Tutorial Quick Start - Revision history

Orfgen: /* Filters */ createImage -> createSingleBand

2017-04-21T21:02:38Z

Filters: createImage -> createSingleBand

← Older revision		Revision as of 21:02, 21 April 2017
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	Class<T> inputType = (Class<T>)input.getClass();		Class<T> inputType = (Class<T>)input.getClass();

	T blurred = GeneralizedImageOps.~~createImage~~(inputType,input.width, input.height);		T blurred = GeneralizedImageOps.createSingleBand(inputType,input.width, input.height);
	GBlurImageOps.gaussian(input, blurred, -1, blurRadius, null);		GBlurImageOps.gaussian(input, blurred, -1, blurRadius, null);
	</syntaxhighlight>		</syntaxhighlight>
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	{		{
	Class<T> inputType = (Class<T>)input.getClass();		Class<T> inputType = (Class<T>)input.getClass();
	T blurred = GeneralizedImageOps.~~createImage~~(inputType, input.width, input.height);		T blurred = GeneralizedImageOps.createSingleBand(inputType, input.width, input.height);
	BlurFilter<T> filterBlur = FactoryBlurFilter.gaussian(inputType, -1, blurRadius);		BlurFilter<T> filterBlur = FactoryBlurFilter.gaussian(inputType, -1, blurRadius);
	filterBlur.process(input,blurred);		filterBlur.process(input,blurred);

Orfgen: Changed Binary Images code to 0.26

2017-04-21T20:44:09Z

Changed Binary Images code to 0.26

← Older revision		Revision as of 20:44, 21 April 2017
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	binary = BinaryImageOps.erode8(binary,null);		binary = BinaryImageOps.erode8(binary, 1, null);
	</syntaxhighlight>		</syntaxhighlight>
	Apply an erode operation on the binary image, writing over the original image reference.		Apply an erode operation once on the binary image, writing over the original image reference.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	BinaryImageOps.erode8(binary,output);		BinaryImageOps.erode8(binary, 1, output);
	</syntaxhighlight>		</syntaxhighlight>
	Apply an erode operation on the binary image, saving results to the output binary image.		Apply an erode operation once on the binary image, saving results to the output binary image.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	BinaryImageOps.erode4(binary,output);		BinaryImageOps.erode4(binary, 1, output);
	</syntaxhighlight>		</syntaxhighlight>
	Apply an erode operation with a 4-connect rule.		Apply an erode operation once with a 4-connect rule.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	int numBlobs = BinaryImageOps.~~labelBlobs4~~(binary,blobs);		int numBlobs = BinaryImageOps.contour(binary, ConnectRule.FOUR, blobs).size();
	</syntaxhighlight>		</syntaxhighlight>
	Detect and label blobs in the binary image using a 4-connect rule. blobs is an image of type GrayS32.		Detect and label blobs in the binary image using a 4-connect rule. blobs is an image of type GrayS32.
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	BufferedImage visualized = VisualizeBinaryData.renderBinary(binary,null);		BufferedImage visualized = VisualizeBinaryData.renderBinary(binary, false, null);
	</syntaxhighlight>		</syntaxhighlight>
	Renders the binary image as a black white image.		Renders the binary image as a black white image. false means the colors are not inverted.

	== Suggested Hardware ==		== Suggested Hardware ==

Peter at 22:34, 21 December 2016

2016-12-21T22:34:26Z

← Older revision		Revision as of 22:34, 21 December 2016
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	</syntaxhighlight>		</syntaxhighlight>
	Renders the binary image as a black white image.		Renders the binary image as a black white image.

			== Suggested Hardware ==

			To do computer vision you need a camera. Here's a list of recommended cameras

			* [http://amzn.to/2igJ9GC Webcam: Logitech C920]
			* [http://amzn.to/2hWGohn 360 Camera: Theta S ]

			Webcams are great for basically everything but structure from motion (SFM) applications. Their images often look better than much more expensive scientific cameras. Unfortunately they have a rolling shutter which breaks SFM algorithms if anything in the scene or the camera is moving.

