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how to crop image with polygon in c#

I'm making a labeling tool.
Goal :By drawing a polygon on the picture, you have to export the image ​​inside the polygon to the outside.
example
extract
This is what I drew in the my program.
But I don't know how to extract this region. I want to know how to extract this area.
I have saved the vertices of the picture above in an object. But I don't know how to extract data from the image through these vertices
========================================
So I found this.
https://www.codeproject.com/Articles/703519/Cropping-Particular-Region-In-Image-Using-Csharp
but it is not work
Can't convert Bitmap to IplImage
It doesn't work for the same reason.
In the post, I am going to use opencvsharp 4.x, but the program I am fixing now is .netframework 3.5, so it does not support opencvsharp 4.x.
What should I do?
============================
I made a function referring to the answer, but it doesn't work...
I want to know why.
void CropImage(Bitmap bitmap, Point[] points)
{
Rectangle rect = PaddingImage(points, bitmap);
TextureBrush textureBrush = new TextureBrush(bitmap);
Bitmap bmp1 = new Bitmap(rect.Width, rect.Height);
using (Graphics g = Graphics.FromImage(bmp1))
{
g.FillPolygon(textureBrush, points);
}
string ima_path = Environment.GetFolderPath(Environment.SpecialFolder.MyDocuments);
bmp1.Save(ima_path + "\\Image.png", ImageFormat.Png);
}
extract Image
original
If you use a small polygon, there is no output at all.
You will notice that the two images are slightly different.
It seems to me that the part where the center point is cut and extracted is different. I don't know if what I was thinking is correct.

You would create a new bitmap, at least as large as the bounding box of your polygon. Create a graphics object from this new bitmap. You can then draw the polygon to this bitmap, using the original image as a texture brush. Note that you might need to apply transform matrix to translate from the full image coordinates to the cropped image coordinates.
Note that it looks like you have radiological images. These are typically 16 bit images, so they will need to be converted to 8bit mono, or 24bit RGB before they can be used. This should already be done in the drawing code if you have access to the source. Or you can do it yourself.

this works for me
private Bitmap CropImage(Bitmap bitmap, List<Point> points)
{
int pminx = 9999, pminy = 9999, pmaxx = 0, pmaxy = 0; System.Drawing.Point[] pcol = new System.Drawing.Point[points.Count]; int i = 0;
foreach (Point pc in points)
{
if (pc.X > pmaxx) pmaxx = (int)pc.X;
if (pc.Y > pmaxy) pmaxy = (int)pc.Y;
if (pc.X < pminx) pminx = (int)pc.X;
if (pc.Y < pminy) pminy = (int)pc.Y;
pcol[i] = new System.Drawing.Point((int)pc.X, (int)pc.Y);
i++;
}
TextureBrush textureBrush = new TextureBrush(bitmap);
Bitmap bmpWrk = new Bitmap(bitmap.Width, bitmap.Height);
using (Graphics g = Graphics.FromImage(bmpWrk))
{
g.FillPolygon(textureBrush, pcol);
}
System.Drawing.Rectangle CropRect = new System.Drawing.Rectangle(pminx, pminy, pmaxx - pminx, pmaxy - pminy);
return bmpWrk.Clone(CropRect, bmpWrk.PixelFormat);
}

How to increase accuracy of OCR text detection in tesseract with emgucv?

Well I am not able to get good accuracy of text detection in tesseract. Please check code and image below.
Mat imgInput = CvInvoke.Imread(#"D:\workspace\raw2\IMG_20200625_194541.jpg", ImreadModes.AnyColor);
int kernel_size = 11;
//Dilation
Mat imgDilatedEdges = new Mat();
CvInvoke.Dilate(
imgInput,
imgDilatedEdges,
CvInvoke.GetStructuringElement(
ElementShape.Rectangle,
new Size(kernel_size, kernel_size),
new Point(1, 1)),
new Point(1, 1),
1,
BorderType.Default,
new MCvScalar(0));
//Blur
Mat imgBlur = new Mat();
CvInvoke.MedianBlur(imgDilatedEdges, imgBlur, kernel_size);
//Abs diff
Mat imgAbsDiff = new Mat();
CvInvoke.AbsDiff(imgInput, imgBlur, imgAbsDiff);
Mat imgNorm = imgAbsDiff;
//Normalize
CvInvoke.Normalize(imgAbsDiff, imgNorm, 0, 255, NormType.MinMax, DepthType.Default);
Mat imgThreshhold = new Mat();
//getting threshhold value
double thresholdval = CvInvoke.Threshold(imgAbsDiff, imgThreshhold, 230, 0, ThresholdType.Trunc);
//Normalize
CvInvoke.Normalize(imgThreshhold, imgThreshhold, 0, 255, NormType.MinMax, DepthType.Default);
imgThreshhold.Save(#"D:\workspace\ocr_images\IMG_20200625_194541.jpg");
//contrast correction
Mat lab = new Mat();
CvInvoke.CvtColor(imgThreshhold, lab, ColorConversion.Bgr2Lab);
VectorOfMat colorChannelB = new VectorOfMat();
CvInvoke.Split(lab, colorChannelB);
CvInvoke.CLAHE(colorChannelB[0], 3.0, new Size(12, 12), colorChannelB[0]);
Mat clahe = new Mat();
//merge
CvInvoke.Merge(colorChannelB, clahe);
Image<Bgr, byte> output = new Image<Bgr, byte>(#"D:\workspace\ocr_images\IMG_20200625_194541.jpg");
Bitmap bmp = output.ToBitmap();
//setting image to 300 dpi since tesseract likes that
bmp.SetResolution(300, 300);
bmp.Save(#"D:\workspace\ocr_images\IMG_20200625_194541.jpg");
I am not getting expected accuracy. Please check how image is converted.
source image
converted image
I have posted few images above that you can refer. For first image i am getting garbage data. For last two images i am getting partial data.
Converting image to gray scale and playing with threshold gives better output.
I want to understand that if in case threshold is the key part then how i will be able to get dynamic threshhold value for each new image? It is going to work as service so user will simply pass the image and get the result. My app should be intelligent enough to process and understand image.
Do i have to adjust contrast, threshold more accurately? If yes how i will do that? or image itself is faulty I mean noise causing problem.
Please let me know what i am doing wrong in the algorithm or anything which will help me to understand issue. Any one who is aware of please tell me what should be ideal steps for image preprocessing for OCR?
I am using csharp, emucv and tesseract.
Any suggestion will be highly appreciated.

