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# 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.

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