317 lines
12 KiB
ReStructuredText
317 lines
12 KiB
ReStructuredText
Object Detection
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================
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.. highlight:: cpp
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gpu::HOGDescriptor
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------------------
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.. ocv:struct:: gpu::HOGDescriptor
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The class implements Histogram of Oriented Gradients ([Dalal2005]_) object detector. ::
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struct CV_EXPORTS HOGDescriptor
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{
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enum { DEFAULT_WIN_SIGMA = -1 };
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enum { DEFAULT_NLEVELS = 64 };
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enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };
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HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
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Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
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int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
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double threshold_L2hys=0.2, bool gamma_correction=true,
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int nlevels=DEFAULT_NLEVELS);
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size_t getDescriptorSize() const;
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size_t getBlockHistogramSize() const;
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void setSVMDetector(const vector<float>& detector);
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static vector<float> getDefaultPeopleDetector();
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static vector<float> getPeopleDetector48x96();
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static vector<float> getPeopleDetector64x128();
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void detect(const GpuMat& img, vector<Point>& found_locations,
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double hit_threshold=0, Size win_stride=Size(),
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Size padding=Size());
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void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
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double hit_threshold=0, Size win_stride=Size(),
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Size padding=Size(), double scale0=1.05,
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int group_threshold=2);
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void getDescriptors(const GpuMat& img, Size win_stride,
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GpuMat& descriptors,
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int descr_format=DESCR_FORMAT_COL_BY_COL);
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Size win_size;
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Size block_size;
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Size block_stride;
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Size cell_size;
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int nbins;
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double win_sigma;
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double threshold_L2hys;
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bool gamma_correction;
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int nlevels;
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private:
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// Hidden
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}
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Interfaces of all methods are kept similar to the ``CPU HOG`` descriptor and detector analogues as much as possible.
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gpu::HOGDescriptor::HOGDescriptor
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-------------------------------------
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Creates the ``HOG`` descriptor and detector.
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.. ocv: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)
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:param win_size: Detection window size. Align to block size and block stride.
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:param block_size: Block size in pixels. Align to cell size. Only (16,16) is supported for now.
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:param block_stride: Block stride. It must be a multiple of cell size.
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:param cell_size: Cell size. Only (8, 8) is supported for now.
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:param nbins: Number of bins. Only 9 bins per cell are supported for now.
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:param win_sigma: Gaussian smoothing window parameter.
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:param threshold_L2hys: L2-Hys normalization method shrinkage.
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:param gamma_correction: Flag to specify whether the gamma correction preprocessing is required or not.
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:param nlevels: Maximum number of detection window increases.
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gpu::HOGDescriptor::getDescriptorSize
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-----------------------------------------
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Returns the number of coefficients required for the classification.
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.. ocv:function:: size_t gpu::HOGDescriptor::getDescriptorSize() const
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gpu::HOGDescriptor::getBlockHistogramSize
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---------------------------------------------
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Returns the block histogram size.
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.. ocv:function:: size_t gpu::HOGDescriptor::getBlockHistogramSize() const
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gpu::HOGDescriptor::setSVMDetector
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--------------------------------------
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Sets coefficients for the linear SVM classifier.
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.. ocv:function:: void gpu::HOGDescriptor::setSVMDetector(const vector<float>& detector)
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gpu::HOGDescriptor::getDefaultPeopleDetector
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------------------------------------------------
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Returns coefficients of the classifier trained for people detection (for default window size).
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.. ocv:function:: static vector<float> gpu::HOGDescriptor::getDefaultPeopleDetector()
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gpu::HOGDescriptor::getPeopleDetector48x96
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----------------------------------------------
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Returns coefficients of the classifier trained for people detection (for 48x96 windows).
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.. ocv:function:: static vector<float> gpu::HOGDescriptor::getPeopleDetector48x96()
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gpu::HOGDescriptor::getPeopleDetector64x128
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-----------------------------------------------
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Returns coefficients of the classifier trained for people detection (for 64x128 windows).
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.. ocv:function:: static vector<float> gpu::HOGDescriptor::getPeopleDetector64x128()
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gpu::HOGDescriptor::detect
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------------------------------
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Performs object detection without a multi-scale window.
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.. ocv:function:: void gpu::HOGDescriptor::detect(const GpuMat& img, vector<Point>& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size())
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:param img: Source image. ``CV_8UC1`` and ``CV_8UC4`` types are supported for now.
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:param found_locations: Left-top corner points of detected objects boundaries.
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: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.
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:param win_stride: Window stride. It must be a multiple of block stride.
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:param padding: Mock parameter to keep the CPU interface compatibility. It must be (0,0).
