Global Analysis
This example shows how to extract global measurements from an image.
First, this example applies the IntensityStatistics algorithm on a color input image. It computes several
statistics from the input image intensities. Each color channel is processed as a separate image and the
example shows how to access to the result of each channel. Results are printed in the standard output.
Then, the color image is binarized with the ColorThresholding algorithm. The total area occupied by the phase between thresholds is given by the Area2d algorithm. The number of detected objects is computed with the ObjectCount algorithm.
See also
Computing color intensity statistics...
- Average intensity [R, G, B] = [197.06, 186.25, 181.10]
- Standard Deviation = [R, G, B] = [10.05, 16.00, 7.79]
- Red intensity range = [151, 212]
- Green intensity range = [0, 207]
- Blue intensity range = [124, 197]
Then, the color image is binarized with the ColorThresholding algorithm. The total area occupied by the phase between thresholds is given by the Area2d algorithm. The number of detected objects is computed with the ObjectCount algorithm.
Color thresholding and binary analysis...
- Total area of detected objects = 46256
- Phase fraction of detected objects = 17.71 %
- Number of detected objects = 65
#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 ( isInitialized() == false ) imagedev::init(); // Open a tif file to analyze auto imageInput = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "micro_nucleus.tif" ); // Extract intensity statistics from an RGB image std::cout << "Computing color intensity statistics... " << std::endl; auto statistics = intensityStatistics( imageInput, IntensityStatistics::RangeMode::MIN_MAX, { 0, 255 } ); std::cout << " - Average intensity [R, G, B] = [" << statistics->mean( 0, 0 ) << ", " << statistics->mean( 1, 0 ) << ", " << statistics->mean( 2, 0 ) << "]" << std::endl; std::cout << " - Standard Deviation = [R, G, B] = [" << statistics->standardDeviation( 0, 0 ) << ", " << statistics->standardDeviation( 1, 0 ) << ", " << statistics->standardDeviation( 2, 0 ) << "]" << std::endl; std::cout << " - Red intensity range = [" << statistics->minimum( 0, 0 ) << ", " << statistics->maximum( 0, 0 ) << "]" << std::endl; std::cout << " - Green intensity range = [" << statistics->minimum( 1, 0 ) << ", " << statistics->maximum( 1, 0 ) << "]" << std::endl; std::cout << " - Blue intensity range = [" << statistics->minimum( 2, 0 ) << ", " << statistics->maximum( 2, 0 ) << "]" << std::endl; // Threshold image pixels having an intensity lower than 190 in each RGB channel std::cout << "Color thresholding and binary analysis... " << std::endl; auto imageOutput = colorThresholding( imageInput, { 0, 190 }, { 0, 190 }, { 0, 190 } ); // Measure the object area auto area = area2d( imageOutput ); std::cout << " - Total area of detected objects = " << area->area( 0 ) << std::endl; std::cout << " - Phase fraction of detected objects = " << ( 100.0 * area->areaFraction( 0 ) ) << " %" << std::endl; // Count the number of detected objects auto count = objectCount( imageOutput ); std::cout << " - Number of detected objects = " << count.outputMeasurement->count( 0 ) << std::endl; // Rescale binary the image intensities between 0 and 255 for visualization // and save it imageOutput = rescaleIntensity( imageOutput, RescaleIntensity::OutputType::UNSIGNED_INTEGER_8_BIT, RescaleIntensity::RangeMode::MIN_MAX, { 2, 98 }, { 0, 255 }, { 0, 255 } ); ioformat::writeView( imageInput, "T04_01_output.png" ); std::cout << "This example ran successfully." << std::endl; } catch ( const imagedev::Exception& error ) { // Print potential exception in the standard output std::cerr << "ImageDev 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; namespace T04_01_GlobalAnalysis { 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 tif file to analyze ImageView imageInput = ViewIO.ReadImage("Data/images/micro_nucleus.tif") as ImageView; // Extract intensity statistics from an RGB image Console.WriteLine("Computing color intensity statistics... "); var statistics = Processing.IntensityStatistics(imageInput); Console.WriteLine(" - Average intensity [R, G, B] = [" + statistics.mean(0, 0).ToString("0.00") + ", " + statistics.mean(1, 0).ToString("0.00") + ", " + statistics.mean(2, 0).ToString("0.00") + "]"); Console.WriteLine(" - Standard Deviation = [R, G, B] = [" + statistics.standardDeviation(0, 0).ToString("0.00") + ", " + statistics.standardDeviation(1, 0).ToString("0.00") + ", " + statistics.standardDeviation(2, 0).ToString("0.00") + "]"); Console.WriteLine(" - Red intensity range = [" + statistics.minimum(0, 0) + ", " + statistics.maximum(0, 0) + "]"); Console.WriteLine(" - Green intensity range = [" + statistics.minimum(1, 0) + ", " + statistics.maximum(1, 0) + "]"); Console.WriteLine(" - Blue intensity range = [" + statistics.minimum(2, 0) + ", " + statistics.maximum(2, 0) + "]"); // Threshold image pixels having an intensity lower than 190 in each RGB channel Console.WriteLine("Color thresholding and binary analysis... "); var imageOutput = Processing.ColorThresholding( imageInput, new double[] { 0, 190 }, new double[] { 0, 190 }, new double[] { 0, 190 }); // Measure the object area var area = Processing.Area2d(imageOutput); Console.WriteLine(" - Total area of detected objects = " + area.area(0)); Console.WriteLine(" - Phase fraction of detected objects = " + (100.0 * area.areaFraction(0)).ToString("0.00") + " %"); // Count the number of detected objects var count = Processing.ObjectCount(imageOutput); Console.WriteLine(" - Number of detected objects = " + count.outputMeasurement.count(0)); // Rescale binary the image intensities between 0 and 255 for visualization and save it imageOutput = Processing.RescaleIntensity(imageOutput, RescaleIntensity.OutputType.UNSIGNED_INTEGER_8_BIT); ViewIO.WriteView(imageOutput, "T04_01_output.png"); // Notify the garbage collector that the created images can be freed imageInput.Dispose(); imageOutput.Dispose(); } catch (Exception error) { // Print potential exception in the standard output System.Console.WriteLine("HelloImageDev 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 # Open a tif file to analyze image_input = ioformat.read_image(imagedev_data.get_image_path("micro_nucleus.tif")) # Extract intensity statistics from an RGB image print("Computing color intensity statistics... ") statistics = imagedev.intensity_statistics(image_input) print(" - Average intensity [R, G, B] = [" + "{:.2f}".format(statistics.mean(0, 0)) + ", " + "{:.2f}".format(statistics.mean(1, 0)) + ", " + "{:.2f}".format(statistics.mean(2, 0)) + "]") print(" - Standard Deviation = [R, G, B] = [" + "{:.2f}".format(statistics.standard_deviation(0, 0)) + ", " + "{:.2f}".format(statistics.standard_deviation(1, 0)) + ", " + "{:.2f}".format(statistics.standard_deviation(2, 0)) + "]") print(" - Red intensity range = [" + str(statistics.minimum(0, 0)) + ", " + str(statistics.maximum(0, 0)) + "]") print(" - Green intensity range = [" + str(statistics.minimum(1, 0)) + ", " + str(statistics.maximum(1, 0)) + "]") print(" - Blue intensity range = [" + str(statistics.minimum(2, 0)) + ", " + str(statistics.maximum(2, 0)) + "]") # Threshold image pixels having an intensity lower than 190 in each RGB channel print("Color thresholding and binary analysis... ") image_output = imagedev.color_thresholding( image_input, [0, 190], [0, 190], [0, 190]) # Measure the object area area = imagedev.area_2d(image_output) print(" - Total area of detected objects = " + str(area.area(0))) print(" - Phase fraction of detected objects = " + "{:.2f}".format(100.0 * area.area_fraction(0)) + " %") # Count the number of detected objects count = imagedev.object_count(image_output) print(" - Number of detected objects = " + str(count[1].count(0))) # Rescale binary the image intensities between 0 and 255 for visualization and save it image_output = imagedev.rescale_intensity(image_output, imagedev.RescaleIntensity.UNSIGNED_INTEGER_8_BIT) ioformat.write_view(image_output, "T04_01_output.png")
See also