ImageDev

Measurement Browsing

This example shows how to automatically parse the content of a label analysis and export it in a csv file.
First, a grayscale image is opened and segmented to generate a label image containing 8 objects.

Then, an analysis is performed with three measurements selected. It computes for each object its area, a shape factor, and a set of oriented diameters. The FeretDiameter measurement generates by default an array of 10 diameters corresponding to different orientations with a pitch of 18 degrees.

A first loop shows how to introspect the analysis to get the name of each selected measurement and deploy the array of diameters. A second loop shows how to introspect the analysis to print the measurement results for each object and deploys the diameter distribution. This step demonstrates the ability to browse the content of an analysis without making assumptions about the measurements that have been selected within.

Label Area2d(mm^2) InverseCircularity2d() FeretDiameter2d[0](mm) FeretDiameter2d[1](mm)
1 1058.00 1.23 35.00 38.73
2 1361.00 3.70 47.00 49.31
3 1086.00 1.26 38.00 35.54
4 293.00 1.81 20.00 18.40

In practice, the analysis can be directly converted to a spreadsheet structure with the toDataFrame method. It generates an IOLink DataFrameView object. In Python, this object can be directly printed in the standard output.

Index [label] Area2d InverseCircularity2d ... FeretDiameter2d[direction=9]
0 1058 1.2297213077545166 ... 38.72925567626953
1 1361 3.698363780975342 ... 49.00188064575195
2 1086 1.2601970434188843 ... 42.93851089477539
3 293 1.8089686632156372 ... 22.111291885375977

Finally, IOFormat allows the export of a DataFrameView object in a csv file that can be visualized as an Excel table. This method is more straightforward for exporting an analysis. The previous one gives you more freedom to customize the export for your needs.

#include <ImageDev/ImageDev.h>
#include <ioformat/IOFormat.h>
#include <string.h>

using namespace imagedev;
using namespace ioformat;
using namespace iolink;

int
main( int argc, char* argv[] )
{
    int status = 0;

    try
    {
        // ImageDev library initialization if not done
        if ( isInitialized() == false )
            imagedev::init();

        // Open a grayscale image from a tif file
        auto imageInput = readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "objects.tif" );

        // Threshold and label the binary input
        auto imageBin = thresholdingByCriterion( imageInput,
                                                 ThresholdingByCriterion::ComparisonCriterion::GREATER_THAN_OR_EQUAL_TO,
                                                 40 );
        auto imageLab = labeling2d( imageBin, Labeling2d::LABEL_8_BIT, Labeling2d::CONNECTIVITY_8 );

        // Calibrate this image to match 1 pixel to 1.4 mm
        Vector3d spacing{ 1.4, 1.4, 1 };
        imageLab->setSpatialSpacing( spacing );
        imageLab->setSpatialUnit( "mm" );

        // Define the analysis features to be computed
        AnalysisMsr::Ptr analysis = std::make_shared< AnalysisMsr >();
        analysis->select( NativeMeasurements::area2d );
        analysis->select( NativeMeasurements::inverseCircularity2d );
        analysis->select( NativeMeasurements::feretDiameter2d );

        // Launch the feature extraction on the segmented image
        labelAnalysis( imageLab, imageInput, analysis );

        // Print the analysis table header
        std::string lineToPrint( "Label\t" );
        for ( const auto& measure : analysis->getMeasurements() )
        {
            // Build and print the table header
            if ( measure->shape().size() == 1 )
                // The measurement is a scalar value
                lineToPrint += measure->name() + "(" + measure->information().physicalUnit() + ")\t";
            else if ( measure->shape().size() == 2 )
                // The measurement is an array, loop on it
                for ( size_t j = 0; j < measure->shape()[1]; ++j )
                    lineToPrint += measure->name() + "[" + std::to_string( j ) + "](" +
                                   measure->information().physicalUnit() + ")\t";
        }
        std::cout << lineToPrint << std::endl;

