Example Interpolation - Revision history

Peter at 23:06, 17 January 2022

2022-01-17T23:06:28Z

← Older revision		Revision as of 23:06, 17 January 2022
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/imageprocessing/ExampleInterpolation.java ExampleInterpolation.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.40/examples/src/main/java/boofcv/examples/imageprocessing/ExampleInterpolation.java ExampleInterpolation.java]

	Concepts:		Concepts:
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	// imagePath = "small_sunflower.jpg";		// imagePath = "small_sunflower.jpg";

	BufferedImage buffered = UtilImageIO.~~loadImage~~(UtilIO.pathExample(imagePath));		BufferedImage buffered = UtilImageIO.loadImageNotNull(UtilIO.pathExample(imagePath));
	~~ListDisplayPanel~~ gui = new ListDisplayPanel();		var gui = new ListDisplayPanel();

	gui.addImage(buffered, "Original");		gui.addImage(buffered, "Original");

Peter at 19:00, 12 July 2021

2021-07-12T19:00:25Z

Peter at 02:22, 22 December 2020

2020-12-22T02:22:17Z

← Older revision		Revision as of 02:22, 22 December 2020
Line 11:		Line 11:

	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/blob/v0.26/examples/src/boofcv/examples/imageprocessing/ExampleInterpolation.java ExampleInterpolation.java]		* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/imageprocessing/ExampleInterpolation.java ExampleInterpolation.java]

	Concepts:		Concepts:
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	*/		*/
	public class ExampleInterpolation {		public class ExampleInterpolation {
			public static void main( String[] args ) {
	public static void main(String[] args) {
	String imagePath;		String imagePath;
	imagePath = "eye01.jpg";		imagePath = "eye01.jpg";
Line 39:		Line 38:
	ListDisplayPanel gui = new ListDisplayPanel();		ListDisplayPanel gui = new ListDisplayPanel();

	gui.addImage(buffered,"Original");		gui.addImage(buffered, "Original");

	// For sake of simplicity assume it's a gray scale image. Interpolation functions exist for planar and		// For sake of simplicity assume it's a gray scale image. Interpolation functions exist for planar and
	// interleaved color images too		// interleaved color images too
	GrayF32 input = ConvertBufferedImage.convertFrom(buffered, (GrayF32)null);		GrayF32 input = ConvertBufferedImage.convertFrom(buffered, (GrayF32)null);
	GrayF32 scaled = input.createNew(500,500*input.height/input.width);		GrayF32 scaled = input.createNew(500, 500*input.height/input.width);

	for( InterpolationType type : InterpolationType.values() ) {		for (InterpolationType type : InterpolationType.values()) {
	// Create the single band (gray scale) interpolation function for the input image		// Create the single band (gray scale) interpolation function for the input image
	InterpolatePixelS<GrayF32> interp = FactoryInterpolation.		InterpolatePixelS<GrayF32> interp = FactoryInterpolation.
	createPixelS(0,255,type,BorderType.EXTENDED,input.getDataType());		createPixelS(0, 255, type, BorderType.EXTENDED, input.getDataType());

	// Tell it which image is being interpolated		// Tell it which image is being interpolated
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	float origX = x*input.width/(float)scaled.width;		float origX = x*input.width/(float)scaled.width;

	scaled.data[indexScaled++] = interp.get(origX,origY);		scaled.data[indexScaled++] = interp.get(origX, origY);
	}		}
	}		}

	// Add the results to the output		// Add the results to the output
	BufferedImage out = ConvertBufferedImage.convertTo(scaled,null,true);		BufferedImage out = ConvertBufferedImage.convertTo(scaled, null, true);
	gui.addImage(out,type.toString());		gui.addImage(out, type.toString());
	}		}

	ShowImages.showWindow(gui,"Example Interpolation", true);		ShowImages.showWindow(gui, "Example Interpolation", true);
	}		}
	}		}
	</syntaxhighlight>		</syntaxhighlight>

Peter: Created page with " file:Example_nearestneighbor_eye.png|Nearest Neighbo..."

2016-12-08T03:43:00Z

Created page with "<center> <gallery caption="Enlarging a small image with different interpolation methods" heights=160 widths=350 perrow=2 > file:Example_nearestneighbor_eye.png|Nearest Neighbo..."

New page

<center>
<gallery caption="Enlarging a small image with different interpolation methods" heights=160 widths=350 perrow=2 >
file:Example_nearestneighbor_eye.png|Nearest Neighbor
file:Example_bilinear_eye.jpg|Bilinear
file:Example_bicubic_eye.jpg|Bicubic
file:Example_polynomial_eye.jpg|Polynomial
</gallery>
</center>

BoofCV provides abstract interfaces to apply different interpolation methods to all of its images. Bilinear is by far the most widely used interpolation method in computer vision.

