mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
325 lines
13 KiB
325 lines
13 KiB
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. |
|
|
|
.. 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. |
|
|
|
.. 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. 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. |
|
|
|
.. 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 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. |
|
|
|
.. 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 for HAAR and only new type of OpenCV XML cascade supported for LBP. |
|
|
|
|
|
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()) |
|
|
|
.. ocv:function:: int gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4) |
|
|
|
: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 maxObjectSize: Maximum possible object size. Objects larger than that are ignored. Used for second signature and supported only for LBP cascades. |
|
|
|
:param scaleFactor: Parameter specifying how much the image size is reduced at each image scale. |
|
|
|
:param minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it. |
|
|
|
: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.
|
|
|