allow any statement about detection quality, as the detector might be overfitting.
* Detector quality must be determined using an independent test set.
* @param hog
*/
static void detectTrainingSetTest(const HOGDescriptor& hog, const double hitThreshold, const vector<string>& posFileNames, const vector<string>& negFileNames) {
unsigned int truePositives = 0;
unsigned int trueNegatives = 0;
unsigned int falsePositives = 0;
unsigned int falseNegatives = 0;
vector<Point> foundDetection;
// Walk over positive training samples, generate images and detect
for (vector<string>::const_iterator posTrainingIterator = posFileNames.begin(); posTrainingIterator != posFileNames.end(); ++posTrainingIterator) {
const Mat imageData = imread(*posTrainingIterator, 0);
hog.detect(imageData, foundDetection, hitThreshold, winStride, trainingPadding);
if (foundDetection.size() > 0) {
++truePositives;
falseNegatives += foundDetection.size() - 1;
} else {
++falseNegatives;
}
}
// Walk over negative training samples, generate images and detect
for (vector<string>::const_iterator negTrainingIterator = negFileNames.begin(); negTrainingIterator != negFileNames.end(); ++negTrainingIterator) {
const Mat imageData = imread(*negTrainingIterator, 0);
hog.detect(imageData, foundDetection, hitThreshold, winStride, trainingPadding);
if (foundDetection.size() > 0) {
falsePositives += foundDetection.size();
} else {
++trueNegatives;
}
}
printf("Results:\n\tTrue Positives: %u\n\tTrue Negatives: %u\n\tFalse Positives: %u\n\tFalse Negatives: %u\n", truePositives, trueNegatives, falsePositives, falseNegatives);
}
/**
* Test detection with custom HOG description vector
* @param hog
* @param hitThreshold threshold value for detection
* @param imageData
*/
static void detectTest(const HOGDescriptor& hog, const double hitThreshold, Mat& imageData) {
vector<Rect> found;
Size padding(Size(32, 32));
Size winStride(Size(8, 8));
hog.detectMultiScale(imageData, found, hitThreshold, winStride, padding);
showDetections(found, imageData);
}
// </editor-fold>
/**
* Main program entry point
* @param argc unused
* @param argv unused
* @return EXIT_SUCCESS (0) or EXIT_FAILURE (1)
*/
int main(int argc, char** argv) {
// <editor-fold defaultstate="collapsed" desc="Init">
HOGDescriptor hog; // Use standard parameters here
hog.winSize = Size(64, 128); // Default training images size as used in paper
// Get the files to train from somewhere
static vector<string> positiveTrainingImages;
static vector<string> negativeTrainingImages;
static vector<string> validExtensions;
validExtensions.push_back("jpg");
validExtensions.push_back("png");
validExtensions.push_back("ppm");
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Read image files">
getFilesInDirectory(posSamplesDir, positiveTrainingImages, validExtensions);
getFilesInDirectory(negSamplesDir, negativeTrainingImages, validExtensions);
/// Retrieve the descriptor vectors from the samples
unsigned long overallSamples = positiveTrainingImages.size() + negativeTrainingImages.size();
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Calculate HOG features and save to file">
// Make sure there are actually samples to train
if (overallSamples == 0) {
printf("No training sample files found, nothing to do!\n");
return EXIT_SUCCESS;
}
/// @WARNING: This is really important, some libraries (e.g. ROS) seems to set the system locale which takes decimal commata instead of points which causes the file input parsing to fail
setlocale(LC_ALL, "C"); // Do not use the system locale
setlocale(LC_NUMERIC,"C");
setlocale(LC_ALL, "POSIX");
printf("Reading files, generating HOG features and save them to file '%s':\n", featuresFile.c_str());
float percent;
/**
* Save the calculated descriptor vectors to a file in a format that can be used by SVMlight for training
* @NOTE: If you split these steps into separate steps:
* 1. calculating features into memory (e.g. into a cv::Mat or vector< vector<float> >),
* 2. saving features to file / directly inject from memory to machine learning algorithm,
* the program may consume a considerable amount of main memory
*/
fstream File;
File.open(featuresFile.c_str(), ios::out);
if (File.good() && File.is_open()) {
// Remove following line for libsvm which does not support comments
// File << "# Use this file to train, e.g. SVMlight by issuing $ svm_learn -i 1 -a weights.txt " << featuresFile.c_str() << endl;
