使用 GIMP/Sobel 与 OpenCV/Sobel 进行边缘检测

Question

在 Linux Ubuntu 22.04 上使用 OpenCV 版本 4.9.0 的 C++ API，我尝试自动检测第一个扫描图像中描绘的多个商店收据，复制生成的子图像，旋转它们并将它们提供给 OCR 例程进一步加工：

绿色区域不在原始区域中，但用于掩盖收据中可能存在的任何 PII。

我尝试使用 OpenCV 文档和 StackOverflow 上的各种示例中的代码，但我很难让它正常工作，直到我尝试在 GIMP 中执行图像预处理步骤。在这里，经过一些实验，我发现使用 Sobel 算法的 “Filter->Edge-Detect->Edge...” 选项对于生成图像最成功，我可以将其提供给我编写的 C++ 代码（代码如下）。

这是 GIMP 对话框，其中包含我使用的设置（算法=Sobel，金额=10，边框行为=“clamp”）：

生成的图像：

这是应用阈值 35 后的二值图像：

最后，OpenCV 代码在收据周围生成这些旋转矩形（太小或太大的矩形将被丢弃）：

所以这是我的问题： 如何让 OpenCV 对原始图像执行与 GIMP 相同的操作？

这是我的代码，改编自 OpenCV 示例以及 这个 SO 线程：

#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, char* argv[]) {

  int min_area = 10000;
  int max_height = 800;
  double thr = 220;

  Mat img, orig_img;
  
  if (argc < 2) {
    cout << "Usage: " << argv[0] << " [FILE PATH] [THRESHOLD (1-255, optional)]" << endl;
    return 0;
  } else {
    orig_img = imread(argv[1]);
    if ( orig_img.empty() ) {
      cout << "WARNING: the input image was empty!" << endl;
      return EXIT_FAILURE;
    }
    img = orig_img;
  }
  
  if (argc > 2) {
    long val = strtol(argv[2], nullptr, 10);
    if (val > 0 && val < 256) {
      thr = val;
    }
  }

  //-------------------------------------------
  // Resize if height is larger than 800 pixels:
  Mat tmp;
  double h = img.size().height;
  double w = img.size().width;
  double df = 1.0;
  cout << "\nOriginal image height:\t" << static_cast<int>(h) << endl;
  cout << "Original image width:\t" << static_cast<int>(w) << "\n" << endl;
  if (h > max_height) {
    df = (double)max_height / h;
    resize(img, tmp, Size(), df, df, INTER_NEAREST_EXACT);
    img = tmp;
  }

  Mat img2, img3;
  cvtColor(img, img2, COLOR_BGR2GRAY);
  blur( img2, img3, Size(3,3) );
  threshold(img3, img2, thr, 255, THRESH_BINARY);
  
  Mat element = getStructuringElement(MORPH_CROSS, Size(3, 3), Point(1, 1));
  erode(img2, img2, element); // without it find contours fails on some rects
  
  // Show images:
  imshow("img", img);
  imshow("img2", img2);
  waitKey();
  
  // preprocessing done, search rectangles
  vector<vector<Point> > contours;
  
  // vector<Vec4i> hierarchy;
  findContours(img2, contours, /* hierarchy, */ RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
  
  vector<RotatedRect> rects;
  for (int i = 0; i < contours.size(); i++) {
    // if (hierarchy[i][2] > 0) continue;
  
    // capture inner contour
    RotatedRect rr = minAreaRect(contours[i]);
    if (rr.size.area() < min_area) continue; // too small
  
    rr.size.width += 8;
    rr.size.height += 8; // expand to outlier rect if needed
    rects.push_back(rr);
    
    cout << "***************\nRectangle dimensions:"
         << "\nRect " << i << ":"
         << "\n\twidth:\t" << rr.size.width
         << "\n\theight:\t" << rr.size.height
         << "\n\tarea:\t" << rr.size.width * rr.size.height
         << "\n***************\n"
         << endl;
  }
  
