Java DIP – Useful Resources ”; Previous Next The following resources contain additional information on Java DIP. Please use them to get more in-depth knowledge on this topic. Useful Links on Java DIP Digital image processing – Wikipedia reference for Java DIP. Useful Books on Java DIP To enlist your site on this page, please drop an email to [email protected] Print Page Previous Next Advertisements ”;
Java DIP – Applying Box Filter ”; Previous Next We apply Box filter that blurs an image. A Box filter could be of dimensions 3×3, 5×5, 9×9 etc. We use OpenCV function filter2D to apply Box filter to images. It can be found under Imgproc package. Its syntax is given below − filter2D(src, dst, depth , kernel, anchor, delta, BORDER_DEFAULT ); The function arguments are described below − Sr.No. Argument & Description 1 src It is source image. 2 dst It is destination image. 3 depth It is the depth of dst. A negative value (such as -1) indicates that the depth is the same as the source. 4 kernel It is the kernel to be scanned through the image. 5 anchor It is the position of the anchor relative to its kernel. The location Point (-1, -1) indicates the center by default. 6 delta It is a value to be added to each pixel during the convolution. By default it is 0. 7 BORDER_DEFAULT We let this value by default. Apart from the filter2D() method, there are other methods provided by the Imgproc class. They are described briefly − Sr.No. Method & Description 1 cvtColor(Mat src, Mat dst, int code, int dstCn) It converts an image from one color space to another. 2 dilate(Mat src, Mat dst, Mat kernel) It dilates an image by using a specific structuring element. 3 equalizeHist(Mat src, Mat dst) It equalizes the histogram of a grayscale image. 4 filter2D(Mat src, Mat dst, int depth, Mat kernel, Point anchor, double delta) It convolves an image with the kernel. 5 GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX) It blurs an image using a Gaussian filter. 6 integral(Mat src, Mat sum) It calculates the integral of an image. Example The following example demonstrates the use of Imgproc class to apply Box filter to an image of Grayscale. import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; public class convolution { public static void main( String[] args ) { try { int kernelSize = 9; System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); Mat source = Highgui.imread(“grayscale.jpg”, Highgui.CV_LOAD_IMAGE_GRAYSCALE); Mat destination = new Mat(source.rows(),source.cols(),source.type()); Mat kernel = Mat.ones(kernelSize,kernelSize, CvType.CV_32F); for(int i=0; i<kernel.rows(); i++) { for(int j=0; j<kernel.cols(); j++) { double[] m = kernel.get(i, j); for(int k =0; k<m.length; k++) { m[k] = m[k]/(kernelSize * kernelSize); } kernel.put(i,j, m); } } Imgproc.filter2D(source, destination, -1, kernel); Highgui.imwrite(“output.jpg”, destination); } catch (Exception e) { System.out.println(“Error: ” + e.getMessage()); } } } Output When you execute the given code, the following output is seen − Original Image In this example we convolve our image with the following filter (kernel). This filter results in blurring an image as its size increases. This original image has been convolved with the box filter of size 5, which is given below − Box filter of size 5 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 1/25 Convolved Image (with Box Filter of Size 5) Convolved Image (with Box Filter of Size 9) Print Page Previous Next Advertisements ”;
Discuss Java DIP ”; Previous Next Digital Image Processing (DIP) deals with manipulation of digital images using a digital computer. This tutorial gives a simple and practical approach of implementing algorithms used in digital image processing. After completing this tutorial, you should find yourself at a moderate level of expertise, from where you should be able to take yourself to next levels. Print Page Previous Next Advertisements ”;
Java DIP – OpenCV Color Space Conversion ”; Previous Next In order to change color space of one image to another using OpenCV, we read image into BufferedImage and convert it into Mat Object. Its syntax is given below − File input = new File(“digital_image_processing.jpg”); BufferedImage image = ImageIO.read(input); //convert Buffered Image to Mat. OpenCv allows many color conversion types, all of which can be found in the Imgproc class. Some of the types are described briefly − Sr.No. Color Conversion Type 1 COLOR_RGB2BGR 2 COLOR_RGB2BGRA 3 COLOR_RGB2GRAY 4 COLOR_RGB2HLS 5 COLOR_RGB2HSV 6 COLOR_RGB2Luv 7 COLOR_RGB2YUV 8 COLOR_RGB2Lab From any of the color conversion type, just pass the appropriate one into method cvtColor() in the Imgproc class. Its syntax is given below − Imgproc.cvtColor(source mat, destination mat1, Color_Conversion_Code); The method cvtColor() takes three parameters which are the source image matrix, the destination image matrix and the color conversion type. Apart from the cvtColor() method, there are other methods provide by the Imgproc class. They are described briefly − Sr.No. Method & Description 1 cvtColor(Mat src, Mat dst, int code, int dstCn) It converts an image from one color space to another. 