OpenCV  3.4.7-dev
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 /**************************************************************************************
2 The structered edge demo requires you to provide a model.
3 This model can be found at the opencv_extra repository on Github on the following link:
4 https://github.com/opencv/opencv_extra/blob/master/testdata/cv/ximgproc/model.yml.gz
5 ***************************************************************************************/
6 
7 #include <opencv2/ximgproc.hpp>
8 #include "opencv2/highgui.hpp"
10 #include <iostream>
11 
12 using namespace cv;
13 using namespace cv::ximgproc;
14 
15 const char* keys =
16 {
17  "{i || input image name}"
18  "{m || model name}"
19  "{o || output image name}"
20 };
21 
22 int main( int argc, const char** argv )
23 {
24  bool printHelp = ( argc == 1 );
25  printHelp = printHelp || ( argc == 2 && String(argv[1]) == "--help" );
26  printHelp = printHelp || ( argc == 2 && String(argv[1]) == "-h" );
27 
28  if ( printHelp )
29  {
30  std::cout << "\nThis sample demonstrates structured forests for fast edge detection\n"
31  "Call:\n"
32  " structured_edge_detection -i=in_image_name -m=model_name [-o=out_image_name]\n\n";
33  return 0;
34  }
35 
36  CommandLineParser parser(argc, argv, keys);
37  if ( !parser.check() )
38  {
39  parser.printErrors();
40  return -1;
41  }
42 
43  String modelFilename = parser.get<String>("m");
44  String inFilename = parser.get<String>("i");
45  String outFilename = parser.get<String>("o");
46 
47  Mat image = imread(inFilename, 1);
48  if ( image.empty() )
49  CV_Error(Error::StsError, String("Cannot read image file: ") + inFilename);
50 
51  if ( modelFilename.size() == 0)
52  CV_Error(Error::StsError, String("Empty model name"));
53 
54  image.convertTo(image, DataType<float>::type, 1/255.0);
55 
56  Mat edges(image.size(), image.type());
57 
59  createStructuredEdgeDetection(modelFilename);
60  pDollar->detectEdges(image, edges);
61 
62  // computes orientation from edge map
63  Mat orientation_map;
64  pDollar->computeOrientation(edges, orientation_map);
65 
66  // suppress edges
67  Mat edge_nms;
68  pDollar->edgesNms(edges, orientation_map, edge_nms, 2, 0, 1, true);
69 
70  if ( outFilename.size() == 0 )
71  {
72  namedWindow("edges", 1);
73  imshow("edges", edges);
74  namedWindow("edges nms", 1);
75  imshow("edges nms", edge_nms);
76  waitKey(0);
77  }
78  else
79  imwrite(outFilename, 255*edges);
80 
81  return 0;
82 }
bool imwrite(const String &filename, InputArray img, const std::vector< int > &params=std::vector< int >())
Saves an image to a specified file.
unknown /unspecified error
Definition: base.hpp:71
Mat imread(const String &filename, int flags=IMREAD_COLOR)
Loads an image from a file.
#define CV_Error(code, msg)
Call the error handler.
Definition: base.hpp:347
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
Definition: affine.hpp:51
Designed for command line parsing.
Definition: utility.hpp:862
Ptr< StructuredEdgeDetection > createStructuredEdgeDetection(const String &model, Ptr< const RFFeatureGetter > howToGetFeatures=Ptr< RFFeatureGetter >())
void namedWindow(const String &winname, int flags=WINDOW_AUTOSIZE)
Creates a window.
Definition: brightedges.hpp:47
Template class for smart pointers with shared ownership.
Definition: cvstd.hpp:261
Template "trait" class for OpenCV primitive data types.
Definition: traits.hpp:112
Definition: cvstd.hpp:458
size_t size() const
n-dimensional dense array class
Definition: mat.hpp:804
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());
    pDollar->detectEdges(image, edges);
  4. Show results
    if ( outFilename == "" )
    {
    cv::namedWindow("edges", 1);
    cv::imshow("edges", edges);
    }
    else
    cv::imwrite(outFilename, 255*edges);

Literature

For more information, refer to the following papers : [49] [124]