private void testWithLines(StringBuilder sb) { var reader = new StreamReader(new MemoryStream(Encoding.UTF8.GetBytes(sb.ToString()))); Predict.doPredict(reader, writer, testModel); }
private int selected; //for the pick state // Use this for initialization void Start() { canvas.SetActive(false); normalState.SetActive(true); drawingState.SetActive(false); pickState.SetActive(false); wrongState.SetActive(false); editState.SetActive(false); systemState.SetActive(false); canvasOpen = false; predictor = GameObject.FindGameObjectWithTag("Predictor").GetComponent <Predict> (); selector = GameObject.FindGameObjectWithTag("ScaleSelection").GetComponent <Selection> (); scaler = GameObject.FindGameObjectWithTag("ScaleSelection").GetComponent <ScaleScript> (); mapping = new int[10]; mapping [0] = 4; mapping [1] = 6; mapping [2] = 5; mapping [3] = 8; mapping [4] = 10; mapping [5] = 1; mapping [6] = 2; mapping [7] = 3; mapping [8] = 7; mapping [9] = 9; }
public static List <StockControlModelPredict> GetPredictModel(int beverageId) { DateTime lastWeek = DateTime.Today.AddDays(-7); using (var context = new KoreanStudyCafeEntities()) { var query = from x in context.BeverageRecords orderby x.DayQuarter where x.BeverageID == beverageId && x.Date == lastWeek select x; List <BeverageRecord> list = query.ToList(); var query2 = from x in list select new StockControlModelPredict { UserCount = x.UserCount, DayQuater = x.DayQuarter, Usage = Predict.Predictor(beverageId, x.DayQuarter, x.UserCount, GetDay(DateTime.Now)) }; return(query2.ToList()); //return list.ConvertAll(x => new StockConreolModelPredictCoffee //{ // UserCount = x.UserCount, // DayQuater = x.DayQuarter, // Usage = Predict.Predictor(1, x.DayQuarter, x.UserCount, GetDay(DateTime.Now)) //}); } }
static void Main(string[] args) { string[] files = Directory.GetFiles(@"C:\Users\brush\Documents\Visual Studio 2012\Projects\SetSpotter\SetSpotter\testimages\"); int i = 0; Predict red = new Predict(@"C:\Users\brush\Desktop\round2\red.txt.model"); Predict purple = new Predict(@"C:\Users\brush\Desktop\round2\purple.txt.model"); Predict green = new Predict(@"C:\Users\brush\Desktop\round2\green.txt.model"); //foreach (string file in files) { FoundColorSpaces foundColorSpaces = ColorSpaceFinder.Find(@"C:\Users\brush\Desktop\IMAG2112.jpg"); FoundBlobs foundBlobs = BlobFinder.Find(foundColorSpaces, 80, 25, 90, 50, 1.2, 2.2); foreach (Blob blob in foundBlobs.Blobs) { //Bitmap color = foundColorSpaces.OriginalColorSpace.Clone(blob.Rectangle, PixelFormat.Format24bppRgb); Bitmap color = ColorSpaceFinder.FindColorCorrectedForBlob(foundColorSpaces, blob); ColorTypeEnum colorType = ColorSpaceFinder.FindShapeColor(color, red, purple, green); using (Graphics g = Graphics.FromImage(color)) { g.DrawString(colorType.ToString(), new Font("Arial", 12), Brushes.Black, new PointF(0, 5)); } color.Save(@"C:\users\brush\desktop\blobs\" + (i++).ToString() + ".bmp"); } } return; }
