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Merge pull request #3012 from vpisarev:clean_objdetect

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modules/objdetect/doc/erfilter.rst deleted 100644 → 0
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modules/objdetect/doc/latent_svm.rst deleted 100644 → 0
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Latent SVM
===============================================================
Discriminatively Trained Part Based Models for Object Detection
---------------------------------------------------------------
The object detector described below has been initially proposed by
P.F. Felzenszwalb in [Felzenszwalb2010]_. It is based on a
Dalal-Triggs detector that uses a single filter on histogram of
oriented gradients (HOG) features to represent an object category.
This detector uses a sliding window approach, where a filter is
applied at all positions and scales of an image. The first
innovation is enriching the Dalal-Triggs model using a
star-structured part-based model defined by a "root" filter
(analogous to the Dalal-Triggs filter) plus a set of parts filters
and associated deformation models. The score of one of star models
at a particular position and scale within an image is the score of
the root filter at the given location plus the sum over parts of the
maximum, over placements of that part, of the part filter score on
its location minus a deformation cost easuring the deviation of the
part from its ideal location relative to the root. Both root and
part filter scores are defined by the dot product between a filter
(a set of weights) and a subwindow of a feature pyramid computed
from the input image. Another improvement is a representation of the
class of models by a mixture of star models. The score of a mixture
model at a particular position and scale is the maximum over
components, of the score of that component model at the given
location.
In OpenCV there are C implementation of Latent SVM and C++ wrapper of it.
C version is the structure :ocv:struct:`CvObjectDetection` and a set of functions
working with this structure (see :ocv:func:`cvLoadLatentSvmDetector`,
:ocv:func:`cvReleaseLatentSvmDetector`, :ocv:func:`cvLatentSvmDetectObjects`).
C++ version is the class :ocv:class:`LatentSvmDetector` and has slightly different
functionality in contrast with C version - it supports loading and detection
of several models.
There are two examples of Latent SVM usage: ``samples/c/latentsvmdetect.cpp``
and ``samples/cpp/latentsvm_multidetect.cpp``.
.. highlight:: c
CvLSVMFilterPosition
--------------------
.. ocv:struct:: CvLSVMFilterPosition
Structure describes the position of the filter in the feature pyramid.
.. ocv:member:: unsigned int l
level in the feature pyramid
.. ocv:member:: unsigned int x
x-coordinate in level l
.. ocv:member:: unsigned int y
y-coordinate in level l
CvLSVMFilterObject
------------------
.. ocv:struct:: CvLSVMFilterObject
Description of the filter, which corresponds to the part of the object.
.. ocv:member:: CvLSVMFilterPosition V
ideal (penalty = 0) position of the partial filter
from the root filter position (V_i in the paper)
.. ocv:member:: float fineFunction[4]
vector describes penalty function (d_i in the paper)
pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
.. ocv:member:: int sizeX
.. ocv:member:: int sizeY
Rectangular map (sizeX x sizeY),
every cell stores feature vector (dimension = p)
.. ocv:member:: int numFeatures
number of features
.. ocv:member:: float *H
matrix of feature vectors to set and get
feature vectors (i,j) used formula H[(j * sizeX + i) * p + k],
where k - component of feature vector in cell (i, j)
CvLatentSvmDetector
-------------------
.. ocv:struct:: CvLatentSvmDetector
Structure contains internal representation of trained Latent SVM detector.
.. ocv:member:: int num_filters
total number of filters (root plus part) in model
.. ocv:member:: int num_components
number of components in model
.. ocv:member:: int* num_part_filters
array containing number of part filters for each component
.. ocv:member:: CvLSVMFilterObject** filters
root and part filters for all model components
.. ocv:member:: float* b
biases for all model components
.. ocv:member:: float score_threshold
confidence level threshold
CvObjectDetection
-----------------
.. ocv:struct:: CvObjectDetection
Structure contains the bounding box and confidence level for detected object.
.. ocv:member:: CvRect rect
bounding box for a detected object
.. ocv:member:: float score
confidence level
cvLoadLatentSvmDetector
-----------------------
Loads trained detector from a file.
.. ocv:function:: CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename)
:param filename: Name of the file containing the description of a trained detector
cvReleaseLatentSvmDetector
--------------------------
Release memory allocated for CvLatentSvmDetector structure.
.. ocv:function:: void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector)
:param detector: CvLatentSvmDetector structure to be released
cvLatentSvmDetectObjects
------------------------
Find rectangular regions in the given image that are likely to contain objects
and corresponding confidence levels.
