Difference between revisions of "Example Binary Image"
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Image:example_binary.png|Thresholded Binary Image. | Image:example_binary.png|Thresholded Binary Image. | ||
Image:example_binary_labeled.png|Labeled Binary Image | Image:example_binary_labeled.png|Labeled Binary Image | ||
Image:example_binary_contour.png|Contour Image | |||
</gallery> | </gallery> | ||
</center> | </center> | ||
In a binary image each pixel can have a value of 0 or 1. Binary images are easy to compute and fast to process, which makes them popular in many applications. BoofCV contains many operations for creating and manipulating binary images. The example below demonstrates a few of ones contained inside of BinaryImageOps. | |||
Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0. | Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.14/examples/src/boofcv/examples/ExampleBinaryOps.java ExampleBinaryOps.java] | ||
Concepts: | Concepts: | ||
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= Basic Example = | = Basic Example = | ||
<syntaxhighlight lang="java"> | <syntaxhighlight lang="java"> | ||
/** | |||
* Demonstrates how to create binary images by thresholding, applying binary morphological operations, and | |||
* then extracting detected features by finding their contours. | |||
* | |||
* @author Peter Abeles | |||
*/ | |||
public class ExampleBinaryOps { | |||
// the | public static void main( String args[] ) { | ||
// load and convert the image into a usable format | |||
BufferedImage image = UtilImageIO.loadImage("../data/applet/particles01.jpg"); | |||
// convert into a usable format | // convert into a usable format | ||
ImageFloat32 input = ConvertBufferedImage.convertFromSingle(image, null, ImageFloat32.class); | ImageFloat32 input = ConvertBufferedImage.convertFromSingle(image, null, ImageFloat32.class); | ||
ImageUInt8 binary = new ImageUInt8(input.width,input.height); | ImageUInt8 binary = new ImageUInt8(input.width,input.height); | ||
ImageSInt32 | ImageSInt32 label = new ImageSInt32(input.width,input.height); | ||
// the mean pixel value is often a reasonable threshold when creating a binary image | // the mean pixel value is often a reasonable threshold when creating a binary image | ||
double mean = ImageStatistics.mean(input); | double mean = ImageStatistics.mean(input); | ||
// create a binary image | // create a binary image by thresholding | ||
ThresholdImageOps.threshold(input,binary,(float)mean,true); | ThresholdImageOps.threshold(input,binary,(float)mean,true); | ||
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// The null in the input indicates that it should internally declare the work image it needs | // The null in the input indicates that it should internally declare the work image it needs | ||
// this is less efficient, but easier to code. | // this is less efficient, but easier to code. | ||
ImageUInt8 filtered = BinaryImageOps.erode8(binary,null); | |||
filtered = BinaryImageOps.dilate8(filtered, null); | |||
// Detect blobs inside the image using an 8-connect rule | |||
List<Contour> contours = BinaryImageOps.contour(filtered, 8, label); | |||
// | // colors of contours | ||
int | int colorExternal = 0xFFFFFF; | ||
int colorInternal = 0xFF2020; | |||
// | // display the results | ||
BufferedImage | BufferedImage visualBinary = VisualizeBinaryData.renderBinary(binary, null); | ||
ShowImages.showWindow( | BufferedImage visualFiltered = VisualizeBinaryData.renderBinary(filtered, null); | ||
BufferedImage visualLabel = VisualizeBinaryData.renderLabeled(label, contours.size(), null); | |||
BufferedImage visualContour = VisualizeBinaryData.renderContours(contours,colorExternal,colorInternal, | |||
input.width,input.height,null); | |||
ShowImages.showWindow(visualBinary,"Binary Original"); | |||
ShowImages.showWindow(visualFiltered,"Binary Filtered"); | |||
ShowImages.showWindow(visualLabel,"Labeled Blobs"); | |||
ShowImages.showWindow(visualContour,"Contours"); | |||
} | } | ||
} | |||
</syntaxhighlight> | </syntaxhighlight> |
Revision as of 10:14, 14 April 2013
In a binary image each pixel can have a value of 0 or 1. Binary images are easy to compute and fast to process, which makes them popular in many applications. BoofCV contains many operations for creating and manipulating binary images. The example below demonstrates a few of ones contained inside of BinaryImageOps.
Example File: ExampleBinaryOps.java
Concepts:
- Image Thresholding
- Morphological Operations
- Binary Labeling
- Pixel Math
- Image Rendering
Relevant Applets:
Basic Example
/**
* Demonstrates how to create binary images by thresholding, applying binary morphological operations, and
* then extracting detected features by finding their contours.
*
* @author Peter Abeles
*/
public class ExampleBinaryOps {
public static void main( String args[] ) {
// load and convert the image into a usable format
BufferedImage image = UtilImageIO.loadImage("../data/applet/particles01.jpg");
// convert into a usable format
ImageFloat32 input = ConvertBufferedImage.convertFromSingle(image, null, ImageFloat32.class);
ImageUInt8 binary = new ImageUInt8(input.width,input.height);
ImageSInt32 label = new ImageSInt32(input.width,input.height);
// the mean pixel value is often a reasonable threshold when creating a binary image
double mean = ImageStatistics.mean(input);
// create a binary image by thresholding
ThresholdImageOps.threshold(input,binary,(float)mean,true);
// remove small blobs through erosion and dilation
// The null in the input indicates that it should internally declare the work image it needs
// this is less efficient, but easier to code.
ImageUInt8 filtered = BinaryImageOps.erode8(binary,null);
filtered = BinaryImageOps.dilate8(filtered, null);
// Detect blobs inside the image using an 8-connect rule
List<Contour> contours = BinaryImageOps.contour(filtered, 8, label);
// colors of contours
int colorExternal = 0xFFFFFF;
int colorInternal = 0xFF2020;
// display the results
BufferedImage visualBinary = VisualizeBinaryData.renderBinary(binary, null);
BufferedImage visualFiltered = VisualizeBinaryData.renderBinary(filtered, null);
BufferedImage visualLabel = VisualizeBinaryData.renderLabeled(label, contours.size(), null);
BufferedImage visualContour = VisualizeBinaryData.renderContours(contours,colorExternal,colorInternal,
input.width,input.height,null);
ShowImages.showWindow(visualBinary,"Binary Original");
ShowImages.showWindow(visualFiltered,"Binary Filtered");
ShowImages.showWindow(visualLabel,"Labeled Blobs");
ShowImages.showWindow(visualContour,"Contours");
}
}