Object Detection

gpu::HOGDescriptor

The class implements Histogram of Oriented Gradients ([Dalal2005]) object detector.

struct CV_EXPORTS HOGDescriptor
{
    enum { DEFAULT_WIN_SIGMA = -1 };
    enum { DEFAULT_NLEVELS = 64 };
    enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };

    HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
                  Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
                  int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
                  double threshold_L2hys=0.2, bool gamma_correction=true,
                  int nlevels=DEFAULT_NLEVELS);

    size_t getDescriptorSize() const;
    size_t getBlockHistogramSize() const;

    void setSVMDetector(const vector<float>& detector);

    static vector<float> getDefaultPeopleDetector();
    static vector<float> getPeopleDetector48x96();
    static vector<float> getPeopleDetector64x128();

    void detect(const GpuMat& img, vector<Point>& found_locations,
                double hit_threshold=0, Size win_stride=Size(),
                Size padding=Size());

    void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
                          double hit_threshold=0, Size win_stride=Size(),
                          Size padding=Size(), double scale0=1.05,
                          int group_threshold=2);

    void getDescriptors(const GpuMat& img, Size win_stride,
                        GpuMat& descriptors,
                        int descr_format=DESCR_FORMAT_COL_BY_COL);

    Size win_size;
    Size block_size;
    Size block_stride;
    Size cell_size;
    int nbins;
    double win_sigma;
    double threshold_L2hys;
    bool gamma_correction;
    int nlevels;

private:
    // Hidden
}

Interfaces of all methods are kept similar to the CPU HOG descriptor and detector analogues as much as possible.

gpu::HOGDescriptor::HOGDescriptor

Creates the HOG descriptor and detector.

gpu::HOGDescriptor::getDescriptorSize

Returns the number of coefficients required for the classification.

gpu::HOGDescriptor::getBlockHistogramSize

Returns the block histogram size.

gpu::HOGDescriptor::setSVMDetector

Sets coefficients for the linear SVM classifier.

gpu::HOGDescriptor::getDefaultPeopleDetector

Returns coefficients of the classifier trained for people detection (for default window size).

gpu::HOGDescriptor::getPeopleDetector48x96

Returns coefficients of the classifier trained for people detection (for 48x96 windows).

gpu::HOGDescriptor::getPeopleDetector64x128

Returns coefficients of the classifier trained for people detection (for 64x128 windows).

gpu::HOGDescriptor::detect

Performs object detection without a multi-scale window.

gpu::HOGDescriptor::detectMultiScale

Performs object detection with a multi-scale window.

gpu::HOGDescriptor::getDescriptors

Returns block descriptors computed for the whole image.

The function is mainly used to learn the classifier.

gpu::CascadeClassifier_GPU

Cascade classifier class used for object detection. Supports HAAR and LBP cascades.

class CV_EXPORTS CascadeClassifier_GPU
{
public:
        CascadeClassifier_GPU();
        CascadeClassifier_GPU(const string& filename);
        ~CascadeClassifier_GPU();

        bool empty() const;
        bool load(const string& filename);
        void release();

        /* Returns number of detected objects */
        int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());
        int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);

        /* Finds only the largest object. Special mode if training is required.*/
        bool findLargestObject;

        /* Draws rectangles in input image */
        bool visualizeInPlace;

        Size getClassifierSize() const;
};

gpu::CascadeClassifier_GPU::CascadeClassifier_GPU

Loads the classifier from a file. Cascade type is detected automatically by constructor parameter.

gpu::CascadeClassifier_GPU::empty

Checks whether the classifier is loaded or not.

gpu::CascadeClassifier_GPU::load

Loads the classifier from a file. The previous content is destroyed.

gpu::CascadeClassifier_GPU::release

Destroys the loaded classifier.

gpu::CascadeClassifier_GPU::detectMultiScale

Detects objects of different sizes in the input image.

The detected objects are returned as a list of rectangles.

The function returns the number of detected objects, so you can retrieve them as in the following example:

gpu::CascadeClassifier_GPU cascade_gpu(...);

Mat image_cpu = imread(...)
GpuMat image_gpu(image_cpu);

GpuMat objbuf;
int detections_number = cascade_gpu.detectMultiScale( image_gpu,
          objbuf, 1.2, minNeighbors);

Mat obj_host;
// download only detected number of rectangles
objbuf.colRange(0, detections_number).download(obj_host);

Rect* faces = obj_host.ptr<Rect>();
for(int i = 0; i < detections_num; ++i)
   cv::rectangle(image_cpu, faces[i], Scalar(255));

imshow("Faces", image_cpu);

[Dalal2005]

Navneet Dalal and Bill Triggs. Histogram of oriented gradients for human detection. 2005.