Hi !

LearnOpenCV is an amazing resource to learn about OpenCV. And, it has lot of scenarios of real life problem solved with OpenCV. Most of the samples are in C++ or Python, so I decided to pick one related to pose estimation, and using .Net 5 in a Winforms App, build something like this:

The main model is OpenPose (see references). The model is amazing, and also works fast: ~1 FPS. There are other variations here, detecting face, Body, Foot, Face, and Hands Estimation, and more. I’ll try and share some of the other models usage in C# in next posts.

Now as usual, a huge code snippet with only the frame recognition and processing to detect the body joints.

private void CaptureCameraCallback()
{
    while (true)
    {
        if (!_run) continue;
        var startTime = DateTime.Now;

        _capture.Read(_image);
        if (_image.Empty()) return;
        var imageRes = new Mat();
        Cv2.Resize(_image, imageRes, new Size(320, 240));
        if (_detectPose)
        {

            var frameWidth = imageRes.Cols;
            var frameHeight = imageRes.Rows;

            const int inWidth = 368;
            const int inHeight = 368;

            // Convert Mat to batch of images
            using var inpBlob = CvDnn.BlobFromImage(imageRes, 1.0 / 255, new Size(inWidth, inHeight), new Scalar(0, 0, 0), false, false);

            _netPose.SetInput(inpBlob);

            using var output = _netPose.Forward();
            var H = output.Size(2);
            var W = output.Size(3);

            var points = new List<Point>();

            for (var n = 0; n < nPoints; n++)
            {
                // Probability map of corresponding body's part.
                using var probMap = new Mat(H, W, MatType.CV_32F, output.Ptr(0, n));
                var p = new Point2f(-1, -1);

                Cv2.MinMaxLoc(probMap, out _, out var maxVal, out _, out var maxLoc);

                var x = (frameWidth * maxLoc.X) / W;
                var y = (frameHeight * maxLoc.Y) / H;

                if (maxVal > thresh)
                {
                    p = maxLoc;
                    p.X *= (float)frameWidth / W;
                    p.Y *= (float)frameHeight / H;

                    Cv2.Circle(imageRes, (int)p.X, (int)p.Y, 8, Scalar.Azure, -1);
                    //Cv2.PutText(imageRes, Cv2.Format(n), new Point((int)p.X, (int)p.Y), HersheyFonts.HersheyComplex, 1, new Scalar(0, 0, 255), 1);
                }

                points.Add((Point)p);
            }

            WriteTextSafe(@$"Joints {nPoints} found");

            var nPairs = 14; //(POSE_PAIRS).Length / POSE_PAIRS[0].Length;

            for (var n = 0; n < nPairs; n++)
            {
                // lookup 2 connected body/hand parts
                var partA = points[posePairs[n][0]];
                var partB = points[posePairs[n][1]];
                if (partA.X <= 0 || partA.Y <= 0 || partB.X <= 0 || partB.Y <= 0)
                    continue;
                Cv2.Line(imageRes, partA, partB, new Scalar(0, 255, 255), 8);
                Cv2.Circle(imageRes, partA.X, partA.Y, 8, new Scalar(0, 0, 255), -1);
                Cv2.Circle(imageRes, partB.X, partB.Y, 8, new Scalar(0, 0, 255), -1);
            }

        }
// rest of the code to calc FPS and display the image
    }
}

Super fun ! and check the references for the model and support files download location.

References

• OpenCVSharp, https://github.com/shimat/opencvsharp
• Wikipedia, Canny Edge Detector
• OpenCV, Canny tutorial
• OpenCV, FAST tutorial
• Wikipedia, FAST
• OpenCV, Perform face detection using Haar Cascades
• OpenCV, Load Caffe framework models
• Caffe Model Zoo
• LearnOpenCV, Multi-Person Pose Estimation in OpenCV using OpenPose
• GitHub, CMU-Perceptual, OpenPose