// Iterate over sample images
for (unsigned long currentFile = 0; currentFile < overallSamples; ++currentFile) {
storeCursor();
vector<float> featureVector;
// Get positive or negative sample image file path
const string currentImageFile = (currentFile < positiveTrainingImages.size() ? positiveTrainingImages.at(currentFile) : negativeTrainingImages.at(currentFile - positiveTrainingImages.size()));
// Output progress
if ( (currentFile+1) % 10 == 0 || (currentFile+1) == overallSamples ) {
percent = ((currentFile+1) * 100 / overallSamples);
printf("%5lu (%3.0f%%):\tFile '%s'", (currentFile+1), percent, currentImageFile.c_str());
fflush(stdout);
resetCursor();
}
// Calculate feature vector from current image file
calculateFeaturesFromInput(currentImageFile, featureVector, hog);
if (!featureVector.empty()) {
/* Put positive or negative sample class to file,
* true=positive, false=negative,
* and convert positive class to +1 and negative class to -1 for SVMlight
*/
File << ((currentFile < positiveTrainingImages.size()) ? "+1" : "-1");
// Save feature vector components
for (unsigned int feature = 0; feature < featureVector.size(); ++feature) {
File << " " << (feature + 1) << ":" << featureVector.at(feature);
}
File << endl;
}
}
printf("\n");
File.flush();
File.close();
} else {
printf("Error opening file '%s'!\n", featuresFile.c_str());
return EXIT_FAILURE;
}
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Pass features to machine learning algorithm">
/// Read in and train the calculated feature vectors
printf("Calling %s\n", TRAINHOG_SVM_TO_TRAIN::getInstance()->getSVMName());
TRAINHOG_SVM_TO_TRAIN::getInstance()->read_problem(const_cast<char*> (featuresFile.c_str()));
TRAINHOG_SVM_TO_TRAIN::getInstance()->train(); // Call the core libsvm training procedure
printf("Training done, saving model file!\n");
TRAINHOG_SVM_TO_TRAIN::getInstance()->saveModelToFile(svmModelFile);
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Generate single detecting feature vector from calculated SVM support vectors and SVM model">
printf("Generating representative single HOG feature vector using svmlight!\n");
vector<float> descriptorVector;
vector<unsigned int> descriptorVectorIndices;
// Generate a single detecting feature vector (v1 | b) from the trained support vectors, for use e.g. with the HOG algorithm
TRAINHOG_SVM_TO_TRAIN::getInstance()->getSingleDetectingVector(descriptorVector, descriptorVectorIndices);
// And save the precious to file system
saveDescriptorVectorToFile(descriptorVector, descriptorVectorIndices, descriptorVectorFile);
// </editor-fold>
// <editor-fold defaultstate="collapsed" desc="Test detecting vector">
// Detector detection tolerance threshold
const double hitThreshold = TRAINHOG_SVM_TO_TRAIN::getInstance()->getThreshold();
// Set our custom detecting vector
hog.setSVMDetector(descriptorVector);
printf("Testing training phase using training set as test set (just to check if training is ok - no detection quality conclusion with this!)\n");
detectTrainingSetTest(hog, hitThreshold, positiveTrainingImages, negativeTrainingImages);
printf("Testing custom detection using camera\n");
VideoCapture cap(0); // open the default camera
if(!cap.isOpened()) { // check if we succeeded
printf("Error opening camera!\n");
return EXIT_FAILURE;
}
Mat testImage;
while ((cvWaitKey(10) & 255) != 27) {
cap >> testImage; // get a new frame from camera
// cvtColor(testImage, testImage, CV_BGR2GRAY); // If you want to work on grayscale images
detectTest(hog, hitThreshold, testImage);
imshow("HOG custom detection", testImage);
}
// </editor-fold>
return EXIT_SUCCESS;
}
如果我以＆＃34; ./ objectdetectmain.cpp＆＃34;执行它，它会给出如下错误：
./objectdetectmain.cpp: line 24: using: command not found
./objectdetectmain.cpp: line 25: using: command not found
./objectdetectmain.cpp: line 29: static: command not found
./objectdetectmain.cpp: line 31: static: command not found
./objectdetectmain.cpp: line 33: static: command not found
./objectdetectmain.cpp: line 35: static: command not found
./objectdetectmain.cpp: line 37: static: command not found
./objectdetectmain.cpp: line 40: syntax error near unexpected token `('
./objectdetectmain.cpp: line 40: `static const Size trainingPadding = Size(0,0);
答案 0 :(得分：3)
您似乎正在尝试将源作为程序运行。您需要先将编译成可执行文件。 E.g。
g++ -Wall -g objectdetectmain.cpp -o objectdetectmain
将为您提供一个名为objectdetectmain的可执行文件（注意缺少.cpp扩展名），您可以运行该文件：
./objectdetectmain