  Mat debugImg;
  img.copyTo(debugImg);

  for (RotatedRect rr : rects) {
    Point2f points[4];
    rr.points(points);
    for (int i = 0; i < 4; i++) {
      int ii = (i + 1) % 4;
      line(debugImg, points[i], points[ii], CV_RGB(255, 0, 0), 2);
    }
  }
  imshow("debug", debugImg);
  waitKey();
}

感谢您的帮助！

Answer 1

与此同时，我通过在 OpenCV 中使用 Scharr 算法以及调整参数，获得了更好的结果。我的目标是使这个过程尽可能自动化，所以我希望用户输入最少的信息。

这是我迄今为止所掌握的内容（改编自此处找到的 OpenCV Sobel 示例）：

#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace cv;
using namespace std;
int main( int argc, char** argv )
{
  cv::CommandLineParser parser(argc, argv,
                               "{@input   |lena.jpg|input image}"
                               "{ksize   k|1|ksize (hit 'K' to increase its value at run time)}"
                               "{scale   s|1|scale (hit 'S' to increase its value at run time)}"
                               "{delta   d|0|delta (hit 'D' to increase its value at run time)}"
                               "{help    h|false|show help message}");
  cout << "The sample uses Sobel or Scharr OpenCV functions for edge detection\n\n";
  parser.printMessage();
  cout << "\nPress 'ESC' to exit program.\nPress 'R' to reset values ( ksize will be -1 equal to Scharr function )";
  // First we declare the variables we are going to use
  Mat image,src, src_gray;
  Mat grad;
  const String window_name = "Sobel Demo - Simple Edge Detector";
  int ksize = parser.get<int>("ksize");
  int scale = parser.get<int>("scale");
  int delta = parser.get<int>("delta");
  int ddepth = CV_16S;
  String imageName = parser.get<String>("@input");
  // As usual we load our source image (src)
  image = imread( samples::findFile( imageName ), IMREAD_COLOR ); // Load an image
  // Check if image is loaded fine
  if( image.empty() )
  {
    printf("Error opening image: %s\n", imageName.c_str());
    return EXIT_FAILURE;
  }

  //-------------------------------------------
  // Resize if height is larger than 800 pixels:
  int max_height = 800;
  Mat tmp;
  double h = image.size().height;
  double w = image.size().width;
  double df = 1.0;
  cout << "\nOriginal image height:\t" << static_cast<int>(h) << endl;
  cout << "Original image width:\t" << static_cast<int>(w) << "\n" << endl;
  if (h > max_height) {
    df = (double)max_height / h;
    resize(image, tmp, Size(), df, df, INTER_NEAREST_EXACT);
    image = tmp;
  }

  double thr = 125; // Start with a threshold value somewhere between 0 and 255

  for (;;)
  {
    // Remove noise by blurring with a Gaussian filter ( kernel size = 3 )
    GaussianBlur(image, src, Size(3, 3), 0, 0, BORDER_DEFAULT);
    // Convert the image to grayscale
    cvtColor(src, src_gray, COLOR_BGR2GRAY);
    Mat grad_x, grad_y;
    Mat abs_grad_x, abs_grad_y;
    Sobel(src_gray, grad_x, ddepth, 1, 0, ksize, scale, delta, BORDER_DEFAULT);
    Sobel(src_gray, grad_y, ddepth, 0, 1, ksize, scale, delta, BORDER_DEFAULT);
    // converting back to CV_8U
    convertScaleAbs(grad_x, abs_grad_x);
    convertScaleAbs(grad_y, abs_grad_y);
    addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad);
    
    imshow(window_name, grad);
    char key = (char)waitKey(0);
    
    int old_ksize, old_scale, old_delta, old_threshold;
    
