本文整理汇总了Java中org.opencv.core.Mat.channels方法的典型用法代码示例。如果您正苦于以下问题:Java Mat.channels方法的具体用法?Java Mat.channels怎么用?Java Mat.channels使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.opencv.core.Mat
的用法示例。
在下文中一共展示了Mat.channels方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: SimplestColorBalance
import org.opencv.core.Mat; //导入方法依赖的package包/类
/**
* Simplest Color Balance. Performs color balancing via histogram
* normalization.
*
* @param img input color or gray scale image
* @param percent controls the percentage of pixels to clip to white and black. (normally, choose 1~10)
* @return Balanced image in CvType.CV_32F
*/
public static Mat SimplestColorBalance(Mat img, int percent) {
if (percent <= 0)
percent = 5;
img.convertTo(img, CvType.CV_32F);
List<Mat> channels = new ArrayList<>();
int rows = img.rows(); // number of rows of image
int cols = img.cols(); // number of columns of image
int chnls = img.channels(); // number of channels of image
double halfPercent = percent / 200.0;
if (chnls == 3) Core.split(img, channels);
else channels.add(img);
List<Mat> results = new ArrayList<>();
for (int i = 0; i < chnls; i++) {
// find the low and high precentile values (based on the input percentile)
Mat flat = new Mat();
channels.get(i).reshape(1, 1).copyTo(flat);
Core.sort(flat, flat, Core.SORT_ASCENDING);
double lowVal = flat.get(0, (int) Math.floor(flat.cols() * halfPercent))[0];
double topVal = flat.get(0, (int) Math.ceil(flat.cols() * (1.0 - halfPercent)))[0];
// saturate below the low percentile and above the high percentile
Mat channel = channels.get(i);
for (int m = 0; m < rows; m++) {
for (int n = 0; n < cols; n++) {
if (channel.get(m, n)[0] < lowVal) channel.put(m, n, lowVal);
if (channel.get(m, n)[0] > topVal) channel.put(m, n, topVal);
}
}
Core.normalize(channel, channel, 0.0, 255.0 / 2, Core.NORM_MINMAX);
channel.convertTo(channel, CvType.CV_32F);
results.add(channel);
}
Mat outval = new Mat();
Core.merge(results, outval);
return outval;
}
示例2: matToBufferedImage
import org.opencv.core.Mat; //导入方法依赖的package包/类
public static Image matToBufferedImage(Mat m)
{
// just a simple convertor from web, this code is the fastest one
int type = BufferedImage.TYPE_BYTE_GRAY;
if ( m.channels() > 1 ) {
type = BufferedImage.TYPE_3BYTE_BGR;
}
int bufferSize = m.channels()*m.cols()*m.rows();
byte [] b = new byte[bufferSize];
m.get(0,0,b);
BufferedImage image = new BufferedImage(m.cols(),m.rows(), type);
final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
System.arraycopy(b, 0, targetPixels, 0, b.length);
return image;
}
示例3: hsvToBgrScalar
import org.opencv.core.Mat; //导入方法依赖的package包/类
public static Scalar hsvToBgrScalar(Scalar hsvin)
{
Mat bgr = new Mat();
Mat hsv = new Mat(1, 1, CvType.CV_8UC3, hsvin);
Imgproc.cvtColor(hsv, bgr, Imgproc.COLOR_HSV2BGR);
byte[] bgr_data = new byte[(int)(bgr.total() * bgr.channels())];
bgr.get(0,0, bgr_data);
return new Scalar(bgr_data[0], bgr_data[1], bgr_data[2]);
}
示例4: matToBufferedImage
import org.opencv.core.Mat; //导入方法依赖的package包/类
public static Image matToBufferedImage(Mat m)
{
// just a simple convertor from web, this code is the fastest one
