文檔圖像傾斜校正算法(2)——直線檢測傾斜校正

文檔圖像傾斜校正算法(2)——直線檢測傾斜校正

原理：檢測文本塊中的直線，根據直線的傾斜角完成傾斜矯正
適用範圍：爲避免背景中可能存在的直線干擾，應先截取到圖像中的帶有表格線的區域，在該區域上進行直線檢測，利用檢測到的直線的傾斜角完成圖像的矯正。

#include <opencv2/opencv.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <vector>
#include <numeric>
#define MY_SKEW 14

//圖像旋轉1：旋轉（截取圖像）Crop
//         Mat img ：圖像輸入，單通道或者三通道
//         Mat & imgout ：圖像輸出
//         int degree ：圖像要旋轉的角度
//         int border_value：圖像旋轉填充值
int rotateImage1(Mat img,Mat & imgout, int degree,int border_value)
{
    if( img.empty())
        return 1;
    degree = -degree;//warpAffine默認的旋轉方向是逆時針，所以加負號表示轉化爲順時針
    double angle = degree  * CV_PI / 180.; // 弧度  
    double a = sin(angle), b = cos(angle);
    int width = img.cols;
    int height = img.rows;
    int width_rotate = int(width * fabs(b)-height * fabs(a));//height * fabs(a) +
    int height_rotate = int(height * fabs(b)-width * fabs(a));//width * fabs(a) +
    if(width_rotate<=20||height_rotate<=20)
    {
        width_rotate = 20;
        height_rotate = 20;
    }
    //旋轉數組map
    // [ m0  m1  m2 ] ===>  [ A11  A12   b1 ]
    // [ m3  m4  m5 ] ===>  [ A21  A22   b2 ]
    float map[6];
    Mat map_matrix = Mat(2, 3, CV_32F, map);
    // 旋轉中心
    CvPoint2D32f center = cvPoint2D32f(width / 2, height / 2);
    CvMat map_matrix2 = map_matrix;
    cv2DRotationMatrix(center, degree, 1.0, &map_matrix2);//計算二維旋轉的仿射變換矩陣
    map[2] += (width_rotate - width) / 2;
    map[5] += (height_rotate - height) / 2;
    //Mat img_rotate;
    //對圖像做仿射變換
    //CV_WARP_FILL_OUTLIERS - 填充所有輸出圖像的象素。
    //如果部分象素落在輸入圖像的邊界外，那麼它們的值設定爲 fillval.
    //CV_WARP_INVERSE_MAP - 指定 map_matrix 是輸出圖像到輸入圖像的反變換，
    int chnnel =img.channels();
    if(chnnel == 3)
        warpAffine(img, imgout, map_matrix, Size(width_rotate, height_rotate), 1, 0, Scalar(border_value,border_value,border_value));
    else
        warpAffine(img, imgout, map_matrix, Size(width_rotate, height_rotate), 1, 0, border_value);
    return 0;
}

//投影傾斜校正：增值稅傾斜矯正方法舉例
//         const Mat rgbimgin ：圖像輸入，三通道
//         Mat & rgbimgout ：矯正後的圖像輸出
//         int &theta ：圖像傾斜的角度
int skew_correction_line(const Mat rgbimgin,  Mat & rgbimgout, int &theta)
{

    if (rgbimgin.empty() || rgbimgin.channels() != 3)
    {
        return 1;
    }

    Mat imgout_crop = rgbimgin.clone();

    Mat imgout;
    float zoom_ratio = 400.0 / imgout_crop.rows;
    resize(imgout_crop, imgout, Size(0, 0), zoom_ratio, zoom_ratio, 1);
    Mat Gray;
    cvtColor(imgout, Gray, COLOR_RGB2GRAY);
    medianBlur(Gray, Gray, 3);
    Mat Bin;
    adaptiveThreshold(Gray, Bin, 255, CV_ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY, 111, 5.0);
    Bin = 255 - Bin;
    vector<Vec4i> lines;
    HoughLinesP(Bin, lines, 1, CV_PI / 180, 100, 100, 4);
    if (lines.size() <= 0)
    {
        theta = 0;
        rgbimgout = rgbimgin.clone();
        return 0;
    }
    Mat  Lineimg(Bin.rows, Bin.cols, CV_8UC1, Scalar::all(255));
    int result = 0;
    for (size_t i = 0; i < lines.size(); i++)
    {
        Vec4i l = lines[i];
        line(Lineimg, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0), 1, CV_AA);
        float param = float(int(l[1]) - int(l[3])) / float(abs(l[2] - l[0]));
        int tt = atan(param) * 180 / PI;
        if (tt > 45)
            tt = tt - 90;
        if (tt < -45)
            tt = 90 + tt;
        result = result + tt;
    }
    for (size_t i = 0; i < lines.size(); i++)
    {
        Vec4i l = lines[i];
        line(imgout, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(186, 88, 255), 1, CV_AA);
    }
    theta = result / int(lines.size());
    rotateImage1(rgbimgin, rgbimgout, theta, 0);
    return 0;
}