.. ocv:function:: CvSeq* cvLatentSvmDetectObjects( IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold=0.5f, int numThreads=-1 )
:param image: image
:param detector: LatentSVM detector in internal representation
:param storage: Memory storage to store the resultant sequence of the object candidate rectangles
:param overlap_threshold: Threshold for the non-maximum suppression algorithm
:param numThreads: Number of threads used in parallel version of the algorithm
.. highlight:: cpp
LatentSvmDetector
-----------------
.. ocv:class:: LatentSvmDetector
This is a C++ wrapping class of Latent SVM. It contains internal representation of several
trained Latent SVM detectors (models) and a set of methods to load the detectors and detect objects
using them.
LatentSvmDetector::ObjectDetection
----------------------------------
.. ocv:struct:: LatentSvmDetector::ObjectDetection
Structure contains the detection information.
.. ocv:member:: Rect rect
bounding box for a detected object
.. ocv:member:: float score
confidence level
.. ocv:member:: int classID
class (model or detector) ID that detect an object
LatentSvmDetector::LatentSvmDetector
------------------------------------
Two types of constructors.
.. ocv:function:: LatentSvmDetector::LatentSvmDetector()
.. ocv:function:: LatentSvmDetector::LatentSvmDetector(const vector<String>& filenames, const vector<String>& classNames=vector<String>())
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
:param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
LatentSvmDetector::~LatentSvmDetector
-------------------------------------
Destructor.
.. ocv:function:: LatentSvmDetector::~LatentSvmDetector()
LatentSvmDetector::~clear
-------------------------
Clear all trained models and their names stored in an class object.
.. ocv:function:: void LatentSvmDetector::clear()
LatentSvmDetector::load
-----------------------
Load the trained models from given ``.xml`` files and return ``true`` if at least one model was loaded.
.. ocv:function:: bool LatentSvmDetector::load( const vector<String>& filenames, const vector<String>& classNames=vector<String>() )
:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
:param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
LatentSvmDetector::detect
-------------------------
Find rectangular regions in the given image that are likely to contain objects of loaded classes (models)
and corresponding confidence levels.
.. ocv:function:: void LatentSvmDetector::detect( const Mat& image, vector<ObjectDetection>& objectDetections, float overlapThreshold=0.5f, int numThreads=-1 )
:param image: An image.
:param objectDetections: The detections: rectangulars, scores and class IDs.
:param overlapThreshold: Threshold for the non-maximum suppression algorithm.
:param numThreads: Number of threads used in parallel version of the algorithm.
LatentSvmDetector::getClassNames
--------------------------------
Return the class (model) names that were passed in constructor or method ``load`` or extracted from models filenames in those methods.
.. ocv:function:: const vector<String>& LatentSvmDetector::getClassNames() const
LatentSvmDetector::getClassCount
--------------------------------
Return a count of loaded models (classes).
.. ocv:function:: size_t LatentSvmDetector::getClassCount() const
.. [Felzenszwalb2010] Felzenszwalb, P. F. and Girshick, R. B. and McAllester, D. and Ramanan, D. *Object Detection with Discriminatively Trained Part Based Models*. PAMI, vol. 32, no. 9, pp. 1627-1645, September 2010
modules/objdetect/doc/objdetect.rst
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......@@ -8,5 +8,3 @@ objdetect. Object Detection
:maxdepth: 2
cascade_classification
latent_svm
erfilter
modules/objdetect/include/opencv2/objdetect.hpp
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......@@ -315,8 +315,6 @@ public:
}
#include "opencv2/objdetect/linemod.hpp"
#include "opencv2/objdetect/erfilter.hpp"
#include "opencv2/objdetect/detection_based_tracker.hpp"
#endif
modules/objdetect/src/erfilter.cpp deleted 100644 → 0
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samples/MacOSX/FaceTracker/FaceTracker-Info.plist deleted 100644 → 0
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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple Computer//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>CFBundleDevelopmentRegion</key>
<string>English</string>
<key>CFBundleExecutable</key>
<string>${EXECUTABLE_NAME}</string>
<key>CFBundleIdentifier</key>
<string>de.rwth-aachen.ient.FaceTracker</string>
<key>CFBundleInfoDictionaryVersion</key>
<string>6.0</string>
<key>CFBundlePackageType</key>
