java_opencv/src/main/java/com/acts/opencv/base/CardPlusController.java

package com.acts.opencv.base;

import java.util.ArrayList;
import java.util.Date;
import java.util.List;
import java.util.TreeMap;

import javax.servlet.http.HttpServletResponse;

import org.apache.commons.lang3.StringUtils;
import org.opencv.core.Core;
import org.opencv.core.Core.MinMaxLocResult;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.Point;
import org.opencv.core.Range;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.highgui.Highgui;
import org.opencv.imgproc.Imgproc;
import org.opencv.photo.Photo;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.RequestMapping;

import com.acts.opencv.common.utils.Constants;
import com.acts.opencv.common.utils.OpenCVUtil;
import com.acts.opencv.common.web.BaseController;



@Controller
@RequestMapping(value = "cardPlus")
public class CardPlusController extends BaseController {
	private static final Logger logger = LoggerFactory.getLogger(CardPlusController.class);

	/**
	 * 答题卡识别优化
	 * 创建者 Songer
	 * 创建时间	2018年3月23日
	 */
	@RequestMapping(value = "answerSheet")
	public void answerSheet(HttpServletResponse response, String imagefile, Integer binary_thresh,
			String blue_red_thresh) {
		System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
		logger.info("\n 完整答题卡识别");

		String sourcePath = Constants.PATH + imagefile;
		logger.info("url==============" + sourcePath);
		Mat sourceMat = Highgui.imread(sourcePath, Highgui.CV_LOAD_IMAGE_COLOR);
		long t1 = new Date().getTime();
		String destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk0.png";
		Highgui.imwrite(destPath, sourceMat);
		logger.info("原答题卡图片======" + destPath);
		// 初始图片灰度图
		Mat sourceMat1 = Highgui.imread(sourcePath, Highgui.CV_LOAD_IMAGE_GRAYSCALE);
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk1.png";
		Highgui.imwrite(destPath, sourceMat1);
		logger.info("生成灰度图======" + destPath);
		// 先膨胀 后腐蚀算法，开运算消除细小杂点
		Mat element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(2 * 1 + 1, 2 * 1 + 1));
		Imgproc.morphologyEx(sourceMat1, sourceMat1, Imgproc.MORPH_OPEN, element);
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk2.png";
		Highgui.imwrite(destPath, sourceMat1);
		logger.info("生成膨胀腐蚀后的图======" + destPath);

		// 切割右侧和底部标记位图片
		Mat rightMark = new Mat(sourceMat1, new Rect(sourceMat1.cols() - 100, 0, 100, sourceMat1.rows()));

		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk3.png";
		Highgui.imwrite(destPath, rightMark);
		logger.info("截取右侧定位点图======" + destPath);
		// 平滑处理消除噪点毛刺等等
		Imgproc.GaussianBlur(rightMark, rightMark, new Size(3, 3), 0);
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk4.png";
		Highgui.imwrite(destPath, rightMark);
		logger.info("平滑处理后的右侧定位点图======" + destPath);

		// 根据右侧定位获取水平投影，并获取纵向坐标
		Mat matright = horizontalProjection(rightMark);
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk5.png";
		Highgui.imwrite(destPath, matright);
		logger.info("右侧水平投影图======" + destPath);
		// 获取y坐标点，返回的是横向条状图集合
		List<Rect> listy = getBlockRect(matright, 1, 0);

		Mat footMark = new Mat(sourceMat1, new Rect(0, sourceMat1.rows() - 150, sourceMat1.cols(), 50));
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk6.png";
		Highgui.imwrite(destPath, footMark);
		logger.info("截取底部定位点图======" + destPath);

		Imgproc.GaussianBlur(footMark, footMark, new Size(3, 3), 0);
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk7.png";
		Highgui.imwrite(destPath, footMark);
		logger.info("平滑处理后的底部定位点图======" + destPath);

		// 根据底部定位获取垂直投影，并获取横向坐标
		Mat matbootom = verticalProjection(footMark);

		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk8.png";
		Highgui.imwrite(destPath, matbootom);
		logger.info("底部垂直投影图======" + destPath);
		// 获取x坐标点，返回的是竖向的柱状图集合
		List<Rect> listx = getBlockRect(matbootom, 0, 0);



