Auball

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Line 13: Line 13:
  
 
The code in the function is explained below.
 
The code in the function is explained below.
 +
 +
The images are obtained with the "ucamserver" and the test image "ball08.png", that is loaded into image-pool image 78.
 +
 +
>> poolset img=78 loadpng=./ball08.png
 +
>> ball img=78 filt debug
 +
        Using gauss filter (5x5)
 +
        Hough circles found 3 circles
 +
        AUBall::  0 (361, 93, 27) Hue=122, sat=114 - blue(122)=  0 off, red(166)=-44 off (OK: blue)
 +
        AUBall::  1 (469, 91, 30) Hue=163, sat=187 - blue(122)= 41 off, red(166)= -3 off (OK: red)
 +
        AUBall::  2 ( 61, 45, 24) Hue=141, sat=127 - blue(122)= 19 off, red(166)=-25 off (NOT: low intensity)
 +
        <ball Cnt="2" blue=1, red=1/>
  
 
=== Sourse image ===
 
=== Sourse image ===
Line 18: Line 29:
 
The code in the file ufuncball.cpp to get the sourse image is
 
The code in the file ufuncball.cpp to get the sourse image is
  
−
66 UImage * img;
+
[[File:code_get_image.png]]
−
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.
 
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()'.
+
Line 87 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 ===
 
=== convert to HSV ===
Line 52: Line 47:
 
  101  // split into planes
 
  101  // split into planes
 
  102  splitIntoHSVChannels(img, imgHSV, imgHue, imgSat, imgInt, imgRGB);
 
  102  splitIntoHSVChannels(img, imgHSV, imgHue, imgSat, imgInt, imgRGB);
 +
 +
[[File:code_split_hsv.png]]
  
 
This is a call to a function that converts to HSV and splits the HSV image into 3 grayscale images  
 
This is a call to a function that converts to HSV and splits the HSV image into 3 grayscale images  
  
−
502 void splitIntoHSVChannels(UImage * img, UImage * imgHSV, UImage * imgHue, UImage * imgSat, UImage * imgInt, UImage * imgRGB)
 
−
...
 
−
521  if (img->usableIpl and not img->usableMat)
 
−
522    img->toMat();
 
−
523  cv::cvtColor(img->mat, imgHSV->mat, cv::COLOR_BGR2HSV);
 
−
524  // result is returned in:
 
−
525  imgHSV->fromMat("BGR"); // the RGB is to be displayed as RGB, but the colors will represent HSV
 
−
526  imgHSV->setName("HSV_format");
 
−
527  // split channels
 
−
528  cv::Mat channel[3];
 
−
529  cv::split(imgHSV->mat, channel);
 
−
530  // hue plane
 
−
531  if (imgHue->tryLock())
 
−
532  { // make hue visible as gray scale image in image pool
 
−
533    imgHue->mat = channel[0].clone();
 
−
534    imgHue->setName("hue");
 
−
535    imgHue->fromMat("GRAY");
 
−
536    imgHue->unlock();
 
−
537  }
 
  
−
This function takes 5 images as parameters. The source updates the cv::Mat version of the image in line 522, and then makes a color conversion from BGR to HSV using the 'cv::COLOR_BGR2HSV' parameter in line 523. The image is then loaded back to the image pool and given a new name in lines 525 and 526.
+
[[File:code_split_into_hsv_do.png]]
  
−
The splitting of the color planes are in line 529, and the remaining lines are to get images back to the image pool.
+
This function takes 6 images as parameters. The source updates the cv::Mat version of the image in line 536, and then makes a color conversion from BGR to HSV using the 'cv::COLOR_BGR2HSV' parameter in line 537. The image is then loaded back to the image pool and given a new name in lines 539 and 540.
 +
 
 +
The splitting of the color planes are in line 543 into an array with 3 cv::Mat objects.
 +
The remaining lines are to get images back to the image pool.
  
