javacv提取图像指纹直方图

JavaCV可以用来提取图像的指纹，其中一种方法是使用直方图。下面是提取图像指纹的基本步骤： 1. 加载图像：使用JavaCV中的cvLoadImage()函数加载图像。 2. 转换图像：将图像转换为灰度图像，使用JavaCV中的cvCvtColor()函数。 3. 计算直方图：使用JavaCV中的cvCalcHist()函数计算灰度直方图。 4. 归一化直方图：使用JavaCV中的cvNormalizeHist()函数将直方图归一化。 5. 提取特征向量：使用JavaCV中的cvGetHistValue()函数提取直方图中的特征向量。 6. 返回特征向量：将特征向量作为图像的指纹返回。需要注意的是，直方图方法只是图像指纹提取的一种方法，还有其他方法可以使用，如局部二值模式（LBP）等。

javacv提取图像指纹直方图实现代码

下面是使用JavaCV提取图像指纹，使用直方图方法的示例代码： ```java import static org.bytedeco.opencv.global.opencv_core.CV_32FC1; import static org.bytedeco.opencv.global.opencv_core.CV_WHOLE_SEQ; import static org.bytedeco.opencv.global.opencv_core.addWeighted; import static org.bytedeco.opencv.global.opencv_core.cvConvertScale; import static org.bytedeco.opencv.global.opencv_core.cvCreateHist; import static org.bytedeco.opencv.global.opencv_core.cvCreateImage; import static org.bytedeco.opencv.global.opencv_core.cvGetSize; import static org.bytedeco.opencv.global.opencv_core.cvMinMaxLoc; import static org.bytedeco.opencv.global.opencv_core.cvNormalize; import static org.bytedeco.opencv.global.opencv_imgcodecs.CV_LOAD_IMAGE_GRAYSCALE; import static org.bytedeco.opencv.global.opencv_imgcodecs.imread; import static org.bytedeco.opencv.global.opencv_imgproc.CV_COMP_CHISQR; import static org.bytedeco.opencv.global.opencv_imgproc.CV_HIST_ARRAY; import static org.bytedeco.opencv.global.opencv_imgproc.CV_INTER_LINEAR; import static org.bytedeco.opencv.global.opencv_imgproc.cvCalcHist; import static org.bytedeco.opencv.global.opencv_imgproc.cvCompareHist; import static org.bytedeco.opencv.global.opencv_imgproc.cvEqualizeHist; import static org.bytedeco.opencv.global.opencv_imgproc.cvGetMinMaxHistValue; import static org.bytedeco.opencv.global.opencv_imgproc.cvResize; import static org.bytedeco.opencv.global.opencv_imgproc.cvSetHistBinRanges; import static org.bytedeco.opencv.global.opencv_imgproc.cvSetImageROI; import static org.bytedeco.opencv.global.opencv_imgproc.cvSplit; import org.bytedeco.javacpp.FloatPointer; import org.bytedeco.javacpp.Pointer; import org.bytedeco.opencv.opencv_core.CvArr; import org.bytedeco.opencv.opencv_core.CvHistogram; import org.bytedeco.opencv.opencv_core.CvMat; import org.bytedeco.opencv.opencv_core.CvSize; import org.bytedeco.opencv.opencv_core.IplImage; public class ImageFingerprint { // 提取图像指纹 public static float[] extract(IplImage image) { // 将图像转换为灰度图像 IplImage grayImage = cvCreateImage(cvGetSize(image), image.depth(), 1); cvCvtColor(image, grayImage, CV_INTER_LINEAR); // 直方图均衡化 cvEqualizeHist(grayImage, grayImage); // 分离图像通道 IplImage imageR = cvCreateImage(cvGetSize(image), image.depth(), 1); IplImage imageG = cvCreateImage(cvGetSize(image), image.depth(), 1); IplImage imageB = cvCreateImage(cvGetSize(image), image.depth(), 1); cvSplit(image, imageR, imageG, imageB, null); // 计算RGB三个通道的直方图 int histSize = 256; CvHistogram histR = cvCreateHist(1, new int[] { histSize }, CV_HIST_ARRAY); CvHistogram histG = cvCreateHist(1, new int[] { histSize }, CV_HIST_ARRAY); CvHistogram histB = cvCreateHist(1, new int[] { histSize }, CV_HIST_ARRAY); cvCalcHist(new IplImage[] { imageR }, histR, 0, null); cvCalcHist(new IplImage[] { imageG }, histG, 0, null); cvCalcHist(new IplImage[] { imageB }, histB, 0, null); // 归一化直方图 float minVal[] = new float[1]; float maxVal[] = new float[1]; FloatPointer minValPointer = new FloatPointer(minVal); FloatPointer maxValPointer = new FloatPointer(maxVal); cvGetMinMaxHistValue(histR, minValPointer, maxValPointer, null, null); cvNormalize(histR, histR, 1, 0, CV_COMP_CHISQR); cvGetMinMaxHistValue(histG, minValPointer, maxValPointer, null, null); cvNormalize(histG, histG, 1, 0, CV_COMP_CHISQR); cvGetMinMaxHistValue(histB, minValPointer, maxValPointer, null, null); cvNormalize(histB, histB, 1, 0, CV_COMP_CHISQR); // 合并三个通道的直方图 int dim = 3; CvSize size = cvGetSize(image); CvMat data = CvMat.create(size.height(), size.width(), CV_32FC1); CvHistogram hist = cvCreateHist(dim, new int[] { histSize, histSize, histSize }, CV_HIST_ARRAY); Pointer pointer = hist.bins(); int ptrOffset = 0; for (int i = 0; i < histSize; i++) { for (int j = 0; j < histSize; j++) { for (int k = 0; k < histSize; k++) { ((FloatPointer) pointer).put(ptrOffset++, (float) (cvGetHistValue_3D(histR, i, j, k) * 0.33 + cvGetHistValue_3D(histG, i, j, k) * 0.33 + cvGetHistValue_3D(histB, i, j, k) * 0.33)); } } } cvSetHistBinRanges(hist, new FloatPointer(new float[] { 0, 256, 0, 256, 0, 256 })); cvCalcHist(data, hist, 0, null); // 归一化直方图 cvNormalize(hist, hist, 1, 0, CV_COMP_CHISQR); // 提取特征向量 float[] featureVector = new float[histSize * histSize * histSize]; FloatPointer featureVectorPointer = new FloatPointer(featureVector); cvGetHistValue_3D(hist, featureVectorPointer); // 释放内存 grayImage.release(); imageR.release(); imageG.release(); imageB.release(); histR.release(); histG.release(); histB.release(); hist.release(); data.release(); return featureVector; } // 计算两张图像的相似度 public static float similarity(IplImage image1, IplImage image2) { // 提取图像指纹 float[] featureVector1 = extract(image1); float[] featureVector2 = extract(image2); // 比较相似度 CvHistogram hist1 = cvCreateHist(1, new int[] { featureVector1.length }, CV_HIST_ARRAY); CvHistogram hist2 = cvCreateHist(1, new int[] { featureVector2.length }, CV_HIST_ARRAY); FloatPointer featureVector1Pointer = new FloatPointer(featureVector1); FloatPointer featureVector2Pointer = new FloatPointer(featureVector2); cvSetHistBinRanges(hist1, new FloatPointer(new float[] { 0, 1 })); cvSetHistBinRanges(hist2, new FloatPointer(new float[] { 0, 1 })); cvCalcHist(new Pointer[] { featureVector1Pointer }, hist1, 0, null); cvCalcHist(new Pointer[] { featureVector2Pointer }, hist2, 0, null); cvNormalize(hist1, hist1, 1, 0, CV_COMP_CHISQR); cvNormalize(hist2, hist2, 1, 0, CV_COMP_CHISQR); float similarity = (float) cvCompareHist(hist1, hist2, CV_COMP_CHISQR); // 释放内存 hist1.release(); hist2.release(); return similarity; } public static void main(String[] args) { IplImage image1 = imread("image1.jpg", CV_LOAD_IMAGE_GRAYSCALE); IplImage image2 = imread("image2.jpg", CV_LOAD_IMAGE_GRAYSCALE); float similarity = similarity(image1, image2); System.out.println("相似度：" + similarity); } } ``` 这个示例代码中的extract()方法用于提取图像指纹，similarity()方法用于计算两张图像的相似度。

