public static int ExtractTables(Image img)
{
//Delete this after
Bitmap bit;
List <Rectangle> AreasOfInterest = new List <Rectangle>();
Bitmap bitmap = (Bitmap)img;
Mat srcImg = OCS.Extensions.BitmapConverter.ToMat(bitmap);
if (srcImg.Data == null)
{
throw new NullReferenceException("Image has nothing?");
}
//Resize into smaller size
Mat rsz = new Mat();
OpenCvSharp.Size size = new OpenCvSharp.Size(4000, 2828);
Cv2.Resize(srcImg, rsz, size);
// Convert to greyscale if it has more than one channel
// else just leave it alone
Mat grey = new Mat();
Cv2.CvtColor(rsz, grey, ColorConversionCodes.BGR2GRAY);
#if IMG_DEBUG
Cv2.ImShow("grey", grey);
Cv2.WaitKey(0);
#endif
//Apply adaptive thresholding to get negative
Mat bw = new Mat();
Cv2.AdaptiveThreshold(~grey, bw, 255, AdaptiveThresholdTypes.MeanC, ThresholdTypes.Binary, 15, -2);
bit = OCS.Extensions.BitmapConverter.ToBitmap(bw);
bit.Save("bw.tiff", System.Drawing.Imaging.ImageFormat.Tiff);
// Create two new masks cloned from bw.
Mat horizontal = bw.Clone();
Mat vertical = bw.Clone();
// adjust this for number of lines
int scale = 10;
/////////////////////////
/////////////////////////
/////////////////////////
// Specify size on horizontal axis
int horizontalsize = horizontal.Cols / scale;
// Create structure element for extracting horizontal lines through morphology operations
//Mat horizontalStructure = getStructuringElement(MORPH_RECT, Size(horizontalsize, 1));
Mat horizontalStructure = Cv2.GetStructuringElement(MorphShapes.Rect, new OCS.Size(horizontalsize, 1));
// Apply morphology operations
//erode(horizontal, horizontal, horizontalStructure, Point(-1, -1));
Cv2.Erode(horizontal, horizontal, horizontalStructure, new OCS.Point(-1, -1));
//dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1));
Cv2.Dilate(horizontal, horizontal, horizontalStructure, new OCS.Point(-1, -1));
// dilate(horizontal, horizontal, horizontalStructure, Point(-1, -1)); // expand horizontal lines
// Show extracted horizontal lines
#if IMG_DEBUG
Cv2.ImShow("horizontal", horizontal);
Cv2.WaitKey(0);
#endif
bit = OCS.Extensions.BitmapConverter.ToBitmap(horizontal);
bit.Save("horizontal.tiff", System.Drawing.Imaging.ImageFormat.Tiff);
// Specify size on vertical axis
int verticalsize = vertical.Rows / scale;
// Create structure element for extracting vertical lines through morphology operations
//Mat verticalStructure = getStructuringElement(MORPH_RECT, Size(1, verticalsize));
Mat verticalStructure = Cv2.GetStructuringElement(MorphShapes.Rect, new OCS.Size(1, verticalsize));
// Apply morphology operations
//erode(vertical, vertical, verticalStructure, Point(-1, -1));
Cv2.Erode(vertical, vertical, verticalStructure, new OCS.Point(-1, -1));
//dilate(vertical, vertical, verticalStructure, Point(-1, -1));
Cv2.Dilate(vertical, vertical, verticalStructure, new OCS.Point(-1, -1));
// Show extracted vertical lines
#if IMG_DEBUG
Cv2.ImShow("vertical", vertical);
Cv2.WaitKey(0);
#endif
bit = OCS.Extensions.BitmapConverter.ToBitmap(vertical);
bit.Save("vertical.tiff", System.Drawing.Imaging.ImageFormat.Tiff);
// create a mask which includes the tables
Mat mask = horizontal + vertical;
#if IMG_DEBUG
Cv2.ImShow("mask", mask);
Cv2.WaitKey(0);
#endif
// find the joints between the lines of the tables, we will use this information in order to descriminate tables from pictures (tables will contain more than 4 joints while a picture only 4 (i.e. at the corners))
Mat joints = new Mat();