OCR Line detecting

I am trying to detectect the bounding box of sentences in an Image. I am using Emgu OpenCV in C# using HougLinesP method to extract lines, but I am obviously doing something wrong. I have looked at many examples and estimate the skew level with houghLines is pretty much what I am trying to do.
Using that sample image I do some pre-processing (Thresholding, canny, ect) and end up with http://snag.gy/sWCuO.jpg, but then when I do HoughLines and draw the lines on the original image, I get http://snag.gy/ESKmR.jpg .
Here is an extract of my code:
using (MemStorage stor = new MemStorage())
{
Image<Hsv, byte> imgHSV = new Image<Hsv, byte>(bitmap);
Image<Gray, Byte> gray = imgHSV.Convert<Gray, Byte>().PyrDown().PyrUp();
CvInvoke.cvCanny(gray, EdgeMap, 100, 400, 3);
IntPtr lines = CvInvoke.cvHoughLines2(EdgeMap, stor,
Emgu.CV.CvEnum.HOUGH_TYPE.CV_HOUGH_PROBABILISTIC, 1, Math.PI / 360, 10,
gray.Width / 4, 20);
Seq<LineSegment2D> segments = new Seq<LineSegment2D>(lines, stor);
ar = segments.ToArray();
}
Graphics g = Graphics.FromImage(OriginalImage);
foreach (LineSegment2D line in ar)
{
g.DrawLine(new Pen(Color.Blue),
new Point(line.P1.X, line.P1.Y),
new Point(line.P2.X, line.P2.Y));
}
g.Save();
Any help would be appreciated.

You can try two approaches:
1- Utilize the frequency domain. Example here
2- After pre-processing, extract the contours, collect all the points (or at least collect all points that are not black); find the minimum bounding rectangle with its angle. Example here

Resize Image<Gray, byte> without scaling. Emgu CV

I am using Image.InRange to create a mask from an image. In order to keep performance at its maximum, I am using the Image.ROI to crop the image and prior to using the InRange method. In order to to actually work with image though, I need it to have the same dimensions as the original, but all that is apparent to me is how to scale an image, not change the dimensions preserving the image.
Here is the code in question:
public Image<Gray, byte> Process(Image<Bgr, byte> frameIn, Rectangle roi)
{
Image<Bgr, byte> rectFrame = null;
Image<Gray, byte> mask = null;
if (roi != Rectangle.Empty)
{
rectFrame = frameIn.Copy(roi);
}
else
{
rectFrame = frameIn;
}
if (Equalize)
{
rectFrame._EqualizeHist();
}
mask = rectFrame.InRange(minColor, maxColor);
mask ._Erode(Iterations);
mask ._Dilate(Iterations);
if (roi != Rectangle.Empty)
{
//How do I give the image its original dimensions?
}
return mask;
}
Thank you,
Chris

I will assume you wish to return the mask with the same size as framIn the easiest way is to copy the mask to a new image that has the same size as framIn. You could if your application isn't time sensitive make mask the same size of framIn set its ROI and then do your operations. This does take longer to process and isn't the best practice.
Anyway here is hopefully the code your after if not let me know and I'll correct it accordingly.
if (roi != Rectangle.Empty)
{
//Create a blank image with the correct size
Image<Gray, byte> mask_return = new Image<Gray, byte>(frameIn.Size);
//Set its ROI to the same as Mask and in the centre of the image (you may wish to change this)
mask_return.ROI = new Rectangle((mask_return.Width - mask.Width) / 2, (mask_return.Height - mask.Height) / 2, mask.Width, mask.Height);
//Copy the mask to the return image
CvInvoke.cvCopy(mask, mask_return, IntPtr.Zero);
//Reset the return image ROI so it has the same dimensions
mask_return.ROI = new Rectangle(0, 0, frameIn.Width, frameIn.Height);
//Return the mask_return image instead of the mask
return mask_return;
}
return mask;
Hope this helps,
Cheers,
Chris

How to make white blob tracking for video or camera capture on Emgu?

