PixelError (Fiji Javadocs 0-SNAPSHOT API)

java.lang.Object
- trainableSegmentation.metrics.Metrics
- - trainableSegmentation.metrics.PixelError

```
public class PixelError
extends Metrics
```
This class implements the pixel error metric

Field Summary
- Fields inherited from class trainableSegmentation.metrics.Metrics
  originalLabels, proposedLabels, verbose

Constructor Summary

Constructors
Constructor and Description

PixelError(ij.ImagePlus originalLabels, ij.ImagePlus proposedLabels)
Initialize pixel error metric

Constructors
Constructor and Description
`PixelError(ij.ImagePlus originalLabels, ij.ImagePlus proposedLabels)` Initialize pixel error metric

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`getMetricValue()` Calculate the pixel error in 2D between some original labels and the corresponding proposed labels (without thresholding).
`double`	`getMetricValue(double binaryThreshold)` Calculate the pixel error in 2D between some original labels and the corresponding proposed labels.
`Callable<Double>`	`getPixelErrorConcurrent(ij.process.ImageProcessor image1, ij.process.ImageProcessor image2)` Get pixel error between two image in a concurrent way (to be submitted to an Executor Service).
`Callable<Double>`	`getPixelErrorConcurrent(ij.process.ImageProcessor image1, ij.process.ImageProcessor image2, double binaryThreshold)` Get pixel error between two image in a concurrent way (to be submitted to an Executor Service).
`double`	`getPixelErrorMaximalFScore(double minThreshold, double maxThreshold, double stepThreshold)` Get the best F-score of the pixel error over a set of thresholds
`ClassificationStatistics`	`getPrecisionRecallStats(double binaryThreshold)` Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
`ArrayList<ClassificationStatistics>`	`getPrecisionRecallStats(double minThreshold, double maxThreshold, double stepThreshold)` Calculate the precision-recall values based on pixel error between some 2D original labels and the corresponding proposed labels.
`ClassificationStatistics`	`getPrecisionRecallStats(double binaryThreshold, ij.ImagePlus mask)` Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
`Callable<ClassificationStatistics>`	`getPrecisionRecallStatsConcurrent(ij.process.ImageProcessor image1, ij.process.ImageProcessor image2, double binaryThreshold)` Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
`Callable<ClassificationStatistics>`	`getPrecisionRecallStatsConcurrent(ij.process.ImageProcessor image1, ij.process.ImageProcessor image2, ij.process.ImageProcessor mask, double binaryThreshold)` Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service).
`ClassificationStatistics[]`	`getPrecisionRecallStatsPerSlice(double binaryThreshold)` Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels.
`static void`	`main(String[] args)` Main method for calculate the pixel error metrics from the command line
`ClassificationStatistics`	`precisionRecallStats(ij.process.ImageProcessor label, ij.process.ImageProcessor proposal, double binaryThreshold)` Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels.
`ClassificationStatistics`	`precisionRecallStats(ij.process.ImageProcessor label, ij.process.ImageProcessor proposal, ij.process.ImageProcessor mask, double binaryThreshold)` Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels.

Methods inherited from class trainableSegmentation.metrics.Metrics
getMinimumMetricValue, setVerboseMode

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - PixelError
```
public PixelError(ij.ImagePlus originalLabels,
                  ij.ImagePlus proposedLabels)
```
    Initialize pixel error metric
    
    Parameters:
    
    originalLabels - original labels (single 2D image or stack)
    
    proposedLabels - proposed new labels (single 2D image or stack of the same as as the original labels)
- Method Detail
  - getMetricValue
```
public double getMetricValue(double binaryThreshold)
```
    Calculate the pixel error in 2D between some original labels and the corresponding proposed labels. Both image are binarized.
    
    Specified by:
    
    getMetricValue in class Metrics
    
    Parameters:
    
    binaryThreshold - threshold value to binarize proposal (larger than 0 and smaller than 1)
    
    Returns:
    
    pixel error
  - getPixelErrorConcurrent
```
public Callable<Double> getPixelErrorConcurrent(ij.process.ImageProcessor image1,
                                                ij.process.ImageProcessor image2,
                                                double binaryThreshold)
```
    Get pixel error between two image in a concurrent way (to be submitted to an Executor Service). Both images are binarized.
    
    Parameters:
    
    image1 - first image
    
    image2 - second image
    
    binaryThreshold - threshold to apply to both images
    
    Returns:
    
    pixel error
  - getMetricValue
```
public double getMetricValue()
```
    Calculate the pixel error in 2D between some original labels and the corresponding proposed labels (without thresholding).
    
    Returns:
    
    pixel error
  - getPixelErrorConcurrent
```
public Callable<Double> getPixelErrorConcurrent(ij.process.ImageProcessor image1,
                                                ij.process.ImageProcessor image2)
```
    Get pixel error between two image in a concurrent way (to be submitted to an Executor Service).
    
    Parameters:
    
    image1 - first image
    
    image2 - second image
    
    Returns:
    
    pixel error
  - getPrecisionRecallStats
```
public ArrayList<ClassificationStatistics> getPrecisionRecallStats(double minThreshold,
                                                                   double maxThreshold,
                                                                   double stepThreshold)
```
    Calculate the precision-recall values based on pixel error between some 2D original labels and the corresponding proposed labels.
    
