Object Detection ================ .. highlight:: cpp .. index:: gpu::HOGDescriptor gpu::HOGDescriptor ------------------ .. cpp:class:: gpu::HOGDescriptor This class provides a histogram of Oriented Gradients [Navneet Dalal and Bill Triggs. Histogram of oriented gradients for human detection. 2005.] descriptor and 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& detector); static vector getDefaultPeopleDetector(); static vector getPeopleDetector48x96(); static vector getPeopleDetector64x128(); void detect(const GpuMat& img, vector& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size()); void detectMultiScale(const GpuMat& img, vector& 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. .. index:: gpu::HOGDescriptor::HOGDescriptor gpu::HOGDescriptor::HOGDescriptor ------------------------------------- .. cpp:function:: 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) Creates the ``HOG`` descriptor and detector. :param win_size: Detection window size. Align to block size and block stride. :param block_size: Block size in pixels. Align to cell size. Only (16,16) is supported for now. :param block_stride: Block stride. It must be a multiple of cell size. :param cell_size: Cell size. Only (8, 8) is supported for now. :param nbins: Number of bins. Only 9 bins per cell are supported for now. :param win_sigma: Gaussian smoothing window parameter. :param threshold_L2Hys: L2-Hys normalization method shrinkage. :param gamma_correction: Flag to specify whether the gamma correction preprocessing is required or not. :param nlevels: Maximum number of detection window increases. .. index:: gpu::HOGDescriptor::getDescriptorSize gpu::HOGDescriptor::getDescriptorSize ----------------------------------------- .. cpp:function:: size_t gpu::HOGDescriptor::getDescriptorSize() const Returns the number of coefficients required for the classification. .. index:: gpu::HOGDescriptor::getBlockHistogramSize gpu::HOGDescriptor::getBlockHistogramSize --------------------------------------------- .. cpp:function:: size_t gpu::HOGDescriptor::getBlockHistogramSize() const Returns the block histogram size. .. index:: gpu::HOGDescriptor::setSVMDetector gpu::HOGDescriptor::setSVMDetector -------------------------------------- .. cpp:function:: void gpu::HOGDescriptor::setSVMDetector(const vector\& detector) Sets coefficients for the linear SVM classifier. .. index:: gpu::HOGDescriptor::getDefaultPeopleDetector gpu::HOGDescriptor::getDefaultPeopleDetector ------------------------------------------------ .. cpp:function:: static vector gpu::HOGDescriptor::getDefaultPeopleDetector() Returns coefficients of the classifier trained for people detection (for default window size). .. index:: gpu::HOGDescriptor::getPeopleDetector48x96 gpu::HOGDescriptor::getPeopleDetector48x96 ---------------------------------------------- .. cpp:function:: static vector gpu::HOGDescriptor::getPeopleDetector48x96() Returns coefficients of the classifier trained for people detection (for 48x96 windows). .. index:: gpu::HOGDescriptor::getPeopleDetector64x128 gpu::HOGDescriptor::getPeopleDetector64x128 ----------------------------------------------- .. cpp:function:: static vector gpu::HOGDescriptor::getPeopleDetector64x128() Returns coefficients of the classifier trained for people detection (for 64x128 windows). .. index:: gpu::HOGDescriptor::detect gpu::HOGDescriptor::detect ------------------------------ .. cpp:function:: void gpu::HOGDescriptor::detect(const GpuMat\& img, vector\& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size()) Performs object detection without a multi-scale window. :param img: Source image. ``CV_8UC1`` and ``CV_8UC4`` types are supported for now. :param found_locations: Left-top corner points of detected objects boundaries. :param 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. :param win_stride: Window stride. It must be a multiple of block stride. :param padding: Mock parameter to keep the CPU interface compatibility. It must be (0,0). .. index:: gpu::HOGDescriptor::detectMultiScale gpu::HOGDescriptor::detectMultiScale ---------------------------------------- .. cpp:function:: void gpu::HOGDescriptor::detectMultiScale(const