			The Theta S is a 360 camera composed of two fisheye cameras. Interestingly it is one of the few consumer grade cameras to provide a global shutter! Making it useful for SFM applications.

			(The above links are Amazon affiliate. If you do plan on purchasing one of those cameras please help finance BoofCV and click on those links.)

Peter at 02:26, 21 December 2016

2016-12-21T02:26:55Z

← Older revision		Revision as of 02:26, 21 December 2016
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	== The Basics ==		== The Basics ==

	BoofCV supports 3 types of images; Gray (single band images), Planar (multi-band in a planar format), and Interleaved (~~tradtional~~ multi-band image format). The first two, gray and planar are fully supported while interleaved is partially supported. Gray and planar images are just easier to work with most of the time which is why they are fully supported. Interleaved is only supported where there is a performance advantage that was significant.		BoofCV supports 3 types of images; Gray (single band images), Planar (multi-band in a planar format), and Interleaved (traditional multi-band image format). The first two, gray and planar are fully supported while interleaved is partially supported. Gray and planar images are just easier to work with most of the time which is why they are fully supported. Interleaved is only supported where there is a performance advantage that was significant.

Peter at 02:26, 21 December 2016

2016-12-21T02:26:42Z

← Older revision		Revision as of 02:26, 21 December 2016
Line 52:		Line 52:

	== The Basics ==		== The Basics ==

			BoofCV supports 3 types of images; Gray (single band images), Planar (multi-band in a planar format), and Interleaved (tradtional multi-band image format). The first two, gray and planar are fully supported while interleaved is partially supported. Gray and planar images are just easier to work with most of the time which is why they are fully supported. Interleaved is only supported where there is a performance advantage that was significant.


	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	GrayU8 image = new GrayU8(100,150);		GrayU8 image = new GrayU8(100,150);

Peter at 02:22, 21 December 2016

2016-12-21T02:22:49Z

← Older revision		Revision as of 02:22, 21 December 2016
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	The following tutorial is intended to provide just enough information for you to quickly set up and start development with BoofCV. If you are not familiar with the [http://java.oracle.com Java programming language] or its associated development tools, you must fix that first because BoofCV is written entirely in Java. It is highly recommended that you use a tool like [https://gradle.org Gradle~~] or [https://gradle.org/ Maven~~] to build your own project and have it download the jars for you. If you enjoy doing things the slow and tedious way we are there for you and provide all the jars.		The following tutorial is intended to provide just enough information for you to quickly set up and start development with BoofCV. If you are not familiar with the [http://java.oracle.com Java programming language] or its associated development tools, you must fix that first because BoofCV is written entirely in Java. It is highly recommended that you use a tool like [https://gradle.org Gradle] to build your own project and have it download the jars for you. If you enjoy doing things the slow and tedious way we are there for you and provide all the jars.

	== Step One: Obtaining ==		== Step One: Obtaining ==

Peter at 02:17, 21 December 2016

2016-12-21T02:17:41Z

← Older revision		Revision as of 02:17, 21 December 2016
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	* Maven and Gradle [[Download:BoofCV\|Download Page]]		* Maven and Gradle [[Download:BoofCV\|Download Page]]

	== Step Two: Running Examples ==		== Step Two: Running Examples and Demonstrations ==

	~~For this step~~ you ~~must have the source code checked out from github or downloaded~~. ~~Just having the jars~~ is not ~~enough. Do that now if you haven't already~~.		Examples are short pieces of code which are designed to be easy to understand and show you how to perform some task. Demonstrations are more complex applications which visualize different aspects of an algorithm. The code for a demonstration is not designed to be easy to learn from and can be quite complex due to its integration with a GUI.

