Object Detection

gpu::HOGDescriptor

struct 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.

Note

• An example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/cpp/peopledetect.cpp
• A GPU example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/gpu/hog.cpp
• (Python) An example applying the HOG descriptor for people detection can be found at opencv_source_code/samples/python2/peopledetect.py

gpu::HOGDescriptor::HOGDescriptor

Creates the HOG descriptor and detector.

C++: gpu::HOGDescriptor::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)

Parameters:

• win_size – Detection window size. Align to block size and block stride.
• block_size – Block size in pixels. Align to cell size. Only (16,16) is supported for now.
• block_stride – Block stride. It must be a multiple of cell size.
• cell_size – Cell size. Only (8, 8) is supported for now.
• nbins – Number of bins. Only 9 bins per cell are supported for now.
• win_sigma – Gaussian smoothing window parameter.
• threshold_L2hys – L2-Hys normalization method shrinkage.
• gamma_correction – Flag to specify whether the gamma correction preprocessing is required or not.
• nlevels – Maximum number of detection window increases.

gpu::HOGDescriptor::getDescriptorSize

Returns the number of coefficients required for the classification.

C++: size_t gpu::HOGDescriptor::getDescriptorSize() const

gpu::HOGDescriptor::getBlockHistogramSize

Returns the block histogram size.

C++: size_t gpu::HOGDescriptor::getBlockHistogramSize() const

gpu::HOGDescriptor::setSVMDetector

Sets coefficients for the linear SVM classifier.

C++: void gpu::HOGDescriptor::setSVMDetector(const vector<float>& detector)

gpu::HOGDescriptor::getDefaultPeopleDetector

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

C++: static vector<float> gpu::HOGDescriptor::getDefaultPeopleDetector()

gpu::HOGDescriptor::getPeopleDetector48x96

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

C++: static vector<float> gpu::HOGDescriptor::getPeopleDetector48x96()

gpu::HOGDescriptor::getPeopleDetector64x128

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

C++: static vector<float> gpu::HOGDescriptor::getPeopleDetector64x128()

gpu::HOGDescriptor::detect

Performs object detection without a multi-scale window.

C++: void gpu::HOGDescriptor::detect(const GpuMat& img, vector<Point>& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size())

Parameters:

• img – Source image. CV_8UC1 and CV_8UC4 types are supported for now.
• found_locations – Left-top corner points of detected objects boundaries.
• hit_threshold – Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.
• win_stride – Window stride. It must be a multiple of block stride.
• padding – Mock parameter to keep the CPU interface compatibility. It must be (0,0).

gpu::HOGDescriptor::detectMultiScale

Performs object detection with a multi-scale window.

C++: void gpu::HOGDescriptor::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)

Parameters:

• img – Source image. See gpu::HOGDescriptor::detect() for type limitations.
• found_locations – Detected objects boundaries.
• hit_threshold – Threshold for the distance between features and SVM classifying plane. See gpu::HOGDescriptor::detect() for details.
• win_stride – Window stride. It must be a multiple of block stride.
• padding – Mock parameter to keep the CPU interface compatibility. It must be (0,0).
• scale0 – Coefficient of the detection window increase.
• group_threshold – Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See groupRectangles() .

gpu::HOGDescriptor::getDescriptors

Returns block descriptors computed for the whole image.

C++: void gpu::HOGDescriptor::getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL)

Parameters:

• img – Source image. See gpu::HOGDescriptor::detect() for type limitations.
• win_stride – Window stride. It must be a multiple of block stride.
• descriptors – 2D array of descriptors.
• descr_format –

  Descriptor storage format:

  • DESCR_FORMAT_ROW_BY_ROW - Row-major order.
  • DESCR_FORMAT_COL_BY_COL - Column-major order.

The function is mainly used to learn the classifier.

gpu::CascadeClassifier_GPU

class 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;
};

Note

• A cascade classifier example can be found at opencv_source_code/samples/gpu/cascadeclassifier.cpp
• A Nvidea API specific cascade classifier example can be found at opencv_source_code/samples/gpu/cascadeclassifier_nvidia_api.cpp

gpu::CascadeClassifier_GPU::CascadeClassifier_GPU

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

C++: gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string& filename)

Parameters:

• filename – Name of the file from which the classifier is loaded. Only the old haar classifier (trained by the haar training application) and NVIDIA’s nvbin are supported for HAAR and only new type of OpenCV XML cascade supported for LBP.

gpu::CascadeClassifier_GPU::empty

Checks whether the classifier is loaded or not.

C++: bool gpu::CascadeClassifier_GPU::empty() const

gpu::CascadeClassifier_GPU::load

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

C++: bool gpu::CascadeClassifier_GPU::load(const string& filename)

Parameters:

• filename – Name of the file from which the classifier is loaded. Only the old haar classifiers (trained by the haar training application) and NVIDIA’s nvbin are supported for HAAR and only new type of OpenCV XML cascade supported for LBP. The working haar classifiers can be found under data\haarcascades_GPU\.

gpu::CascadeClassifier_GPU::release

Destroys the loaded classifier.

C++: void gpu::CascadeClassifier_GPU::release()

gpu::CascadeClassifier_GPU::detectMultiScale

Detects objects of different sizes in the input image.

C++: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size())

C++: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize=Size(), double scaleFactor=1.1, int minNeighbors=4)

Parameters:

• image – Matrix of type CV_8U containing an image where objects should be detected.
• objectsBuf – Buffer to store detected objects (rectangles). If it is empty, it is allocated with the default size. If not empty, the function searches not more than N objects, where N = sizeof(objectsBufer's data)/sizeof(cv::Rect).
• maxObjectSize – Maximum possible object size. Objects larger than that are ignored. Used for second signature and supported only for LBP cascades.
• scaleFactor – Parameter specifying how much the image size is reduced at each image scale.
• minNeighbors – Parameter specifying how many neighbors each candidate rectangle should have to retain it.
• minSize – Minimum possible object size. Objects smaller than that are ignored.

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);

See also

CascadeClassifier::detectMultiScale()

[Dalal2005]

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