2 dilate(Mat src, Mat dst, Mat kernel) It dilates an image by using a specific structuring element. 3 equalizeHist(Mat src, Mat dst) It equalizes the histogram of a grayscale image. 4 filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta) It convolves an image with the kernel. 5 GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX) It blurs an image using a Gaussian filter. 6 integral(Mat src, Mat sum) It calculates the integral of an image. Example The following example demonstrates the use of Imgproc class to convert an image from one color space to another. import java.awt.image.BufferedImage; import java.awt.image.DataBufferByte; import java.io.File; import javax.imageio.ImageIO; import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.imgproc.Imgproc; public class Main { public static void main( String[] args ) { try { System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); File input = new File(“digital_image_processing.jpg”); BufferedImage image = ImageIO.read(input); byte[] data = ((DataBufferByte) image.getRaster().getDataBuffer()).getData(); Mat mat = new Mat(image.getHeight(),image.getWidth(), CvType.CV_8UC3); mat.put(0, 0, data); Mat mat1 = new Mat(image.getHeight(), image.getWidth(), CvType.CV_8UC3); Imgproc.cvtColor(mat, mat1, Imgproc.COLOR_RGB2HSV); byte[] data1 = new byte[mat1.rows()*mat1.cols()*(int)(mat1.elemSize())]; mat1.get(0, 0, data1); BufferedImage image1 = new BufferedImage(mat1.cols(), mat1.rows(), 5); image1.getRaster().setDataElements(0, 0, mat1.cols(), mat1.rows(), data1); File ouptut = new File(“hsv.jpg”); ImageIO.write(image1, “jpg”, ouptut); } catch (Exception e) { System.out.println(“Error: ” + e.getMessage()); } } } Output When you execute the given example, it converts an image name digital_image_processing.jpg to its equivalent HSV color space image and writes it on hard disk with name hsv.jpg. Original Image (RGB) Converted Image (HSV) Print Page Previous Next Advertisements ”;
Java DIP – Robinson Operator ”; Previous Next Robinson compass masks are yet another type of derivative masks which are used for edge detection. This operator is also known as direction mask. In this operator we take one mask and rotate it in all the eight major directions to get edges of the eight directions. We are going to use OpenCV function filter2D to apply Robinson operator to images. It can be found under Imgproc package. Its syntax is given below − filter2D(src, dst, depth , kernel, anchor, delta, BORDER_DEFAULT ); The function arguments are described below − Sr.No. Argument & Description 1 src It is source image. 2 dst It is destination image. 3 depth It is the depth of dst. A negative value (such as -1) indicates that the depth is the same as the source. 4 kernel It is the kernel to be scanned through the image. 5 anchor It is the position of the anchor relative to its kernel. The location Point(-1, -1) indicates the center by default. 6 delta It is a value to be added to each pixel during the convolution. By default it is 0. 7 BORDER_DEFAULT We let this value by default. Apart from the filter2D method, there are other methods provided by the Imgproc class. They are described briefly − Sr.No. Method & Description 1 cvtColor(Mat src, Mat dst, int code, int dstCn) It converts an image from one color space to another. 2 dilate(Mat src, Mat dst, Mat kernel) It dilates an image by using a specific structuring element. 3 equalizeHist(Mat src, Mat dst) It equalizes the histogram of a grayscale image. 4 filter2D(Mat src, Mat dst, int depth, Mat kernel, Point anchor, double delta) It convolves an image with the kernel. 5 GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX) It blurs an image using a Gaussian filter. 6 integral(Mat src, Mat sum) It calculates the integral of an image. Example The following example demonstrates the use of Imgproc class to apply Robinson operator to an image of Grayscale. import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; public class convolution { public static void main( String[] args ) { try { int kernelSize = 9; System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); Mat source = Highgui.imread(“grayscale.jpg”, Highgui.CV_LOAD_IMAGE_GRAYSCALE); Mat destination = new Mat(source.rows(),source.cols(),source.type()); Mat kernel = new Mat(kernelSize,kernelSize, CvType.CV_32F) { { put(0,0,-1); put(0,1,0); put(0,2,1); put(1,0-2); put(1,1,0); put(1,2,2); put(2,0,-1); put(2,1,0); put(2,2,1); } }; Imgproc.filter2D(source, destination, -1, kernel); Highgui.imwrite(“output.jpg”, destination); } catch (Exception e) { System.out.println(“Error: ” + e.getMessage()); } } } Output When you execute the given code, the following output is seen − Original Image This original image is convolved with the Robinson operator of North edges as given below − North Direction Mask -1 0 1 -2 0 2 -1 0 1 Convolved Image(Robinson North) This original image has also been convolved with the Robinson operator of East edges as given below − East Direction Mask -1 -2 -1 0 0 0 1 2 1 Convolved Image(Robinson East) Print Page Previous Next Advertisements ”;
