mirror of
https://github.com/opencv/opencv.git
synced 2024-12-15 01:39:10 +08:00
315 lines
11 KiB
ReStructuredText
315 lines
11 KiB
ReStructuredText
Object Detection
|
|
================
|
|
|
|
.. highlight:: cpp
|
|
|
|
|
|
|
|
gpu::HOGDescriptor
|
|
------------------
|
|
.. ocv: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.
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::HOGDescriptor
|
|
-------------------------------------
|
|
Creates the ``HOG`` descriptor and detector.
|
|
|
|
.. 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)
|
|
|
|
: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.
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::getDescriptorSize
|
|
-----------------------------------------
|
|
Returns the number of coefficients required for the classification.
|
|
|
|
.. ocv:function:: size_t gpu::HOGDescriptor::getDescriptorSize() const
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::getBlockHistogramSize
|
|
---------------------------------------------
|
|
Returns the block histogram size.
|
|
|
|
.. ocv:function:: size_t gpu::HOGDescriptor::getBlockHistogramSize() const
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::setSVMDetector
|
|
--------------------------------------
|
|
Sets coefficients for the linear SVM classifier.
|
|
|
|
.. ocv:function:: void gpu::HOGDescriptor::setSVMDetector(const vector<float>& detector)
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::getDefaultPeopleDetector
|
|
------------------------------------------------
|
|
Returns coefficients of the classifier trained for people detection (for default window size).
|
|
|
|
.. ocv:function:: static vector<float> gpu::HOGDescriptor::getDefaultPeopleDetector()
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::getPeopleDetector48x96
|
|
----------------------------------------------
|
|
Returns coefficients of the classifier trained for people detection (for 48x96 windows).
|
|
|
|
.. ocv:function:: static vector<float> gpu::HOGDescriptor::getPeopleDetector48x96()
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::getPeopleDetector64x128
|
|
-----------------------------------------------
|
|
Returns coefficients of the classifier trained for people detection (for 64x128 windows).
|
|
|
|
.. ocv:function:: static vector<float> gpu::HOGDescriptor::getPeopleDetector64x128()
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::detect
|
|
------------------------------
|
|
Performs object detection without a multi-scale window.
|
|
|
|
.. ocv:function:: void gpu::HOGDescriptor::detect(const GpuMat& img, vector<Point>& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size())
|
|
|
|
: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).
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::detectMultiScale
|
|
----------------------------------------
|
|
Performs object detection with a multi-scale window.
|
|
|
|
.. 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)
|
|
|
|
:param img: Source image. See :ocv: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 :ocv: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 :ocv:func:`groupRectangles` .
|
|
|
|
|
|
|
|
gpu::HOGDescriptor::getDescriptors
|
|
--------------------------------------
|
|
Returns block descriptors computed for the whole image.
|
|
|
|
.. ocv:function:: void gpu::HOGDescriptor::getDescriptors(const GpuMat& img, Size win_stride, GpuMat& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL)
|
|
|
|
:param img: Source image. See :ocv: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.
|
|
|
|
The function is mainly used to learn the classifier.
|
|
|
|
|
|
|
|
gpu::CascadeClassifier_GPU
|
|
--------------------------
|
|
.. ocv:class:: gpu::CascadeClassifier_GPU
|
|
|
|
Cascade classifier class 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;
|
|
};
|
|
|
|
|
|
|
|
gpu::CascadeClassifier_GPU::CascadeClassifier_GPU
|
|
-----------------------------------------------------
|
|
Loads the classifier from a file.
|
|
|
|
.. ocv:function:: gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string& filename)
|
|
|
|
: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.
|
|
|
|
|
|
|
|
gpu::CascadeClassifier_GPU::empty
|
|
-------------------------------------
|
|
Checks whether the classifier is loaded or not.
|
|
|
|
.. ocv:function:: bool gpu::CascadeClassifier_GPU::empty() const
|
|
|
|
|
|
|
|
gpu::CascadeClassifier_GPU::load
|
|
------------------------------------
|
|
Loads the classifier from a file. The previous content is destroyed.
|
|
|
|
.. ocv:function:: bool gpu::CascadeClassifier_GPU::load(const string& filename)
|
|
|
|
: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.
|
|
|
|
|
|
|
|
gpu::CascadeClassifier_GPU::release
|
|
---------------------------------------
|
|
Destroys the loaded classifier.
|
|
|
|
.. ocv:function:: void gpu::CascadeClassifier_GPU::release()
|
|
|
|
|
|
|
|
gpu::CascadeClassifier_GPU::detectMultiScale
|
|
------------------------------------------------
|
|
Detects objects of different sizes in the input image.
|
|
|
|
.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor=1.2, int minNeighbors=4, Size minSize=Size())
|
|
|
|
:param image: Matrix of type ``CV_8U`` containing an image where objects should be detected.
|
|
|
|
: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)``.
|
|
|
|
: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 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);
|
|
|
|
|
|
.. seealso:: :ocv:func:`CascadeClassifier::detectMultiScale`
|
|
|
|
|
|
|
|
.. [Dalal2005] Navneet Dalal and Bill Triggs. *Histogram of oriented gradients for human detection*. 2005.
|