public Record getPredict(string ctsMarker, ClientObjectAttribute cl) { Program dbscan = new Program(); //Debug.Log("!!!!!!!!!!!!!Start Dbscan!!!!!!!!!!!!!"); List <int> clusterIds; List <Cluster> clusterDes; HashSet <Record[]> _clusters = dbscan.Dbscan(out clusterIds, out clusterDes); Predict predict = new Predict(); List <Record> gazePoints = preGaze.getGazePoints(getAllBrowseRecord()); List <Record> predictedCluster = predict.GetPredictedCluster( new Vector3(cl.CameraPosX, cl.CameraPosY, cl.CameraPosZ), new Vector3(cl.CameraRotX, cl.CameraRotY, cl.CameraRotZ), _clusters, clusterDes ); Record posRange = getRangeRecord(predictedCluster); Vector3 tmpPos = pathHistory[ctsMarker].cameraPos; Vector3 rot; float x_sum = 0, y_sum = 0, z_sum = 0, x_rot = 0, y_rot = 0, z_rot = 0; foreach (var j in predictedCluster) { x_sum += (float)j.posX; y_sum += (float)j.posY; z_sum += (float)j.posZ; x_rot += (float)j.rotX; y_rot += (float)j.rotY; z_rot += (float)j.rotZ; } float x_ave = x_sum / predictedCluster.Count; float y_ave = y_sum / predictedCluster.Count; float z_ave = z_sum / predictedCluster.Count; float x_averot = x_rot / predictedCluster.Count; float y_averot = y_rot / predictedCluster.Count; float z_averot = z_rot / predictedCluster.Count; if (tmpPos.x <= posRange.posX && tmpPos.y <= posRange.posY && tmpPos.z <= posRange.posZ && tmpPos.x >= posRange.rotX && tmpPos.y >= posRange.rotY && tmpPos.z >= posRange.rotZ) { rot = new Vector3(x_averot, y_averot, z_averot); } else { rot = new Vector3(x_ave, y_ave, z_ave); rot = rot - tmpPos; } //------ /*if (predictedCluster != null) * { * //把块变成坐标系 * var exchangeAxis = new ExchangeAxis(); * Record pos = new Record(exchangeAxis.ModelIndex_to_unityPos(x_ave, y_ave, z_ave), new Vector3(x_averot, y_averot, z_averot)); * return pos; * * } * else { Debug.Log("No suggestion"); }*/ return(new Record(MoveForwardPredict(pathHistory[ctsMarker], rot), rot)); }
public Record getPredict(string ctsMarker, ClientObjectAttribute cl) { Debug.Log("Start predict"); Predict predict = new Predict(); List <Record> gazePoints = preGaze.getGazePoints(getAllBrowseRecord()); Cluster predictedCluster = predict.GetPredictedCluster( new Vector3(cl.CameraPosX, cl.CameraPosY, cl.CameraPosZ), new Vector3(cl.CameraRotX, cl.CameraRotY, cl.CameraRotZ), clusterNet, gazePoints); //Record posRange = getRangeRecord(predictedCluster); Vector3 tmpPos = pathHistory[ctsMarker].cameraPos; //float x_sum = 0, y_sum = 0, z_sum = 0, x_rot = 0, y_rot = 0, z_rot = 0; //foreach (var j in predictedCluster) //{ // x_sum += (float)j.posX; // y_sum += (float)j.posY; // z_sum += (float)j.posZ; // x_rot += (float)j.rotX; // y_rot += (float)j.rotY; // z_rot += (float)j.rotZ; //} //float x_ave = x_sum / predictedCluster.Count; //float y_ave = y_sum / predictedCluster.Count; //float z_ave = z_sum / predictedCluster.Count; Vector3 cluterPos = predictedCluster.center; //float x_averot = x_rot / predictedCluster.Count; //float y_averot = y_rot / predictedCluster.Count; //float z_averot = z_rot / predictedCluster.Count; Vector3 rot = cluterPos - tmpPos; //if (tmpPos.x <= posRange.posX && tmpPos.y <= posRange.posY && tmpPos.z <= posRange.posZ // && tmpPos.x >= posRange.rotX && tmpPos.y >= posRange.rotY && tmpPos.z >= posRange.rotZ) //{ // rot = new Vector3(x_averot, y_averot, z_averot); //} //else //{ // rot = new Vector3(x_ave, y_ave, z_ave); // rot = rot - tmpPos; //} //------ /*if (predictedCluster != null) * { * //把块变成坐标系 * var exchangeAxis = new ExchangeAxis(); * Record pos = new Record(exchangeAxis.ModelIndex_to_unityPos(x_ave, y_ave, z_ave), new Vector3(x_averot, y_averot, z_averot)); * return pos; * * } * else { Debug.Log("No suggestion"); }*/ return(new Record(MoveForwardPredict(pathHistory[ctsMarker], rot), rot)); }
