public static IImageStatistics Create(ImageProperties imageProperties, ushort[] array)
{
using (MyStopWatch.Measure()) {
long sum = 0;
long squareSum = 0;
int count = array.Count();
ushort min = ushort.MaxValue;
ushort oldmin = min;
ushort max = 0;
ushort oldmax = max;
long maxOccurrences = 0;
long minOccurrences = 0;
/* Array mapping: pixel value -> total number of occurrences of that pixel value */
int[] pixelValueCounts = new int[ushort.MaxValue + 1];
for (var i = 0; i < array.Length; i++)
{
ushort val = array[i];
sum += val;
squareSum += (long)val * val;
pixelValueCounts[val]++;
min = Math.Min(min, val);
if (min != oldmin)
{
minOccurrences = 0;
}
if (val == min)
{
minOccurrences += 1;
}
max = Math.Max(max, val);
if (max != oldmax)
{
maxOccurrences = 0;
}
if (val == max)
{
maxOccurrences += 1;
}
oldmin = min;
oldmax = max;
}
double mean = sum / (double)count;
double variance = (squareSum - count * mean * mean) / (count);
double stdev = Math.Sqrt(variance);
var occurrences = 0;
double median = 0d;
int median1 = 0, median2 = 0;
var medianlength = array.Length / 2.0;
/* Determine median out of histogram array */
for (ushort i = 0; i < ushort.MaxValue; i++)
{
occurrences += pixelValueCounts[i];
if (occurrences > medianlength)
{
median1 = i;
median2 = i;
break;
}
else if (occurrences == medianlength)
{
median1 = i;
for (int j = i + 1; j <= ushort.MaxValue; j++)
{
if (pixelValueCounts[j] > 0)
{
median2 = j;
break;
}
}
break;
}
}
median = (median1 + median2) / 2.0;
/* Determine median Absolute Deviation out of histogram array and previously determined median
* As the histogram already has the values sorted and we know the median,
* we can determine the mad by beginning from the median and step up and down
* By doing so we will gain a sorted list automatically, because MAD = DetermineMedian(|xn - median|)
* So starting from the median will be 0 (as median - median = 0), going up and down will increment by the steps
*/
var medianAbsoluteDeviation = 0.0d;
occurrences = 0;
var idxDown = median1;
var idxUp = median2;
while (true)
{
if (idxDown >= 0 && idxDown != idxUp)
{
occurrences += pixelValueCounts[idxDown] + pixelValueCounts[idxUp];
}
else
{
occurrences += pixelValueCounts[idxUp];
}
if (occurrences > medianlength)
{
medianAbsoluteDeviation = Math.Abs(idxUp - median);
break;
}
idxUp++;
idxDown--;
if (idxUp > ushort.MaxValue)
{
break;
}
}
var maxPossibleValue = (ushort)((1 << imageProperties.BitDepth) - 1);
var factor = (double)HISTOGRAMRESOLUTION / maxPossibleValue;
var histogram = pixelValueCounts
.Select((value, index) => new { Index = index, Value = value })
.GroupBy(
x => Math.Floor((double)Math.Min(maxPossibleValue, x.Index) * factor),
x => x.Value)
.Select(g => new OxyPlot.DataPoint(g.Key, g.Sum()))
.OrderBy(item => item.X).ToImmutableList();
var statistics = new ImageStatistics();
statistics.StDev = stdev;
statistics.Mean = mean;
statistics.Median = median;
statistics.MedianAbsoluteDeviation = medianAbsoluteDeviation;
statistics.Max = max;
statistics.MaxOccurrences = maxOccurrences;
statistics.Min = min;
statistics.MinOccurrences = minOccurrences;
statistics.Histogram = histogram;
return(statistics);
}
}
public ImageData(IImageArray imageArray, int width, int height, int bitDepth, bool isBayered, ImageMetaData metaData)
{
this.Data = imageArray;
this.MetaData = metaData;
isBayered = metaData.Camera.SensorType != SensorType.Monochrome ? true : isBayered;
this.Properties = new ImageProperties(width: width, height: height, bitDepth: bitDepth, isBayered: isBayered, gain: metaData.Camera.Gain);
this.StarDetectionAnalysis = new StarDetectionAnalysis();
this.Statistics = new Nito.AsyncEx.AsyncLazy <IImageStatistics>(async() => await Task.Run(() => ImageStatistics.Create(this)));
}