GpuMat\& img, vector\& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size(), double scale0=1.05, int group_threshold=2) Performs object detection with a multi-scale window. :param img: Source image. See :cpp:func:`gpu::HOGDescriptor::detect` for type limitations. :param found_locations: Detected objects boundaries. :param hit_threshold: Threshold for the distance between features and SVM classifying plane. See :cpp:func:`gpu::HOGDescriptor::detect` for details. :param win_stride: Window stride. It must be a multiple of block stride. :param padding: Mock parameter to keep the CPU interface compatibility. It must be (0,0). :param scale0: Coefficient of the detection window increase. :param 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 :cpp:func:`groupRectangles` . .. index:: gpu::HOGDescriptor::getDescriptors gpu::HOGDescriptor::getDescriptors -------------------------------------- .. cpp:function:: void gpu::HOGDescriptor::getDescriptors(const GpuMat\& img, Size win_stride, GpuMat\& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL) Returns block descriptors computed for the whole image. The function is mainly used to learn the classifier. :param img: Source image. See :cpp:func:`gpu::HOGDescriptor::detect` for type limitations. :param win_stride: Window stride. It must be a multiple of block stride. :param descriptors: 2D array of descriptors. :param descr_format: Descriptor storage format: * **DESCR_FORMAT_ROW_BY_ROW** Row-major order. * **DESCR_FORMAT_COL_BY_COL** Column-major order. .. index:: gpu::CascadeClassifier_GPU gpu::CascadeClassifier_GPU -------------------------- .. cpp:class:: gpu::CascadeClassifier_GPU This cascade classifier class is used for object detection. :: 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()); /* Finds only the largest object. Special mode if training is required.*/ bool findLargestObject; /* Draws rectangles in input image */ bool visualizeInPlace; Size getClassifierSize() const; }; .. index:: gpu::CascadeClassifier_GPU::CascadeClassifier_GPU gpu::CascadeClassifier_GPU::CascadeClassifier_GPU ----------------------------------------------------- .. cpp:function:: gpu::CascadeClassifier_GPU(const string\& filename) Loads the classifier from a file. :param 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. .. index:: gpu::CascadeClassifier_GPU::empty .. _gpu::CascadeClassifier_GPU::empty: gpu::CascadeClassifier_GPU::empty ------------------------------------- .. cpp:function:: bool gpu::CascadeClassifier_GPU::empty() const Checks whether the classifier is loaded or not. .. index:: gpu::CascadeClassifier_GPU::load .. _gpu::CascadeClassifier_GPU::load: gpu::CascadeClassifier_GPU::load ------------------------------------ .. cpp:function:: bool gpu::CascadeClassifier_GPU::load(const string\& filename) Loads the classifier from a file. The previous content is destroyed. :param 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. .. index:: gpu::CascadeClassifier_GPU::release gpu::CascadeClassifier_GPU::release --------------------------------------- .. cpp:function:: void gpu::CascadeClassifier_GPU::release() Destroys the loaded classifier. .. index:: gpu::CascadeClassifier_GPU::detectMultiScale gpu::CascadeClassifier_GPU::detectMultiScale ------------------------------------------------ .. cpp:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat\& image, GpuMat\& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size()) Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles. :param image: Matrix of type ``CV_8U`` containing an image where objects should be detected. :param objects: 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)``. :param scaleFactor: Value to specify how much the image size is reduced at each image scale. :param minNeighbors: Value to specify how many neighbours each candidate rectangle has to retain. :param minSize: Minimum possible object size. Objects smaller than that are ignored. 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(); for(int i = 0; i < detections_num; ++i) cv::rectangle(image_cpu, faces[i], Scalar(255)); imshow("Faces", image_cpu); See Also: :cpp:func:`CascadeClassifier::detectMultiScale`