LabelCountByRegion
Computes, for each region of a first label image, the number of objects contained in a second label image.
Access to parameter description
This algorithm can be used, for instance, to compute the number of markers contained in each cell of a life science image, or the number of inclusions in each phase of a material science image.
If the region image is a binary image, it is considered as a single region and the algorithm amounts to computing the number of objects masked by this image.
If the region image is a label image, the number of objects is given for each label of the region image.
See also
Access to parameter description
This algorithm can be used, for instance, to compute the number of markers contained in each cell of a life science image, or the number of inclusions in each phase of a material science image.
If the region image is a binary image, it is considered as a single region and the algorithm amounts to computing the number of objects masked by this image.
If the region image is a label image, the number of objects is given for each label of the region image.
See also
Function Syntax
This function returns outputMeasurement.
// Function prototype
LabelCountByRegionMsr::Ptr labelCountByRegion( std::shared_ptr< iolink::ImageView > inputObjectImage, std::shared_ptr< iolink::ImageView > inputLabelImage, LabelCountByRegionMsr::Ptr outputMeasurement = nullptr );
This function returns outputMeasurement.
// Function prototype.
label_count_by_region(input_object_image: idt.ImageType,
input_label_image: idt.ImageType,
output_measurement: Union[Any, None] = None) -> LabelCountByRegionMsr
This function returns outputMeasurement.
// Function prototype.
public static LabelCountByRegionMsr
LabelCountByRegion( IOLink.ImageView inputObjectImage,
IOLink.ImageView inputLabelImage,
LabelCountByRegionMsr outputMeasurement = null );
Class Syntax
Parameters
| Parameter Name | Description | Type | Supported Values | Default Value | |
|---|---|---|---|---|---|
 |
inputObjectImage |
The input binary or label image representing the regions to be analyzed. | Image | Binary or Label | nullptr |
|
inputLabelImage |
The input label image containing the particles to be counted. This image must have same dimensions as the region input image. | Image | Label | nullptr |
 |
outputMeasurement |
The output measurement result providing one count per region. | LabelCountByRegionMsr | nullptr | |
| Parameter Name | Description | Type | Supported Values | Default Value | |
|---|---|---|---|---|---|
|
input_object_image |
The input binary or label image representing the regions to be analyzed. | image | Binary or Label | None |
|
input_label_image |
The input label image containing the particles to be counted. This image must have same dimensions as the region input image. | image | Label | None |
|
output_measurement |
The output measurement result providing one count per region. | LabelCountByRegionMsr | None | |
| Parameter Name | Description | Type | Supported Values | Default Value | |
|---|---|---|---|---|---|
|
inputObjectImage |
The input binary or label image representing the regions to be analyzed. | Image | Binary or Label | null |
|
inputLabelImage |
The input label image containing the particles to be counted. This image must have same dimensions as the region input image. | Image | Label | null |
|
outputMeasurement |
The output measurement result providing one count per region. | LabelCountByRegionMsr | null | |
Object Examples
auto polystyrene_sep_label = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene_sep_label.vip" ); LabelCountByRegion labelCountByRegionAlgo; labelCountByRegionAlgo.setInputObjectImage( polystyrene_sep_label ); labelCountByRegionAlgo.setInputLabelImage( polystyrene_sep_label ); labelCountByRegionAlgo.execute(); std::cout << "region: " << labelCountByRegionAlgo.outputMeasurement()->region( 0 ) ;
polystyrene_sep_label = imagedev.read_vip_image(imagedev_data.get_image_path("polystyrene_sep_label.vip"))
label_count_by_region_algo = imagedev.LabelCountByRegion()
label_count_by_region_algo.input_object_image = polystyrene_sep_label
label_count_by_region_algo.input_label_image = polystyrene_sep_label
label_count_by_region_algo.execute()
print("region: ", str(label_count_by_region_algo.output_measurement.region(0)))
ImageView polystyrene_sep_label = Data.ReadVipImage( @"Data/images/polystyrene_sep_label.vip" );
LabelCountByRegion labelCountByRegionAlgo = new LabelCountByRegion
{
inputObjectImage = polystyrene_sep_label,
inputLabelImage = polystyrene_sep_label
};
labelCountByRegionAlgo.Execute();
Console.WriteLine( "region: " + labelCountByRegionAlgo.outputMeasurement.region( 0 ) );
Function Examples
auto polystyrene_sep_label = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene_sep_label.vip" ); auto result = labelCountByRegion( polystyrene_sep_label, polystyrene_sep_label ); std::cout << "region: " << result->region( 0 ) ;
polystyrene_sep_label = imagedev.read_vip_image(imagedev_data.get_image_path("polystyrene_sep_label.vip"))
result = imagedev.label_count_by_region(polystyrene_sep_label, polystyrene_sep_label)
print("region: ", str(result.region(0)))
ImageView polystyrene_sep_label = Data.ReadVipImage( @"Data/images/polystyrene_sep_label.vip" ); LabelCountByRegionMsr result = Processing.LabelCountByRegion( polystyrene_sep_label, polystyrene_sep_label ); Console.WriteLine( "region: " + result.region( 0 ) );