Example Code:
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.26/examples/src/boofcv/examples/imageprocessing/ExampleInterpolation.java ExampleInterpolation.java]

Concepts:
* Interpolation
* Image Distortion

= Example Code =

<syntaxhighlight lang="java">
/**
* Interpolation is used to convert an image, which is discrete by its nature, into a (piecewise) smooth function.
* Interpolation is in many CV applications, such as feature detection, and when distorting images. In this
* example a low resolution is scaled up using several different techniques to make the differences easily visible.
* For computer vision applications bilinear interpolation is almost always used.
*
* @author Peter Abeles
*/
public class ExampleInterpolation {

public static void main(String[] args) {
String imagePath;
imagePath = "eye01.jpg";
// imagePath = "small_sunflower.jpg";

BufferedImage buffered = UtilImageIO.loadImage(UtilIO.pathExample(imagePath));
ListDisplayPanel gui = new ListDisplayPanel();

gui.addImage(buffered,"Original");

// For sake of simplicity assume it's a gray scale image. Interpolation functions exist for planar and
// interleaved color images too
GrayF32 input = ConvertBufferedImage.convertFrom(buffered, (GrayF32)null);
GrayF32 scaled = input.createNew(500,500*input.height/input.width);

for( InterpolationType type : InterpolationType.values() ) {
// Create the single band (gray scale) interpolation function for the input image
InterpolatePixelS<GrayF32> interp = FactoryInterpolation.
createPixelS(0,255,type,BorderType.EXTENDED,input.getDataType());

// Tell it which image is being interpolated
interp.setImage(input);

// Manually apply scaling to the input image. See FDistort() for a built in function which does
// the same thing and is slightly more efficient
for (int y = 0; y < scaled.height; y++) {
// iterate using the 1D index for added performance. Altertively there is the set(x,y) operator
int indexScaled = scaled.startIndex + y*scaled.stride;
float origY = y*input.height/(float)scaled.height;

for (int x = 0; x < scaled.width; x++) {
float origX = x*input.width/(float)scaled.width;

scaled.data[indexScaled++] = interp.get(origX,origY);
}
}

// Add the results to the output
BufferedImage out = ConvertBufferedImage.convertTo(scaled,null,true);
gui.addImage(out,type.toString());
}

ShowImages.showWindow(gui,"Example Interpolation", true);
}
}
</syntaxhighlight>

← Older revision		Revision as of 19:00, 12 July 2021
Line 11:		Line 11:

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

	Concepts:		Concepts:
Line 23:		Line 23:
	/**		/**
	* Interpolation is used to convert an image, which is discrete by its nature, into a (piecewise) smooth function.		* Interpolation is used to convert an image, which is discrete by its nature, into a (piecewise) smooth function.
	* Interpolation is in many CV applications, such as feature detection, and when distorting images. In this		* Interpolation is in many CV applications, such as feature detection, and when distorting images. In this
	* example a low resolution is scaled up using several different techniques to make the differences easily visible.		* example a low resolution is scaled up using several different techniques to make the differences easily visible.
	* For computer vision applications bilinear interpolation is almost always used.		* For computer vision applications bilinear interpolation is almost always used.
Line 40:		Line 40:
	gui.addImage(buffered, "Original");		gui.addImage(buffered, "Original");

	// For sake of simplicity assume it's a gray scale image. Interpolation functions exist for planar and		// For sake of simplicity assume it's a gray scale image. Interpolation functions exist for planar and
	// interleaved color images too		// interleaved color images too
	GrayF32 input = ConvertBufferedImage.convertFrom(buffered, (GrayF32)null);		GrayF32 input = ConvertBufferedImage.convertFrom(buffered, (GrayF32)null);
Line 53:		Line 53:
	interp.setImage(input);		interp.setImage(input);

	// Manually apply scaling to the input image. See FDistort() for a built in function which does		// Manually apply scaling to the input image. See FDistort() for a built in function which does
	// the same thing and is slightly more efficient		// the same thing and is slightly more efficient
	for (int y = 0; y < scaled.height; y++) {		for (int y = 0; y < scaled.height; y++) {
	// iterate using the 1D index for added performance. Altertively there is the set(x,y) operator		// iterate using the 1D index for added performance. Altertively there is the set(x,y) operator
	int indexScaled = scaled.startIndex + y*scaled.stride;		int indexScaled = scaled.startIndex + y*scaled.stride;
	float origY = y*input.height/(float)scaled.height;		float origY = y*input.height/(float)scaled.height;