    if(key == 27)
    {
      break;
    }
    if (key == 'k' || key == 'K')
    {
      old_ksize = ksize;
      ksize = ksize < 30 ? ksize+2 : -1;
      cout << "Changed 'ksize' from " << old_ksize << " to " << ksize << endl;
    }
    if (key == 's' || key == 'S')
    {
      old_scale = scale;
      scale++;
      cout << "Changed 'scale' from " << old_scale << " to " << scale << endl;
    }
    if (key == 'd' || key == 'D')
    {
      old_delta = delta;
      delta++;
      cout << "Changed 'delta' from " << old_delta << " to " << delta << endl;
    }
    if (key == 'r' || key == 'R')
    {
      scale =  1;
      ksize = -1;
      delta =  0;
      cout << "Reset to Scharr algorithm." << endl;
    }
    if (key == '+') {
      old_threshold = (int)thr;
      if (thr < 250) {
        thr += 5;
        cout << "Increasing threshold from " << old_threshold << " to " << (int)thr << endl;
        key = 'f';
      }
    }
    if (key == '-') {
      old_threshold = (int)thr;
      if (thr > 5) {
        thr -= 5;
        cout << "Decreasing threshold from " << old_threshold << " to " << (int)thr << endl;
        key = 'f';
      }
    }
    if (key == 'f' || key == 'F') {
      Mat img2, img3;
      int min_area = 10000;
      // not necessary here:
      // cvtColor(img, img2, COLOR_BGR2GRAY);
      blur( grad, img2, Size(3,3) );
      threshold(img2, img3, thr, 255, THRESH_BINARY);
      
      Mat element = getStructuringElement(MORPH_CROSS, Size(3, 3), Point(1, 1));
      erode(img3, img3, element); // without it find contours fails on some rects

      // preprocessing done, search rectanges
      vector<vector<Point> > contours;
      
      // vector<Vec4i> hierarchy;
      findContours(img3, contours, /* hierarchy, */ RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
      
      vector<RotatedRect> rects;
      for (int i = 0; i < contours.size(); i++) {
        // if (hierarchy[i][2] > 0) continue;
      
        // capture inner contour
        RotatedRect rr = minAreaRect(contours[i]);
        if (rr.size.area() < min_area) continue; // too small?
        if (rr.size.width > img3.size().width - 5) continue; // rectangle encloses entire image?
      
        rr.size.width += 8;
        rr.size.height += 8; // expand to outlier rect if needed
        rects.push_back(rr);
        
        cout << "***************\nRectangle dimensions found:"
             << "\nRect " << i << ":"
             << "\n\twidth:\t" << rr.size.width
             << "\n\theight:\t" << rr.size.height
             << "\n\tarea:\t" << rr.size.width * rr.size.height
             << "\n***************\n"
             << endl;
      }
      
      Mat debugImg;
      image.copyTo(debugImg);
    
      for (RotatedRect rr : rects) {
        Point2f points[4];
        rr.points(points);
        for (int i = 0; i < 4; i++) {
          int ii = (i + 1) % 4;
          line(debugImg, points[i], points[ii], CV_RGB(255, 0, 0), 2);
        }
      }
      imshow("debug", debugImg);
      waitKey();
      destroyWindow("debug");
    }
  }

    cout << "\n************************\nParameters:"
         << "\n\tksize     = " << ksize
         << "\n\tscale     = " << scale
         << "\n\tdelta     = " << delta
         << "\n\tthreshold = " << thr 
         << "\n************************" 
         << endl;

  return EXIT_SUCCESS;
}

阈值从 125 开始，可以根据需要增加或减少。当按

"+"

或

"-"

时，阈值可以增加或减少 5，这也会自动将任何矩形应用于原始图像并在新窗口中显示。更改其中一个参数（如原始 Sobel 示例代码中所示）将按预期工作，但必须按下

"f"

或

"F"

键才能显示矩形叠加层。

当我使用 Scharr（将 -1 作为

ksize

传递）与

scale=5

和

delta=1

并将阈值降低到 120 时，似乎会产生最好的结果。但我仍然想知道如何自动化一切......最小用户输入将提供有关扫描的收据数量的输入，并告诉他们确保它们不重叠。但如果可能的话，所有参数等都应该从图像中自动确定。

谢谢。