private void Init() { isFirstLoad = true; Predictions = null; python = !string.IsNullOrEmpty(pythonPath) ? new PythonExecutor(pythonPath) : new PythonExecutor(); python.AddStandartOutputErrorFilters("Using TensorFlow backend."); python.AddStandartOutputErrorFilters("CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected"); client = new PipeClient(); predict = new Predict(client); python.OnPythonError += Python_OnPythonError; TinfoBox.Start(initText, title); python.RunScript("main.py"); client.Connect("openstsm"); }
void Brain.IObserver.onPredict(Predict response) { if (response?.prediction == null) { _logger.warning($"Received null prediction from server..."); return; } var snap = new BodyVectorFloat(); for (int i = 0; i < snap.length; ++i) { snap[i] = response.prediction[i]; } _logger.info($"Received prediction from server: {_targetSnapshot}"); _targetSnapshot = snap; }
protected void Page_Load(object sender, EventArgs e) { ServiceReference1.Service1Client service = new ServiceReference1.Service1Client(); ServiceReference1.UserDetail user = (ServiceReference1.UserDetail)HttpContext.Current.Session["User"]; if (user.AccessLvl == 1) { ServiceReference1.reportDonation[] donations = service.getAllDailyDonations(service.getNPOWithManager(user.userValue.UserID)); ServiceReference1.reportDonation[] don = Predict.prodictValue(donations, 6); dates = new string[don.Count()]; amounts = new double[don.Count()]; for (int i = 0; i < don.Count(); i++) { dates[i] = don[i].Date; amounts[i] = don[i].Amount; } } }
public void RunSelectiveSearch() { Predict predict; PythonExecutor python = new PythonExecutor(@"C:\Users\kutiatore\AppData\Local\Programs\Python\Python35\python.exe"); python.AddStandartOutputErrorFilters("Using TensorFlow backend."); python.AddStandartOutputErrorFilters("CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected"); PipeClient client = new PipeClient(); predict = new Predict(client); try { python.RunScript("main.py"); client.Connect("openstsm"); predict = new Predict(client); bool run = predict.LoadModel(@"E:\\Storage\\Python\\OpenSTSM\\ML\\models\\model.model"); run = predict.ImageDimessionCorrections("E:\\Libraries\\Desktop\\Visa Docs\\test_selective_1.png"); run = predict.RunSelectiveSearch(80, 1.0f); string results = predict.RunPrediction(5, 3, 5, 1, 8, 2, true); client.Close(); python.Close(); } catch (System.Exception e) { System.Diagnostics.Debug.WriteLine("---------------------"); System.Diagnostics.Debug.WriteLine(e.StackTrace); System.Diagnostics.Debug.WriteLine("---------------------"); if (client.isConnected()) { client.Close(); } python.Close(); } }
public VersionController(Predict predict, ITextClassifier textClassifier) { _predict = predict; _textClassifier = textClassifier; }
public static async Task <Predict> GetImgUrlPrediction(string accessToken, List <string> imgURLs) { ServicePointManager.ServerCertificateValidationCallback += new RemoteCertificateValidationCallback(ValidateServerCertificate); // // MESSAGE CONTENT //string postData = "client_id="+ clientId + "&client_secret="+ clientSecret + "&grant_type=client_credentials"; List <PredictInput> inputs = new List <PredictInput>(); foreach (string imgUrl in imgURLs) { inputs.Add(new PredictInput { Data = new PredictImage { Image = new PredictImageData { Url = imgUrl } } }); } var ins = new { Inputs = inputs }; string postData = LowercaseJsonSerializer.SerializeObject(ins); byte[] byteArray = Encoding.UTF8.GetBytes(postData); // // CREATE REQUEST HttpWebRequest Request = (HttpWebRequest)WebRequest.Create(_apiEndPoint + _predictPath); Request.Method = "POST"; Request.KeepAlive = false; Request.ContentType = "application/json"; Request.Headers.Add("cache-control", "no-cache"); Request.Headers.Add("authorization", "Bearer " + accessToken); Stream dataStream = await Request.GetRequestStreamAsync(); await dataStream.WriteAsync(byteArray, 0, byteArray.Length); dataStream.Close(); // // SEND MESSAGE try { WebResponse Response = await Request.GetResponseAsync(); StreamReader Reader = new StreamReader(Response.GetResponseStream()); string responseLine = await Reader.ReadToEndAsync(); Reader.Close(); HttpStatusCode ResponseCode = ((HttpWebResponse)Response).StatusCode; if (!ResponseCode.Equals(HttpStatusCode.OK)) { Predict error = Newtonsoft.Json.JsonConvert.DeserializeObject <Predict>(responseLine); return(error); } Predict predict = Newtonsoft.Json.JsonConvert.DeserializeObject <Predict>(responseLine); int ind = 0; foreach (string imgUrl in imgURLs) { var outputs = predict.Outputs; outputs[ind].Data.Concepts[0].ImageName = imgUrl; ind++; } return(predict); } catch (WebException e) { using (WebResponse response = e.Response) { HttpWebResponse httpResponse = (HttpWebResponse)response; using (Stream data = response.GetResponseStream()) using (var reader = new StreamReader(data)) { string text = reader.ReadToEnd(); Predict error = Newtonsoft.Json.JsonConvert.DeserializeObject <Predict>(text); return(error); } } } return(new Predict { Status = new PredictStatus { Code = 0, Description = "Undefined Error" } }); }
public static async Task <Predict> GetFolderImgsPrediction(string accessToken, string folder_path) { ServicePointManager.ServerCertificateValidationCallback += new RemoteCertificateValidationCallback(ValidateServerCertificate); // // MESSAGE CONTENT //string postData = "client_id="+ clientId + "&client_secret="+ clientSecret + "&grant_type=client_credentials"; List <PredictInput> inputs = new List <PredictInput>(); var imgPaths = Directory.GetFiles(folder_path, "*.*", SearchOption.AllDirectories).ToList(); foreach (string imgPath in imgPaths) { Bitmap source = new Bitmap(imgPath); int x = source.Width - 120; int y = source.Height - 120; Bitmap CroppedImage = source.Clone(new Rectangle(x, y, 120, 120), source.PixelFormat); System.IO.MemoryStream ms = new System.IO.MemoryStream(); CroppedImage.Save(ms, System.Drawing.Imaging.ImageFormat.Jpeg); byte[] imageArray = ms.ToArray(); //byte[] imageArray = System.IO.File.ReadAllBytes(imgPath); string base64ImageRepresentation = Convert.ToBase64String(imageArray); inputs.Add(new PredictInput { Data = new PredictImage { Image = new PredictImageData { Base64 = base64ImageRepresentation } } }); } var ins = new { Inputs = inputs }; string postData = LowercaseJsonSerializer.SerializeObject(ins); byte[] byteArray = Encoding.UTF8.GetBytes(postData); // // CREATE REQUEST HttpWebRequest Request = (HttpWebRequest)WebRequest.Create(_apiEndPoint + _predictPath); Request.Method = "POST"; Request.KeepAlive = false; Request.ContentType = "application/json"; Request.Headers.Add("cache-control", "no-cache"); Request.Headers.Add("authorization", "Bearer " + accessToken); Stream dataStream = await Request.GetRequestStreamAsync(); await dataStream.WriteAsync(byteArray, 0, byteArray.Length); dataStream.Close(); // // SEND MESSAGE try { WebResponse Response = await Request.GetResponseAsync(); StreamReader Reader = new StreamReader(Response.GetResponseStream()); string responseLine = await Reader.ReadToEndAsync(); Reader.Close(); HttpStatusCode