		// 高阶处理：增加HSV颜色查找，查找红色像素点
		Mat matRed = findColorbyHSV(sourceMat, 156, 180);
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk9.png";
		Highgui.imwrite(destPath, matRed);
		logger.info("HSV找出红色像素点======" + destPath);

		Mat dstNoRed = new Mat(sourceMat1.rows(), sourceMat1.cols(), sourceMat1.type());
		dstNoRed = OpenCVUtil.dilation(sourceMat1);
		// Imgproc.threshold(sourceMat1, dstNoRed, 190, 255, Imgproc.THRESH_BINARY);
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk10.png";
		Highgui.imwrite(destPath, dstNoRed);
		logger.info("原灰度图的图片======" + destPath);

		Photo.inpaint(dstNoRed, matRed, dstNoRed, 1, Photo.INPAINT_NS);
		// findBlackColorbyHSV(sourceMat);
		// for (int i = 0;i<dstNoRed.rows();i++) {
		// for (int j = 0; j < dstNoRed.cols(); j++) {
		// if(matRed.get(i, j)[0]==255){//代表识别出的红色区域
		// dstNoRed.put(i,j,255);
		// }
		// }
		// }

		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk11.png";
		Highgui.imwrite(destPath, dstNoRed);
		logger.info("去除红颜色后的图片======" + destPath);

		Mat grayHistogram1 = getGrayHistogram(dstNoRed);
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk12.png";
		Highgui.imwrite(destPath, grayHistogram1);
		logger.info("灰度直方图图片1======" + destPath);

		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk13.png";
		Mat answerMat = dstNoRed.submat(new Rect(41, 895, 278, 133));
		Mat grayHistogram2 = getGrayHistogram(answerMat);
		Highgui.imwrite(destPath, grayHistogram2);
		logger.info("灰度直方图图片2======" + destPath);

		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk14.png";
		Imgproc.threshold(dstNoRed, dstNoRed, binary_thresh, 255, Imgproc.THRESH_BINARY_INV);
		Highgui.imwrite(destPath, dstNoRed);
		logger.info("去除红色基础上进行二值化======" + destPath);
		String redvalue = StringUtils.split(blue_red_thresh, ",")[0];
		String bluevalue = StringUtils.split(blue_red_thresh, ",")[1];
		System.out.println(bluevalue + "			" + redvalue);
		TreeMap<Integer, String> resultMap = new TreeMap<Integer, String>();
		StringBuffer resultValue = new StringBuffer();
		for (int no = 0; no < listx.size(); no++) {
			Rect rectx = listx.get(no);
			for (int an = 0; an < listy.size(); an++) {
				Rect recty = listy.get(an);
				Mat selectdst = new Mat(dstNoRed, new Range(recty.y, recty.y + recty.height), new Range(rectx.x,
						rectx.x
						+ rectx.width));
				// 本来是在每个区域内进行二值化，后来挪至了14步，整体进行二值化，因此注释掉此处2行
				// Mat selectdst = new Mat(select.rows(), select.cols(), select.type());
				// Imgproc.threshold(select, selectdst, 170, 255, Imgproc.THRESH_BINARY);

				// System.out.println("rectx.x, recty.y=="+rectx.x+","+recty.y+"rectx.width,recty.height=="+rectx.width+","+recty.height);
				double p100 = Core.countNonZero(selectdst) * 100 / (selectdst.size().area());
				String que_answer = getQA(no, an);
				Integer que = Integer.valueOf(que_answer.split("_")[0]);
				String answer = que_answer.split("_")[1];
				// System.out.println(Core.countNonZero(selectdst) + "/" + selectdst.size().area());
				System.out.println(que_answer + ":			" + p100);