        VectorXu64 index;
        // Print all measurement results for each label
        for ( int i = 0; i < analysis->labelCount(); ++i )
        {
            lineToPrint = std::to_string( i + 1 ) + "\t";
            for ( const auto& measure : analysis->getMeasurements() )
            {
                index = measure->shape();
                index[0] = i;
                if ( measure->shape().size() == 1 )
                    // The measurement is a scalar value
                    lineToPrint += std::to_string( measure->toDouble( index ) ) + "\t";
                else if ( measure->shape().size() == 2 )
                    // The measurement is an array, loop on it
                    for ( size_t j = 0; j < measure->shape()[1]; ++j )
                    {
                        index[1] = j;
                        lineToPrint += std::to_string( measure->toDouble( index ) ) + "\t";
                    }
            }
            std::cout << lineToPrint << std::endl;
        }

        // Export the analysis in a dataframe and save it in a csv file
        auto dataframe = analysis->toDataFrame();
        writeView( dataframe, "T04_03_analysis.csv" );

        std::cout << "This example ran successfully." << std::endl;
    }
    catch ( const imagedev::Exception& error )
    {
        // Print potential exception in the standard output
        std::cerr << "T04_03_MeasurementBrowsing exception: " << error.what() << std::endl;
        status = -1;
    }

    // ImageDev library finalization
    imagedev::finish();

    // Check if we must ask for an enter key to close the program
    if ( !( ( argc == 2 ) && strcmp( argv[1], "--no-stop-at-end" ) == 0 ) )
        std::cout << "Press Enter key to close this window." << std::endl, getchar();

    return status;
}
using System;
using ImageDev;
using IOLink;
using IOFormat;
using System.Linq;

namespace T04_03_MeasurementBrowsing
{
    class Program
    {
        static void Main( string[] args )
        {
            int status = 0;

            try
            {
                // Initialize the ImageDev library if not done
                if ( Initialization.IsInitialized() == false )
                    Initialization.Init();

                // Open a grayscale image from a tif file
                ImageView imageInput = ViewIO.ReadImage( "Data/images/objects.tif" );

                // Threshold and label the binary input
                var imageBin = Processing.ThresholdingByCriterion(
                    imageInput, ThresholdingByCriterion.ComparisonCriterion.GREATER_THAN_OR_EQUAL_TO, 40 );
                var imageLab = Processing.Labeling2d( imageBin );

                // Calibrate this image to match 1 pixel to 1.4 mm
                Vector3d spacing = new Vector3d( 1.4, 1.4, 1 );
                imageLab.SpatialSpacing = spacing;
                imageLab.SpatialUnit = "mm";

                // Define the analysis features to be computed
                AnalysisMsr analysis = new AnalysisMsr();
                analysis.Select( NativeMeasurements.Area2d );
                analysis.Select( NativeMeasurements.InverseCircularity2d );
                analysis.Select( NativeMeasurements.FeretDiameter2d );

                // Launch the feature extraction on the segmented image
                Processing.LabelAnalysis( imageLab, imageInput, analysis );

                // Print the analysis table header
                string lineToPrint = "Label\t";
                foreach ( var measure in analysis.Measurements() )
                {
                    // Build and print the table header
                    if ( measure.Shape().Length == 1 )
                        // The measurement is a scalar value
                        lineToPrint += measure.Name() + "(" + measure.information.physicalUnit + ")\t";
                    else if ( measure.Shape().Length == 2 )
                        // The measurement is an array, loop on it
                        for ( int j = 0; j < measure.Shape()[1]; ++j )
                            lineToPrint += measure.Name() + "[" + j + "](" + measure.information.physicalUnit + ")\t";
                }
                Console.WriteLine( lineToPrint );

                int[] index;
                // Print all measurement results for each label
                for ( int i = 0; i < analysis.LabelCount(); ++i )
                {
                    lineToPrint = ( i + 1 ) + "\t";
                    foreach ( var measure in analysis.Measurements() )
                    {
                        index = measure.Shape();
                        index[0] = i;
                        if ( measure.Shape().Length == 1 )
                            // The measurement is a scalar value
                            lineToPrint +=
                                measure.ToDouble( index.Select( item => ( long )item ).ToArray() ).ToString() + "\t";
                        else if ( measure.Shape().Length == 2 )
                            // The measurement is an array, loop on it
                            for ( int j = 0; j < measure.Shape()[1]; ++j )
                            {
                                index[1] = j;
                                lineToPrint +=
                                    measure.ToDouble( index.Select( item => ( long )item ).ToArray() ).ToString() +
                                    "\t";
                            }
                    }
                    Console.WriteLine( lineToPrint );
                }