	~~Before you try to run the examples make sure you have all the data they use! The~~ [https://github.com/lessthanoptimal/BoofCV~~-Data data] is stored in a submodule in boofcv~~/data, which is initially empty if you pulled the source code from Github. If you download the source code then you should have the entire data directory. The easiest way to get the data directory is to pull it from git:		Source Code:
	~~<syntaxhighlight lang="bash">~~		* [https://github.com/lessthanoptimal/BoofCV/tree/master/examples boofcv/examples]
	cd boofcv/		* [https://github.com/lessthanoptimal/BoofCV/tree/master/demonstrations boofcv/demonstrations]
	~~git submodule init~~
	~~git submodule update~~
	~~</syntaxhighlight>~~
	~~Once you do that you are almost ready to run the~~ examples.

	~~BoofCV is built using Gradle. Gradle can be imported into IntelliJ or Eclipse. For instructions on how to do that see the project~~ [https://github.com/lessthanoptimal/BoofCV/~~blob~~/master/~~README.md README.md~~] ~~document. Then in your IDE you can navigate to the examples directory, right click and select run.~~

	~~If you have Gradle installed in your system, then it's very easy~~ to run ~~examples~~. ~~For full instructions see [https://github.com/lessthanoptimal/BoofCV/blob/master/examples/readme.txt examples/readme~~.~~txt]~~		The easiest way to run an example or demonstration is to launch their respective master applications. You can also load up the source code in your favorite IDE and run the applications directly.

	<syntaxhighlight lang="bash">		<syntaxhighlight lang="bash">
	~~gradle exampleRun -Pwhich=~~boofcv.examples.~~imageprocessing~~.~~ExampleBinaryOps~~		cd boofcv
			./gradlew examples
			java -jar examples/examples.jar
			./gradlew demonstrations
			java -jar demonstrations/demonstrations.jar
	</syntaxhighlight>		</syntaxhighlight>

	~~You can now explore all the example. I recommend trying them out on your own images/data as well as changing parameters and seeing what happens.~~

	= HELP ME!! =		= HELP ME!! =

Peter at 15:16, 29 March 2016

2016-03-29T15:16:42Z

← Older revision		Revision as of 15:16, 29 March 2016
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	ThresholdImageOps.threshold(image, binary, 23, true);		ThresholdImageOps.threshold(image, binary, 23, true);
	</syntaxhighlight>		</syntaxhighlight>
	Creates a binary image by thresholding the input image. Binary must be of type ~~ImageUInt8~~.		Creates a binary image by thresholding the input image. Binary must be of type GrayU8.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">

Peter at 05:45, 29 March 2016

2016-03-29T05:45:36Z

← Older revision		Revision as of 05:45, 29 March 2016
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static void procedural( ~~ImageU8~~ input )		public static void procedural( GrayU8 input )
	{		{
	~~ImageU8~~ blurred = new ~~ImageU8~~(input.width,input.height);		GrayU8 blurred = new GrayU8(input.width,input.height);
	BlurImageOps.gaussian(input,blurred,-1,blurRadius,null);		BlurImageOps.gaussian(input,blurred,-1,blurRadius,null);
	</syntaxhighlight>		</syntaxhighlight>

