Auball

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Ball finder plugin

A plugin intended for finding coloured balls in a camera image. This example is an image from a Kinect camera, looking at my desk.

Figure 18. There is 2 or 3 coloured objects, 2 red in the front and a darker blue in the back.

Sourse image

The code in the file ufuncball.cpp to get the sourse image is

66 UImage * img;
67 if (isOK)
68 { // resource is available, get image to analyze
69   // get source image
70   if (extra != NULL)
71     // image is provided (push command)
72     img = (UImage *) extra;
73   else
74     img = imgPool->getImage(varSourceImg->getInt(), false);
75   isOK = img != NULL;
76   if (isOK)
77     isOK = img->height() > 1;
78   if (not isOK)
79   { // no image
80     sendWarning("No source image");
81   }
82 }

The UImage type is a class that holds a version of the image that can be transferred to the client and a cv::Mat version for openCV manipulation.

Line 74 get the image from the image pool with the number specified in the (global) variable "poolImg=18" with the call 'varSourceImg->getInt()'.

convert to HSV

The same image in HSV format

Figure 45. A high hue value shown as blue, a high saturation in green (e.g. the dark red in the front and the blue lit in the back), the intensity (value) is shown in red (ex. the cup in the front). White objects have a hue of 0, the hue of dark objects is unreliable.

Crop

The image is cropped to the interesting Range Of Interest, here the top 100 rows are removed (parameter 'topLine').

Split into planes

The individual planes shown as grayscale images.

Figure 46. Hue, 0 is yellow (dark like the table, green is about 50 (rather dark), blue is about 115 (brighter), red is about 170 (bright). Maximum is 179, then it is back to 0.

Figure 47. Saturation, the brighter the more saturated the colour.

Figure 48. Intensity, this is a grayscale version of the original image.

Hue filtered image

The hue is used as the main filter, here emphasizing the two selected hues 'redHue=175' and 'blueHue=112'. Low saturation and low intensity are further removed.

Figure 53. A grayscale image, where all pixels with hue values of the two colours ('redHue' and 'blueHue') in the range specified by 'colLim' are set to white (255), if the hue is further away, then the highest distance from the desired hue is used. If the saturation is below 'limitSat=70' then the value is set to 0 (dark), the same if the intensity is below 'limitVal=70'.

Opening

The filtered image is likely to have smaller areas enhanced, these are removed/reduced by an opening filter.

Figure 54. Result of an opening operation with a 3x3 (all ones) erosion (twice, if 'opening=2') followed by dilation the same number of times.

Smoothing

The resulting image is then smoothed to get softer edges better suitable for a canny edge detector.

Figure 55. The smoothing is a Gauss blur with a mask size of 5 ('filter="1 5"').

Hough transform

The Hough transform is performed on the filtered image with a number of parameters ('hough="700 70 5").

The used canny filter has a high limit of 700 (and a low limit of 350 (half)). The second parameter 70 represents the voting of there is a circle with a centre at this position. The last parameter 5 is the resolution of the centre position, in this case all centre votes within 5 pixels are counted.

The Hough transform found these circles.

Hough circles found 3 circles
AUBall::  0 (268,278, 39) HSV=179 178 176 - blueish(112)= 67, redish(175)=  4 (OK=1)
AUBall::  1 (524, 58, 37) HSV=112 112 112 - blueish(112)=  0, redish(175)=-63 (OK=2)
AUBall::  2 (230,376, 38) HSV=170 171 169 - blueish(112)= 58, redish(175)= -5 (OK=1)
Ball:: biggest circle at x=268.5, y=277.5, radius=38.9936, color=1 (of 3)

The numbers in brackets are pixel position and circle radius, then the HSV values and how far the hue is from the two colors. In the last bracket is the colour detection (1 for red and 2 for blue).

Figure 50. The found circles (balls?) are shown in the original image with blue and red circles. The removed top part is shown as a red line.

Canny

The Hough includes a canny edge detector, to make the image binary.

Figure 51. A replica of the Canny edge filtered image that the Hough transform uses as the basis for estimation.

Parameters

In the demo code there is a - rather high - number of parameters, these are

  poolImg=18                 (r/w) image pool number to use as source
  poolDebugImg=45            (r/w) first image pool number to use for interim images
  redHue=175                 (r/w) hue value (in HSV formet) for red ball range [0-180]
                             (~120=red)
  redCnt=2                   (r) Number of red balls found in last image
  blueHue=112                (r/w) hue value (in HSV formet) for blue ball range
                             [0-180] (~0=blue) 
  blueCnt=1                  (r) Number of blue balls found in last image
  BallSize=0.12              (r/w) Size of the ball (diameter [m])
  topLine=100                (r/w) is the topmost line that could be a ball on the
                             floor.
  size=14 80                 (r/w) size limits of ball in pixels [min max]
  hough=700 70 5             (r/w) params for Hough (canny high [0-1000], hough vote
                             [0..255], Hough resolution 1(fine)..8(rough))
  colLim=13 13               (rw) Color limit (+/-) for circle center hue match [red
                             blue] [0..180]
  mrc=1                      (rw) Should result be send to MRC (smrcl)
  debug=1                    (rw) make more debug images and printout
  filter=1 5                 (rw) smooth image before detect [filter 0-1, size NxN]
  opening=2                  (rw) Opening before Hough circles
  limitSat=70                (rw) do not use pixels with saturation lower than this
                             (0..255)
  limitVal=70                (rw) do not use pixels with a lower V-value (intensity)
                             lower than this (0..255)