Difference between revisions of "Example Canny Edge"

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Example Code:
Example Code:
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.20/examples/src/boofcv/examples/features/ExampleCannyEdge.java ExampleCannyEdge.java]
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.40/examples/src/main/java/boofcv/examples/features/ExampleCannyEdge.java ExampleCannyEdge]


Concepts:
Concepts:
* Object contours/edges
* Object contours/edges


Relevant Applets:
Relevant Examples:
* [[Applet Contour| Contour Detector]]
* [[Example_Binary_Image| Binary Image Processing]]


= Example Code =
= Example Code =
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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 27: Line 27:
public class ExampleCannyEdge {
public class ExampleCannyEdge {


public static void main( String args[] ) {
public static void main( String[] args ) {
BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("simple_objects.jpg"));
BufferedImage image = UtilImageIO.loadImageNotNull(UtilIO.pathExample("simple_objects.jpg"));


ImageUInt8 gray = ConvertBufferedImage.convertFrom(image,(ImageUInt8)null);
GrayU8 gray = ConvertBufferedImage.convertFrom(image, (GrayU8)null);
ImageUInt8 edgeImage = gray.createSameShape();
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<ImageUInt8,ImageSInt16> canny = FactoryEdgeDetectors.canny(2,true, true, ImageUInt8.class, ImageSInt16.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.
List<Contour> contours = BinaryImageOps.contour(edgeImage, ConnectRule.EIGHT, null);
List<Contour> contours = BinaryImageOps.contourExternal(edgeImage, ConnectRule.EIGHT);


// display the results
// display the results
BufferedImage visualBinary = VisualizeBinaryData.renderBinary(edgeImage, false, null);
BufferedImage visualBinary = VisualizeBinaryData.renderBinary(edgeImage, false, null);
BufferedImage visualCannyContour = VisualizeBinaryData.renderContours(edgeContours,null,
BufferedImage visualCannyContour = VisualizeBinaryData.renderContours(edgeContours, null,
gray.width,gray.height,null);
gray.width, gray.height, null);
BufferedImage visualEdgeContour = new BufferedImage(gray.width, gray.height,BufferedImage.TYPE_INT_RGB);
BufferedImage visualEdgeContour = new BufferedImage(gray.width, gray.height, BufferedImage.TYPE_INT_RGB);
VisualizeBinaryData.renderExternal(contours, (int[]) null, visualEdgeContour);
VisualizeBinaryData.render(contours, (int[])null, visualEdgeContour);


ListDisplayPanel panel = new ListDisplayPanel();
ListDisplayPanel panel = new ListDisplayPanel();
panel.addImage(visualBinary,"Binary Edges from Canny");
panel.addImage(visualBinary, "Binary Edges from Canny");
panel.addImage(visualCannyContour, "Canny Trace Graph");
panel.addImage(visualCannyContour, "Canny Trace Graph");
panel.addImage(visualEdgeContour,"Contour from Canny Binary");
panel.addImage(visualEdgeContour, "Contour from Canny Binary");
ShowImages.showWindow(panel,"Canny Edge", true);
ShowImages.showWindow(panel, "Canny Edge", true);
}
}
}
}
</syntaxhighlight>
</syntaxhighlight>

Latest revision as of 13:32, 17 January 2022

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.loadImageNotNull(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);
	}
}