java直方图文件相似度比较指纹提取存储不用javacv

要实现指纹提取并进行存储，可以使用Java自带的图像处理库ImageIO来读取图像文件，并使用Java自带的数据分析库Apache Commons Math来计算直方图和指纹，并将指纹存储在文件中。以下是一个示例代码： ```java import java.awt.image.BufferedImage; import java.io.File; import java.io.IOException; import java.io.PrintWriter; import javax.imageio.ImageIO; import org.apache.commons.math3.stat.descriptive.moment.Mean; import org.apache.commons.math3.stat.descriptive.summary.Sum; public class FingerprintExtract { public static void main(String[] args) throws IOException { // 读取图像文件 BufferedImage img = ImageIO.read(new File("image.jpg")); // 计算直方图 int[] hist = calcHistogram(img); // 计算指纹 int[] fingerprint = calcFingerprint(hist); // 存储指纹 saveFingerprint(fingerprint, "fingerprint.txt"); } private static int[] calcHistogram(BufferedImage img) { int[] hist = new int[256]; for (int y = 0; y < img.getHeight(); y++) { for (int x = 0; x < img.getWidth(); x++) { int pixel = img.getRGB(x, y); int r = (pixel >> 16) & 0xff; int g = (pixel >> 8) & 0xff; int b = pixel & 0xff; int gray = (r + g + b) / 3; hist[gray]++; } } return hist; } private static int[] calcFingerprint(int[] hist) { int[] fingerprint = new int[64]; double[] histNorm = normalize(hist); for (int i = 0; i < fingerprint.length; i++) { int start = i * 4; double max = Double.NEGATIVE_INFINITY; int maxIndex = -1; for (int j = start; j < start + 4; j++) { if (histNorm[j] > max) { max = histNorm[j]; maxIndex = j; } } fingerprint[i] = maxIndex; } return fingerprint; } private static double[] normalize(int[] hist) { double[] histNorm = new double[hist.length]; int numPixels = hist.length; Mean mean = new Mean(); double histMean = mean.evaluate(hist); for (int i = 0; i < hist.length; i++) { histNorm[i] = hist[i] / (double) numPixels - histMean; } return histNorm; } private static void saveFingerprint(int[] fingerprint, String filename) throws IOException { PrintWriter writer = new PrintWriter(filename); for (int i = 0; i < fingerprint.length; i++) { writer.print(fingerprint[i]); if (i < fingerprint.length - 1) { writer.print(","); } } writer.close(); } } ``` 该代码读取一个图像文件，计算它的直方图和指纹，并将指纹存储在文件中。其中，`calcHistogram`方法用于计算直方图，`calcFingerprint`方法用于计算指纹，`normalize`方法用于将直方图归一化，以便计算指纹。`saveFingerprint`方法用于将指纹存储在文件中。指纹用逗号分隔，并存储在一个文本文件中。

javacv提取图像指纹 直方图