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.
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.
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.
See also
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:
# Initialize the ImageDev library if not done
if not imagedev.is_initialized(): imagedev.init()
# 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))
# ImageDev library finalization
imagedev.finish()
See also