 
=== Crop ===
 
=== Crop ===
  
−
The image is cropped to the interesting Range Of Interest, here the top 100 rows are removed (parameter 'topLine').
+
The image is cropped to the interesting Range Of Interest, here the top 200 rows are removed (parameter 'topLine').
  
 
NB! the parameters used in this example is listed at the bottom of this page.
 
NB! the parameters used in this example is listed at the bottom of this page.
Line 88: Line 69:
  
 
The individual planes shown as grayscale images.
 
The individual planes shown as grayscale images.
 +
 +
[[File:ball_5271_48_val.png | 500px]]
 +
[[File:ball08_int.png | 400px]]
 +
 +
Figure 48. Intensity, this is the grayscale version of the original image and has 3 lines marked, these are used in the analysis to the left. The balls has a rather low intensity, but never gets below a value of 50.
  
 
[[File:ball_5271_46_hue.png | 400px]]
 
[[File:ball_5271_46_hue.png | 400px]]
 +
[[File:ball08_hue.png | 400px]]
  
−
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 46. Hue, 0 is yellow (dark like the table, green is about 50 (rather dark), the blue ball is about 120, the red ball is about 165. Maximum is 179, then it is folds back to 0. The plot shows 3 lines and it is clear that the red ball does not differ too much from the surrounding floor colour. It is further visible that there some colour variation across the ball.
  
 
[[File:ball_5271_47_sat.png | 400px]]
 
[[File:ball_5271_47_sat.png | 400px]]
 +
[[File:ball08_sat.png | 400px]]
  
−
Figure 47. Saturation, the brighter the more saturated the colour.
+
Figure 47. Saturation, the brighter the more saturated the colour. It is visible that the balls have a better saturation than most of the rest of the image.
  
−
[[File:ball_5271_48_val.png | 500px]]
+
=== Enhanced image ===
  
−
Figure 48. Intensity, this is a grayscale version of the original image with 3 lines marked, that is used in the analysis.
+
The enhanced image is an attempt to promote the characteristic hue, saturation and intensity of the balls, in such a way that a following edge filter has an easy task in finding the ball edges and suppress all other edges.
  
−
=== Hue filtered image ===
+
The hue is used as the main enhancement, here emphasizing the two selected hues 'redHue=166' and 'blueHue=112'.
−
 
+
−
The hue is used as the main filter, here emphasizing the two selected hues 'redHue=166' and 'blueHue=112'. Low saturation and low intensity are further removed.
+
  
 
[[File:ball_5271_54_opened.png | 400px]]
 
[[File:ball_5271_54_opened.png | 400px]]
 +
[[File:ball08_enh.png | 400px]]
 +
 +
Figure 53. A grayscale image where the ball characteristics are promoted (towards 255 (white)).
 +
 +
The enhanced image is constructed from HSV pixel values so that each enhanced pixel e = f(h,s,v)
 +
 +
[[File:code_enhanced_expression.png]]
  
−
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.  
+
Some of these values Ls = redHue, Lb = blueHue and 13 (redLim and blueLim) are setable parameters (see list at bottom of this page).
−
If the saturation is below 'limitSat=70' then the value is set to 0 (dark), the same if the intensity is below 'limitVal=70'.
+
The hue value is from 0 to 179. The red range if folded if reached outside these limits.
  
 
==== Code ====
 
==== Code ====
Line 117: Line 110:
 
  122  use = enhanceDesiredHue(use, imgSat->mat, varLimitSat->getInt(), imgInt->mat, varLimitValue->getInt());
 
  122  use = enhanceDesiredHue(use, imgSat->mat, varLimitSat->getInt(), imgInt->mat, varLimitValue->getInt());
  
−
The colour and colour span is fetched in 599-602 below, the saturation and intensity values are transferred as parameters.
+
The enhanced image is constructed from HSV pixel values so that each enhanced pixel e = f(h,s,v)
  
−
597 cv::Mat enhanceDesiredHue(cv::Mat use, cv::Mat sat, int limitSat, cv::Mat val, int limitVal)
+
[[File:code_enhanced_expression.png]]
−
598 { // enhance contrast based on expected hue (color tone)
+
 
