| public static FindContours ( InputOutputArray image, Mat &contours, OutputArray hierarchy, RetrievalModes mode, ContourApproximationModes method, Point offset = null ) : void | ||
| image | InputOutputArray | Source, an 8-bit single-channel image. Non-zero pixels are treated as 1’s. /// Zero pixels remain 0’s, so the image is treated as binary. /// The function modifies the image while extracting the contours. |
| contours | Mat | Detected contours. Each contour is stored as a vector of points. |
| hierarchy | OutputArray | Optional output vector, containing information about the image topology. /// It has as many elements as the number of contours. For each i-th contour contours[i], /// the members of the elements hierarchy[i] are set to 0-based indices in contours of the next /// and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. /// If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative. |
| mode | RetrievalModes | Contour retrieval mode |
| method | ContourApproximationModes | Contour approximation method |
| offset | Point | Optional offset by which every contour point is shifted. /// This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context. |
| return | void | |
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);
}
//OpenCVを使用して、座標を求める。
void OpenCVTexture(Texture2D texture)
{
Mat newMat = Unity.TextureToMat(texture);
//画像をCv2.Equalsで変化があるかグローバルのoldMatと比較して検知しようとしたが、できなかった。
//Convert image to grayscale
Mat imgGray = new Mat();
Cv2.CvtColor(newMat, imgGray, ColorConversionCodes.BGR2GRAY);
//Debug.Log(Cv2.Equals(imgGray, imgGray));
// Clean up image using Gaussian Blur
Mat imgGrayBlur = new Mat();
Cv2.GaussianBlur(imgGray, imgGrayBlur, new Size(5, 5), 0);
//Extract edges
Mat cannyEdges = new Mat();
Cv2.Canny(imgGrayBlur, cannyEdges, 10.0, 70.0);
//Do an invert binarize the image
Mat mask = new Mat();
Cv2.Threshold(cannyEdges, mask, 70.0, 255.0, ThresholdTypes.BinaryInv);
// Extract Contours
Point[][] contours; //特徴点が格納される変数。
HierarchyIndex[] hierarchy; //特徴点の階層が格納される。
Cv2.FindContours(cannyEdges, out contours, out hierarchy, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple, null); //特徴点を検出する。
PointChangeNumSendUDP(contours); //Pointが変化しなければ送信を実装しようとしている途中。
//StartCoroutine(udpSendCoroutine(contours));
//輪郭描画
int width = (int)transform.GetComponent <RectTransform>().sizeDelta.x;
int height = (int)transform.GetComponent <RectTransform>().sizeDelta.y;
Mat Contours = new Mat(width, height, MatType.CV_8UC3, new Scalar(0, 0, 0)); //初期値として黒い画面を作成する。
Cv2.DrawContours(Contours, contours, -1, new Scalar(0, 255, 0, 255), 1); //MatにCountours(特徴点)を描画する。
Texture2D changedTex = Unity.MatToTexture(Contours); //MatをTexture2Dへ変更
GetComponent <RawImage>().texture = changedTex; //RaxImageにTexture2Dを書き込み。
//MatをDisposeする。
newMat.Dispose();
imgGray.Dispose();
imgGrayBlur.Dispose();
cannyEdges.Dispose();
mask.Dispose();
Contours.Dispose();
//TextureをDestryしないとメモリーリークを送りました。
MonoBehaviour.Destroy(texture);
if (changedTex != oldChangedTex)
{
MonoBehaviour.Destroy(oldChangedTex);
oldChangedTex = changedTex;
}
}