                // Export the analysis in a dataframe and save it in a csv file
                DataFrameView dataframe = analysis.ToDataFrame();
                ViewIO.WriteView( dataframe, "T04_03_analysis.csv" );

                // Notify the garbage collector that the created images can be freed
                imageInput.Dispose();
                imageBin.Dispose();
                imageLab.Dispose();

                Console.WriteLine( "This example ran successfully." );
            }
            catch ( Exception error )
            {
                // Print potential exception in the standard output
                System.Console.WriteLine( "T04_03_MeasurementBrowsing exception: " + error.ToString() );
                status = -1;
            }

            // ImageDev library finalization
            Initialization.Finish();

            // Check if we must ask for an enter key to close the program
            if ( !( ( args.Length >= 1 ) && ( args[0] == "--no-stop-at-end" ) ) )
            {
                System.Console.WriteLine( "Press Enter key to close this window." );
                System.Console.ReadKey();
            }

            System.Environment.Exit( status );
        }
    }
}
import imagedev
import imagedev_data
import ioformat
import iolink

try:
    # Open a grayscale image from a tif file
    image_input = ioformat.read_image(imagedev_data.get_image_path('objects.tif'))

    # Threshold and label the binary input
    image_bin = imagedev.thresholding_by_criterion(image_input, comparison_value=40)
    image_lab = imagedev.labeling_2d(image_bin, imagedev.Labeling2d.LabelType.LABEL_8_BIT)

    # Calibrate this image to match 1 pixel to 1.4 mm
    image_lab.spatial_spacing = iolink.Vector3d(1.4, 1.4, 1)
    image_lab.spatial_unit = 'mm'

    # Define the analysis features to be computed
    analysis = imagedev.AnalysisMsr()
    analysis.select(imagedev.native_measurements.Area2d)
    analysis.select(imagedev.native_measurements.InverseCircularity2d)
    analysis.select(imagedev.native_measurements.FeretDiameter2d)

    # Launch the feature extraction on the segmented image
    imagedev.label_analysis(image_lab, image_input, analysis)

    # Print the analysis table header
    line_to_print = 'Label\t'
    for measure in analysis.measurements:
        # Build and print the table header
        if len(measure.shape) == 1:
            # The measurement is a scalar value
            line_to_print += measure.name + '(' + measure.information.physical_unit + ')\t'
        elif len(measure.shape) == 2:
            # The measurement is an array, loop on it
            for j in range(0, measure.shape[1]):
                line_to_print += measure.name + '[' + str(j) + '](' + measure.information.physical_unit + ')\t'
    print(line_to_print)

    # Print all measurement results for each label
    for i in range(0, analysis.label_count):
        line_to_print = str(i + 1) + '\t\t'
        for measure in analysis.measurements:
            if len(measure.shape) == 1:
                # The measurement is a scalar value
                line_to_print += '{:.2f}'.format(measure.value(i)) + '\t\t'
            elif len(measure.shape) == 2:
                # The measurement is an array, loop on it
                for j in range(0, measure.shape[1]):
                    line_to_print += '{:.2f}'.format(measure.value(i, j)) + '\t\t\t\t'
        print(line_to_print)

    # Export the analysis in a dataframe and save it in a csv file
    dataframe = analysis.to_data_frame()
    print(dataframe)
    ioformat.write_view(dataframe, 'T04_03_analysis.csv')

    print("This example ran successfully.")
except Exception as error:
    # Print potential exception in the standard output
    print("T04_03_MeasurementBrowsing exception: " + str(error))


See also