−
599  int redhue  = varRed->getInt();
+
Some of these values Ls = redHue, Lb = blueHue and 13 (redLim and blueLim) are setable parameters (see list at bottom of this page).
−
600  int bluehue = varBlue->getInt();
+
The hue value is from 0 to 179. The red range if folded if reached outside these limits.
−
601  int redLim = varColorLim->getInt(0);
+
 
−
602  int blueLim = varColorLim->getInt(1);
+
These parameters are fetched at the start of the enhancement function
−
603  //
+
 
−
604  UImage * imgRoi2 = imgPool->getImage(varDebugImg->getInt() + 8, true);
+
[[File:code_enhancement_function_start.png]]
−
605  imgRoi2->mat = use.clone();
+
−
606 ...
+
  
 
The resulting image is set to be available in the image pool as image 45+8 = 53.
 
The resulting image is set to be available in the image pool as image 45+8 = 53.
  
−
Each pixel in the resulting image is based on the same pixel position in the HSV planes.
+
Each pixel in the resulting image is based on the same pixel position in the HSV planes as described above, the actual code is:
−
The saturation and intensity limits are tested by line 617. Then the distance from the desired red and blue colour are calculated in lines 619-624 and 625-631 respectively. If within range then the result is set to 255 (max white), else reduced with an increased contrast of a factor 3 (line 638 and 645).
+
  
−
617        if (*cpSat > limitSat and *cpVal > limitVal)
+
[[File:code_enhanced_actual_code.png]]
−
618        {
+
−
619          int dred = *cp - redhue;
+
−
621          if (dred > 90)
+
−
622            dred -= 180;
+
−
623          else if (dred < -90)
+
−
624            dred += 180;
+
−
625          int dblue = *cp - bluehue;
+
−
627          if (dblue > 90)
+
−
628            dblue -= 180;
+
−
629          else if (dblue < -90)
+
−
630            dblue += 180;
+
−
631          v = absi(dblue);
+
−
632          if (v > absi(dred))
+
−
633          {
+
−
634            v = absi(dred);
+
−
635            if (v < redLim)
+
−
636              v = 255;
+
−
637            else
+
−
638              v = 155 - v * 3;
+
−
639          }
+
−
640          else
+
−
641          {
+
−
642            if (v < blueLim)
+
−
643              v = 255;
+
−
644            else
+
−
645              v = 155 - v * 3;
+
−
646          }
+
−
647          if (v < 0)
+
−
648            v = 0;
+
−
649        }
+
−
650        *cp2 = v; // save into image
+
  
 
=== Opening ===
 
=== Opening ===
  
−
The filtered image is likely to have smaller areas enhanced, these are removed/reduced by an opening filter.
+
The enhanced image is likely to have smaller areas similar to the ball enhanced, these are removed/reduced by an opening filter.
  
 
[[File:ball_5271_53_enhanced.png | 400px]]
 
[[File:ball_5271_53_enhanced.png | 400px]]
Line 178: Line 137:
 
====Code====
 
====Code====
  
−
The opening code is straight forward - a number of erodes followed by the same number of dilate.
+
The opening code is straight forward - a number of erodes followed by the same number of dilations.
  
−
124    cv::Mat buffer;
+
 
−
125    cv::Mat mask = (cv::Mat_<char>(3,3) <<
+
[[File:code_opening_filter.png]]
−
126    1,  1,  1,
+
−
127    1,  1,  1, 
+
−
128    1,  1,  1);
+
−
129    cv::erode(use, buffer, mask, cv::Point(-1,-1), varOpening->getInt(0));
+
−
130    cv::dilate(buffer, use, mask, cv::Point(-1,-1), varOpening->getInt(0));
+
  
 
=== Smoothing ===
 
=== Smoothing ===
Line 193: Line 147:
  
 
[[File:ball_5271_55_filtered.png | 400px]]
 
[[File:ball_5271_55_filtered.png | 400px]]
 +
[[File:ball08_enh_filt.png | 400px]]
  
 
Figure 55. The smoothing is a Gauss blur with a mask size of 5 ('filter="1 5"').
 
