17.4 Training
About1 Introduction2 Image Recognition3 TransApplet4 API5 Interface6 Input7 Image Display8 Preprocessing9 Processing10 Normalization11 Parameter Class12 Image Signatures13 Unsupervised Filters14 BioFilters15 NeuralFilters16 Dynamic Library17 NeuralNet Filter18 Parameters19 Input Options20 Database Input21 Video Input22  Live Video Input23  Counting & Tracking24  Counting 25  Batch Job26 ImageFinder for DOS27 ImageHunt 28 Support Packages

17.1  Key Segment 
17.2  Menu 
17.3 API 
17.4 Training  
17.5 1:N Design 
17.6 1:N Implementation 
17.7 Results 
17.8 Another Test 
[Home][17 NeuralNet Filter][17.4 Training ]

 

17.4   Training

Training here means setting up the NeuralNet Filter. After specifying a key segment, click “NeuralNet/Training” in Figure 17.3 to complete the training. You should see this message:

    Training ...

Training End!

Double click menu item “NeuralNet/Training” and enter:

        private void menuItem109_Click(object sender, System.EventArgs e)

                {

                this.mainMenuToAPI.neuralNet_Training ( textBox1.Text  );

                }       

Here, mainMenuToAPI is an object, which will implement all functions. As we discussed earlier, the main form simply links menu items to functions in the mainMenuToAPI object. The implementation is:

        public bool neuralNet_Training ( string b)

                {

                if ( ! System.IO .File .Exists (b) )

                {

                    appendText ( "Please enter a valid Key!\n");

                    return false;

                }

         

                int x =0, y = 0, w =0, h = 0;

                try

                {

                    x = int.Parse (f.textBox3.Text );

                    y = int.Parse (f.textBox4.Text );

                    w = int.Parse (f.textBox5.Text );

                    h = int.Parse (f.textBox6.Text );

                }

                catch

                {

                    setText ("Invalid Training Segment!\n");

                    x = 0;

                    y = 0;

                    w = 0;

                    h = 0;

                    return false;

                }

         

                script.neuralNetFilter.train ( b, x, y, w, h) ;

                return true;

                }

The following code simply makes sure the key image exists:

        if ( ! System.IO .File .Exists (b) )

                {

                    appendText ( "Please enter a valid Key!\n");

                    return false;

                }

The next section of code computes  (x, y, w, h):

            int x =0, y = 0, w =0, h = 0;

                try

                {

                    x = int.Parse (f.textBox3.Text );

                    y = int.Parse (f.textBox4.Text );

                    w = int.Parse (f.textBox5.Text );

                    h = int.Parse (f.textBox6.Text );

                }

                catch

                {

                    setText ("Invalid Training Segment!\n");

                    x = 0;

                    y = 0;

                    w = 0;

                    h = 0;

                    return false;

                }

The last section of code trains the neural net:

        script.neuralNetFilter.train ( b, x, y, w, h) ;

 

[Home][About][1 Introduction][2 Image Recognition][3 TransApplet][4 API][5 Interface][6 Input][7 Image Display][8 Preprocessing][9 Processing][10 Normalization][11 Parameter Class][12 Image Signatures][13 Unsupervised Filters][14 BioFilters][15 NeuralFilters][16 Dynamic Library][17 NeuralNet Filter][18 Parameters][19 Input Options][20 Database Input][21 Video Input][22 Live Video Input][23 Counting & Tracking][24 Counting ][25 Batch Job][26 ImageFinder for DOS][27 ImageHunt ][28 Support Packages]

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