Java DIP – Enhancing Image Contrast ”; Previous Next In this chapter learn how to enhance the contrast of an image using histogram equalization. We use the OpenCV function equalizeHist() method. It can be found under Imgproc package. Its syntax is given below − Imgproc.equalizeHist(source, destination); The parameters are described below − Sr.No. Parameter & Description 1 Source It is 8-bit single channel source image. 2 Destination It is the destination image. Apart from the equalizeHist() method, there are other methods provided by the Imgproc class. They are described briefly − Sr.No. Method & Description 1 cvtColor(Mat src, Mat dst, int code, int dstCn) It converts an image from one color space to another. 2 dilate(Mat src, Mat dst, Mat kernel) It dilates an image by using a specific structuring element. 3 equalizeHist(Mat src, Mat dst) It equalizes the histogram of a grayscale image. 4 filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta) It convolves an image with the kernel. 5 GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX) It blurs an image using a Gaussian filter. 6 integral(Mat src, Mat sum) It calculates the integral of an image. Example The following example demonstrates the use of Imgproc class to enhance contrast of an image − import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; public class Main { static int width; static int height; static double alpha = 2; static double beta = 50; public static void main( String[] args ) { try { System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); Mat source = Highgui.imread(“grayscale.jpg”, Highgui.CV_LOAD_IMAGE_GRAYSCALE); Mat destination = new Mat(source.rows(),source.cols(),source.type()); Imgproc.equalizeHist(source, destination); Highgui.imwrite(“contrast.jpg”, destination); } catch (Exception e) { System.out.println(“error: ” + e.getMessage()); } } } Output When you execute the given code, the following output is seen − Original Image Enhanced Contrast Image Print Page Previous Next Advertisements ”;
Java DIP – Applying Watermark ”; Previous Next In this chapter we learn two ways of applying watermark on images. These ways are − Applying Text Watermark Applying Image watermark Applying Text Watermark We use OpenCV function putText to apply text watermark to image. It can be found under Core package. Its syntax is given below − Core.putText(source, Text, Point, fontFace ,fontScale , color); The parameters of this function are described below − Sr.No. Parameter & Description 1 Source It is source image. 2 Text It is the string text that would appear on the image. 3 Point It is the point where text should appear on image. 4 fontFace Font type. For example − FONT_HERSHEY_SIMPLEX, FONT_HERSHEY_PLAIN, FONT_HERSHEY_COMPLEX etc. 5 fontScale It is font scale factor that is multiplied by the font-specific base size. 6 color It is text color. Apart from the putText method, there are other methods provided by the Core class. They are described briefly − Sr.No. Method & Description 1 normalize(Mat src, Mat dst, double alpha, double beta, int norm_type) It normalizes the norm or value range of an array. 2 perspectiveTransform(Mat src, Mat dst, Mat m) It performs the perspective matrix transformation of vectors. 3 phase(Mat x, Mat y, Mat angle) It calculates the rotation angle of 2D vectors. 4 rectangle(Mat img, Point pt1, Point pt2, Scalar color) It draws a simple, thick, or filled up-right rectangle. 5 reduce(Mat src, Mat dst, int dim, int rtype, int dtype) It reduces a matrix to a vector. 6 transform(Mat src, Mat dst, Mat m) It performs the matrix transformation of every array element. Example The following example demonstrates the use of Core class to apply text watermark to an image − import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; public class Main { public static void main( String[] args ) { try{ System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); Mat source = Highgui.imread(“digital_image_processing.jpg”, Highgui.CV_LOAD_IMAGE_COLOR); Mat destination = new Mat(source.rows(),source.cols(), source.type()); Core.putText(source, “Tutorialspoint.com”, new Point (source.rows()/2,source.cols()/2), Core.FONT_ITALIC,new Double(1),new Scalar(255)); Highgui.imwrite(“watermarked.jpg”, source); } catch (Exception e) { System.out.println(“Error: “+e.getMessage()); } } } Output When you execute the given code, the following output is seen − Original Image Text Watermarked Image Applying Image Watermark on Image We are going to use OpenCV function addWeighted to apply image watermark to image. It can be found under Core package. Its syntax is given below − Core.addWeighted(InputArray src1, alpha, src2 (Watermark image), beta, gamma, OutputArray dst); The parameters of this function are described below − Sr.No. Parameter & Description 1 src1 It is first input array. 