OpenCV  3.1.0
Open Source Computer Vision
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
Definition: affine.hpp:51
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::ximgproc
Definition: disparity_filter.hpp:45
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
cv::Mat
n-dimensional dense array class
Definition: mat.hpp:740
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 : [33] [87]

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