opencv之检测试卷上的答题线

注:此教程是对贾志刚老师的opencv课程学习的一个记录,在此表示对贾老师的感谢.
需求:寻找英语试卷填空题的下划线，这个对后期的切图与自动识别都比较重要。

解决思路:通过图像形态学操作来寻找直线，霍夫获取位置信息与显示
直接用霍夫直线检测,很难准确将直线检测出来.

//霍夫直线检测
void detectLines(int, void *) {
    
    
    Canny(roiImage, dst, threshold_value, threshold_value * 2, 3, false);
    //threshold(roiImage, dst, 0, 255, THRESH_BINARY | THRESH_OTSU);
    vector<Vec4i> lines;
    HoughLinesP(dst, lines, 1, CV_PI / 180.0, 30, 30.0, 0);
    cvtColor(dst, dst, COLOR_GRAY2BGR);
    for (size_t t = 0; t < lines.size(); t++) {
    
    
        Vec4i ln = lines[t];
        line(dst, Point(ln[0], ln[1]), Point(ln[2], ln[3]), Scalar(0, 0, 255), 2, 8, 0);
    }
    imshow(output_lines, dst);
}

效果如下:

正确的操作: 二值化---->形态学操作(直线结构元素检测出很长的直线,然后膨胀)------>霍夫直线检测------->显示
代码:

#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>
#include <string>

using namespace cv;
using namespace std;

int max_count = 255;
int threshold_value = 100;
const char *output_lines = "Hough Lines";
Mat src, roiImage, dst;
int line_length = 50;
int line_length_max = 300;
std::string lines = "lines";
Mat binaryImage, morhpImage;

void detectLines(int, void *);

void morhpologyLines(int, void *);

int main(int argc, char **argv) {
    
    
    src = imread("/home/fuhong/code/cpp/opencv_learning/src/small_case/imgs/case2_2.png", IMREAD_GRAYSCALE);
    if (src.empty()) {
    
    
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input image", CV_WINDOW_AUTOSIZE);
    imshow("input image", src);
    namedWindow(lines, CV_WINDOW_AUTOSIZE);
    Rect roi = Rect(10, 10, src.cols - 20, src.rows - 20);
    roiImage = src(roi);
    imshow("ROI image", roiImage);
//    createTrackbar("threshold:", output_lines, &threshold_value, max_count, detectLines);
//    detectLines(0, 0);
    createTrackbar("threshold:", lines, &line_length, line_length_max, morhpologyLines);
    morhpologyLines(0, 0);

    waitKey(0);
    return 0;
}

void detectLines(int, void *) {
    
    
    Canny(roiImage, dst, threshold_value, threshold_value * 2, 3, false);
    //threshold(roiImage, dst, 0, 255, THRESH_BINARY | THRESH_OTSU);
    vector<Vec4i> lines;
    HoughLinesP(dst, lines, 1, CV_PI / 180.0, 30, 30.0, 0);
    cvtColor(dst, dst, COLOR_GRAY2BGR);
    for (size_t t = 0; t < lines.size(); t++) {
    
    
        Vec4i ln = lines[t];
        line(dst, Point(ln[0], ln[1]), Point(ln[2], ln[3]), Scalar(0, 0, 255), 2, 8, 0);
    }
    imshow(output_lines, dst);
}

void morhpologyLines(int, void *) {
    
    ![在这里插入图片描述](https://img-blog.csdnimg.cn/20200918002224924.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hvbmdnZV9zbWlsZQ==,size_16,color_FFFFFF,t_70#pic_center)

    // binary image
//    Mat binaryImage, morhpImage;
    threshold(roiImage, binaryImage, 0, 255, THRESH_BINARY_INV | THRESH_OTSU);
    imshow("binary", binaryImage);

    // morphology operation
    Mat kernel = getStructuringElement(MORPH_RECT, Size(line_length, 1), Point(-1, -1));
    morphologyEx(binaryImage, morhpImage, MORPH_OPEN, kernel, Point(-1, -1));
    imshow(lines, morhpImage);

    // dilate image
    kernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
    dilate(morhpImage, morhpImage, kernel);
    imshow("morphology lines", morhpImage);

    // hough lines
    vector<Vec4i> lines;
    HoughLinesP(morhpImage, lines, 1, CV_PI / 180.0, 30, 20.0, 0);
    Mat resultImage = roiImage.clone();
    cvtColor(resultImage, resultImage, COLOR_GRAY2BGR);
    for (size_t t = 0; t < lines.size(); t++) {
    
    
        Vec4i ln = lines[t];
        line(resultImage, Point(ln[0], ln[1]), Point(ln[2], ln[3]), Scalar(0, 0, 255), 2, 8, 0);
    }
    imshow(output_lines, resultImage);
    return;
}

效果如下所示: