Bhairav Pardiwala
Published

Machine Learning with Windows 10 IoT Core

Use machine learning with IoT to identify and connect with common scenarios in life (for example, webcam-based door security).

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Machine Learning with Windows 10 IoT Core

Things used in this project

Hardware components

logitech webcam c210
×1
NVIDIA Jetson nvidia GPU gtx 750TI
×1
Raspberry Pi 2 Model B
Raspberry Pi 2 Model B
×1

Software apps and online services

Visual Studio 2015
Microsoft Visual Studio 2015
Windows 10 IoT Core
Microsoft Windows 10 IoT Core
Microsoft CNTK
Virtual DUB
VLC

Story

Read more

Code

Asp.Net Back end api Code

C#
The Aps.net Backend Api code thhat allows IOT app to upload a photo to it
  //http://localhost:63838/api/Door/UploadFiles
        [HttpPost()]
        public String UploadFiles()
        {
            int iUploadedCnt = 0;

            
            // DEFINE THE PATH WHERE WE WANT TO SAVE THE FILES.
            string sPath = "";

            String randomfilename = Guid.NewGuid().ToString();
            String randomoutputname = Guid.NewGuid().ToString();
            sPath = System.Web.Hosting.HostingEnvironment.MapPath("~/tempstorage/");

            System.Web.HttpFileCollection httpfilecollection = System.Web.HttpContext.Current.Request.Files;

          
            int iCnt = 0;
                System.Web.HttpPostedFile hpf = httpfilecollection[iCnt];

                if (hpf.ContentLength > 0)
                {
                   
                    if (!File.Exists(sPath + randomfilename))
                    {
                       
                        hpf.SaveAs(sPath + randomfilename);

                   

                    System.Diagnostics.Process process = new System.Diagnostics.Process();
                    process.StartInfo.FileName = "iotcheck";
                    process.StartInfo.WorkingDirectory = @"C:\iotcheck";

                    process.StartInfo.Arguments = @""""+sPath + randomoutputname +@""""+" "+ @""""+sPath + randomfilename+@"""";
                   
                    process.StartInfo.WindowStyle = System.Diagnostics.ProcessWindowStyle.Hidden;
                    process.Start();
                    process.WaitForExit();
                    while (!process.HasExited)
                    {
                        Thread.Sleep(500);
                    }

                   

                    String returnStr= File.ReadLines(sPath + randomoutputname).First();

                    File.Delete(sPath + randomoutputname);
                    File.Delete(sPath + randomfilename);

                    return returnStr;

                    //   test=Utility.EvaluationSingleImage(CNTK.DeviceDescriptor.DefaultDevice());


                }
                }
            


            // RETURN A MESSAGE (OPTIONAL).
            if (iUploadedCnt > 0)
            {
                return "invalid";
            }
            else
            {
                return "Please upload a file to this api";
            }
        }

Console app code

C#
The actual console app called with 2 arguments ["output file name" "input file name"]
This is the actual app that checks that the scene given corresponds to what trained value
 class Program
    {
        static void Main(string[] args)
        {
            if(args.Length==2)
            {
               List<ValPerc> output= EvaluationSingleImage(DeviceDescriptor.DefaultDevice(), args[1]);

                ValPerc actualVal = null;
                double maxperc=0;
                foreach(var item in output)
                {
                    if(Convert.ToDouble(item.percent)>maxperc)
                    {
                        maxperc = (Convert.ToDouble(item.percent));
                        actualVal = item;
                    }
                }
                if(actualVal!=null)
                {
                    OutputFile(args[0], actualVal.val);
                }
                else
                {
                    OutputFile(args[0], "invalid");
                }

            }
            else
            {
                OutputFile(args[0], "invalid params");
            }

        }
        public static void OutputFile(String filename,String message)
        {
            using (StreamWriter outputFile = new StreamWriter(filename))
            {
                    outputFile.WriteLine(message);
            }
        }
        public static List<valperc> EvaluationSingleImage(DeviceDescriptor device,String fileName)
        {
            const string outputName = "z";
            var inputDataMap = new Dictionary<Variable, Value>();

