用java大图中寻找小图位置

先说下思路：
因为是大图中寻找小图，所以小图必须是大图的一部分，那么对应的他们具有相同的像素点，所以为了一遍就可以搜出来，从小图中抽取若干个像素点（本次DEMO只选区了5个），从大图中找到像素与第一个点满足的，然后直接进行对比第二个点。。。到N个。都符合，说明就找到了，然后为了进行验证，对图片进行了相似度运算。
看下结果，开始还想做优化，但是看了下用的时间82毫秒，最后加上验证才1秒，貌似挺快的。就算了

话不多说，上代码：
SearchPixelPosition核心类

public class SearchPixelPosition {//需要找的图片宽度private int targetWidth;//需要找的图片高度private int targetHeight;/*** 对大图进行所有像素点寻找，知道满足5个点，返回之后到的坐标值* @param path* @param tagert* @return*/public ResultBean getAllRGB(String path, String tagert) {// int[] rgb = new int[3];File file = new File(path);BufferedImage bi = null;try {bi = ImageIO.read(file);} catch (Exception e) {e.printStackTrace();}int width = bi.getWidth();int height = bi.getHeight();int minx = bi.getMinX();int miny = bi.getMinY();System.out.println("width=" + width + ",height=" + height + ".");System.out.println("minx=" + minx + ",miniy=" + miny + ".");ArrayList<PositionBean> setTarget5RGB = setTarget5RGB(tagert);// System.out.println(setTarget5RGB.get(0).x+" "+setTarget5RGB.get(0).y+"// "+setTarget5RGB.get(0).pxrgb);// System.out.println(setTarget5RGB.get(1).x+" "+setTarget5RGB.get(1).y+"// "+setTarget5RGB.get(1).pxrgb);// System.out.println(setTarget5RGB.get(2).x+" "+setTarget5RGB.get(2).y+"// "+setTarget5RGB.get(2).pxrgb);// System.out.println(setTarget5RGB.get(3).x+" "+setTarget5RGB.get(3).y+"// "+setTarget5RGB.get(3).pxrgb);// System.out.println(setTarget5RGB.get(4).x+" "+setTarget5RGB.get(4).y+"// "+setTarget5RGB.get(4).pxrgb);long start = System.currentTimeMillis();for (int i = minx; i < width; i++) {for (int j = miny; j < height; j++) {int pixel = bi.getRGB(i, j);// rgb[0] = (pixel & 0xff0000) >> 16;// rgb[1] = (pixel & 0xff00) >> 8;// rgb[2] = (pixel & 0xff);//依次对比5个点。if (setTarget5RGB != null) {PositionBean p1 = setTarget5RGB.get(0);if (pixel == p1.pxrgb) {int other = 0;PositionBean p2 = setTarget5RGB.get(1);int pixel2 = bi.getRGB(i + (p2.x - p1.x), j);if (pixel2 == p2.pxrgb) {other++;PositionBean p3 = setTarget5RGB.get(2);int pixel3 = bi.getRGB(i + (p3.x - p1.x), j + (p3.y - p1.y));if (pixel3 == p3.pxrgb) {other++;PositionBean p4 = setTarget5RGB.get(3);int pixel4 = bi.getRGB(i, j + (p4.y - p1.y));if (pixel4 == p4.pxrgb) {other++;PositionBean p5 = setTarget5RGB.get(4);int pixel5 = bi.getRGB(i + (p5.x - p1.x), j + (p5.y - p1.y));if (pixel5 == p5.pxrgb) {other++;}}}}if (other == 4) {long end = System.currentTimeMillis();System.out.println("总耗时：" + (end - start));System.out.println("找到了===》》》》横坐标" + i + "纵坐标" + j);ResultBean resultBean = new ResultBean();resultBean.width = targetWidth;resultBean.height = targetHeight;resultBean.x = i - p1.x;resultBean.y = j - p1.y;return resultBean;}}}}}long end = System.currentTimeMillis();System.out.println("搜索坐标耗时：" + (end - start));return null;}/*** 分别取小图的四个角落和中心点的像素，作为搜图依据* * @param src* @return* @throws Exception*/private ArrayList<PositionBean> get5PointForTager(String src) throws Exception {ArrayList<PositionBean> searchXYList = new ArrayList<>();File file = new File(src);BufferedImage bi = null;try {bi = ImageIO.read(file);} catch (Exception e) {e.printStackTrace();}int width = bi.getWidth();int height = bi.getHeight();targetWidth = width;targetHeight = height;if (width >= 10 && height >= 10) {int px1 = (int) (width * 0.25);int py1 = (int) (height * 0.25);int px2 = (int) (width * 0.75);int py2 = (int) (height * 0.25);int px3 = (int) (width * 0.5);int py3 = (int) (height * 0.5);int px4 = (int) (width * 0.25);int py4 = (int) (height * 0.75);int px5 = (int) (width * 0.75);int py5 = (int) (height * 0.75);searchXYList.add(new PositionBean(px1, py1));searchXYList.add(new PositionBean(px2, py2));searchXYList.add(new PositionBean(px3, py3));searchXYList.add(new PositionBean(px4, py4));searchXYList.add(new PositionBean(px5, py5));} else {throw new Exception("不支持10px以内的搜索");}return searchXYList;}/*** 设置5个点的像素值 和对应的坐标* @param src* @return*/private ArrayList<PositionBean> setTarget5RGB(String src) {File file = new File(src);BufferedImage bi = null;try {bi = ImageIO.read(file);} catch (Exception e) {e.printStackTrace();}try {ArrayList<PositionBean> get5PointForTager = get5PointForTager(src);for (int i = 0; i < get5PointForTager.size(); i++) {PositionBean positionBean = get5PointForTager.get(i);positionBean.pxrgb = bi.getRGB(positionBean.x, positionBean.y);}return get5PointForTager;} catch (Exception e) {e.printStackTrace();}return null;}}