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gpu::HOGDescriptor::detectMultiScale
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----------------------------------------
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Performs object detection with a multi-scale window.
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.. ocv:function:: 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)
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:param img: Source image. See :ocv:func:`gpu::HOGDescriptor::detect` for type limitations.
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:param found_locations: Detected objects boundaries.
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:param hit_threshold: Threshold for the distance between features and SVM classifying plane. See :ocv:func:`gpu::HOGDescriptor::detect` for details.
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:param win_stride: Window stride. It must be a multiple of block stride.
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:param padding: Mock parameter to keep the CPU interface compatibility. It must be (0,0).
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:param scale0: Coefficient of the detection window increase.
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: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 :ocv:func:`groupRectangles` .
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gpu::HOGDescriptor::getDescriptors
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--------------------------------------
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Returns block descriptors computed for the whole image.
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.. ocv:function:: void gpu::HOGDescriptor::getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL)
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:param img: Source image. See :ocv:func:`gpu::HOGDescriptor::detect` for type limitations.
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:param win_stride: Window stride. It must be a multiple of block stride.
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:param descriptors: 2D array of descriptors.
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:param descr_format: Descriptor storage format:
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* **DESCR_FORMAT_ROW_BY_ROW** - Row-major order.
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* **DESCR_FORMAT_COL_BY_COL** - Column-major order.
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The function is mainly used to learn the classifier.
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gpu::CascadeClassifier_GPU
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--------------------------
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.. ocv:class:: gpu::CascadeClassifier_GPU
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Cascade classifier class used for object detection. Supports HAAR and LBP cascades. ::
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class CV_EXPORTS CascadeClassifier_GPU
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{
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public:
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CascadeClassifier_GPU();
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CascadeClassifier_GPU(const string& filename);
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~CascadeClassifier_GPU();
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bool empty() const;
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bool load(const string& filename);
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void release();
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/* Returns number of detected objects */
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int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size());
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int detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);
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/* Finds only the largest object. Special mode if training is required.*/
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bool findLargestObject;
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/* Draws rectangles in input image */
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bool visualizeInPlace;
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Size getClassifierSize() const;
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};
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gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
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-----------------------------------------------------
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Loads the classifier from a file. Cascade type is detected automatically by constructor parameter.
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.. ocv:function:: gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string& filename)
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: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 for HAAR and only new type of OpenCV XML cascade supported for LBP.
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gpu::CascadeClassifier_GPU::empty
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-------------------------------------
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Checks whether the classifier is loaded or not.
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.. ocv:function:: bool gpu::CascadeClassifier_GPU::empty() const
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gpu::CascadeClassifier_GPU::load
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------------------------------------
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Loads the classifier from a file. The previous content is destroyed.
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.. ocv:function:: bool gpu::CascadeClassifier_GPU::load(const string& filename)
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: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 for HAAR and only new type of OpenCV XML cascade supported for LBP.
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gpu::CascadeClassifier_GPU::release
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---------------------------------------
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Destroys the loaded classifier.
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.. ocv:function:: void gpu::CascadeClassifier_GPU::release()
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gpu::CascadeClassifier_GPU::detectMultiScale
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------------------------------------------------
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Detects objects of different sizes in the input image.
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.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.1, int minNeighbors=4, Size minSize=Size() )
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:param image: Matrix of type ``CV_8U`` containing an image where objects should be detected.
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:param 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)``.
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:param maxObjectSize: Maximum possible object size. Objects larger than that are ignored. Used for second signature and supported only for LBP cascades.
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:param scaleFactor: Parameter specifying how much the image size is reduced at each image scale.
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:param minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it.
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:param minSize: Minimum possible object size. Objects smaller than that are ignored.
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The detected objects are returned as a list of rectangles.
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The function returns the number of detected objects, so you can retrieve them as in the following example: ::
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gpu::CascadeClassifier_GPU cascade_gpu(...);
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Mat image_cpu = imread(...)
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GpuMat image_gpu(image_cpu);
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GpuMat objbuf;
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int detections_number = cascade_gpu.detectMultiScale( image_gpu,
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objbuf, 1.2, minNeighbors);
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Mat obj_host;
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// download only detected number of rectangles
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objbuf.colRange(0, detections_number).download(obj_host);
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Rect* faces = obj_host.ptr<Rect>();
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for(int i = 0; i < detections_num; ++i)
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cv::rectangle(image_cpu, faces[i], Scalar(255));
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imshow("Faces", image_cpu);
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.. seealso:: :ocv:func:`CascadeClassifier::detectMultiScale`
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.. [Dalal2005] Navneet Dalal and Bill Triggs. *Histogram of oriented gradients for human detection*. 2005.
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