            // Load the model.
            Function modelFunc = Function.LoadModel(@"C:\local\cntk\Tutorials\ImageHandsOn\Models\cifar10.cmf", device);

            // Get output variable based on name
            Variable outputVar = modelFunc.Outputs.Where(variable => string.Equals(variable.Name, outputName)).Single();

            // Get input variable. The model has only one single input.
            // The same way described above for output variable can be used here to get input variable by name.
            Variable inputVar = modelFunc.Arguments.First();
            var outputDataMap = new Dictionary<Variable, Value>();
            Value inputVal, outputVal;
            List<List<float>> outputBuffer;

            // Get shape data for the input variable
            NDShape inputShape = inputVar.Shape;
            uint imageWidth = inputShape[0];
            uint imageHeight = inputShape[1];
            uint imageChannels = inputShape[2];
            uint imageSize = inputShape.TotalSize;

            Console.WriteLine("Evaluate single image");

            // Image preprocessing to match input requirements of the model.
            Bitmap bmp = new Bitmap(Bitmap.FromFile(fileName));
            var resized = bmp.Resize((int)imageWidth, (int)imageHeight, true);
            List<float> resizedCHW = resized.ParallelExtractCHW();

            // Create input data map
            inputVal = Value.CreateBatch(inputVar.Shape, resizedCHW, device);
            inputDataMap.Add(inputVar, inputVal);

            // Create ouput data map. Using null as Value to indicate using system allocated memory.
            // Alternatively, create a Value object and add it to the data map.
            outputDataMap.Add(outputVar, null);

            // Start evaluation on the device
            modelFunc.Evaluate(inputDataMap, outputDataMap, device);

            // Get evaluate result as dense output
            outputBuffer = new List<List<float>>();
            outputVal = outputDataMap[outputVar];
            outputVal.CopyVariableValueTo(outputVar, outputBuffer);
            var s = PrintOutput(outputVar.Shape.TotalSize, outputBuffer);
            return s;

        }
        private static List<ValPerc> PrintOutput<T>(uint sampleSize, List<List<T>> outputBuffer)
        {
            Console.WriteLine("The number of sequences in the batch: " + outputBuffer.Count);
            int seqNo = 0;
            uint outputSampleSize = sampleSize;

            System.Collections.Generic.List<ValPerc> listOfOutputs = new System.Collections.Generic.List<ValPerc>();


            foreach (var seq in outputBuffer)
            {
                Console.WriteLine(String.Format("Sequence {0} contains {1} samples.", seqNo++, seq.Count / outputSampleSize));
                uint i = 0;
                uint sampleNo = 0;

                uint cnt = 0;
                foreach (var element in seq)
                {

                    if (i++ % outputSampleSize == 0)
                    {
                        Console.Write(String.Format("    sample {0}: ", sampleNo));
                    }

                    String val = "";
                    switch (cnt)
                    {
                        case 0:
                          //  val = "airplane";
                            val = "closed";
                            break;
                        case 1:
                          //  val = "automobile";
                            val = "open";
                            break;
                        case 2:
                        //not used this is used for cifr 10 example
                            val = "bird";
                            break;
                        case 3:
                        //   //not used this is used for cifr 10 example
                            val = "cat";
                            break;
                        case 4:
                        //   //not used this is used for cifr 10 example
                            val = "deer";
                            break;
                        case 5:
                        //   //not used this is used for cifr 10 example
                            val = "dog";
                            break;
                        case 6:
                           //not used this is used for cifr 10 example
                            val = "frog";
                            break;
                        case 7:
                           //not used this is used for cifr 10 example
                            val = "horse";
                            break;
                        case 8:
                           //not used this is used for cifr 10 example
                            val = "ship";
                            break;

                        case 9:
                           //not used this is used for cifr 10 example
                            val = "truck";
                            break;

                    }

                    listOfOutputs.Add(new ValPerc { percent = element.ToString(), val = val });
                    Console.Write(element);
                    if (i % outputSampleSize == 0)
                    {
                        Console.WriteLine(".");
                        sampleNo++;
                    }
                    else
                    {
                        Console.Write(",");
                    }
                    cnt += 1;
                }