Peter at 06:11, 28 March 2016

2016-03-28T06:11:27Z

← Older revision		Revision as of 06:11, 28 March 2016
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	== The Basics ==		== The Basics ==
	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	~~ImageUInt8~~ image = new ~~ImageUInt8~~ (100,150);		GrayU8 image = new GrayU8(100,150);
	</syntaxhighlight>		</syntaxhighlight>
	Creating an unsigned 8-bit integer single band image with width=100 and height=150.		Creating an unsigned 8-bit integer single band image with width=100 and height=150.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	~~ImageFloat32~~ image = new ~~ImageFloat32~~(100,150);		GrayF32 image = new GrayF32(100,150);
	</syntaxhighlight>		</syntaxhighlight>
	Creating a floating point single band image with width=100 and height=150.		Creating a floating point single band image with width=100 and height=150.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	~~MultiSpectral~~<~~ImageUInt8~~> image = new ~~MultiSpectral~~<~~ImageUInt8~~>(~~ImageUInt8~~.class,100,200,3);		Planar<GrayU8> image = new Planar<GrayU8>(GrayU8.class,100,200,3);
	</syntaxhighlight>		</syntaxhighlight>
	Creates a color ~~multi spectral~~ image with 3 bands using ~~ImageUInt8~~ for each band.		Creates a color planar image with 3 bands using GrayU8 for each band.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	~~ImageFloat32~~ image = UtilImageIO.loadImage("test.png",~~ImageFloat32~~.class);		GrayF32 image = UtilImageIO.loadImage("test.png",GrayF32.class);
	</syntaxhighlight>		</syntaxhighlight>
	Loads a single band image of type ~~ImageFloat32~~ from a file.		Loads a single band image of type GrayF32 from a file.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static void function( ~~ImageFloat32~~ image )		public static void function( GrayF32 image )
	{		{
	float pixel = image.get(5,23);		float pixel = image.get(5,23);
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static void function( ~~ImageUInt8~~ image )		public static void function( GrayU8 image )
	{		{
	int pixel = image.get(5,23);		int pixel = image.get(5,23);
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static void function( ~~ImageInteger~~ image )		public static void function( GrayI image )
	{		{
	int pixel = image.get(5,23);		int pixel = image.get(5,23);
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static void function( ~~MultiSpectral~~<~~ImageUInt8~~> image )		public static void function( Planar<GrayU8> image )
	{		{
	int pixel = image.getBand(0).get(5,23);		int pixel = image.getBand(0).get(5,23);
	image.getBand(0).set(5,23,50);		image.getBand(0).set(5,23,50);
	</syntaxhighlight>		</syntaxhighlight>
	~~MultiSpectral~~ images are essentially arrays of ~~ImageSingleBands~~. To set or get a pixel value first access the particular band that needs to be changed then use the standard accessors inside of ImageSingleBand.		Planar images are essentially arrays of ImageGray. To set or get a pixel value first access the particular band that needs to be changed then use the standard accessors inside of ImageSingleBand.

	== Filters ==		== Filters ==

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static void procedural( ~~ImageUInt8~~ input )		public static void procedural( ImageU8 input )
	{		{
	~~ImageUInt8~~ blurred = new ~~ImageUInt8~~(input.width,input.height);		ImageU8 blurred = new ImageU8(input.width,input.height);
	BlurImageOps.gaussian(input,blurred,-1,blurRadius,null);		BlurImageOps.gaussian(input,blurred,-1,blurRadius,null);
	</syntaxhighlight>		</syntaxhighlight>
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static <T extends ~~ImageSingleBand~~, D extends ~~ImageSingleBand~~>		public static <T extends ImageGray, D extends ImageGray>
	void generalized( T input )		void generalized( T input )
	{		{
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static <T extends ~~ImageSingleBand~~, D extends ~~ImageSingleBand~~>		public static <T extends ImageGray, D extends ImageGray>
	void filter( T input )		void filter( T input )
	{		{
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	public static <T extends ~~ImageSingleBand~~, D extends ~~ImageSingleBand~~>		public static <T extends ImageGray, D extends ImageGray>
	void example( T input , Class<D> derivType ) {		void example( T input , Class<D> derivType ) {
	AnyImageDerivative<T,D> deriv = GImageDerivativeOps.createDerivatives((Class<T>)input.getClass(),derivType);		AnyImageDerivative<T,D> deriv = GImageDerivativeOps.createDerivatives((Class<T>)input.getClass(),derivType);
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	int numBlobs = BinaryImageOps.labelBlobs4(binary,blobs);		int numBlobs = BinaryImageOps.labelBlobs4(binary,blobs);
	</syntaxhighlight>		</syntaxhighlight>
	Detect and label blobs in the binary image using a 4-connect rule. blobs is an image of type ~~ImageSInt32~~.		Detect and label blobs in the binary image using a 4-connect rule. blobs is an image of type GrayS32.

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">