ResponseCode = ((HttpWebResponse)Response).StatusCode; if (!ResponseCode.Equals(HttpStatusCode.OK)) { Predict error = Newtonsoft.Json.JsonConvert.DeserializeObject <Predict>(responseLine); return(error); } Predict predict = Newtonsoft.Json.JsonConvert.DeserializeObject <Predict>(responseLine); int ind = 0; foreach (string imgPath in imgPaths) { var outputs = predict.Outputs; outputs[ind].Data.Concepts[0].ImageName = imgPath; ind++; } return(predict); } catch (WebException e) { using (WebResponse response = e.Response) { HttpWebResponse httpResponse = (HttpWebResponse)response; using (Stream data = response.GetResponseStream()) using (var reader = new StreamReader(data)) { string text = reader.ReadToEnd(); Predict error = Newtonsoft.Json.JsonConvert.DeserializeObject <Predict>(text); return(error); } } } return(new Predict { Status = new PredictStatus { Code = 0, Description = "Undefined Error" } }); }
public async Task <Predict> PredictByFolderPath(string folder_path) { Predict response = await ClarifaiHttpClient.GetFolderImgsPrediction(AccessToken, folder_path); return(response); }
public async Task <Predict> PredictByImgURL(List <string> urls) { Predict response = await ClarifaiHttpClient.GetImgUrlPrediction(AccessToken, urls); return(response); }
public static ColorTypeEnum FindShapeColor(Bitmap shapeBitmap, Predict red, Predict green, Predict purple) { List<HSL> pixels = new List<HSL>(); for (int y = 0; y < shapeBitmap.Height; y++) { for (int x = 0; x < shapeBitmap.Width; x++) { Color color = shapeBitmap.GetPixel(x, y); pixels.Add(HSL.FromRGB(new RGB(color.R, color.G, color.B))); } } HSL[] subset = pixels.OrderBy(m => m.Luminance).Take(10).ToArray(); int redCount = 0, greenCount = 0, purpleCount = 0; foreach(HSL pixel in subset) { double[] input = new double[] { pixel.Hue, pixel.Saturation * 360.0, pixel.Luminance * 360.0}; double isRed = red.Compute(input); double isPurple = purple.Compute(input); double isGreen = green.Compute(input); if (isRed > isPurple && isRed > isGreen) redCount++; else if (isPurple > isRed && isPurple > isGreen) purpleCount++; else if (isGreen > isRed && isGreen > isPurple) greenCount++; } if (redCount > greenCount && redCount > purpleCount) return ColorTypeEnum.Red; else if (greenCount > redCount && greenCount > purpleCount) return ColorTypeEnum.Green; else if (purpleCount > redCount && purpleCount > greenCount) return ColorTypeEnum.Purple; else return ColorTypeEnum.Unknown; }
private void Start() { predict = gameObject.GetComponent <Predict>(); rotate = new RotateTowards(); enemyCount = new List <GameObject>(); }
// Use this for initialization void Start() { canvas.SetActive (false); normalState.SetActive (true); drawingState.SetActive (false); pickState.SetActive (false); wrongState.SetActive (false); editState.SetActive (false); systemState.SetActive (false); canvasOpen = false; predictor = GameObject.FindGameObjectWithTag ("Predictor").GetComponent<Predict> (); selector = GameObject.FindGameObjectWithTag ("ScaleSelection").GetComponent<Selection> (); scaler = GameObject.FindGameObjectWithTag ("ScaleSelection").GetComponent<ScaleScript> (); mapping = new int[10]; mapping [0] = 4; mapping [1] = 6; mapping [2] = 5; mapping [3] = 8; mapping [4] = 10; mapping [5] = 1; mapping [6] = 2; mapping [7] = 3; mapping [8] = 7; mapping [9] = 9; }
public void Predict_WhenInit_ReturnNew() { var output = new Predict(); Assert.IsInstanceOf(typeof(Predict), output); }