Delphi-OpenCV/samples/Classes/CvKNearest/Class_2D_Point_Classification.dpr

// *****************************************************************
// Delphi-OpenCV Class Demo
// Copyright (C) 2013 Project Delphi-OpenCV
// ****************************************************************
// Contributor:
// laentir Valetov
// email:laex@bk.ru
// ****************************************************************
// You may retrieve the latest version of this file at the GitHub,
// located at git://github.com/Laex/Delphi-OpenCV.git
// ****************************************************************
// The contents of this file are used with permission, subject to
// the Mozilla Public License Version 1.1 (the "License"); you may
// not use this file except in compliance with the License. You may
// obtain a copy of the License at
// http://www.mozilla.org/MPL/MPL-1_1Final.html
//
// Software distributed under the License is distributed on an
// "AS IS" basis, WITHOUT WARRANTY OF ANY KIND, either express or
// implied. See the License for the specific language governing
// rights and limitations under the License.
// *******************************************************************
// Original:
// http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html#cvknearest-is-regression
// *******************************************************************

program Class_2D_Point_Classification;

{$APPTYPE CONSOLE}
{$POINTERMATH ON}
{$R *.res}

uses
  System.SysUtils,
  System.Classes,
  Core.types_c,
  core_c,
  highgui_c,
  ml;

Const
  K10: Integer = 10;

Var
  i, j, K, accuracy : Integer;
  response          : float;
  train_sample_count: Integer = 100;
  rng_state         : TCvRNG;
  trainData         : pCvMat;
  trainClasses      : pCvMat;
  img               : pIplImage;
  _sample           : array [0 .. 1] of float;
  sample            : TCvMat;
  trainData1,      //
    trainData2,    //
    trainClasses1, //
    trainClasses2: TCvMat;
  knn            : ICvKNearest;
  nearests       : pCvMat;
  t              : TCvScalar;
  pt             : TCvPoint;

begin
  try
    rng_state := CvRNG(-1);
    trainData := cvCreateMat(
      train_sample_count,
      2,
      CV_32FC1);
    trainClasses := cvCreateMat(
      train_sample_count,
      1,
      CV_32FC1);
    img := cvCreateImage(
      cvSize(500, 500),
      8,
      3);
    sample := CvMat(
      1,
      2,
      CV_32FC1,
      @_sample);
    cvZero(img);
    // form the training samples
    cvGetRows(
      trainData,
      @trainData1,
      0,
      train_sample_count div 2);
    cvRandArr(
      @rng_state,
      @trainData1,
      CV_RAND_NORMAL,
      cvScalar(200, 200),
      cvScalar(50, 50));

    cvGetRows(
      trainData,
      @trainData2,
      train_sample_count div 2,
      train_sample_count);
    cvRandArr(
      @rng_state,
      @trainData2,
      CV_RAND_NORMAL,
      cvScalar(300, 300),
      cvScalar(50, 50));

    cvGetRows(
      trainClasses,
      @trainClasses1,
      0,
      train_sample_count div 2);
    cvSet(
      @trainClasses1,
      cvScalar(1));

    cvGetRows(
      trainClasses,
      @trainClasses2,
      train_sample_count div 2,
      train_sample_count);
    cvSet(
      @trainClasses2,
      cvScalar(2));

    // learn classifier
    knn := CreateCvKNearest(
      trainData,
      trainClasses,
      nil,
      false,
      K10);
    nearests := cvCreateMat(
      1,
      K10,
      CV_32FC1);

    for i := 0 to img^.height - 1 do
    begin
      for j := 0 to img^.width - 1 do
      begin
        pFloat(sample.data)[0] := j;
        pFloat(sample.data)[1] := i;
        // estimate the response and get the neighbors' labels
        response := knn.find_nearest(
          @sample,
          K10,
          nil,
          nil,
          nearests,
          nil);

        // compute the number of neighbors representing the majority
        accuracy := 0;
        for K    := 0 to K10 - 1 do
        begin
          if (pFloat(nearests^.data)[K] = response) then
            Inc(accuracy);
        end;
        // highlight the pixel depending on the accuracy (or confidence)
        if response = 1 then
        begin
          if accuracy > 5 then
            t := CV_RGB(
              180,
              0,
              0)
          else
            CV_RGB(
              180,
              120,
              0);
        end
        else
        begin
          if accuracy > 5 then
            t := CV_RGB(
              0,
              180,
              0)
          else
            CV_RGB(
              120,
              120,
              0);
        end;
        cvSet2D(
          img,
          i,
          j,
          t);
      end;
    end;

    // display the original training samples
    for i := 0 to (train_sample_count div 2) - 1 do
    begin
      pt.x := cvRound(pFloat(trainData1.data)[i * 2]);
      pt.y := cvRound(pFloat(trainData1.data)[i * 2 + 1]);
      cvCircle(
        img,
        pt,
        2,
        CV_RGB(255, 0, 0),
        CV_FILLED);
      pt.x := cvRound(pFloat(trainData2.data)[i * 2]);
      pt.y := cvRound(pFloat(trainData2.data)[i * 2 + 1]);
      cvCircle(
        img,
        pt,
        2,
        CV_RGB(0, 255, 0),
        CV_FILLED);
    end;

    cvNamedWindow(
      'classifier result',
      1);
    cvShowImage(
      'classifier result',
      img);
    cvWaitKey(0);

    cvReleaseMat(trainClasses);
    cvReleaseMat(trainData);
  except
    on E: Exception do
      WriteLn(
        E.ClassName,
        ': ',
        E.Message);
  end;

end.