Figure 55. The smoothing is a Gauss blur with a mask size of 5 ('filter="1 5"').
Line 200: Line 155:
 
Blur filtering is optional but recommended.
 
Blur filtering is optional but recommended.
  
−
137    if (varFilter->getBool())
+
[[File:code_enh_gauss.png]]
−
138    {
+
−
139      int n = varFilter->getInt(1);
+
−
140      if (true)
+
−
141      {
+
−
142        printf("Using median filter (%dx%d)\n", n,n);
+
−
143        cv::medianBlur(use, buffer, n);
+
−
144      }
+
−
145      else
+
−
146      {
+
−
147        printf("Using gauss filter (%dx%d)\n", n,n);
+
−
148        cv::GaussianBlur(use, buffer, cv::Size(n, n), 0 , 0);
+
−
149      }
+
−
150      use = buffer.clone();
+
−
151    }
+
  
−
A median blur is used here, but a Gaussian blur was probably better.
+
A Gaussian blur is used here, but a median could be used too.
  
 
=== Hough transform ===
 
=== Hough transform ===
Line 236: Line 177:
  
 
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. The white ring is a rejected circle.
 
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. The white ring is a rejected circle.
 +
The green squares in the circles ar the area, where the colour of the ball is checked (see below).
  
 
====Code====
 
====Code====
  
−
The result of the Hough transform is delivered in a vector with 3 floats (line 165).
+
The result of the Hough transform is delivered in a vector with 3 floats (line 191).
  
−
164    // prepare result circles
+
[[File:code_hough.png]]
−
165    std::vector<cv::Vec3f> circles;
+
−
166    std::vector<int> usable; // 0 = not, 1 = red, 2= blue
+
−
167    // find circles
+
−
168    cv::HoughCircles(use, circles, cv::HOUGH_GRADIENT, varHough->getInt(2),
+
−
169                      use.rows/16,  // change this value to detect circles with different distances to each other
+
−
170                      varHough->getInt(0), // Canny top parameter [0..1000]
+
−
171                      varHough->getInt(1), // circle quality limit
+
−
172                      varSize->getInt(0),  // minimum radius in pixels
+
−
173                      varSize->getInt(1)  // maximum radius in pixels
+
−
174                );
+
−
175    isOK = circles.size() > 0;
+
−
176    printf("Hough circles found %d circles\n", (int)circles.size());
+
  
−
=== Canny ===
+
===Ball check===
  
−
The Hough includes a canny edge detector, to make the image binary.
+
The code in this function filters the found circles
  
−
[[File:ball_5271_51_canny.png | 400px]]
+
674        void findUsableCircles(std::vector<cv::Vec3f> & circles,
 +
                                  std::vector<UCircle> & usable,
 +
                                  UImage * imgHue, int tl,
 +
                                  UImage * imgAnn,
 +
                                  UImage * imgSat,
 +
                                  UImage * imgVal)
 +
            {
 +
                ...
  
−
Figure 51. A replica of the Canny edge filtered image that the Hough transform uses as the basis for estimation.
+
For each circle, the average hue, saturation and value are tested in a square area in this code.
  
−
===Ball color===
+
[[File:code_get_centre_values.png]]
  
−
The code
+
The average value is calculated in a subroutine (line  888), and further draws the used square in the annotated image (if used with the debug option).
  
−
179      findUsableCircles(circles, usable, imgHue, varTopLine->getInt(), imgRGB);
+
===Ball overlap===
  
−
checkes the colour in the centre of the found circles, and annotates with 1 or 2 if the colour is within the red or blue range, and further paints circles in the RGB image. Other circles are deleted.
+
There is further an overlap filter. If two Hough circles overlap, then the circle with the best colour (hue) match is selected (from line 759).
 +
 
 +
 
 +
=== Canny ===
 +
 
 +
The Hough includes a canny edge detector, to make the image binary.
 +
 
 +
[[File:ball_5271_51_canny.png | 400px]]
 +
 
 +
Figure 51. A replica of the Canny edge filtered image that the Hough transform uses as the basis for estimation.
  