int type = BufferedImage.TYPE_BYTE_GRAY;
if ( m.channels() > 1 ) {
type = BufferedImage.TYPE_3BYTE_BGR;
}
int bufferSize = m.channels()*m.cols()*m.rows();
byte [] b = new byte[bufferSize];
m.get(0,0,b);
BufferedImage image = new BufferedImage(m.cols(),m.rows(), type);
final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
System.arraycopy(b, 0, targetPixels, 0, b.length);
return image;
}
示例5: getOpenCvLines
import org.opencv.core.Mat; //导入方法依赖的package包/类
public static List<Line> getOpenCvLines(Mat original, int scale, double minLength) {
Mat raw = new Mat();
Imgproc.resize(original.clone(), raw, new Size((int) (original.size().width/scale), (int) (original.size().height/scale)));
if(raw.channels() > 1) {
Imgproc.cvtColor(raw, raw, Imgproc.COLOR_RGB2GRAY);
}
Imgproc.equalizeHist(raw, raw);
Imgproc.blur(raw, raw, new Size(3,3));
//Line Segment Detection 2
Mat linesM1 = new Mat();
//LineSegmentDetector detector = Imgproc.createLineSegmentDetector(Imgproc.LSD_REFINE_ADV, 0.6, 0.3, 2.6, 22.5, 0, 0.3,256);
//LineSegmentDetector detector = Imgproc.createLineSegmentDetector(Imgproc.LSD_REFINE_STD, 0.5, 0.4,2.0, 19.5, 0, 0.6, 32);
//Reference for final glyph detection
detector.detect(raw, linesM1);
ArrayList<Line> lines = new ArrayList<Line>();
for (int x = 0; x < linesM1.rows(); x++) {
double[] vec = linesM1.get(x, 0);
Point start = new Point(vec[0],vec[1]);
Point end = new Point(vec[2], vec[3]);
Line line = new Line(start, end);
line = new Line(new Point((int)line.x1*scale, (int) line.y1*scale), new Point((int)line.x2*scale, (int)line.y2*scale));
if(line.length() > minLength) lines.add(line);
}
raw.release();
linesM1.release();
return lines;
}
示例6: Mat2BufferedImage
import org.opencv.core.Mat; //导入方法依赖的package包/类
public static BufferedImage Mat2BufferedImage(Mat m) {
//Method converts a Mat to a Buffered Image
int type = BufferedImage.TYPE_BYTE_GRAY;
if ( m.channels() > 1 ) {
type = BufferedImage.TYPE_3BYTE_BGR;
}
int bufferSize = m.channels()*m.cols()*m.rows();
byte [] b = new byte[bufferSize];
m.get(0,0,b); // get all the pixels
BufferedImage image = new BufferedImage(m.cols(),m.rows(), type);
final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
System.arraycopy(b, 0, targetPixels, 0, b.length);
return image;
}
示例7: mat2image
import org.opencv.core.Mat; //导入方法依赖的package包/类
public BufferedImage mat2image(Mat mat){
if (mat.channels()!=3 && mat.channels()!=1) return null;
nByte = mat.channels();
allocateSpace(mat);
mat.get(0, 0, data);
image.getRaster().setDataElements(0, 0, mat.cols(),mat.rows(), data);
return image;
}
示例8: blkEstimateEachChannel
import org.opencv.core.Mat; //导入方法依赖的package包/类
public static double blkEstimateEachChannel(Mat blkIm, double airlight, double lambda, double fTrans) {
double Trans = 0.0;
double nTrans = Math.floor(1.0 / fTrans * 128);
double fMinCost = Double.MAX_VALUE;
int numberOfPixels = blkIm.rows() * blkIm.cols() * blkIm.channels();
int nCounter = 0;
while (nCounter < (int) (1 - fTrans) * 10) {
// initial dehazing process to calculate the loss information
Mat channel = blkIm.clone();
