opencv--feature2d之FeatureDetector

来源：互联网发布：上古卷轴5无心捏脸数据编辑：程序博客网时间：2024/04/29 12:28

FaceRecognizer::onInit时直接创建了描述符对象：

descriptor_detector_ = new cv::GridAdaptedFeatureDetector();

descriptor_extractor_ = new cv::SurfDescriptorExtractor;

bowExtractor_ = new cv::BOWImgDescriptorExtractor();

训练过程，首先根据样本图像生成提取描述符。然后使用svm分类器。

1.cvtColor 将原始图像转换为灰度图像。

2.归一化直方图。

3.检测灰度图像的关键点，生成关键点。descriptor_detector_->detect

4.根据关键点，从灰度图像提取描述符。生成提取描述符。descriptor_extractor_->compute

5.将生成的提取描述符作为人的描述符。如果模型中没有描述符则直接赋值，如果已有至少一个则匹配插入。

识别检测过程：根据输入图像生成提取描述符，然后使用SVM进行预测。

FeatureDetector

class FeatureDetector : public Algorithm

Abstract base class for 2D image feature detectors.

class CV_EXPORTS FeatureDetector{public:    virtual ~FeatureDetector();    void detect( const Mat& image, vector<KeyPoint>& keypoints,                 const Mat& mask=Mat() ) const;    void detect( const vector<Mat>& images,                 vector<vector<KeyPoint> >& keypoints,                 const vector<Mat>& masks=vector<Mat>() ) const;    virtual void read(const FileNode&);    virtual void write(FileStorage&) const;    static Ptr<FeatureDetector> create( const string& detectorType );protected:...};Parameters:detectorType – Feature detector type.
The following detector types are supported:
"FAST" – FastFeatureDetector
"STAR" – StarFeatureDetector
"SIFT" – SIFT (nonfree module)
"SURF" – SURF (nonfree module)
"ORB" – ORB
"BRISK" – BRISK
"MSER" – MSER
"GFTT" – GoodFeaturesToTrackDetector
"HARRIS" – GoodFeaturesToTrackDetector with Harris detector enabled
"Dense" – DenseFeatureDetector
"SimpleBlob" – SimpleBlobDetector
BOWImgDescriptorExtractor::compute

0 0