				if (p100 >= Integer.valueOf(bluevalue)) {// 蓝色
					Core.rectangle(sourceMat, new Point(rectx.x, recty.y), new Point(rectx.x + rectx.width, recty.y
							+ recty.height), new Scalar(255, 0, 0), 2);
					// logger.info(que_answer + ":填涂");
					if (StringUtils.isNotEmpty(resultMap.get(que))) {
						resultMap.put(que, resultMap.get(que) + "," + answer);
					} else {
						resultMap.put(que, answer);
					}
				} else if (p100 > Integer.valueOf(redvalue) && p100 < Integer.valueOf(bluevalue)) {// 红色
					Core.rectangle(sourceMat, new Point(rectx.x, recty.y), new Point(rectx.x + rectx.width, recty.y
							+ recty.height), new Scalar(0, 0, 255), 2);
					// logger.info(que_answer + ":临界");
					if (StringUtils.isNotEmpty(resultMap.get(que))) {
						resultMap.put(que, resultMap.get(que) + ",(" + answer + ")");
					} else {
						resultMap.put(que, "(" + answer + ")");
					}
				} else {// 绿色
					Core.rectangle(sourceMat, new Point(rectx.x, recty.y), new Point(rectx.x + rectx.width, recty.y
							+ recty.height), new Scalar(0, 255, 0), 1);
					// logger.info(que_answer + ":未涂");
				}
			}
		}

		// for (Object result : resultMap.keySet()) {
		for (int i = 1; i <= 100; i++) {
			// logger.info("key=" + result + " value=" + resultMap.get(result));
			resultValue.append("  " + i + "=" + (StringUtils.isEmpty(resultMap.get(i)) ? "未填写" : resultMap.get(i)));
			if (i % 5 == 0) {
				resultValue.append("<br>");
			}
		}
		destPath = Constants.PATH + Constants.DEST_IMAGE_PATH + "dtk15.png";
		Highgui.imwrite(destPath, sourceMat);
		logger.info("框选填图区域，绿色为选项，蓝色为填图，红色为临界======" + destPath);
		long t2 = new Date().getTime();
		System.out.println(t2 - t1);

		// logger.info("输出最终结果：" + resultValue.toString());

		renderString(response, resultValue.toString());
	}

	/**
	 * 绘制灰度直方图用于调整识别区域阈值判断
	 * @Author 王嵩
	 * @param 输入Mat对象img
	 * @return Mat
	 * @Date 2018年3月28日
	 * 更新日志
	 * 2018年3月28日 王嵩  首次创建
	 *
	 */
	public Mat getGrayHistogram(Mat img) {
		List<Mat> images = new ArrayList<Mat>();
		images.add(img);
		MatOfInt channels = new MatOfInt(0); // 图像通道数，0表示只有一个通道
		MatOfInt histSize = new MatOfInt(256); // CV_8U类型的图片范围是0~255，共有256个灰度级
		Mat histogramOfGray = new Mat(); // 输出直方图结果，共有256行，行数的相当于对应灰度值，每一行的值相当于该灰度值所占比例
		MatOfFloat histRange = new MatOfFloat(0, 255);
		Imgproc.calcHist(images, channels, new Mat(), histogramOfGray, histSize, histRange, false); // 计算直方图
		MinMaxLocResult minmaxLoc = Core.minMaxLoc(histogramOfGray);
		// 按行归一化
		// Core.normalize(histogramOfGray, histogramOfGray, 0, histogramOfGray.rows(), Core.NORM_MINMAX, -1, new Mat());

		// 创建画布
		int histImgRows = 600;
		int histImgCols = 1300;
		System.out.println("---------" + histSize.get(0, 0)[0]);
		int colStep = (int) Math.floor(histImgCols / histSize.get(0, 0)[0]);// 舍去小数，不能四舍五入，有可能列宽不够
		Mat histImg = new Mat(histImgRows, histImgCols, CvType.CV_8UC3, new Scalar(255, 255, 255)); // 重新建一张图片，绘制直方图


		int max = (int) minmaxLoc.maxVal;
		System.out.println("--------" + max);
		double bin_u = (double) (histImgRows - 20) / max; // max: 最高条的像素个数，则 bin_u 为单个像素的高度，因为画直方图的时候上移了20像素，要减去
		int kedu = 0;
		for (int i = 1; kedu <= minmaxLoc.maxVal; i++) {
			kedu = i * max / 10;
			// 在图像中显示文本字符串
			Core.putText(histImg, kedu + "", new Point(0, histImgRows - kedu * bin_u), 1, 1, new Scalar(0, 0, 0));
		}