channel = preDehaze(channel, airlight, nTrans);
// find the pixels with over-255 value and below-0 value, and
// calculate the sum of information loss
double nSumOfLoss = 0.0;
for (int i = 0; i < channel.rows(); i++) {
for (int j = 0; j < channel.cols(); j++) {
if (channel.get(i, j)[0] > 255.0) nSumOfLoss += (channel.get(i, j)[0] - 255.0) * (channel.get(i, j)[0] - 255.0);
else if (channel.get(i, j)[0] < 0.0) nSumOfLoss += channel.get(i, j)[0] * channel.get(i, j)[0];
}
}
// calculate the value of sum of square out
double nSumOfSquareOuts = Core.sumElems(channel.mul(channel)).val[0];
// calculate the value of sum of out
double nSumOfOuts = Core.sumElems(channel).val[0];
// calculate the mean value of the block image
double fMean = nSumOfOuts / numberOfPixels;
// calculate the cost function
double fCost = lambda * nSumOfLoss / numberOfPixels - (nSumOfSquareOuts / numberOfPixels - fMean * fMean);
// find the minimum cost and the related transmission
if (nCounter == 0 || fMinCost > fCost) {
fMinCost = fCost;
Trans = fTrans;
}
fTrans = fTrans + 0.1;
nTrans = 1.0 / fTrans * 128;
nCounter = nCounter + 1;
}
return Trans;
}
示例9: toBufferedImage
import org.opencv.core.Mat; //导入方法依赖的package包/类
public BufferedImage toBufferedImage(Mat m) {
int type = BufferedImage.TYPE_BYTE_GRAY;
if (m.channels() > 1) type = BufferedImage.TYPE_3BYTE_BGR;
int bufferSize = m.channels() * m.cols() * m.rows();
byte[] b = new byte[bufferSize];
m.get(0, 0, b); // get all the pixels
BufferedImage image = new BufferedImage(m.cols(), m.rows(), type);
final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
System.arraycopy(b, 0, targetPixels, 0, b.length);
return image;
}
示例10: mat2BI
import org.opencv.core.Mat; //导入方法依赖的package包/类
public static BufferedImage mat2BI(Mat matrix) {
final int cols = matrix.cols();
final int rows = matrix.rows();
final int elemSize = (int) matrix.elemSize();
final byte[] data = new byte[cols * rows * elemSize];
int type;
matrix.get(0, 0, data);
switch (matrix.channels()) {
case 1:
type = BufferedImage.TYPE_BYTE_GRAY;
break;
case 3:
type = BufferedImage.TYPE_3BYTE_BGR;
// bgr to rgb
byte b;
for (int i = 0; i < data.length; i = i + 3) {
b = data[i];
data[i] = data[i + 2];
data[i + 2] = b;
}
break;
default:
return null;
}
final BufferedImage image2 = new BufferedImage(cols, rows, type);
image2.getRaster().setDataElements(0, 0, cols, rows, data);
return image2;
}
示例11: covertImage2Gray
import org.opencv.core.Mat; //导入方法依赖的package包/类
/**
* 将源图像转换为灰度图
* @param srcImg
* @return
*/
public static Mat covertImage2Gray(Mat srcImg) {
Mat gray = new Mat(); // 存储灰度图
if (srcImg.channels() == 3) {
Imgproc.cvtColor(srcImg, gray, Imgproc.COLOR_BGR2GRAY);
} else if (srcImg.channels() == 4) {
Imgproc.cvtColor(srcImg, gray, Imgproc.COLOR_BGRA2GRAY);
} else {
gray = srcImg;
}
return gray;
}
示例12: matToBufferedImage
import org.opencv.core.Mat; //导入方法依赖的package包/类
public static BufferedImage matToBufferedImage(Mat matrix) {
int cols = matrix.cols();
int rows = matrix.rows();
int elemSize = (int) matrix.elemSize();
byte[] data = new byte[cols * rows * elemSize];
int type;
matrix.get(0, 0, data);