    Parameters:
    
    minThreshold - minimum threshold value to binarize the input images
    
    maxThreshold - maximum threshold value to binarize the input images
    
    stepThreshold - threshold step value to use during binarization
    
    Returns:
    
    pixel error value and derived statistics for each threshold
  - getPrecisionRecallStats
```
public ClassificationStatistics getPrecisionRecallStats(double binaryThreshold)
```
    Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels. Both images are binarized.
    
    Parameters:
    
    binaryThreshold - threshold value to binarize proposal (larger than 0 and smaller than 1)
    
    Returns:
    
    pixel error value and derived statistics
  - getPrecisionRecallStatsPerSlice
```
public ClassificationStatistics[] getPrecisionRecallStatsPerSlice(double binaryThreshold)
```
    Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels. Both images are binarized and the results are presented per slice.
    
    Parameters:
    
    binaryThreshold - threshold value to binarize proposal (larger than 0 and smaller than 1)
    
    Returns:
    
    pixel error value and derived statistics per input slice
  - getPrecisionRecallStats
```
public ClassificationStatistics getPrecisionRecallStats(double binaryThreshold,
                                                        ij.ImagePlus mask)
```
    Calculate the pixel error and its derived statistics in 2D between some original labels and the corresponding proposed labels. Both images are binarized.
    
    Parameters:
    
    binaryThreshold - threshold value to binarize proposal (larger than 0 and smaller than 1)
    
    mask - mask image
    
    Returns:
    
    pixel error value and derived statistics
  - getPrecisionRecallStatsConcurrent
```
public Callable<ClassificationStatistics> getPrecisionRecallStatsConcurrent(ij.process.ImageProcessor image1,
                                                                            ij.process.ImageProcessor image2,
                                                                            double binaryThreshold)
```
    Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service). Both images are binarized.
    
    Parameters:
    
    image1 - first image
    
    image2 - second image
    
    binaryThreshold - threshold to apply to both images
    
    Returns:
    
    pixel error value and derived statistics
  - getPrecisionRecallStatsConcurrent
```
public Callable<ClassificationStatistics> getPrecisionRecallStatsConcurrent(ij.process.ImageProcessor image1,
                                                                            ij.process.ImageProcessor image2,
                                                                            ij.process.ImageProcessor mask,
                                                                            double binaryThreshold)
```
    Get pixel error value and derived statistics between two images in a concurrent way (to be submitted to an Executor Service). Both images are binarized.
    
    Parameters:
    
    image1 - first image
    
    image2 - second image
    
    mask - mask image
    
    binaryThreshold - threshold to apply to both images
    
    Returns:
    
    pixel error value and derived statistics
  - precisionRecallStats
```
public ClassificationStatistics precisionRecallStats(ij.process.ImageProcessor label,
                                                     ij.process.ImageProcessor proposal,
                                                     double binaryThreshold)
```
    Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels. Both image are binarized.
    
    Parameters:
    
    label - 2D image with the original labels
    
    proposal - 2D image with the proposed labels
    
    binaryThreshold - threshold value to binarize the input images
    
    Returns:
    
    rand index value and derived statistics
  - precisionRecallStats
```
public ClassificationStatistics precisionRecallStats(ij.process.ImageProcessor label,
                                                     ij.process.ImageProcessor proposal,
                                                     ij.process.ImageProcessor mask,
                                                     double binaryThreshold)
```
    Calculate the pixel error and derived statistics between some 2D original labels and the corresponding proposed labels. Both image are binarized.
    
    Parameters:
    
    label - 2D image with the original labels
    
    proposal - 2D image with the proposed labels
    
    mask - 2D image representing the binary mask
    
    binaryThreshold - threshold value to binarize the input images
    
    Returns:
    
    classification statistics
  - getPixelErrorMaximalFScore
```
public double getPixelErrorMaximalFScore(double minThreshold,
                                         double maxThreshold,
                                         double stepThreshold)
```
    Get the best F-score of the pixel error over a set of thresholds
    
    Parameters:
    
    minThreshold - minimum threshold value to binarize the input images
    
    maxThreshold - maximum threshold value to binarize the input images
    
    stepThreshold - threshold step value to use during binarization
    
    Returns:
    
    maximal F-score of the pixel error
  - main
```
public static void main(String[] args)
```
    Main method for calculate the pixel error metrics from the command line
    
    Parameters:
    
    args - arguments to decide the action

Class PixelError

Field Summary

Fields inherited from class trainableSegmentation.metrics.Metrics

Constructor Summary

Method Summary

Methods inherited from class trainableSegmentation.metrics.Metrics

Methods inherited from class java.lang.Object

Constructor Detail

PixelError

Method Detail

getMetricValue

getPixelErrorConcurrent

getMetricValue

getPixelErrorConcurrent

getPrecisionRecallStats

getPrecisionRecallStats

getPrecisionRecallStatsPerSlice

getPrecisionRecallStats

getPrecisionRecallStatsConcurrent

getPrecisionRecallStatsConcurrent

precisionRecallStats

precisionRecallStats

getPixelErrorMaximalFScore

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