Difference between revisions of "Example Canny Edge"

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Example Code:
Example Code:
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/features/ExampleCannyEdge.java ExampleCannyEdge]
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/features/ExampleCannyEdge.java ExampleCannyEdge]


Concepts:
Concepts:
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<syntaxhighlight lang="java">
<syntaxhighlight lang="java">
/**
/**
  * Demonstration of the Canny edge detection algorithm. In this implementation the output can be a binary image and/or
  * Demonstration of the Canny edge detection algorithm. In this implementation the output can be a binary image and/or
  * a graph describing each contour.
  * a graph describing each contour.
  *
  *
Line 34: Line 34:


// 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<GrayU8,GrayS16> canny = FactoryEdgeDetectors.canny(2,true, true, GrayU8.class, GrayS16.class);
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
canny.process(gray,0.1f,0.3f,edgeImage);
canny.process(gray,0.1f,0.3f,edgeImage);


// First get the contour created by canny
// First get the contour created by canny
List<EdgeContour> edgeContours = canny.getContours();
List<EdgeContour> edgeContours = canny.getContours();
// The 'edgeContours' is a tree graph that can be difficult to process. An alternative is to extract
// 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.
// 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.
// Note that you are only interested in external contours.

Revision as of 10:37, 12 July 2021

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 Examples:

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.contourExternal(edgeImage, ConnectRule.EIGHT);

		// 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.render(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);
	}
}