I want to make program using C# with Emgu that can detect white blobs on images from camera and also track it. Also, the program can return IDs of tracked blobs
Frame1: http://www.freeimagehosting.net/uploads/ff2ac19054.jpg
Frame2: http://www.freeimagehosting.net/uploads/09e20e5dd6.jpg

The Emgu sample project "VideoSurveilance" in the Emgu.CV.Example solution (Emgu.CV.Example.sln) demonstrates blob tracking and assigns ID's to them.
I'm a newbie to OpenCV but it seems to me that the tracking of only "white" blobs may be harder than it sounds. For example, the blobs in your sample picture aren't really "white" are they? What I think you are really trying to do is "get the blobs that are brighter than the background by a certain amount" i.e. find a gray blob on a black background or a white blob on a gray background.

It depends what's your background like. If it is constantly dark like on those images you attached, then you should be able to extract those "white" blobs with some threshold. For any smarter segmentation you'll need to use some other features as well (e.g. like correlation if your object is color consistent).

I cannot say the code will work because I haven't tested it.
The general idea is to take the captured frame (assuming you're capturing frames) and filter out the noise by modifying the saturation and value(brightness). This modified HSV image is then processed as greyscale. Blobs can be labeled by looping through the blob collection generated by the tracker and assigned id's and bounding boxes.
Also, you may be interested in AForge.net and the related article: Hands Gesture Recognition on the mechanics and implementation of using the histogram for computer vision.
This is a modified version of custom tracker code found on the nui forums:
static void Main(){
Capture capture = new Capture(); //create a camera captue
Image<Bgr, Byte> img = capture.QuerySmallFrame();
OptimizeBlobs(img);
BackgroundStatisticsModel bsm = new BackgroundStatisticsModel(img, Emgu.CV.CvEnum.BG_STAT_TYPE.FGD_STAT_MODEL);
bsm.Update(img);
BlobSeq oldBlobs = new BlobSeq();
BlobSeq newBlobs = new BlobSeq();
ForgroundDetector fd = new ForgroundDetector(Emgu.CV.CvEnum.FORGROUND_DETECTOR_TYPE.FGD);
BlobDetector bd = new BlobDetector(Emgu.CV.CvEnum.BLOB_DETECTOR_TYPE.CC);
BlobTracker bt = new BlobTracker(Emgu.CV.CvEnum.BLOBTRACKER_TYPE.CC);
BlobTrackerAutoParam btap = new BlobTrackerAutoParam();
btap.BlobDetector = bd;
btap.ForgroundDetector = fd;
btap.BlobTracker = bt;
btap.FGTrainFrames = 5;
BlobTrackerAuto bta = new BlobTrackerAuto(btap);
Application.Idle += new EventHandler(delegate(object sender, EventArgs e)
{ //run this until application closed (close button click on image viewer)
//******* capture image *******
img = capture.QuerySmallFrame();
OptimizeBlobs(img);
bd.DetectNewBlob(img, bsm.Foreground, newBlobs, oldBlobs);
List<MCvBlob> blobs = new List<MCvBlob>(bta);
MCvFont font = new MCvFont(Emgu.CV.CvEnum.FONT.CV_FONT_HERSHEY_SIMPLEX, 1.0, 1.0);
foreach (MCvBlob blob in blobs)
{
img.Draw(Rectangle.Round(blob), new Gray(255.0), 2);
img.Draw(blob.ID.ToString(), ref font, Point.Round(blob.Center), new Gray(255.0));
}
Image<Gray, Byte> fg = bta.GetForgroundMask();
});
}
public Image<Gray, Byte> OptimizeBlobs(Image<Gray, Byte img)
{
// can improve image quality, but expensive if real-time capture
img._EqualizeHist();
// convert img to temporary HSV object
Image<Hsv, Byte> imgHSV = img.Convert<Hsv, Byte>();
// break down HSV
Image<Gray, Byte>[] channels = imgHSV.Split();
Image<Gray, Byte> imgHSV_saturation = channels[1]; // saturation channel
Image<Gray, Byte> imgHSV_value = channels[2]; // value channel
//use the saturation and value channel to filter noise. [you will need to tweak these values]
Image<Gray, Byte> saturationFilter = imgHSV_saturation.InRange(new Gray(0), new Gray(80));
Image<Gray, Byte> valueFilter = imgHSV_value.InRange(new Gray(200), new Gray(255));
// combine the filters to get the final image to process.
Image<Gray, byte> imgTarget = huefilter.And(saturationFilter);
return imgTarget;
}