kinectdepthsmoothingbin.zip
KinectDepthSmoothingBin3.zip
kinectdepthsmoothingsrc.zip
KinectDepthSmoothing
Properties
Settings.settings
KinectDepthSmoothingSrc3.zip
Settings.settings
KinectDepthSmoothing_bin_.zip
KinectDepthSmoothing_src_.zip
Settings.settings

using System.Threading.Tasks;
using System.Windows;
using System.Collections.Generic;
namespace KinectDepthSmoothing
{
public partial class MainWindow : Window
{
private short[] CreateFilteredDepthArray(short[] depthArray, int width, int height)
{
/////////////////////////////////////////////////////////////////////////////////////
// I will try to comment this as well as I can in here, but you should probably refer
// to my Code Project article for a more in depth description of the method.
/////////////////////////////////////////////////////////////////////////////////////
short[] smoothDepthArray = new short[depthArray.Length];
// We will be using these numbers for constraints on indexes
int widthBound = width  1;
int heightBound = height  1;
// We process each row in parallel
Parallel.For(0, 240, depthArrayRowIndex =>
{
// Process each pixel in the row
for (int depthArrayColumnIndex = 0; depthArrayColumnIndex < 320; depthArrayColumnIndex++)
{
var depthIndex = depthArrayColumnIndex + (depthArrayRowIndex * 320);
// We are only concerned with eliminating 'white' noise from the data.
// We consider any pixel with a depth of 0 as a possible candidate for filtering.
if (depthArray[depthIndex] == 0)
{
// From the depth index, we can determine the X and Y coordinates that the index
// will appear in the image. We use this to help us define our filter matrix.
int x = depthIndex % 320;
int y = (depthIndex  x) / 320;
// The filter collection is used to count the frequency of each
// depth value in the filter array. This is used later to determine
// the statistical mode for possible assignment to the candidate.
short[,] filterCollection = new short[24,2];
// The inner and outer band counts are used later to compare against the threshold
// values set in the UI to identify a positive filter result.
int innerBandCount = 0;
int outerBandCount = 0;
// The following loops will loop through a 5 X 5 matrix of pixels surrounding the
// candidate pixel. This defines 2 distinct 'bands' around the candidate pixel.
// If any of the pixels in this matrix are non0, we will accumulate them and count
// how many non0 pixels are in each band. If the number of non0 pixels breaks the
// threshold in either band, then the average of all non0 pixels in the matrix is applied
// to the candidate pixel.
for (int yi = 2; yi < 3; yi++)
{
for (int xi = 2; xi < 3; xi++)
{
// yi and xi are modifiers that will be subtracted from and added to the
// candidate pixel's x and y coordinates that we calculated earlier. From the
// resulting coordinates, we can calculate the index to be addressed for processing.
// We do not want to consider the candidate pixel (xi = 0, yi = 0) in our process at this point.
// We already know that it's 0
if (xi != 0  yi != 0)
{
// We then create our modified coordinates for each pass
var xSearch = x + xi;
var ySearch = y + yi;
// While the modified coordinates may in fact calculate out to an actual index, it
// might not be the one we want. Be sure to check to make sure that the modified coordinates
// match up with our image bounds.
if (xSearch >= 0 && xSearch <= widthBound && ySearch >= 0 && ySearch <= heightBound)
{
var index = xSearch + (ySearch * width);
// We only want to look for non0 values
if (depthArray[index] != 0)
{
// We want to find count the frequency of each depth
for (int i = 0; i < 24; i++)
{
if (filterCollection[i, 0] == depthArray[index])
{
// When the depth is already in the filter collection
// we will just increment the frequency.
filterCollection[i, 1]++;
break;
}
else if (filterCollection[i, 0] == 0)
{
// When we encounter a 0 depth in the filter collection
// this means we have reached the end of values already counted.
// We will then add the new depth and start it's frequency at 1.
filterCollection[i, 0] = depthArray[index];
filterCollection[i, 1]++;
break;
}
}
// We will then determine which band the non0 pixel
// was found in, and increment the band counters.
if (yi != 2 && yi != 2 && xi != 2 && xi != 2)
innerBandCount++;
else
outerBandCount++;
}
}
}
}
}
// Once we have determined our inner and outer band nonzero counts, and accumulated all of those values,
// we can compare it against the threshold to determine if our candidate pixel will be changed to the
// statistical mode of the nonzero surrounding pixels.
if (innerBandCount >= innerBandThreshold  outerBandCount >= outerBandThreshold)
{
short frequency = 0;
short depth = 0;
// This loop will determine the statistical mode
// of the surrounding pixels for assignment to
// the candidate.
for (int i = 0; i < 24; i++)
{
// This means we have reached the end of our
// frequency distribution and can break out of the
// loop to save time.
if (filterCollection[i,0] == 0)
break;
if (filterCollection[i, 1] > frequency)
{
depth = filterCollection[i, 0];
frequency = filterCollection[i, 1];
}
}
smoothDepthArray[depthIndex] = depth;
}
}
else
{
// If the pixel is not zero, we will keep the original depth.
smoothDepthArray[depthIndex] = depthArray[depthIndex];
}
}
});
return smoothDepthArray;
}
}
}

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Karl Sanford
Software Developer
Open Systems Technologies
United States
First learned to program in 1997 on my TI83 and have been doing it ever since, with a foray into networking and infrastructure.
Mostly a C# junky (Win\Web Forms, WP7.5/8, WPF and MVC), though I have experience with many other technologies and products.
I have also been trying to learn and apply more in the area of AI; focusing on computer vision, natural language processing, and classification.
In my spare time, I love to tinker with electronics and various useless DIY projects.
My brain is a shark... if it stops moving, it will die. I'm always looking to learn more.