2 alpha It is the weight of the first array elements. 3 src2 It is the second input array of the same size and channel number as src1. 4 beta It is the weight of the second array elements. 5 gamma It is the scalar added to each sum. 6 dst It is the output array that has the same size and number of channels as the input arrays. Example The following example demonstrates the use of Core class to apply image watermark to an image − import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; public class Main { public static void main( String[] args ) { try{ System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); Mat source = Highgui.imread(“digital_image_processing.jpg”, Highgui.CV_LOAD_IMAGE_COLOR); Mat waterMark = Highgui.imread(“watermark.png”, Highgui.CV_LOAD_IMAGE_COLOR); Rect ROI = new Rect(waterMark.rows() * 4,waterMark.cols(), waterMark.cols(),waterMark.rows()); Core.addWeighted(source.submat(ROI), 0.8, waterMark, 0.2, 1, source.submat(ROI)); Highgui.imwrite(“watermarkedImage.jpg”, source); } catch (Exception e) { System.out.println(“Error: ” + e.getMessage()); } } } Output When you execute the given code, the following output is seen − Original Image The Watermark Image Watermarked Image Print Page Previous Next Advertisements ”;
Java DIP – Prewitt Operator ”; Previous Next Prewitt operator is used for edge detection in an image. It detects two types of edges: vertical edges and horizontal edges. We use OpenCV function filter2D to apply Prewitt operator to images. It can be found under Imgproc package. Its syntax is given below − filter2D(src, dst, depth , kernel, anchor, delta, BORDER_DEFAULT ); The function arguments are described below − Sr.No. Argument & Description 1 src It is source image. 2 dst It is destination image. 3 depth It is the depth of dst. A negative value (such as -1) indicates that the depth is the same as the source. 4 kernel It is the kernel to be scanned through the image. 5 anchor It is the position of the anchor relative to its kernel. The location Point (-1, -1) indicates the center by default. 6 delta It is a value to be added to each pixel during the convolution. By default it is 0. 7 BORDER_DEFAULT We let this value by default. Apart from the filter2D method, there are other methods provide by the Imgproc class. They are described briefly − Sr.No. Method & Description 1 cvtColor(Mat src, Mat dst, int code, int dstCn) It converts an image from one color space to another. 2 dilate(Mat src, Mat dst, Mat kernel) It dilates an image by using a specific structuring element. 3 equalizeHist(Mat src, Mat dst) It equalizes the histogram of a grayscale image. 4 filter2D(Mat src, Mat dst, int depth, Mat kernel, Point anchor, double delta) It convolves an image with the kernel. 5 GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX) It blurs an image using a Gaussian filter. 6 integral(Mat src, Mat sum) It calculates the integral of an image. Example The following example demonstrates the use of Imgproc class to apply Prewitt operator to an image of Grayscale. import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; public class convolution { public static void main( String[] args ) { try { int kernelSize = 9; System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); Mat source = Highgui.imread(“grayscale.jpg”, Highgui.CV_LOAD_IMAGE_GRAYSCALE); Mat destination = new Mat(source.rows(),source.cols(),source.type()); Mat kernel = new Mat(kernelSize,kernelSize, CvType.CV_32F) { { put(0,0,-1); put(0,1,0); put(0,2,1); put(1,0-1); put(1,1,0); put(1,2,1); put(2,0,-1); put(2,1,0); put(2,2,1); } }; Imgproc.filter2D(source, destination, -1, kernel); Highgui.imwrite(“output.jpg”, destination); } catch (Exception e) { System.out.println(“Error: ” + e.getMessage()); } } } Output When you execute the given code, the following output is seen − Original Image This original image is convolved with the Prewitt operator of vertical edges as given below − Vertical direction -1 0 1 -1 0 1 -1 0 1 Convolved Image(Vertical Direction) This original image has also been convolved with the Prewitt operator of horizontal edges, which is given below − Horizontal Direction -1 -1 -1 0 0 0 1 1 1 Convolved Image(Horizontal Direction) Print Page Previous Next Advertisements ”;
