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Open Source Computer Vision Library
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314 lines
11 KiB
314 lines
11 KiB
Object Detection |
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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. :: |
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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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/* 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. |
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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. |
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gpu::CascadeClassifier_GPU::empty |
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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. |
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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=1.2, 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 scaleFactor: Value to specify how much the image size is reduced at each image scale. |
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:param minNeighbors: Value to specify how many neighbours each candidate rectangle has to retain. |
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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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