看下调用

public class MainInTest {public static void main(String[] args) {String src = "/Users/mac_py/Desktop/cocl.png";String dest = "/Users/mac_py/Desktop/cocl-n-y.png";String target = "/Users/mac_py/Desktop/cocl-n.png";long start = System.currentTimeMillis();try {// ScalImage.zoomImage(src, dest,320,180);// SnippingImage.saveImageWithSize(568,850,240,106,src,"/Users/mac_py/Desktop/cocl-n.png");// SnippingImage.saveImageWithSize(71,106,30,13,src,"/Users/mac_py/Desktop/cocl-n-s-y.png");// ScalImage.zoomImage(src, dest,30,13);// SearchPixelPosition.getAllRGB(src);SearchPixelPosition searchPixelPosition = new SearchPixelPosition();ResultBean result = searchPixelPosition.getAllRGB(src, target);if (result != null) {SnippingImage.saveImageWithSize(result.x, result.y, result.width, result.height, src,"/Users/mac_py/Desktop/cocl-ai.png");ImagePHash p = new ImagePHash();System.out.println("进行相似度计算");String image1 = p.getHash(new FileInputStream(new File(target)));String image2 = p.getHash(new FileInputStream(new File("/Users/mac_py/Desktop/cocl-ai.png")));System.out.println("相似度为" + (p.distance(image1, image2)==0?"相似度100%":"不相似"));}} catch (Exception e) {e.printStackTrace();}long end = System.currentTimeMillis();System.out.println("总共耗时：" + (end - start));}
}

相似度计算核心类 ImagePHash

/* * 汉明距离越大表明图片差异越大，如果不相同的数据位不超过5，就说明两张图片很相似；如果大于10，就说明这是两张不同的图片。*/
public class ImagePHash {private int size = 32;private int smallerSize = 8;public ImagePHash() {initCoefficients();}public ImagePHash(int size, int smallerSize) {this.size = size;this.smallerSize = smallerSize;initCoefficients();}public int distance(String s1, String s2) {int counter = 0;for (int k = 0; k < s1.length(); k++) {if (s1.charAt(k) != s2.charAt(k)) {counter++;}}return counter;}/*** 返回图片二进制流的字符串* @param is 输入流* @return* @throws Exception*/public String getHash(InputStream is) throws Exception {BufferedImage img = ImageIO.read(is);/** 简化图片尺寸*/img = resize(img, size, size);/**  减少图片颜色*/img = grayscale(img);double[][] vals = new double[size][size];for (int x = 0; x < img.getWidth(); x++) {for (int y = 0; y < img.getHeight(); y++) {vals[x][y] = getBlue(img, x, y);}}/** 计算DTC 采用32*32尺寸*/long start = System.currentTimeMillis();double[][] dctVals = applyDCT(vals);System.out.println("DCT: " + (System.currentTimeMillis() - start));/** 计算平均值DTC*/double total = 0;for (int x = 0; x < smallerSize; x++) {for (int y = 0; y < smallerSize; y++) {total += dctVals[x][y];}}total -= dctVals[0][0];double avg = total / (double) ((smallerSize * smallerSize) - 1);/** 计算hash值*/String hash = "";for (int x = 0; x < smallerSize; x++) {for (int y = 0; y < smallerSize; y++) {if (x != 0 && y != 0) {hash += (dctVals[x][y] > avg ? "1" : "0");}}}return hash;}private BufferedImage resize(BufferedImage image, int width, int height) {BufferedImage resizedImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);Graphics2D g = resizedImage.createGraphics();g.drawImage(image, 0, 0, width, height, null);g.dispose();return resizedImage;}private ColorConvertOp colorConvert = new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null);private BufferedImage grayscale(BufferedImage img) {colorConvert.filter(img, img);return img;}private static int getBlue(BufferedImage img, int x, int y) {return (img.getRGB(x, y)) & 0xff;}private double[] c;private void initCoefficients() {c = new double[size];for (int i = 1; i < size; i++) {c[i] = 1;}c[0] = 1 / Math.sqrt(2.0);}private double[][] applyDCT(double[][] f) {int N = size;double[][] F = new double[N][N];for (int u = 0; u < N; u++) {for (int v = 0; v < N; v++) {double sum = 0.0;for (int i = 0; i < N; i++) {for (int j = 0; j < N; j++) {sum += Math.cos(((2 * i + 1) / (2.0 * N)) * u * Math.PI)* Math.cos(((2 * j + 1) / (2.0 * N)) * v * Math.PI) * (f[i][j]);}}sum *= ((c[u] * c[v]) / 4.0);F[u][v] = sum;}}return F;}public static void main(String[] args) {ImagePHash p = new ImagePHash();String image1;String image2;try {image1 = p.getHash(new FileInputStream(new File("/Users/mac_py/Desktop/cocl-n-sc.png")));image2 = p.getHash(new FileInputStream(new File("/Users/mac_py/Desktop/cocl-n-s-y.png")));System.out.println("得分为 " + p.distance(image1, image2));} catch (FileNotFoundException e) {e.printStackTrace();} catch (Exception e) {e.printStackTrace();}}
}

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