Perform OMR on Images

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Aspose.OMR for Java is a simple and lightweight API that makes performing OMR operation on images a breeze. The upcoming examples demonstrate how easy it is to get started with performing OMR on images.

Perform OMR operation on Images

Aspose.OMR for Java provides simple to use features for performing OMR operation. For a simple OMR operation, you only need two things, the prepared template (special markers will be drawn over the user’s form and the image(s) to perform OMR operation on. Aspose.OMR provides TemplateProcessor.RecognizeImage() method that takes an image path and returns a string output. The following code sample shows the use of OmrEngine and TemplateProcessor to perform OMR operation on two images.

// For complete examples and data files, please go to https://github.com/aspose-omr/Aspose.OMR-for-Java
String TemplateName = "Sheet.omr";
String[] UserImages = new String[] { "Sheet1.jpg", "Sheet2.jpg" };
String[] UserImagesNoExt = new String[] { "Sheet1", "Sheet2" };

String sourceDirectory = Utils.getSourceDirectory();
String outputDirectory = Utils.combine(Utils.getOutputDirectory(), "Result");
String templatePath = Utils.combine(Utils.getSourceDirectory(), TemplateName);

// initialize engine and get template processor providing path to the .omr file
OmrEngine engine = new OmrEngine();
TemplateProcessor templateProcessor = engine.getTemplateProcessor(templatePath);
System.out.println("Template loaded.");

// images loop
for (int i = 0; i < UserImages.length; i++) {
    // path to the image to be recognized
    String imagePath = Utils.combine(sourceDirectory, UserImages[i]);
    System.out.println("Processing image: " + imagePath);

    // recognize image and receive result
    RecognitionResult result = templateProcessor.recognizeImage(imagePath);

    // export results as csv string
    String csvResult = result.getCsv();

    String json = result.getJson();

    // save csv to the output folder
    PrintWriter wr = new PrintWriter(new FileOutputStream(Utils.combine(outputDirectory, UserImagesNoExt[i] + ".csv")), true);
    wr.println(csvResult);
}

Perform OMR operation with a threshold setting

Aspose.OMR provides a threshold setting to fine-tune the result of OMR according to your needs. You can set the value of the threshold from 0 to 100 depending on your requirements. By increasing the value of the threshold, the API becomes more strict regarding the highlighting of the answers. TemplateProcessor.RecognizeImage() method takes threshold as the optional 2nd parameter with the default value of 0. The use of the threshold setting is demonstrated in the following code sample.

// For complete examples and data files, please go to https://github.com/aspose-omr/Aspose.OMR-for-Java
String TemplateName = "Sheet.omr";
String[] UserImages = new String[] { "Sheet1.jpg", "Sheet2.jpg" };
String[] UserImagesNoExt = new String[] { "Sheet1", "Sheet2" };

String sourceDirectory = Utils.getSourceDirectory();
String outputDirectory = Utils.combine(Utils.getOutputDirectory(), "Result");
String templatePath = Utils.combine(Utils.getSourceDirectory(), TemplateName);

int customThreshold = 40;

// initialize engine and get template processor providing path to the .omr file
OmrEngine engine = new OmrEngine();
TemplateProcessor templateProcessor = engine.getTemplateProcessor(templatePath);
System.out.println("Template loaded.");

// images loop
for (int i = 0; i < UserImages.length; i++) {
    // path to the image to be recognized
    String imagePath = Utils.combine(sourceDirectory, UserImages[i]);
    System.out.println("Processing image: " + imagePath);

    // recognize image and receive result
    RecognitionResult result = templateProcessor.recognizeImage(imagePath, customThreshold);

    // export results as csv string
    String csvResult = result.getCsv();

    String json = result.getJson();

    String outputName = Utils.combine(outputDirectory, UserImagesNoExt[i] + "_threashold.csv");

    // save csv to the output folder
    PrintWriter wr = new PrintWriter(new FileOutputStream(outputName), true);
    wr.println(csvResult);
    System.out.println("Generated File: " + outputName);
}

Perform OMR operation with Recalculation

Aspose.OMR for Java provides you with the ability to perform recalculation on the image during the OMR operation. There might be some cases where you might want to process an image multiple times by changing the threshold setting to get the desired result. In that case, you may want to process the image again by calling TemplateProcessor.RecognizeImage(). But Aspose.OMR for Java provides a better way to do this by providing the TemplateProcessor.Recalculate() method. This method takes the result of TemplateProcessor.RecognizeImage() as the first parameter and an optional threshold. TemplateProcessor.Recalculate() method improves the efficiency of image processing and saves time as represented by the following code sample.

// For complete examples and data files, please go to https://github.com/aspose-omr/Aspose.OMR-for-Java
String TemplateName = "Sheet.omr";
String[] UserImages = new String[] { "Sheet1.jpg", "Sheet2.jpg" };
String[] UserImagesNoExt = new String[] { "Sheet1", "Sheet2" };

String sourceDirectory = Utils.getSourceDirectory();
String outputDirectory = Utils.combine(Utils.getOutputDirectory(), "Result");
String templatePath = Utils.combine(Utils.getSourceDirectory(), TemplateName);

// init engine and get template processor
OmrEngine engine = new OmrEngine();
TemplateProcessor templateProcessor = engine.getTemplateProcessor(templatePath);
System.out.println("Template loaded.");

// Set custom threshold to use in recalculation
// this value is in range (0..100)
// represents the percentage of required black pixels on bubble image to be recognized
// i.e. the lower the value - the less black pixels required for bubble to be counted as filled and vice versa
int CustomThreshold = 40;

// images loop
for (int i = 0; i < UserImages.length; i++)
{
    String image = UserImages[i];
    String imagePath = Utils.combine(sourceDirectory, image);
    System.out.println("Processing image: " + imagePath);

    // recognize image
    RecognitionResult result = templateProcessor.recognizeImage(imagePath);

    // get export csv string
    String stringRes = result.getCsv();

    // save csv to output folder
    String outputName = combine(outputDirectory, UserImagesNoExt[i] + ".csv");

    PrintWriter wr = new PrintWriter(new FileOutputStream(outputName), true);
    wr.println(stringRes);

    System.out.println("Export done. Path: " + outputName);

    // recalculate recognition results with custom threshold
    templateProcessor.recalculate(result, CustomThreshold);

    // get export csv string
    stringRes = result.getCsv();

    // save recalculated results
    outputName = combine(outputDirectory, UserImagesNoExt[i] + "_recalculated.csv");

    wr = new PrintWriter(new FileOutputStream(outputName), true);
    wr.println(stringRes);

    System.out.println("Recalculated result export done. Path: " + outputName);
    System.out.println();
}