Java基于openCV实现的身份证图像识别系统源代码，识别率达到90%以上

package com.nbsl.cv.utils; import java.util.Vector; import org.opencv.core.Core; import org.opencv.core.CvType; import org.opencv.core.Mat; import org.opencv.core.Rect; import org.opencv.core.Size; import org.opencv.imgproc.Imgproc; /** * 核心 * @author eguid * */ public class CoreFunc { public enum Direction { UNKNOWN, VERTICAL, HORIZONTAL } /** * Get the Sobel Mat of input image! * * @param image * The input image. * @return The Sobel Mat image of input image. */ public static Mat Sobel(Mat image) { if (image.empty()) { System.out.println("Please check the input image!"); return image; } Mat gray = image.clone(); if (3 == gray.channels()) { Imgproc.cvtColor(gray, gray, Imgproc.COLOR_BGR2GRAY); } Mat grad = new Mat(); Mat grad_x = new Mat(); Mat grad_y = new Mat(); Mat abs_grad_x = new Mat(); Mat abs_grad_y = new Mat(); final int scharr_scale = 1; final int scharr_delta = 0; final int scharr_ddpeth = CvType.CV_16S; Imgproc.Sobel(gray, grad_x, scharr_ddpeth, 1, 0, 3, scharr_scale, scharr_delta, Core.BORDER_DEFAULT); Imgproc.Sobel(gray, grad_y, scharr_ddpeth, 0, 1, 3, scharr_scale, scharr_delta, Core.BORDER_DEFAULT); Core.convertScaleAbs(grad_x, abs_grad_x); Core.convertScaleAbs(grad_y, abs_grad_y); Core.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad); return grad; } public static double otsu(Mat image) { int threshold=0; double maxVariance = 0; double w0=0,w1=0;//前景与背景像素点所占比例 double u0=0,u1=0;//前景与背景像素值平均灰度 double[] histogram=new double[256]; double Num=image.cols()*image.rows(); //统计256个bin，每个bin像素的个数 for(int i=0;i<image.rows();i++){ for(int j=0;j<image.cols();j++){ double record = image.get(i, j)[0]; histogram[(int)record]++; //cout<<"Histogram[data[i*image.step+j]]++:;"<<histogram[int(*p++)]++<<endl; } } //前景像素统计 for(int i=0;i<255;i++){ w0=0; w1=0; u0=0; u1=0; for(int j=0;j<=i;j++){ w0=w0+histogram[j];//以i为阈值，统计前景像素个数 u0=u0+j*histogram[j];//以i为阈值，统计前景像素灰度总和 } w0=w0/Num;u0=u0/w0; //背景像素统计 for(int j=i+1;j<=255;j++){ w1=w1+histogram[j];//以i为阈值，统计前景像素个数 u1=u1+j*histogram[j];//以i为阈值，统计前景像素灰度总和 } w1=w1/Num;u1=u1/w1; double variance=w0*w1*(u1-u0)*(u1-u0); //当前类间方差计算 if(variance > maxVariance) { maxVariance = variance; threshold = i; } } //cout<<"threshold:"<<threshold<<endl; return threshold; } public static float[] projectedHistogram(final Mat img, Direction direction) { int sz = 0; switch (direction) { case HORIZONTAL: sz = img.rows(); break; case VERTICAL: sz = img.cols(); break; default: break; } // 统计这一行或一列中，非零元素的个数，并保存到nonZeroMat中 float[] nonZeroMat = new float[sz]; Core.extractChannel(img, img, 0); for (int j = 0; j < sz; j++) { Mat data = (direction == Direction.HORIZONTAL) ? img.row(j) : img.col(j); int count = Core.countNonZero(data); nonZeroMat[j] = count; } // Normalize histogram float max = 0; for (int j = 0; j < nonZeroMat.length; ++j) { max = Math.max(max, nonZeroMat[j]); } if (max > 0) { for (int j = 0; j < nonZeroMat.length; ++j) { nonZeroMat[j] /= max; } } return nonZeroMat; } /** * 将Rect按位置从左到右进行排序 * * @param vecRect * @param out * @return */ public static Vector<Rect> SortRect(final Vector<Rect> vecRect) { Vector<Rect> out = new Vector<Rect>(); Vector<Integer> orderIndex = new Vector<Integer>(); Vector<Integer> xpositions = new Vector<Integer>(); for (int i = 0; i < vecRect.size(); ++i) { orderIndex.add(i); xpositions.add(vecRect.get(i).x); } float min = xpositions.get(0); int minIdx; for (int i = 0; i < xpositions.size(); ++i) { min = xpositions.get(i); minIdx = i; for (int j = i; j < xpositions.size(); ++j) { if (xpositions.get(j) < min) { min = xpositions.get(j); minIdx = j; } } int aux_i = orderIndex.get(i); int aux_min = orderIndex.get(minIdx); orderIndex.remove(i); orderIndex.insertElementAt(aux_min, i); orderIndex.remove(minIdx); orderIndex.insertElementAt(aux_i, minIdx); float aux_xi = xpositions.get(i); float aux_xmin = xpositions.get(minIdx); xpositions.remove(i); xpositions.insertElementAt((int) aux_xmin, i); xpositions.remove(minIdx); xpositions.insertElementAt((int) aux_xi, minIdx); } for (int i = 0; i < orderIndex.size(); i++) out.add(vecRect.get(orderIndex.get(i))); return out; } /** * Assign values to feature * <p> * 样本特征为水平、垂直直方图和低分辨率图像所组成的矢量 * * @param in * @param sizeData - 低分辨率图像size = sizeData*sizeData, 可以为0 * @return */ public static Mat features(final Mat in, final int sizeData) { float[] vhist = projectedHistogram(in, Direction.VERTICAL); float[] hhist = projectedHistogram(in, Direction.HORIZONTAL); Mat lowData = new Mat(); if (sizeData > 0) { Imgproc.resize(in, lowData, new Size(sizeData, sizeData)); } int numCols = vhist.length + hhist.length + lowData.cols() * lowData.rows(); Mat out = Mat.zeros(1, numCols, CvType.CV_32F); //FloatIndexer idx = out.createIndexer(); int j = 0; for (int i = 0; i < vhist.length; ++i, ++j) { out.put(0, j, vhist[i]); // idx.put(0, j, vhist[i]); } for (int i = 0; i < hhist.length; ++i, ++j) { out.put(0, j, hhist[i]); // idx.put(0, j, hhist[i]); } for (int x = 0; x < lowData.cols(); x++) { for (int y = 0; y < lowData.rows(); y++, ++j) { // float val = lowData.ptr(x, y).get() & 0xFF; float val = (float)(lowData.get(x, y)[0]); // idx.put(0, j, val); out.put(0, j, val); } } return out; } }