基于HOG特征的SVM分类器，用于OpenCV中的“对象检测”

时间：2012-05-26 20:27:00

标签： c++ opencv svm object-recognition training-data

我有一个项目，我想检测图像中的对象;我的目标是使用HOG功能。通过使用OpenCV SVM实现，我可以找到用于检测人的代码，并且我阅读了一些关于调整参数以便检测对象而不是人的文章。不幸的是，由于一些原因我不能这样做;首先，我可能错误地调整了参数，其次，我不是C ++中的优秀程序员但我必须使用C ++ / OpenCV ... here你可以找到检测HOG的代码使用C ++ / OpenCV为人们提供的功能。

假设我要检测此image中的对象。现在，我将向您展示我在代码中尝试更改的内容，但它与我无关。

我试图更改的代码：

HOGDescriptor hog;
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());

我尝试使用以下参数更改getDefaultPeopleDetector()，但它不起作用：

(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9, 0,-1, 0, 0.2, true, cv::HOGDescriptor::DEFAULT_NLEVELS)

然后我尝试制作一个矢量，但是当我想打印结果时，它似乎是空的。

vector<float> detector;

HOGDescriptor hog(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8), 9, 0,-1, 0, 0.2, true, cv::HOGDescriptor::DEFAULT_NLEVELS);

hog.setSVMDetector(detector);

拜托，我需要帮助解决这个问题。

3 个答案:

答案 0 :(得分：36)

为了使用opencv HOG描述符和SVM分类器检测任意对象，您需要首先训练分类器。玩这些参数对这里没有帮助，抱歉:(。

从广义上讲，您需要完成以下步骤：

步骤1）准备一些您想要检测的对象的训练图像（正样本）。此外，您还需要准备一些没有感兴趣对象的图像（负样本）。

步骤2）检测训练样本的HOG功能，并使用此功能训练SVM分类器（也在OpenCV中提供）。

步骤3）在HOGDescriptor :: setSVMDetector（）方法中使用经过训练的SVM分类器的系数。

只有这样，您才可以使用peopledetector.cpp示例代码来检测要检测的对象。

答案 1 :(得分：9)

我一直在处理同样的问题，并且因为缺少一些干净的C ++解决方案而感到惊讶，我创建了 ~> this wrapper of SVMLight <~ ，这是一个提供类{{1}的静态库}和SVMTrainer将训练简化为：

SVMClassifier

然后是每个训练样本：

// we are going to use HOG to obtain feature vectors:
HOGDescriptor hog;
hog.winSize = Size(32,48);

// and feed SVM with them:
SVMLight::SVMTrainer svm("features.dat");

直到// obtain feature vector describing sample image: vector<float> featureVector; hog.compute(img, featureVector, Size(8, 8), Size(0, 0)); // and write feature vector to the file: svm.writeFeatureVectorToFile(featureVector, true); // true = positive sample 文件包含所有样本的特征向量，最后您只需调用：

features.dat

一旦你有一个模型文件（或std::string modelName("classifier.dat"); svm.trainAndSaveModel(modelName); 你可以训练分类器）：

features.dat

请查看HOGDescriptor的文档以获取更多信息：）

答案 2 :(得分：7)

我做了类似的事情：使用HOG收集正负图像样本以提取汽车的特征，使用线性SVM训练特征集（我使用SVM光），然后使用模型使用HOG检测汽车multidetect功能。

我得到很多假阳性，然后我使用阳性样本和假阳性+阴性样本重新训练数据。然后再次测试得到的模型。结果检测得到改善（误报率较低）但结果并不令人满意（平均50％命中率和50％误报率）。调整multidetect参数可以改善结果，但不会太多（假阳性减少10％，命中率增加）。

修改 如果您愿意，我可以与您分享源代码，我非常乐于讨论，因为我没有使用HOG获得满意的结果。无论如何，我认为代码可以成为使用HOG进行训练和检测的良好起点

修改：添加代码 

static void calculateFeaturesFromInput(const string& imageFilename, vector<float>& featureVector, HOGDescriptor& hog) 
{
    Mat imageData = imread(imageFilename, 1);
    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
}

... 

int main(int argc, char** argv) {

    // <editor-fold defaultstate="collapsed" desc="Init">
    HOGDescriptor hog; // Use standard parameters here
    hog.winSize.height = 128;
    hog.winSize.width = 64;

    // Get the files to train from somewhere
    static vector<string> tesImages;
    static vector<string> positiveTrainingImages;
    static vector<string> negativeTrainingImages;
    static vector<string> validExtensions;
    validExtensions.push_back("jpg");
    validExtensions.push_back("png");
    validExtensions.push_back("ppm");
    validExtensions.push_back("pgm");
    // </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()) {
        File << "# Use this file to train, e.g. SVMlight by issuing $ svm_learn -i 1 -a weights.txt " << featuresFile.c_str() << endl; // Remove this line for libsvm which does not support comments
        // 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 SVMlight\n");
    SVMlight::getInstance()->read_problem(const_cast<char*> (featuresFile.c_str()));
    SVMlight::getInstance()->train(); // Call the core libsvm training procedure
    printf("Training done, saving model file!\n");
    SVMlight::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
    SVMlight::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">

    cout << "Test Detecting Vector" << endl;
    hog.setSVMDetector(descriptorVector); // Set our custom detecting vector
    cout << "descriptorVector size: " << sizeof(descriptorVector) << endl;

    getFilesInDirectory(tesSamplesDir, tesImages, validExtensions);
    namedWindow("Test Detector", 1);

    for( size_t it = 0; it < tesImages.size(); it++ )
    {
        cout << "Process image " << tesImages[it] << endl;
        Mat image = imread( tesImages[it], 1 );
        detectAndDrawObjects(image, hog);

        for(;;)
        {
            int c = waitKey();
            if( (char)c == 'n')
                break;
            else if( (char)c == '\x1b' )
                exit(0);
        }
    }
    // </editor-fold>
    return EXIT_SUCCESS;
}
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