<string>APPL</string>
<key>CFBundleSignature</key>
<string>????</string>
<key>CFBundleVersion</key>
<string>1.0</string>
</dict>
</plist>
samples/MacOSX/FaceTracker/FaceTracker.cpp deleted 100644 → 0
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#include <OpenCV/OpenCV.h>
#include <cassert>
#include <iostream>
const char * WINDOW_NAME = "Face Tracker";
const CFIndex CASCADE_NAME_LEN = 2048;
char CASCADE_NAME[CASCADE_NAME_LEN] = "~/opencv/data/haarcascades/haarcascade_frontalface_alt2.xml";
using namespace std;
int main (int argc, char * const argv[])
{
const int scale = 2;
// locate haar cascade from inside application bundle
// (this is the mac way to package application resources)
CFBundleRef mainBundle = CFBundleGetMainBundle ();
assert (mainBundle);
CFURLRef cascade_url = CFBundleCopyResourceURL (mainBundle, CFSTR("haarcascade_frontalface_alt2"), CFSTR("xml"), NULL);
assert (cascade_url);
Boolean got_it = CFURLGetFileSystemRepresentation (cascade_url, true,
reinterpret_cast<UInt8 *>(CASCADE_NAME), CASCADE_NAME_LEN);
if (! got_it)
abort ();
// create all necessary instances
cvNamedWindow (WINDOW_NAME, CV_WINDOW_AUTOSIZE);
CvCapture * camera = cvCreateCameraCapture (CV_CAP_ANY);
CvHaarClassifierCascade* cascade = (CvHaarClassifierCascade*) cvLoad (CASCADE_NAME, 0, 0, 0);
CvMemStorage* storage = cvCreateMemStorage(0);
assert (storage);
// you do own an iSight, don't you ?!?
if (! camera)
abort ();
// did we load the cascade?!?
if (! cascade)
abort ();
// get an initial frame and duplicate it for later work
IplImage * current_frame = cvQueryFrame (camera);
IplImage * draw_image = cvCreateImage(cvSize (current_frame->width, current_frame->height), IPL_DEPTH_8U, 3);
IplImage * gray_image = cvCreateImage(cvSize (current_frame->width, current_frame->height), IPL_DEPTH_8U, 1);
IplImage * small_image = cvCreateImage(cvSize (current_frame->width / scale, current_frame->height / scale), IPL_DEPTH_8U, 1);
assert (current_frame && gray_image && draw_image);
// as long as there are images ...
while (current_frame = cvQueryFrame (camera))
{
// convert to gray and downsize
cvCvtColor (current_frame, gray_image, CV_BGR2GRAY);
cvResize (gray_image, small_image, CV_INTER_LINEAR);
// detect faces
CvSeq* faces = cvHaarDetectObjects (small_image, cascade, storage,
1.1, 2, CV_HAAR_DO_CANNY_PRUNING,
cvSize (30, 30));
// draw faces
cvFlip (current_frame, draw_image, 1);
for (int i = 0; i < (faces ? faces->total : 0); i++)
{
CvRect* r = (CvRect*) cvGetSeqElem (faces, i);
CvPoint center;
int radius;
center.x = cvRound((small_image->width - r->width*0.5 - r->x) *scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
cvCircle (draw_image, center, radius, CV_RGB(0,255,0), 3, 8, 0 );
}
// just show the image
cvShowImage (WINDOW_NAME, draw_image);
// wait a tenth of a second for keypress and window drawing
int key = cvWaitKey (100);
if (key == 'q' || key == 'Q')
break;
}
// be nice and return no error
return 0;
}
samples/MacOSX/FaceTracker/FaceTracker.xcodeproj/project.pbxproj deleted 100644 → 0
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samples/MacOSX/FaceTracker/README.txt deleted 100644 → 0
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FaceTracker/REAME.txt
2007-05-24, Mark Asbach <asbach@ient.rwth-aachen.de>
Objective:
This document is intended to get you up and running with an OpenCV Framework on Mac OS X
Building the OpenCV.framework:
In the main directory of the opencv distribution, you will find a shell script called
'make_frameworks.sh' that does all of the typical unixy './configure && make' stuff required
to build a universal binary framework. Invoke this script from Terminal.app, wait some minutes
and you are done.
OpenCV is a Private Framework:
On Mac OS X the concept of Framework bundles is meant to simplify distribution of shared libraries,
accompanying headers and documentation. There are however to subtly different 'flavours' of
Frameworks: public and private ones. The public frameworks get installed into the Frameworks
diretories in /Library, /System/Library or ~/Library and are meant to be shared amongst
applications. The private frameworks are only distributed as parts of an Application Bundle.
This makes it easier to deploy applications because they bring their own framework invisibly to
the user. No installation of the framework is necessary and different applications can bring
different versions of the same framework without any conflict.
Since OpenCV is still a moving target, it seems best to avoid any installation and versioning issues
for an end user. The OpenCV framework that currently comes with this demo application therefore
is a Private Framework.
