7#include "samples_utility.hpp"
13void sobelExtractor(
const Mat img,
const Rect roi,
Mat& feat);
15int main(
int argc,
char** argv ){
19 " Usage: tracker <video_name>\n"
21 " example_tracking_kcf Bolt/img/%04d.jpg\n"
22 " example_tracking_kcf faceocc2.webm\n"
33 param.
desc_pca = TrackerKCF::GRAY | TrackerKCF::CN;
45 tracker->setFeatureExtractor(sobelExtractor);
49 std::string video = argv[1];
54 roi=selectROI(
"tracker",frame);
61 tracker->init(frame,roi);
64 printf(
"Start the tracking process, press ESC to quit.\n");
70 if(frame.rows==0 || frame.cols==0)
74 tracker->update(frame,roi);
77 rectangle( frame, roi,
Scalar( 255, 0, 0 ), 2, 1 );
80 imshow(
"tracker",frame);
83 if(waitKey(1)==27)
break;
89void sobelExtractor(
const Mat img,
const Rect roi,
Mat& feat){
96 if(roi.
x<0){region.
x=0;region.
width+=roi.
x;}
97 if(roi.
y<0){region.
y=0;region.
height+=roi.
y;}
104 patch=img(region).
clone();
105 cvtColor(patch,patch, COLOR_BGR2GRAY);
109 int addTop,addBottom, addLeft, addRight;
110 addTop=region.
y-roi.
y;
112 addLeft=region.
x-roi.
x;
115 copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);
n-dimensional dense array class
Definition mat.hpp:828
CV_NODISCARD_STD Mat clone() const
Creates a full copy of the array and the underlying data.
int cols
Definition mat.hpp:2154
int rows
the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
Definition mat.hpp:2154
Template class for 2D rectangles.
Definition types.hpp:444
_Tp x
x coordinate of the top-left corner
Definition types.hpp:487
_Tp y
y coordinate of the top-left corner
Definition types.hpp:488
_Tp width
width of the rectangle
Definition types.hpp:489
_Tp height
height of the rectangle
Definition types.hpp:490
Class for video capturing from video files, image sequences or cameras.
Definition videoio.hpp:735
void copyMakeBorder(InputArray src, OutputArray dst, int top, int bottom, int left, int right, int borderType, const Scalar &value=Scalar())
Forms a border around an image.
void merge(const Mat *mv, size_t count, OutputArray dst)
Creates one multi-channel array out of several single-channel ones.
std::shared_ptr< _Tp > Ptr
Definition cvstd_wrapper.hpp:23
#define CV_32F
Definition interface.h:78
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0, AlgorithmHint hint=cv::ALGO_HINT_DEFAULT)
Converts an image from one color space to another.
void Sobel(InputArray src, OutputArray dst, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT)
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
Definition tracking.hpp:118
bool compress_feature
activate the pca method to compress the features
Definition tracking.hpp:130
int desc_pca
compressed descriptors of TrackerKCF::MODE
Definition tracking.hpp:133
int desc_npca
non-compressed descriptors of TrackerKCF::MODE
Definition tracking.hpp:134
int compressed_size
feature size after compression
Definition tracking.hpp:132
This part explains how to set custom parameters and use your own feature-extractor function for the CN tracker. If you need a more detailed information to use cv::Tracker, please refer to Introduction to OpenCV Tracker.
Set Custom Parameters
param.
desc_pca = TrackerKCF::GRAY | TrackerKCF::CN;
To set custom paramters, an object should be created. Each tracker algorithm has their own parameter format. So, in this case we should use parameter from cv::TrackerKCF since we are interested in modifying the parameter of this tracker algorithm.
There are several parameters that can be configured as explained in cv::TrackerKCF::Params. For this tutorial, we focussed on the feature extractor functions.
Several feature types can be used in cv::TrackerKCF. In this case, the grayscale value (1 dimension) and color-names features (10 dimension), will be merged as 11 dimension feature and then compressed into 2 dimension as specified in the code.
If you want to use another type of pre-defined feature-extractor function, you can check in cv::TrackerKCF::MODE. We will leave the non-compressed feature as 0 since we want to use a customized function.
Using a custom function
You can define your own feature-extractor function for the CN tracker. However, you need to take care about several things:
- The extracted feature should have the same size as the size of the given bounding box (width and height). For the number of channels you can check the limitation in cv::Mat.
- You can only use features that can be compared using Euclidean distance. Features like local binary pattern (LBP) may not be suitable since it should be compared using Hamming distance.
Since the size of the extracted feature should be in the same size with the given bounding box, we need to take care whenever the given bounding box is partially out of range. In this case, we can copy part of image contained in the bounding box as shown in the snippet below.
if(roi.
x<0){region.
x=0;region.
width+=roi.
x;}
if(roi.
y<0){region.
y=0;region.
height+=roi.
y;}
Whenever the copied image is smaller than the given bounding box, padding should be given to the sides where the bounding box is partially out of frame.
int addTop,addBottom, addLeft, addRight;
copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);
Defining the feature
In this tutorial, the extracted feature is response of the Sobel filter in x and y direction. Those Sobel filter responses are concatenated, resulting a feature with 2 channels.
Sobel(patch, sobel[0],
CV_32F,1,0,1);
Sobel(patch, sobel[1],
CV_32F,0,1,1);
merge(sobel,2,feat);
Post processing
Make sure to normalize the feature with range -0.5 to 0.5