switch (matrix.channels()) {
case 1:
type = BufferedImage.TYPE_BYTE_GRAY;
break;
case 3:
type = BufferedImage.TYPE_3BYTE_BGR;
// bgr to rgb
byte b;
for (int i = 0; i < data.length; i = i + 3) {
b = data[i];
data[i] = data[i + 2];
data[i + 2] = b;
}
break;
default:
return null;
}
BufferedImage bimg = new BufferedImage(cols, rows, type);
bimg.getRaster().setDataElements(0, 0, cols, rows, data);
return bimg;
}
示例13: enhancedMatToBufferedImage
import org.opencv.core.Mat; //导入方法依赖的package包/类
public Image enhancedMatToBufferedImage(Mat m){
int type = BufferedImage.TYPE_BYTE_GRAY;
if ( m.channels() > 1 ) {
type = BufferedImage.TYPE_3BYTE_BGR;
}
int bufferSize = m.channels()*m.cols()*m.rows();
byte [] b = new byte[bufferSize];
m.get(0,0,b); // get all the pixels
BufferedImage image = new BufferedImage(m.cols(),m.rows(), type);
final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
System.arraycopy(b, 0, targetPixels, 0, b.length);
return image;
}
示例14: unevenLightCompensate
import org.opencv.core.Mat; //导入方法依赖的package包/类
/**
* 其主要思路为:
1、求取源图I的平均灰度,并记录rows和cols;
2、按照一定大小,分为N*M个方块,求出每块的平均值,得到子块的亮度矩阵D;
3、用矩阵D的每个元素减去源图的平均灰度,得到子块的亮度差值矩阵E;
4、用双立方差值法,将矩阵E差值成与源图一样大小的亮度分布矩阵R;
5、得到矫正后的图像result=I-R;
* @Title: unevenLightCompensate
* @Description: 光线补偿
* @param image
* @param blockSize
* void
* @throws
*/
public static void unevenLightCompensate(Mat image, int blockSize) {
if(image.channels() == 3) {
Imgproc.cvtColor(image, image, 7);
}
double average = Core.mean(image).val[0];
Scalar scalar = new Scalar(average);
int rowsNew = (int) Math.ceil((double)image.rows() / (double)blockSize);
int colsNew = (int) Math.ceil((double)image.cols() / (double)blockSize);
Mat blockImage = new Mat();
blockImage = Mat.zeros(rowsNew, colsNew, CvType.CV_32FC1);
for(int i = 0; i < rowsNew; i ++) {
for(int j = 0; j < colsNew; j ++) {
int rowmin = i * blockSize;
int rowmax = (i + 1) * blockSize;
if(rowmax > image.rows()) rowmax = image.rows();
int colmin = j * blockSize;
int colmax = (j +1) * blockSize;
if(colmax > image.cols()) colmax = image.cols();
Range rangeRow = new Range(rowmin, rowmax);
Range rangeCol = new Range(colmin, colmax);
Mat imageROI = new Mat(image, rangeRow, rangeCol);
double temaver = Core.mean(imageROI).val[0];
blockImage.put(i, j, temaver);
}
}
Core.subtract(blockImage, scalar, blockImage);
Mat blockImage2 = new Mat();
int INTER_CUBIC = 2;
Imgproc.resize(blockImage, blockImage2, image.size(), 0, 0, INTER_CUBIC);
Mat image2 = new Mat();
image.convertTo(image2, CvType.CV_32FC1);
Mat dst = new Mat();
Core.subtract(image2, blockImage2, dst);
dst.convertTo(image, CvType.CV_8UC1);
}
示例15: norm_0_255
import org.opencv.core.Mat; //导入方法依赖的package包/类
/**
* 图像归一化
* @Title: norm_0_255
* @Description: TODO(这里用一句话描述这个方法的作用)
* @param src
* @return
* Mat
* @throws
*/
public static Mat norm_0_255(Mat src) {
// 创建和返回一个归一化后的图像矩阵
Mat dst = new Mat();
switch(src.channels()) {
case 1: Core.normalize(src, dst, 0, 255, Core.NORM_MINMAX, CvType.CV_8UC1); break;
case 3: Core.normalize(src, dst, 0, 255, Core.NORM_MINMAX, CvType.CV_8UC3); break;
default: src.copyTo(dst);break;
}
return dst;
}