Structured Edge Detection demo

This sample demonstrates structured forests for fast edge detection, and edgeboxes.

The structered edge demo requires you to provide a model. This demo downloads a model from the opencv_extra repository on Github.

Sources:

Contents

Load image

read RGB image

img = cv.imread(fullfile(mexopencv.root(),'test','balloon.jpg'), 'Color',true);
assert(~isempty(img), 'Cannot read image file');

% convert to floating-point in [0,1] range
img = single(img) / 255;

Create object

we load a pre-trained model

modelFilename = fullfile(mexopencv.root(),'test','model.yml.gz');
if exist(modelFilename, 'file') ~= 2
    % download model from GitHub
    url = 'https://cdn.rawgit.com/opencv/opencv_extra/3.3.0/testdata/cv/ximgproc/model.yml.gz';
    disp('Downloading model...')
    urlwrite(url, modelFilename);
end

tic
pDollar = cv.StructuredEdgeDetection(modelFilename);
toc

Elapsed time is 1.868493 seconds.

Detect edges

tic
edges = pDollar.detectEdges(img);
toc

Elapsed time is 0.058288 seconds.

convert grayscale [0,1] float image to 8-bit

e8u = uint8(edges * 255);

computes orientation from edge map

orientation_map = pDollar.computeOrientation(edges);

suppress edges

edges_nms = pDollar.edgesNms(edges, orientation_map);

generate object bounding box proposals using edges

ebx = cv.EdgeBoxes('MaxBoxes',30);
boxes = ebx.getBoundingBoxes(edges_nms, orientation_map);
out = cv.rectangle(img, boxes, 'Color',[0 255 0], 'LineType','AA');

Display result

figure('Position',[200 200 800 600])
subplot(221), imshow(img), title('image')
subplot(222), imshow(e8u), title('edges')
subplot(223), imshow(edges_nms), title('edges NMS')
subplot(224), imshow(out), title('object proposals')