 
=== Reporting ===
 
=== Reporting ===
  
−
The rest of the code (line 205 to line 245) is for reporting the result to the client and to the MRC.
+
The rest of the code in the HandleCommand function (line 208 to line 270) is for reporting the result to the client and to the MRC.
  
 
== Parameters ==
 
== Parameters ==
  
−
In the demo code there is a - rather high - number of parameters, these are
+
The code uses a number of parameters, these are  
  
−
   poolImg=18                 (r/w) image pool number to use as source
+
>> var ball
 +
<help subject="var list" name="ball">
 +
Description:                  camera based ball detect (compiled Feb 16 2020 09:23:37)
 +
   poolImg=77                 (r/w) image pool number to use as source
 
   poolDebugImg=45            (r/w) first image pool number to use for interim images
 
   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]
+
   redHue=166                 (r/w) hue value (in HSV formet) for red ball range [0-180]
 
                               (~120=red)
 
                               (~120=red)
−
   redCnt=2                   (r) Number of red balls found in last image
+
   redCnt=1                   (r) Number of red balls found in last image
−
   blueHue=112               (r/w) hue value (in HSV formet) for blue ball range
+
   blueHue=122               (r/w) hue value (in HSV formet) for blue ball range
 
                               [0-180] (~0=blue)  
 
                               [0-180] (~0=blue)  
 
   blueCnt=1                  (r) Number of blue balls found in last image
 
   blueCnt=1                  (r) Number of blue balls found in last image
 
   BallSize=0.12              (r/w) Size of the ball (diameter [m])
 
   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
+
   topLine=200               (r/w) is the topmost line that could be a ball on the
 
                               floor.
 
                               floor.
−
   size=14 80                 (r/w) size limits of ball in pixels [min max]
+
   size=15 60                 (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
+
   hough=400 40 2             (r/w) params for Hough (canny high [0-1000], hough vote
 
                               [0..255], Hough resolution 1(fine)..8(rough))
 
                               [0..255], Hough resolution 1(fine)..8(rough))
 
   colLim=13 13              (rw) Color limit (+/-) for circle center hue match [red
 
   colLim=13 13              (rw) Color limit (+/-) for circle center hue match [red
Line 299: Line 248:
 
   filter=1 5                (rw) smooth image before detect [filter 0-1, size NxN]
 
   filter=1 5                (rw) smooth image before detect [filter 0-1, size NxN]
 
   opening=2                  (rw) Opening before Hough circles
 
   opening=2                  (rw) Opening before Hough circles
−
   limitSat=70               (rw) do not use pixels with saturation lower than this
+
   limitSat=80               (rw) do not use pixels with saturation lower than this
 
                               (0..255)
 
                               (0..255)
−
   limitVal=70               (rw) do not use pixels with a lower V-value (intensity)
+
   limitVal=50               (rw) do not use pixels with a lower V-value (intensity)
 
                               lower than this (0..255)
 
                               lower than this (0..255)
 +
(H: has time series, L: is logged, R: replay)
 +
</help>
 +
<var info="done"/>
 +
 +
They can be modified with eg:
 +
 +
>> var ball.topLine=175
 +
                              <var name="ball.topLine" typ="d" size="1" value="175"/>
 +
>> var ball.hough="300 40 2"
 +
                              <var name="ball.hough" typ="d" size="3" value="300 40 2" oldValue="400 40 2"/>
 +
 +
To change the top line cut to 150 pixels rather than 200, and change one of the Hough parameters.

Latest revision as of 15:19, 16 February 2020

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[edit] Ball finder plugin

A plugin intended for finding coloured balls in a camera image. This example is an image (ball08.png) from a Kinect camera with 2 balls at daylight.

