Things used in this project

Hardware components:
logitech webcam c210
×1
NVIDIA Jetson nvidia GPU gtx 750TI
×1
R8326274 01
Raspberry Pi 2 Model B
×1
Software apps and online services:
Vs2015logo
Microsoft Visual Studio 2015
10
Microsoft Windows 10 IoT Core
Microsoft CNTK
Virtual DUB
VLC

Code

Asp.Net Back end api CodeC#
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 codeC#
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 classC#
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);
UWP IOT codeC#
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();
        }
Trainmap .txtPlain 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
cifar-10-batches-py\data\train\closed000003.png	0
cifar-10-batches-py\data\train\closed000004.png	0
cifar-10-batches-py\data\train\closed000005.png	0
cifar-10-batches-py\data\train\closed000006.png	0
cifar-10-batches-py\data\train\closed000007.png	0
cifar-10-batches-py\data\train\closed000008.png	0
cifar-10-batches-py\data\train\closed000009.png	0
cifar-10-batches-py\data\train\closed000010.png	0
cifar-10-batches-py\data\train\closed000011.png	0
cifar-10-batches-py\data\train\closed000012.png	0
cifar-10-batches-py\data\train\closed000013.png	0
cifar-10-batches-py\data\train\closed000014.png	0
cifar-10-batches-py\data\train\closed000015.png	0
cifar-10-batches-py\data\train\closed000016.png	0
cifar-10-batches-py\data\train\closed000017.png	0
cifar-10-batches-py\data\train\closed000018.png	0
cifar-10-batches-py\data\train\closed000019.png	0
cifar-10-batches-py\data\train\closed000020.png	0
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cifar-10-batches-py\data\train\closed000024.png	0
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cifar-10-batches-py\data\train\closed000026.png	0
cifar-10-batches-py\data\train\closed000027.png	0
cifar-10-batches-py\data\train\closed000028.png	0
cifar-10-batches-py\data\train\closed000029.png	0
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cifar-10-batches-py\data\train\closed000038.png	0
cifar-10-batches-py\data\train\closed000039.png	0
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cifar-10-batches-py\data\train\closed000064.png	0
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cifar-10-batches-py\data\train\closed000070.png	0
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cifar-10-batches-py\data\train\closed000072.png	0
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cifar-10-batches-py\data\train\closed000075.png	0
cifar-10-batches-py\data\train\closed000076.png	0
cifar-10-batches-py\data\train\closed000077.png	0
cifar-10-batches-py\data\train\closed000078.png	0
cifar-10-batches-py\data\train\closed000079.png	0
cifar-10-batches-py\data\train\closed000080.png	0
cifar-10-batches-py\data\train\closed000081.png	0
cifar-10-batches-py\data\train\closed000082.png	0
cifar-10-batches-py\data\train\closed000083.png	0
cifar-10-batches-py\data\train\closed000084.png	0
cifar-10-batches-py\data\train\closed000085.png	0
cifar-10-batches-py\data\train\closed000086.png	0
cifar-10-batches-py\data\train\closed000087.png	0
cifar-10-batches-py\data\train\closed000088.png	0
cifar-10-batches-py\data\train\closed000089.png	0
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cifar-10-batches-py\data\train\closed000110.png	0
cifar-10-batches-py\data\train\closed000111.png	0
cifar-10-batches-py\data\train\closed000112.png	0
cifar-10-batches-py\data\train\closed000113.png	0
cifar-10-batches-py\data\train\closed000114.png	0
cifar-10-batches-py\data\train\closed000115.png	0
cifar-10-batches-py\data\train\closed000116.png	0
cifar-10-batches-py\data\train\closed000117.png	0
cifar-10-batches-py\data\train\closed000118.png	0
cifar-10-batches-py\data\train\closed000119.png	0
cifar-10-batches-py\data\train\closed000120.png	0
cifar-10-batches-py\data\train\closed000121.png	0
cifar-10-batches-py\data\train\closed000122.png	0
cifar-10-batches-py\data\train\closed000123.png	0
cifar-10-batches-py\data\train\closed000124.png	0
cifar-10-batches-py\data\train\closed000125.png	0
cifar-10-batches-py\data\train\closed000126.png	0
cifar-10-batches-py\data\train\closed000127.png	0
cifar-10-batches-py\data\train\closed000128.png	0
cifar-10-batches-py\data\train\closed000129.png	0
cifar-10-batches-py\data\train\closed000130.png	0
cifar-10-batches-py\data\train\closed000131.png	0
cifar-10-batches-py\data\train\closed000132.png	0
cifar-10-batches-py\data\train\closed000133.png	0
cifar-10-batches-py\data\train\closed000134.png	0
cifar-10-batches-py\data\train\closed000135.png	0
cifar-10-batches-py\data\train\closed000136.png	0
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Credits

12141491 10153568203433548 4330990757421737359 n
Bhairav Pardiwala

Polyglot dev

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