Difference between revisions of "Example Canny Edge"
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Example Code: | Example Code: | ||
* [https://github.com/lessthanoptimal/BoofCV/blob/v0. | * [https://github.com/lessthanoptimal/BoofCV/blob/v0.23/examples/src/boofcv/examples/features/ExampleCannyEdge.java ExampleCannyEdge.java] | ||
Concepts: | Concepts: | ||
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BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg")); | BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg")); | ||
GrayU8 gray = ConvertBufferedImage.convertFrom(image,(GrayU8)null); | |||
GrayU8 edgeImage = gray.createSameShape(); | |||
// Create a canny edge detector which will dynamically compute the threshold based on maximum edge intensity | // Create a canny edge detector which will dynamically compute the threshold based on maximum edge intensity | ||
// It has also been configured to save the trace as a graph. This is the graph created while performing | // It has also been configured to save the trace as a graph. This is the graph created while performing | ||
// hysteresis thresholding. | // hysteresis thresholding. | ||
CannyEdge< | CannyEdge<GrayU8,GrayS16> canny = FactoryEdgeDetectors.canny(2,true, true, GrayU8.class, GrayS16.class); | ||
// The edge image is actually an optional parameter. If you don't need it just pass in null | // The edge image is actually an optional parameter. If you don't need it just pass in null |
Revision as of 21:16, 27 March 2016
Edge or contour detection is a basic computer vision problem. The Canny edge detector is a popular algorithm for detecting edges in an image which uses hystersis thresholding. In BoofCV the Canny edge detector can produce different kinds of output. A binary image containing every pixel which is identified as an edge or a tree graph containing all the selected edge pixels.
Example Code:
Concepts:
- Object contours/edges
Relevant Applets:
Example Code
/**
* Demonstration of the Canny edge detection algorithm. In this implementation the output can be a binary image and/or
* a graph describing each contour.
*
* @author Peter Abeles
*/
public class ExampleCannyEdge {
public static void main( String args[] ) {
BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg"));
GrayU8 gray = ConvertBufferedImage.convertFrom(image,(GrayU8)null);
GrayU8 edgeImage = gray.createSameShape();
// Create a canny edge detector which will dynamically compute the threshold based on maximum edge intensity
// It has also been configured to save the trace as a graph. This is the graph created while performing
// hysteresis thresholding.
CannyEdge<GrayU8,GrayS16> canny = FactoryEdgeDetectors.canny(2,true, true, GrayU8.class, GrayS16.class);
// The edge image is actually an optional parameter. If you don't need it just pass in null
canny.process(gray,0.1f,0.3f,edgeImage);
// First get the contour created by canny
List<EdgeContour> edgeContours = canny.getContours();
// The 'edgeContours' is a tree graph that can be difficult to process. An alternative is to extract
// the contours from the binary image, which will produce a single loop for each connected cluster of pixels.
// Note that you are only interested in external contours.
List<Contour> contours = BinaryImageOps.contour(edgeImage, ConnectRule.EIGHT, null);
// display the results
BufferedImage visualBinary = VisualizeBinaryData.renderBinary(edgeImage, false, null);
BufferedImage visualCannyContour = VisualizeBinaryData.renderContours(edgeContours,null,
gray.width,gray.height,null);
BufferedImage visualEdgeContour = new BufferedImage(gray.width, gray.height,BufferedImage.TYPE_INT_RGB);
VisualizeBinaryData.renderExternal(contours, (int[]) null, visualEdgeContour);
ListDisplayPanel panel = new ListDisplayPanel();
panel.addImage(visualBinary,"Binary Edges from Canny");
panel.addImage(visualCannyContour, "Canny Trace Graph");
panel.addImage(visualEdgeContour,"Contour from Canny Binary");
ShowImages.showWindow(panel,"Canny Edge", true);
}
}