		for (int i = 0; i < histSize.get(0, 0)[0]; i++) { // 画出每一个灰度级分量的比例，注意OpenCV将Mat最左上角的点作为坐标原点
			// System.out.println(i + ":=====" + histogramOfGray.get(i, 0)[0]);
			Core.rectangle(histImg, new Point(colStep * i, histImgRows - 20), new Point(colStep * (i + 1), histImgRows
					- bin_u * Math.round(histogramOfGray.get(i, 0)[0]) - 20),
					new Scalar(0, 0, 0), 1, 8, 0);
			kedu = i * 10;
			// 每隔10画一下刻度
			Core.rectangle(histImg, new Point(colStep * kedu, histImgRows - 20), new Point(colStep * (kedu + 1),
					histImgRows - 20), new Scalar(255, 0, 0), 2, 8, 0);
			Core.putText(histImg, kedu + "", new Point(colStep * kedu, histImgRows - 5), 1, 1, new Scalar(255, 0, 0)); // 附上x轴刻度
		}

		return histImg;

	}

	// 获取题号及选项填涂情况
	public String getQA(int no,int an){
		//返回1A、1B、1C...2A类似这样的返回值
		int first = no + 1 + an / 4 * 20;
		String second = "";
		if (an % 4 == 0) {
			second = "A";
		} else if (an % 4 == 1) {
			second = "B";
		} else if (an % 4 == 2) {
			second = "C";
		} else if (an % 4 == 3) {
			second = "D";
		}
		return first + "_" + second;
	}

	public static void main(String[] args) {
		System.out.println(5 / 3);
	}
	/**
	 * 红色色系0-20，160-180
	 * 蓝色色系100-120
	 * 绿色色系60-80
	 * 黄色色系23-38
	 * 识别出的颜色会标记为白色，其他的为黑色
	 * @param min
	 * @param max
	 */
	public static Mat findColorbyHSV(Mat source, int min, int max) {
		Mat hsv_image = new Mat();
		Imgproc.GaussianBlur(source, source, new Size(3, 3), 0, 0);
		Imgproc.cvtColor(source, hsv_image, Imgproc.COLOR_BGR2HSV);
		// String imagenameb = "D:\\test\\testImge\\ttbefore.jpg";
		// Highgui.imwrite(imagenameb, hsv_image);
		Mat thresholded = new Mat();
		Core.inRange(hsv_image, new Scalar(min, 90, 90), new Scalar(max, 255, 255), thresholded);
		return thresholded;
	}

	/**
	 * 查找黑色
	 * @param source
	 * @param min
	 * @param max
	 * @return
	 */
	public static Mat findBlackColorbyHSV(Mat source) {
		Mat hsv_image = new Mat();
		Imgproc.GaussianBlur(source, source, new Size(3, 3), 0, 0);
		Imgproc.cvtColor(source, hsv_image, Imgproc.COLOR_BGR2HSV);
		String imagenameb = "D:\\test\\testImge\\ttbefore.jpg";
		Highgui.imwrite(imagenameb, hsv_image);
		Mat thresholded = new Mat();
		Core.inRange(hsv_image, new Scalar(0, 0, 0), new Scalar(180, 255, 46), thresholded);
		String ttblack = "D:\\test\\testImge\\ttblack.jpg";
		Highgui.imwrite(ttblack, thresholded);
		return thresholded;
	}

	/**
	 * 水平投影
	 * @param source 传入灰度图片Mat
	 * @return
	 */
	public static Mat horizontalProjection(Mat source) {
		Mat dst = new Mat(source.rows(), source.cols(), source.type());
		// 先进行反转二值化
		Imgproc.threshold(source, dst, 150, 255, Imgproc.THRESH_BINARY_INV);
		// 水平积分投影
		// 每一行的白色像素的个数
		int[] rowswidth = new int[dst.rows()];
		for (int i = 0; i < dst.rows(); i++) {
			for (int j = 0; j < dst.cols(); j++) {
				if (dst.get(i, j)[0] == 255) {
					rowswidth[i]++;
				}
			}
		}
		// 定义一个白色跟原图一样大小的画布
		Mat matResult = new Mat(dst.rows(), dst.cols(), CvType.CV_8UC1, new Scalar(255, 255, 255));
		// 将每一行按照行像素值大小填充像素宽度
		for (int i = 0; i < matResult.rows(); i++) {
			for (int j = 0; j < rowswidth[i]; j++) {
				matResult.put(i, j, 0);
			}
		}
		return matResult;
	}

