OpenCV  3.0.0
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Structured forests for fast edge detection

Introduction

In this tutorial you will learn how to use structured forests for the purpose of edge detection in an image.

Examples

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Note
binarization techniques like Canny edge detector are applicable to edges produced by both algorithms (Sobel and StructuredEdgeDetection::detectEdges).

Source Code

1 #include <opencv2/ximgproc.hpp>
2 #include "opencv2/highgui.hpp"
3 #include "opencv2/core/utility.hpp"
4 
5 using namespace cv;
6 using namespace cv::ximgproc;
7 
8 const char* keys =
9 {
10  "{i || input image name}"
11  "{m || model name}"
12  "{o || output image name}"
13 };
14 
15 int main( int argc, const char** argv )
16 {
17  bool printHelp = ( argc == 1 );
18  printHelp = printHelp || ( argc == 2 && std::string(argv[1]) == "--help" );
19  printHelp = printHelp || ( argc == 2 && std::string(argv[1]) == "-h" );
20 
21  if ( printHelp )
22  {
23  printf("\nThis sample demonstrates structured forests for fast edge detection\n"
24  "Call:\n"
25  " structured_edge_detection -i=in_image_name -m=model_name [-o=out_image_name]\n\n");
26  return 0;
27  }
28 
29  cv::CommandLineParser parser(argc, argv, keys);
30  if ( !parser.check() )
31  {
32  parser.printErrors();
33  return -1;
34  }
35 
36  std::string modelFilename = parser.get<std::string>("m");
37  std::string inFilename = parser.get<std::string>("i");
38  std::string outFilename = parser.get<std::string>("o");
39 
40  cv::Mat image = cv::imread(inFilename, 1);
41  if ( image.empty() )
42  {
43  printf("Cannot read image file: %s\n", inFilename.c_str());
44  return -1;
45  }
46 
47  image.convertTo(image, cv::DataType<float>::type, 1/255.0);
48 
49  cv::Mat edges(image.size(), image.type());
50 
51  cv::Ptr<StructuredEdgeDetection> pDollar =
52  createStructuredEdgeDetection(modelFilename);
53  pDollar->detectEdges(image, edges);
54 
55  if ( outFilename == "" )
56  {
57  cv::namedWindow("edges", 1);
58  cv::imshow("edges", edges);
59 
60  cv::waitKey(0);
61  }
62  else
63  cv::imwrite(outFilename, 255*edges);
64 
65  return 0;
66 }
cv::imwrite
bool imwrite(const String &filename, InputArray img, const std::vector< int > &params=std::vector< int >())
Saves an image to a specified file.
cv::imread
Mat imread(const String &filename, int flags=IMREAD_COLOR)
Loads an image from a file.
cv::imshow
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
cv::CommandLineParser
Designed for command line parsing.
Definition: utility.hpp:612
cv::ximgproc::createStructuredEdgeDetection
Ptr< StructuredEdgeDetection > createStructuredEdgeDetection(const String &model, Ptr< const RFFeatureGetter > howToGetFeatures=Ptr< RFFeatureGetter >())
cv::namedWindow
void namedWindow(const String &winname, int flags=WINDOW_AUTOSIZE)
Creates a window.
cv::Ptr
Template class for smart pointers with shared ownership.
Definition: cvstd.hpp:283
cv::DataType
Template "trait" class for OpenCV primitive data types.
Definition: traits.hpp:106
main
int main(int argc, const char *argv[])
Definition: facerec_demo.cpp:67
cv::Mat
n-dimensional dense array class
Definition: mat.hpp:730
cv::waitKey
int waitKey(int delay=0)
Waits for a pressed key.

Explanation

  1. Load source color image
    cv::Mat image = cv::imread(inFilename, 1);
    if ( image.empty() )
    {
    printf("Cannot read image file: %s\n", inFilename.c_str());
    return -1;
    }
  2. Convert source image to [0;1] range
    image.convertTo(image, cv::DataType<float>::type, 1/255.0);
  3. Run main algorithm
    cv::Mat edges(image.size(), image.type());
    cv::Ptr<StructuredEdgeDetection> pDollar =
    cv::createStructuredEdgeDetection(modelFilename);
    pDollar->detectEdges(image, edges);
  4. Show results
    if ( outFilename == "" )
    {
    cv::namedWindow("edges", 1);
    cv::imshow("edges", edges);
    cv::waitKey(0);
    }
    else
    cv::imwrite(outFilename, 255*edges);

Literature

For more information, refer to the following papers : [31] [77]

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