Ball 5271 18.png Ball 5271 50 annotated.png

Figure 78 (and 50). There are two balls, a red and a blue. These are marked in the second image (50) by the plugin, the white circle is a failed circle candidate. The red line is the top limit, where to look for balls.

The code in the function is explained below.

The images are obtained with the "ucamserver" and the test image "ball08.png", that is loaded into image-pool image 78. 

>> poolset img=78 loadpng=./ball08.png
>> ball img=78 filt debug
        Using gauss filter (5x5)
        Hough circles found 3 circles
        AUBall::  0 (361, 93, 27) Hue=122, sat=114 - blue(122)=  0 off, red(166)=-44 off (OK: blue)
        AUBall::  1 (469, 91, 30) Hue=163, sat=187 - blue(122)= 41 off, red(166)= -3 off (OK: red)
        AUBall::  2 ( 61, 45, 24) Hue=141, sat=127 - blue(122)= 19 off, red(166)=-25 off (NOT: low intensity)
        <ball Cnt="2" blue=1, red=1/>

[edit] Sourse image

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

Code get image.png

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

[edit] convert to HSV

The same image in HSV format

Ball 5271 45 HSV.png

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.

[edit] Code 

101  // split into planes
102  splitIntoHSVChannels(img, imgHSV, imgHue, imgSat, imgInt, imgRGB);

Code split hsv.png

This is a call to a function that converts to HSV and splits the HSV image into 3 grayscale images


Code split into hsv do.png

This function takes 6 images as parameters. The source updates the cv::Mat version of the image in line 536, and then makes a color conversion from BGR to HSV using the 'cv::COLOR_BGR2HSV' parameter in line 537. The image is then loaded back to the image pool and given a new name in lines 539 and 540.

The splitting of the color planes are in line 543 into an array with 3 cv::Mat objects. The remaining lines are to get images back to the image pool.

[edit] Crop

The image is cropped to the interesting Range Of Interest, here the top 200 rows are removed (parameter 'topLine').

NB! the parameters used in this example is listed at the bottom of this page.

[edit] Split into planes

The individual planes shown as grayscale images.

Ball 5271 48 val.png Ball08 int.png

Figure 48. Intensity, this is the grayscale version of the original image and has 3 lines marked, these are used in the analysis to the left. The balls has a rather low intensity, but never gets below a value of 50.

Ball 5271 46 hue.png Ball08 hue.png

Figure 46. Hue, 0 is yellow (dark like the table, green is about 50 (rather dark), the blue ball is about 120, the red ball is about 165. Maximum is 179, then it is folds back to 0. The plot shows 3 lines and it is clear that the red ball does not differ too much from the surrounding floor colour. It is further visible that there some colour variation across the ball.

Ball 5271 47 sat.png Ball08 sat.png

Figure 47. Saturation, the brighter the more saturated the colour. It is visible that the balls have a better saturation than most of the rest of the image.

[edit] Enhanced image

The enhanced image is an attempt to promote the characteristic hue, saturation and intensity of the balls, in such a way that a following edge filter has an easy task in finding the ball edges and suppress all other edges.

The hue is used as the main enhancement, here emphasizing the two selected hues 'redHue=166' and 'blueHue=112'.

Ball 5271 54 opened.png Ball08 enh.png

Figure 53. A grayscale image where the ball characteristics are promoted (towards 255 (white)).

The enhanced image is constructed from HSV pixel values so that each enhanced pixel e = f(h,s,v)

Code enhanced expression.png

Some of these values Ls = redHue, Lb = blueHue and 13 (redLim and blueLim) are setable parameters (see list at bottom of this page). The hue value is from 0 to 179. The red range if folded if reached outside these limits.

