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Suleyman TURKMEN 7 years ago
parent 05b99a4dde
commit d547c6b1a2
5 changed files with 149 additions and 64 deletions
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  1. 29
      modules/imgproc/include/opencv2/imgproc.hpp
  2. 2
      modules/objdetect/include/opencv2/objdetect.hpp
  3. 2
      samples/cpp/facedetect.cpp
  4. 58
      samples/cpp/lsd_lines.cpp
  5. 122
      samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp

29
modules/imgproc/include/opencv2/imgproc.hpp
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@ -1192,6 +1192,7 @@ protected:
/** @example lsd_lines.cpp
An example using the LineSegmentDetector
\image html building_lsd.png "Sample output image" width=434 height=300
*/
/** @brief Line segment detector class
@ -1347,6 +1348,11 @@ operation is shifted.
*/
CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1));
/** @example Smoothing.cpp
Sample code for simple filters
![Sample screenshot](Smoothing_Tutorial_Result_Median_Filter.jpg)
Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details
*/
/** @brief Blurs an image using the median filter.
The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
@ -1549,6 +1555,11 @@ CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
Point anchor = Point(-1,-1),
double delta = 0, int borderType = BORDER_DEFAULT );
/** @example Sobel_Demo.cpp
Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector
![Sample screenshot](Sobel_Derivatives_Tutorial_Result.jpg)
Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details
*/
/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
@ -1681,7 +1692,9 @@ CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
//! @{
/** @example edge.cpp
An example on using the canny edge detector
This program demonstrates usage of the Canny edge detector
Check @ref tutorial_canny_detector "the corresponding tutorial" for more details
*/
/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 .
@ -1908,6 +1921,7 @@ CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
/** @example houghlines.cpp
An example using the Hough line detector
![Sample input image](Hough_Lines_Tutorial_Original_Image.jpg) ![Output image](Hough_Lines_Tutorial_Result.jpg)
*/
/** @brief Finds lines in a binary image using the standard Hough transform.
@ -2105,7 +2119,9 @@ CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles,
//! @{
/** @example morphology2.cpp
An example using the morphological operations
Advanced morphology Transformations sample code
![Sample screenshot](Morphology_2_Tutorial_Result.jpg)
Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details
*/
/** @brief Erodes an image by using a specific structuring element.
@ -2135,6 +2151,11 @@ CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
int borderType = BORDER_CONSTANT,
const Scalar& borderValue = morphologyDefaultBorderValue() );
/** @example Morphology_1.cpp
Erosion and Dilation sample code
![Sample Screenshot-Erosion](Morphology_1_Tutorial_Erosion_Result.jpg)![Sample Screenshot-Dilation](Morphology_1_Tutorial_Dilation_Result.jpg)
Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details
*/
/** @brief Dilates an image by using a specific structuring element.
The function dilates the source image using the specified structuring element that determines the
@ -3377,6 +3398,7 @@ CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
/** @example grabcut.cpp
An example using the GrabCut algorithm
![Sample Screenshot](grabcut_output1.jpg)
*/
/** @brief Runs the GrabCut algorithm.
@ -4399,7 +4421,8 @@ CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
int thickness = 1, int lineType = LINE_8, int shift = 0 );
/** @example contours2.cpp
An example using the drawContour functionality
An example program illustrates the use of cv::findContours and cv::drawContours
\image html WindowsQtContoursOutput.png "Screenshot of the program"
*/
/** @example segment_objects.cpp

2
modules/objdetect/include/opencv2/objdetect.hpp
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@ -216,6 +216,8 @@ public:
};
/** @example facedetect.cpp
This program demonstrates usage of the Cascade classifier class
\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254
*/
/** @brief Cascade classifier class for object detection.
*/

2
samples/cpp/facedetect.cpp
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@ -8,7 +8,7 @@ using namespace cv;
static void help()
{
cout << "\nThis program demonstrates the cascade recognizer. Now you can use Haar or LBP features.\n"
cout << "\nThis program demonstrates the use of cv::CascadeClassifier class to detect objects (Face + eyes). You can use Haar or LBP features.\n"
"This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n"
"It's most known use is for faces.\n"
"Usage:\n"