Use it for targets that result in an Application Bundle:
Since it is a Private Framework, it must be copied to the Frameworks/ directory of an Application
Bundle, which means, it is useless for plain unix console applications. You should create a Carbon
or a Cocoa application target in XCode for your projects. Then add the OpenCV.framework just like
in this demo and add a Copy Files build phase to your target. Let that phase copy to the Framework
directory and drop the OpenCV.framework on the build phase (again just like in this demo code).
The resulting application bundle will be self contained and if you set compiler option correctly
(in the "Build" tab of the "Project Info" window you should find 'i386 ppc' for the architectures),
your application can just be copied to any OS 10.4 Mac and used without further installation.
samples/cpp/linemod.cpp deleted 100644 → 0
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samples/cpp/textdetection.cpp deleted 100644 → 0
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/*
* textdetection.cpp
*
* A demo program of the Extremal Region Filter algorithm described in
* Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
*
* Created on: Sep 23, 2013
* Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es>
*/
#include "opencv2/opencv.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <vector>
#include <iostream>
#include <iomanip>
using namespace std;
using namespace cv;
void show_help_and_exit(const char *cmd);
void groups_draw(Mat &src, vector<Rect> &groups);
void er_show(vector<Mat> &channels, vector<vector<ERStat> > &regions);
int main(int argc, const char * argv[])
{
cout << endl << argv[0] << endl << endl;
cout << "Demo program of the Extremal Region Filter algorithm described in " << endl;
cout << "Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012" << endl << endl;
if (argc < 2) show_help_and_exit(argv[0]);
Mat src = imread(argv[1]);
// Extract channels to be processed individually
vector<Mat> channels;
computeNMChannels(src, channels);
int cn = (int)channels.size();
// Append negative channels to detect ER- (bright regions over dark background)
for (int c = 0; c < cn-1; c++)
channels.push_back(255-channels[c]);
// Create ERFilter objects with the 1st and 2nd stage default classifiers
Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),16,0.00015f,0.13f,0.2f,true,0.1f);
Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.5);
vector<vector<ERStat> > regions(channels.size());
// Apply the default cascade classifier to each independent channel (could be done in parallel)
cout << "Extracting Class Specific Extremal Regions from " << (int)channels.size() << " channels ..." << endl;
cout << " (...) this may take a while (...)" << endl << endl;
for (int c=0; c<(int)channels.size(); c++)
{
er_filter1->run(channels[c], regions[c]);
er_filter2->run(channels[c], regions[c]);
}
// Detect character groups
cout << "Grouping extracted ERs ... ";
vector<Rect> groups;
erGrouping(channels, regions, "trained_classifier_erGrouping.xml", 0.5, groups);
// draw groups
groups_draw(src, groups);
imshow("grouping",src);
cout << "Done!" << endl << endl;
cout << "Press 'e' to show the extracted Extremal Regions, any other key to exit." << endl << endl;
if( waitKey (-1) == 101)
er_show(channels,regions);
// memory clean-up
er_filter1.release();
er_filter2.release();
regions.clear();
if (!groups.empty())
{
groups.clear();
}
}
// helper functions
void show_help_and_exit(const char *cmd)
{
cout << " Usage: " << cmd << " <input_image> " << endl;
cout << " Default classifier files (trained_classifierNM*.xml) must be in current directory" << endl << endl;
exit(-1);
}
void groups_draw(Mat &src, vector<Rect> &groups)
{
for (int i=(int)groups.size()-1; i>=0; i--)
{
if (src.type() == CV_8UC3)
rectangle(src,groups.at(i).tl(),groups.at(i).br(),Scalar( 0, 255, 255 ), 3, 8 );
else
rectangle(src,groups.at(i).tl(),groups.at(i).br(),Scalar( 255 ), 3, 8 );
}
}
void er_show(vector<Mat> &channels, vector<vector<ERStat> > &regions)
{
for (int c=0; c<(int)channels.size(); c++)
{
Mat dst = Mat::zeros(channels[0].rows+2,channels[0].cols+2,CV_8UC1);
for (int r=0; r<(int)regions[c].size(); r++)
{
ERStat er = regions[c][r];
if (er.parent != NULL) // deprecate the root region
{
int newMaskVal = 255;
int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
floodFill(channels[c],dst,Point(er.pixel%channels[c].cols,er.pixel/channels[c].cols),
Scalar(255),0,Scalar(er.level),Scalar(0),flags);
}
}
char buff[10]; char *buff_ptr = buff;
sprintf(buff, "channel %d", c);
imshow(buff_ptr, dst);
}
waitKey(-1);
}
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