Java DIP – Understand Convolution ”; Previous Next Convolution is a mathematical operation on two functions f and g. The function f and g in this case are images, since an image is also a two dimensional function. Performing Convolution In order to perform convolution on an image, following steps are taken − Flip the mask (horizontally and vertically) only once. Slide the mask onto the image. Multiply the corresponding elements and then add them. Repeat this procedure until all values of the image has been calculated. We use OpenCV function filter2D to apply convolution to images. It can be found under Imgproc package. Its syntax is given below − filter2D(src, dst, depth , kernel, anchor, delta, BORDER_DEFAULT ); The function arguments are described below − Sr.No. Argument & Description 1 src It is source image. 2 dst It is destination image. 3 depth It is the depth of dst. A negative value (such as -1) indicates that the depth is the same as the source. 4 kernel It is the kernel to be scanned through the image. 5 anchor It is the position of the anchor relative to its kernel. The location Point (-1, -1) indicates the center by default. 6 delta It is a value to be added to each pixel during the convolution. By default it is 0. 7 BORDER_DEFAULT We let this value by default. Example The following example demonstrates the use of Imgproc class to perform convolution on an image of Grayscale. import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; public class convolution { public static void main( String[] args ) { try { int kernelSize = 3; System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); Mat source = Highgui.imread(“grayscale.jpg”, Highgui.CV_LOAD_IMAGE_GRAYSCALE); Mat destination = new Mat(source.rows(),source.cols(),source.type()); Mat kernel = new Mat(kernelSize,kernelSize, CvType.CV_32F) { { put(0,0,0); put(0,1,0); put(0,2,0); put(1,0,0); put(1,1,1); put(1,2,0); put(2,0,0); put(2,1,0); put(2,2,0); } }; Imgproc.filter2D(source, destination, -1, kernel); Highgui.imwrite(“original.jpg”, destination); } catch (Exception e) { System.out.println(“Error:” + e.getMessage()); } } } Output In this example we convolve our image with the following filter(kernel). This filter results in producing original image as it is − 0 0 0 0 1 0 0 0 0 Original Image Convolved Image Print Page Previous Next Advertisements ”;
Java DIP – Create Zooming Effect ”; Previous Next Zooming is the process of enlarging an image so that the details in the image become more visible and prominent. We use OpenCV function resize to apply zooming to images. It can be found under Imgproc package. Its syntax is given below − Imgproc.resize(source,destination, destination.size(),zoomFactor,zoomFactor,Interpolation); In the resize function, we pass source image, destination image and its size, zooming factor, and the interpolation method to use. The interpolation methods available are described below − Sr.No. Interpolation method & Description 1 INTER_NEAREST It is nearest-neighbour interpolation. 2 INTER_LINEAR It is bilinear interpolation (used by default). 3 INTER_AREA It is resampling using pixel area relation. It may be a preferred method for image decimation, as it gives more-free results. 4 INTER_CUBIC It is a bi-cubic interpolation over 4×4 pixel neighbourhood. 5 INTER_LANCZOS4 It is a Lanczos interpolation over 8×8 pixel neighbourhood. Apart from the resize method, there are other methods provided by the Imgproc class. They are described briefly − Sr.No. Method & Description 1 cvtColor(Mat src, Mat dst, int code, int dstCn) It converts an image from one color space to another. 2 dilate(Mat src, Mat dst, Mat kernel) It dilates an image by using a specific structuring element. 3 equalizeHist(Mat src, Mat dst) It equalizes the histogram of a grayscale image. 4 filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta) It convolves an image with the kernel. 5 GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX) It blurs an image using a Gaussian filter. 6 integral(Mat src, Mat sum) It calculates the integral of an image. Example The following example demonstrates the use of Imgproc class to apply zooming to an image. import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.highgui.Highgui; import org.opencv.imgproc.Imgproc; public class Main { public static void main( String[] args ) { try { int zoomingFactor = 2; System.loadLibrary( Core.NATIVE_LIBRARY_NAME ); Mat source = Highgui.imread(“image.jpg”, Highgui.CV_LOAD_IMAGE_GRAYSCALE); Mat destination = new Mat(source.rows() * zoomingFactor, source.cols()* zoomingFactor,source.type()); Imgproc.resize(source, destination, destination.size(), zoomingFactor,zoomingFactor,Imgproc.INTER_NEAREST); Highgui.imwrite(“zoomed.jpg”, destination); } catch (Exception e) { System.out.println(“Error: “+e.getMessage()); } } } Output When you execute the given code, the following output is seen − Original Image Zoomed Image(Zooming factor − 2) Print Page Previous Next Advertisements ”;