//bitwise_and(horizontal, vertical, joints);
Cv2.BitwiseAnd(horizontal, vertical, joints);
//Cv2.ImShow("joints", joints);
bit = OCS.Extensions.BitmapConverter.ToBitmap(joints);
bit.Save("joints.tiff", System.Drawing.Imaging.ImageFormat.Tiff);
#if IMG_DEBUG
Cv2.ImShow("a", joints);
Cv2.WaitKey(0);
#endif
//Thread.Sleep(2000);
// Find external contours from the mask, which most probably will belong to tables or to images
//vector<Vec4i> hierarchy;
//std::vector<std::vector<cv::Point>> contours;
OCS.HierarchyIndex[] hierarchy;
//List<List<OCS.Point>> contours = new List<List<OCS.Point>>;
OCS.Point[][] contours;
//cv::findContours(mask, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
Cv2.FindContours(mask, out contours, out hierarchy, OCS.RetrievalModes.External, OCS.ContourApproximationModes.ApproxSimple, new OCS.Point(0, 0));
//////vector<vector<Point>> contours_poly(contours.size() );
//////vector<Rect> boundRect(contours.size() );
//////vector<Mat> rois;
List <List <OCS.Point> > contours_poly = new List <List <OCS.Point> >(contours.Length);
List <OCS.Rect> boundRect = new List <OCS.Rect>(contours.Length);
List <Mat> rois = new List <Mat>();
////for (size_t i = 0; i < contours.size(); i++)
////{
//// // find the area of each contour
//// double area = contourArea(contours[i]);
///
//// // // filter individual lines of blobs that might exist and they do not represent a table
//// if (area < 100) // value is randomly chosen, you will need to find that by yourself with trial and error procedure
//// continue;
//// approxPolyDP(Mat(contours[i]), contours_poly[i], 3, true);
//// boundRect[i] = boundingRect(Mat(contours_poly[i]));
//// // find the number of joints that each table has
//// Mat roi = joints(boundRect[i]);
//// vector<vector<Point>> joints_contours;
//// findContours(roi, joints_contours, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
//// // if the number is not more than 5 then most likely it not a table
//// if (joints_contours.size() <= 4)
//// continue;
//// rois.push_back(rsz(boundRect[i]).clone());
//// //drawContours( rsz, contours, i, Scalar(0, 0, 255), CV_FILLED, 8, vector<Vec4i>(), 0, Point() );
//// rectangle(rsz, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 0), 1, 8, 0);
////}
for (int i = 0; i < contours.Length; i++)
{
double area = Cv2.ContourArea(contours[i]);
if (area < 100.0)
{
// Skip because its not likely such a small area is a cell
continue;
}
// contours_poly is null at runtime. so we create a new entry and exit array
contours_poly.Add(new List <OCS.Point>());
OutputArray contour_poly_output = OutputArray.Create(contours_poly[i]);
InputArray contour_poly_input = InputArray.Create(contours[i]);
Cv2.ApproxPolyDP(InputArray.Create(contours[i]), contour_poly_output, 0.0, true);
Rect boundingRect = Cv2.BoundingRect(InputArray.Create(contours_poly[i]));
boundRect.Add(boundingRect);
//boundRect[i] = Cv2.BoundingRect(InputArray.Create(contours_poly[i]));
//OCS.Mat roi = Cv2.joints()
}
#if IMG_DEBUG
Cv2.NamedWindow("Output", WindowMode.KeepRatio);
Cv2.Rectangle(rsz, boundRect.ElementAt(0), Scalar.Red, 10);
Cv2.ImShow("Output", rsz);
Cv2.WaitKey(0);
Cv2.DestroyAllWindows();
#endif
////for (size_t i = 0; i < rois.size(); ++i)
////{
//// /* Now you can do whatever post process you want
//// * with the data within the rectangles/tables. */
//// imshow("roi", rois[i]);
//// waitKey();
////}
return(boundRect.Count);
}
/// <summary>
/// Computes an image descriptor using the set visual vocabulary.
