Tutorial Quick Start

Quick Start

The following is a list of code sniplets which demonstrate how to do various tasks inside of BoofCV and is designed to complement other tutorials and examples.

Term	definition
single band	The image supports only one color
floating point	Image elements are of type float or double
unsigned	Image elements can only be positive integers
signed	Image elements can be either positive or negative integers
generics	Allows strong typing in abstracted code. Introduced in Java 1.5. Click here.

The Basics

ImageUInt8 image = new ImageUInt8 (100,150);

Creating an unsigned 8-bit integer single band image with width=100 and height=150.

ImageFloat32 image = new ImageFloat32(100,150);

Creating a floating point single band image with width=100 and height=150.

ImageFloat32 image = UtilImageIO.loadImage("test.png",ImageFloat32.class);

Loads a single band image of type ImageFloat32 from a file.

public static <T extends ImageBase> T generic( Class<T> imageType ) {
	T image = UtilImageIO.loadImage("test.png",imageType);

Loads an image with the specified type inside a function that uses Java generics.

BufferedImage out = ConvertBufferedImage.convertTo(image,null);

Converts an image into a BufferedImage to provide better integration with Java2D (display/saving). Pixel values must be in the range of 0 to 255.

BufferedImage out = VisualizeImageData.grayMagnitude(derivX,null,-1);

Renders a signed single band image into a gray intensity image.

BufferedImage out = VisualizeImageData.colorizeSign(derivX,null,-1);

Renders a signed single band image into a color intensity image.

BufferedImage out = ConvertBufferedImage.convertTo(image,null);
ShowImages.showWindow(out,"Output");

Displays an image in a window using Java swing.

Pixel Access

The image type must be known to access pixel information. The following show how to access pixels for different image types. For more information on the image data structure and direct access to the raw data array see Tutorial Images for more details

public static void function( ImageFloat32 image )
{
	float pixel = image.get(5,23);
	image.set(5,23,50.3);

Gets and sets the pixel at (5,23). Note that set() and get() functions are image type specific. In other words, you can't access pixel without knowing the image type.

public static void function( ImageUInt8 image )
{
	int pixel = image.get(5,23);
	image.set(5,23,50);

Similar to the above example but for an 8-bit unsigned integer image. Note the image.get() returns 'int' and not 'byte'.

public static void function( ImageInteger image )
{
	int pixel = image.get(5,23);
	image.set(5,23,50);

In fact the same code will work for all integer images, except SInt64 which uses longs and not ints. Internally UInt8 stores its pixels as a byte array, but set() and get() return int because Java internally does not use bytes on the register.

Filters

public static void procedural( ImageUInt8 input )
{
	ImageUInt8 blurred = new ImageUInt8(input.width,input.height);
	BlurImageOps.gaussian(input,blurred,-1,blurRadius,null);

Applies Gaussian blur to an image using a type specific procedural interface.

public static <T extends ImageBase, D extends ImageBase>
void generalized( T input )
{
	Class<T> inputType = (Class<T>)input.getClass();

	T blurred = GeneralizedImageOps.createImage(inputType,input.width, input.height);
	GBlurImageOps.gaussian(input, blurred, -1, blurRadius, null);

Applies Gaussian blur to an image using an abstracted procedural interface. Note the G in front of BlurImageOps that indicates it contains generic functions.

public static <T extends ImageBase, D extends ImageBase>
void filter( T input )
{
	Class<T> inputType = (Class<T>)input.getClass();
	T blurred = GeneralizedImageOps.createImage(inputType, input.width, input.height);
	BlurFilter<T> filterBlur = FactoryBlurFilter.gaussian(inputType, -1, blurRadius);
	filterBlur.process(input,blurred);

Creates an image filter class for computing the Gaussian blur. Provides greater abstraction.

// type specific sobel
GradientSobel.process(blurred, derivX, derivY, FactoryImageBorder.extend(input));
// generic
GImageDerivativeOps.sobel(blurred, derivX, derivY, BorderType.EXTENDED);
// filter
ImageGradient<T,D> gradient = FactoryDerivative.sobel(inputType, derivType);
gradient.process(blurred,derivX,derivY);

Three ways to compute the image gradient using a Sobel kernel.

public static <T extends ImageBase, D extends ImageBase>
void example( T input , Class<D> derivType ) {
	AnyImageDerivative<T,D> deriv = GImageDerivativeOps.createDerivatives((Class<T>)input.getClass(),derivType);

	deriv.setInput(input);
	D derivX = deriv.getDerivative(true);
	D derivXXY = deriv.getDerivative(true,true,false);

Useful class for computing arbitrary image derivatives. Computes 1st order x-derive and then 3rd order xxy derivative.

Binary Images

ThresholdImageOps.threshold(image, binary, 23, true);

Creates a binary image by thresholding the input image. Binary must be of type ImageUInt8.

binary = BinaryImageOps.erode8(binary,null);

Apply an erode operation on the binary image, writing over the original image reference.

BinaryImageOps.erode8(binary,output);

Apply an erode operation on the binary image, saving results to the output binary image.

BinaryImageOps.erode4(binary,output);

Apply an erode operation with a 4-connect rule.

int numBlobs = BinaryImageOps.labelBlobs4(binary,blobs);

Detect and label blobs in the binary image using a 4-connect rule. blobs is an image of type ImageSInt32.

BufferedImage visualized = VisualizeBinaryData.renderLabeled(blobs, numBlobs, null);

Renders the detected blobs in a colored image.

BufferedImage visualized = VisualizeBinaryData.renderBinary(binary,null);

Renders the binary image as a black white image.