58
samples/cpp/lsd_lines.cpp
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@ -1,38 +1,50 @@
#include <iostream>
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace std;
using namespace cv;
int main(int argc, char** argv)
{
std::string in;
cv::CommandLineParser parser(argc, argv, "{@input|../data/building.jpg|input image}{help h||show help message}");
if (parser.has("help"))
cv::CommandLineParser parser(argc, argv,
"{input i|../data/building.jpg|input image}"
"{refine r|false|if true use LSD_REFINE_STD method, if false use LSD_REFINE_NONE method}"
"{canny c|false|use Canny edge detector}"
"{overlay o|false|show result on input image}"
"{help h|false|show help message}");
if (parser.get<bool>("help"))
{
parser.printMessage();
return 0;
}
in = parser.get<string>("@input");
Mat image = imread(in, IMREAD_GRAYSCALE);
parser.printMessage();
String filename = parser.get<String>("input");
bool useRefine = parser.get<bool>("refine");
bool useCanny = parser.get<bool>("canny");
bool overlay = parser.get<bool>("overlay");
Mat image = imread(filename, IMREAD_GRAYSCALE);
if( image.empty() )
{ return -1; }
{
cout << "Unable to load " << filename;
return 1;
}
#if 0
Canny(image, image, 50, 200, 3); // Apply canny edge
#endif
imshow("Source Image", image);
if (useCanny)
{
Canny(image, image, 50, 200, 3); // Apply Canny edge detector
}
// Create and LSD detector with standard or no refinement.
#if 1
Ptr<LineSegmentDetector> ls = createLineSegmentDetector(LSD_REFINE_STD);
#else
Ptr<LineSegmentDetector> ls = createLineSegmentDetector(LSD_REFINE_NONE);
#endif
Ptr<LineSegmentDetector> ls = useRefine ? createLineSegmentDetector(LSD_REFINE_STD) : createLineSegmentDetector(LSD_REFINE_NONE);
double start = double(getTickCount());
vector<Vec4f> lines_std;
@ -44,9 +56,17 @@ int main(int argc, char** argv)
std::cout << "It took " << duration_ms << " ms." << std::endl;
// Show found lines
Mat drawnLines(image);
ls->drawSegments(drawnLines, lines_std);
imshow("Standard refinement", drawnLines);
if (!overlay || useCanny)
{
image = Scalar(0, 0, 0);
}
ls->drawSegments(image, lines_std);
String window_name = useRefine ? "Result - standard refinement" : "Result - no refinement";
window_name += useCanny ? " - Canny edge detector used" : "";
imshow(window_name, image);
waitKey();
return 0;

122
samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp
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@ -1,6 +1,6 @@
/**
* @file Sobel_Demo.cpp
* @brief Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector
* @brief Sample code uses Sobel or Scharr OpenCV functions for edge detection
* @author OpenCV team
*/
@ -8,70 +8,110 @@
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace cv;
using namespace std;
/**
* @function main
*/
int main( int argc, char** argv )
{
cv::CommandLineParser parser(argc, argv,
"{@input |../data/lena.jpg|input image}"
"{ksize k|1|ksize (hit 'K' to increase its value)}"
"{scale s|1|scale (hit 'S' to increase its value)}"
"{delta d|0|delta (hit 'D' to increase its value)}"
"{help h|false|show help message}");
cout << "The sample uses Sobel or Scharr OpenCV functions for edge detection\n\n";
parser.printMessage();
cout << "\nPress 'ESC' to exit program.\nPress 'R' to reset values ( ksize will be -1 equal to Scharr function )";
//![variables]
Mat src, src_gray;
Mat image,src, src_gray;
Mat grad;
const char* window_name = "Sobel Demo - Simple Edge Detector";
int scale = 1;
int delta = 0;
const String window_name = "Sobel Demo - Simple Edge Detector";
int ksize = parser.get<int>("ksize");
int scale = parser.get<int>("scale");
int delta = parser.get<int>("delta");
int ddepth = CV_16S;
//![variables]
//![load]
String imageName("../data/lena.jpg"); // by default
if (argc > 1)
String imageName = parser.get<String>("@input"); // by default
image = imread( imageName, IMREAD_COLOR ); // Load an image
if( image.empty() )
{
imageName = argv[1];
return 1;
}
src = imread( imageName, IMREAD_COLOR ); // Load an image
if( src.empty() )
{ return -1; }
//![load]
//![reduce_noise]
GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
//![reduce_noise]
for (;;)
{
//![reduce_noise]
GaussianBlur(image, src, Size(3, 3), 0, 0, BORDER_DEFAULT);
//![reduce_noise]
//![convert_to_gray]
cvtColor( src, src_gray, COLOR_BGR2GRAY );
//![convert_to_gray]
//![convert_to_gray]
cvtColor(src, src_gray, COLOR_BGR2GRAY);
//![convert_to_gray]
//![sobel]
/// Generate grad_x and grad_y
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
//![sobel]
/// Generate grad_x and grad_y
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
/// Gradient X
//Scharr( src_gray, grad_x, ddepth, 1, 0, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
/// Gradient X
Sobel(src_gray, grad_x, ddepth, 1, 0, ksize, scale, delta, BORDER_DEFAULT);
/// Gradient Y
//Scharr( src_gray, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
//![sobel]
/// Gradient Y
Sobel(src_gray, grad_y, ddepth, 0, 1, ksize, scale, delta, BORDER_DEFAULT);
//![sobel]
//![convert]
convertScaleAbs( grad_x, abs_grad_x );
convertScaleAbs( grad_y, abs_grad_y );
//![convert]
//![convert]
convertScaleAbs(grad_x, abs_grad_x);
convertScaleAbs(grad_y, abs_grad_y);
//![convert]
//![blend]
/// Total Gradient (approximate)
addWeighted( abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad );
//![blend]
//![blend]
/// Total Gradient (approximate)
addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad);
//![blend]
//![display]
imshow( window_name, grad );
waitKey(0);
//![display]
//![display]
imshow(window_name, grad);
char key = (char)waitKey(0);
//![display]
if(key == 27)
{
return 0;
}
if (key == 'k' || key == 'K')
{
ksize = ksize < 30 ? ksize+2 : -1;
}
if (key == 's' || key == 'S')
{
scale++;
}
if (key == 'd' || key == 'D')
{
delta++;
}
if (key == 'r' || key == 'R')
{
scale = 1;
ksize = -1;
delta = 0;
}
}
return 0;
}

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