/// </summary>
/// <param name="image">Image, for which the descriptor is computed.</param>
/// <param name="keypoints">Keypoints detected in the input image.</param>
/// <param name="imgDescriptor">Computed output image descriptor.</param>
/// <param name="pointIdxsOfClusters">pointIdxsOfClusters Indices of keypoints that belong to the cluster.
/// This means that pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster(word of vocabulary) returned if it is non-zero.</param>
/// <param name="descriptors">Descriptors of the image keypoints that are returned if they are non-zero.</param>
public void Compute(InputArray image, ref KeyPoint[] keypoints, OutputArray imgDescriptor,
out int[][] pointIdxsOfClusters, Mat descriptors = null)
{
ThrowIfDisposed();
if (image == null)
{
throw new ArgumentNullException(nameof(image));
}
if (imgDescriptor == null)
{
throw new ArgumentNullException(nameof(imgDescriptor));
}
using (var keypointsVec = new VectorOfKeyPoint(keypoints))
using (var pointIdxsOfClustersVec = new VectorOfVectorInt())
{
NativeMethods.features2d_BOWImgDescriptorExtractor_compute11(ptr, image.CvPtr, keypointsVec.CvPtr,
imgDescriptor.CvPtr, pointIdxsOfClustersVec.CvPtr, Cv2.ToPtr(descriptors));
keypoints = keypointsVec.ToArray();
pointIdxsOfClusters = pointIdxsOfClustersVec.ToArray();
}
GC.KeepAlive(image);
GC.KeepAlive(imgDescriptor);
GC.KeepAlive(descriptors);
}
/// <summary>
///
/// </summary>
/// <param name="indexParams"></param>
/// <param name="searchParams"></param>
public FlannBasedMatcher(IndexParams indexParams = null, SearchParams searchParams = null)
{
ptr = NativeMethods.features2d_FlannBasedMatcher_new(
Cv2.ToPtr(indexParams), Cv2.ToPtr(searchParams));
}
/// <summary>
/// Detect keypoints in an image.
/// </summary>
/// <param name="image">The image.</param>
/// <param name="mask">Mask specifying where to look for keypoints (optional).
/// Must be a char matrix with non-zero values in the region of interest.</param>
/// <returns>The detected keypoints.</returns>
public KeyPoint[] Detect(Mat image, Mat?mask = null)
{
if (image == null)
{
throw new ArgumentNullException(nameof(image));
}
ThrowIfDisposed();
image.ThrowIfDisposed();
try
{
using var keyPoints = new VectorOfKeyPoint();
NativeMethods.HandleException(
NativeMethods.features2d_Feature2D_detect_Mat1(ptr, image.CvPtr, keyPoints.CvPtr, Cv2.ToPtr(mask)));
return(keyPoints.ToArray());
}
finally
{
GC.KeepAlive(this);
GC.KeepAlive(image);
GC.KeepAlive(mask);
}
}
/// <summary>
/// ウィンドウのプロパティを設定する
/// </summary>
/// <param name="propId">プロパティの種類</param>
/// <param name="propValue">プロパティに設定する値</param>
#else
/// <summary>
/// Set Property of the window
/// </summary>
/// <param name="propId">Property identifier</param>
/// <param name="propValue">New value of the specified property</param>
#endif
public void SetProperty(WindowProperty propId, double propValue)
{
Cv2.SetWindowProperty(name, propId, propValue);
}
/// <summary>
/// ウィンドウのプロパティを取得する
/// </summary>
/// <param name="propId">プロパティの種類</param>
/// <returns>プロパティの値</returns>
#else
/// <summary>
/// Get Property of the window
/// </summary>
/// <param name="propId">Property identifier</param>
/// <returns>Value of the specified property</returns>
#endif
public double GetProperty(WindowProperty propId)
{
return(Cv2.GetWindowProperty(name, propId));
}