            }
            return listOfOutputs;



        }
    }

Model for class

C#
The model that Console app uses
 public class ValPerc
    {
        public string val { get; set; }
        public string percent{get;set;}
    }

Sample code i used to generate the label set for the files

C#
This is sample WPF app code that i sued to generate the training set labels
 //String s = @"cifar-10-batches-py\data\train\closednumb.png	0"+Environment.NewLine;
            String s = @"cifar-10-batches-py\data\train\opennumb.png	1" + Environment.NewLine;
            String test = "";
            for(int i=0;i<2876;i++) //2876 is the last frame for the video file 
            {
                test += s.Replace("numb", i.ToString("D6")); //giving it padding
            }
            //000000
            Clipboard.SetText(test);

Trainmap .txt

Plain text
CNTK sample file i had given
Notice the label at the end of the line
0 stands of closed
1 stands for open
cifar-10-batches-py\data\train\closed000000.png is path of file
cifar-10-batches-py\data\train\closed000000.png	0
cifar-10-batches-py\data\train\closed000001.png	0
cifar-10-batches-py\data\train\closed000002.png	0
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UWP IOT code

C#
This is thre front end IOT code
 DispatcherTimer tmr;
        MediaCapture _mediaCapture;

        public MainPage()
        {
            this.InitializeComponent();
            tmr = new DispatcherTimer();
            tmr.Interval = TimeSpan.FromSeconds(5);
            tmr.Tick += Tmr_Tick;
            tmr.Start();
            _mediaCapture = new MediaCapture();

        }

        private async void Tmr_Tick(object sender, object e)
        {







            txtResponse.Text = await CapturePhoto();

        }

        private async Task<String> CapturePhoto()
        {
            var myPictures = await Windows.Storage.StorageLibrary.GetLibraryAsync(Windows.Storage.KnownLibraryId.Pictures);
            StorageFile file = await myPictures.SaveFolder.CreateFileAsync("photo.jpg", CreationCollisionOption.GenerateUniqueName);


            using (var captureStream = new InMemoryRandomAccessStream())
            {

                await _mediaCapture.CapturePhotoToStreamAsync(ImageEncodingProperties.CreateJpeg(), captureStream);

                using (var fileStream = await file.OpenAsync(FileAccessMode.ReadWrite))
                {
                    var decoder = await BitmapDecoder.CreateAsync(captureStream);
                    var encoder = await BitmapEncoder.CreateForTranscodingAsync(fileStream, decoder);

                    var properties = new BitmapPropertySet {
            { "System.Photo.Orientation", new BitmapTypedValue(PhotoOrientation.Normal, PropertyType.UInt16) }
        };
                    await encoder.BitmapProperties.SetPropertiesAsync(properties);

                    await encoder.FlushAsync();


                }
            }
            String ret = await Upload("http://192.168.0.105:8090/api/Door/UploadFiles", file);

            await file.DeleteAsync();
            return ret;
        }

        private async Task<string> Upload(string actionUrl, StorageFile fileToUpload)
        {

            try
            {
                var client = new HttpClient();

                MultipartFormDataContent form = new MultipartFormDataContent();
                HttpContent content;
                //HttpContent content = new StringContent("fileToUpload");
                //form.Add(content, "fileToUpload");
                var stream = await fileToUpload.OpenStreamForReadAsync();
                content = new StreamContent(stream);
                content.Headers.ContentDisposition = new ContentDispositionHeaderValue("form-data")
                {
                    Name = "fileToUpload",
                    FileName = fileToUpload.Name
                };
                form.Add(content);
                var response = await client.PostAsync(actionUrl, form);
                return response.Content.ReadAsStringAsync().Result;
            }
            catch (Exception e)
            {
                return "invalid response";

            }
        }

        private async void Page_Loaded(object sender, RoutedEventArgs e)
        {
            await _mediaCapture.InitializeAsync();

            previewElement.Source = _mediaCapture;
            await _mediaCapture.StartPreviewAsync();
        }

Credits

Bhairav Pardiwala

Bhairav Pardiwala

3 projects • 8 followers
Polyglot dev

Comments