[edit] Code

The filter is implemented in a sub-function: 

121  // enhance contrast based on known hue for red and blue ball 
122  use = enhanceDesiredHue(use, imgSat->mat, varLimitSat->getInt(), imgInt->mat, varLimitValue->getInt());

The enhanced image is constructed from HSV pixel values so that each enhanced pixel e = f(h,s,v)

Code enhanced expression.png

Some of these values Ls = redHue, Lb = blueHue and 13 (redLim and blueLim) are setable parameters (see list at bottom of this page). The hue value is from 0 to 179. The red range if folded if reached outside these limits.

These parameters are fetched at the start of the enhancement function

Code enhancement function start.png

The resulting image is set to be available in the image pool as image 45+8 = 53.

Each pixel in the resulting image is based on the same pixel position in the HSV planes as described above, the actual code is:

Code enhanced actual code.png

[edit] Opening

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

Ball 5271 53 enhanced.png

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.

[edit] Code

The opening code is straight forward - a number of erodes followed by the same number of dilations.


Code opening filter.png

[edit] Smoothing

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

Ball 5271 55 filtered.png Ball08 enh filt.png

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

[edit] Code

Blur filtering is optional but recommended.

Code enh gauss.png

A Gaussian blur is used here, but a median could be used too.

[edit] 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 (361, 93, 27) Hue=122, sat=114 - blue(122)=  0 off, red(166)=-44 off (OK: blue)
AUBall::  1 (469, 91, 30) Hue=163, sat=187 - blue(122)= 41 off, red(166)= -3 off (OK: red)
AUBall::  2 ( 61, 45, 24) Hue=141, sat=127 - blue(122)= 19 off, red(166)=-25 off (NOT: low intensity)

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).

Ball 5271 50 annotated.png

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. The white ring is a rejected circle. The green squares in the circles ar the area, where the colour of the ball is checked (see below).

[edit] Code

The result of the Hough transform is delivered in a vector with 3 floats (line 191).

Code hough.png

[edit] Ball check

The code in this function filters the found circles 

674        void findUsableCircles(std::vector<cv::Vec3f> & circles,
                                  std::vector<UCircle> & usable,
                                  UImage * imgHue, int tl,
                                  UImage * imgAnn,
                                  UImage * imgSat,
                                  UImage * imgVal)
           {
               ...

For each circle, the average hue, saturation and value are tested in a square area in this code.

Code get centre values.png

The average value is calculated in a subroutine (line 888), and further draws the used square in the annotated image (if used with the debug option).

[edit] Ball overlap

There is further an overlap filter. If two Hough circles overlap, then the circle with the best colour (hue) match is selected (from line 759).


[edit] Canny

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

Ball 5271 51 canny.png

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

[edit] Reporting

The rest of the code in the HandleCommand function (line 208 to line 270) is for reporting the result to the client and to the MRC.

[edit] Parameters

The code uses a number of parameters, these are 

>> var ball
<help subject="var list" name="ball">
Description:                  camera based ball detect (compiled Feb 16 2020 09:23:37)
  poolImg=77                 (r/w) image pool number to use as source
  poolDebugImg=45            (r/w) first image pool number to use for interim images
  redHue=166                 (r/w) hue value (in HSV formet) for red ball range [0-180]
                             (~120=red)
  redCnt=1                   (r) Number of red balls found in last image
  blueHue=122                (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=200                (r/w) is the topmost line that could be a ball on the
                             floor.
  size=15 60                 (r/w) size limits of ball in pixels [min max]
  hough=400 40 2             (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=80                (rw) do not use pixels with saturation lower than this
                             (0..255)
  limitVal=50                (rw) do not use pixels with a lower V-value (intensity)
                             lower than this (0..255)
(H: has time series, L: is logged, R: replay)
</help>
<var info="done"/>

They can be modified with eg: 

>> var ball.topLine=175
                              <var name="ball.topLine" typ="d" size="1" value="175"/>
>> var ball.hough="300 40 2"
                              <var name="ball.hough" typ="d" size="3" value="300 40 2" oldValue="400 40 2"/>

To change the top line cut to 150 pixels rather than 200, and change one of the Hough parameters.

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