	/**
	 * 垂直投影
	 * @param source 传入灰度图片Mat
	 * @return
	 */
	public static Mat verticalProjection(Mat source) {
		// 先进行反转二值化
		Mat dst = new Mat(source.rows(), source.cols(), source.type());
		Imgproc.threshold(source, dst, 150, 255, Imgproc.THRESH_BINARY_INV);
		// 垂直积分投影
		// 每一列的白色像素的个数
		int[] colswidth = new int[dst.cols()];
		for (int j = 0; j < dst.cols(); j++) {
			for (int i = 0; i < dst.rows(); i++) {
				if (dst.get(i, j)[0] == 255) {
					colswidth[j]++;
				}
			}
		}
		Mat matResult = new Mat(dst.rows(), dst.cols(), CvType.CV_8UC1, new Scalar(255, 255, 255));
		// 将每一列按照列像素值大小填充像素宽度
		for (int j = 0; j < matResult.cols(); j++) {
			for (int i = 0; i < colswidth[j]; i++) {
				matResult.put(matResult.rows() - 1 - i, j, 0);
			}
		}
		return matResult;
	}

	/**
	 * 图片切块
	 * @param srcMat 传入水平或垂直投影的图片对象Mat
	 * @param proType 传入投影Mat对象的 投影方式0：垂直投影图片,竖向切割；1：水平投影图片，横向切割
	 * @param rowY 由于传来的是原始图片的部分切片，要计算切块的实际坐标位置需要给出切片时所在的坐标，所以需要传递横向切片的y坐标或者纵向切片的横坐标
	 * 如当proType==0时，传入的是切片的垂直投影，那么切成块后能得出x坐标及块宽高度，但是实际y坐标需要加上原切片的y坐标值，所以rowXY为切片的y坐标点，
	 * 同理当proType==1时，rowXY应该为x坐标
	 * @return
	 */
	public static List<Rect> getBlockRect(Mat srcImg, Integer proType, int rowXY) {
		Imgproc.threshold(srcImg, srcImg, 150, 255, Imgproc.THRESH_BINARY_INV);
		// 注意 countNonZero 方法是获取非0像素（白色像素）数量，所以一般要对图像进行二值化反转
		List<Rect> rectList = new ArrayList<Rect>();
		int size = proType == 0 ? srcImg.cols() : srcImg.rows();
		int[] pixNum = new int[size];
		if (proType == 0) {
			for (int i = 0; i < srcImg.cols(); i++) {
				Mat col = srcImg.col(i);
				pixNum[i] = Core.countNonZero(col) > 1 ? Core.countNonZero(col) : 0;
			}
		} else {// 水平投影只关注行
			for (int i = 0; i < srcImg.rows(); i++) {
				Mat row = srcImg.row(i);
				pixNum[i] = Core.countNonZero(row) > 1 ? Core.countNonZero(row) : 0;
			}
		}
		int startIndex = 0;// 记录进入字符区的索引
		int endIndex = 0;// 记录进入空白区域的索引
		boolean inBlock = false;// 是否遍历到了字符区内
		for (int i = 0; i < size; i++) {
			if (!inBlock && pixNum[i] != 0) {// 进入字符区，上升跳变沿
				inBlock = true;
				startIndex = i;
			} else if (pixNum[i] == 0 && inBlock) {// 进入空白区，下降跳变沿存储
				endIndex = i;
				inBlock = false;
				Rect rect = null;
				if (proType == 0) {
					rect = new Rect(startIndex, rowXY, (endIndex - startIndex), srcImg.rows());
				} else {
					rect = new Rect(rowXY, startIndex, srcImg.cols(), (endIndex - startIndex));
				}
				rectList.add(rect);
			}
		}
		return rectList;
	}

}