*JMeh 17 Aug 2017*- HoughTransThe Brinell test presses a 10 mm diameter hardened steel ball with a force of 29.42 kN into the surface of the material to be tested. After that the diameter of the impress is measured and then flows into a formula for calculating Brinell hardness. For a better measurement, the image of the impress is optically magnified on a milk glass disc, the screen. Since the size of the screen and the magnification factor are known, one can easily calculate the real diameter of the imprint if the size of the circle has previously been determined.Therefore, I have installed a high resolution camera (1600x1200) in front of the screen to get a photo of the impress. Now one can use an analysis of the photo to find these points that form the edge of the imprint. With these points, a Hough transform can now be performed to determine the radius of the circle. This is the method which is used here.Since the data volume in a Hough space becomes very large, it is not possible to store the values with standard Tcl commands. Therefore, the VecTcl library was used here.If you want to learn more about Brinell Hardness or Hough Transformation, have a look at

https://en.wikipedia.org/wiki/Brinell_scale

https://en.wikipedia.org/wiki/Hough_transformYes, the performance of the algorithm using VecTcl is far away from being usable, but it is also more of a case study. You can see it working in this screencast (animated GIF):The sample image of the camera can be downloaded here:http://sesam-gmbh.org/images/Downloads/Public/hbcam.pngFor performance reasons, I use the same algorithm but written in C at the real test facility. Here is a table of execution times of several implementations:

Language | CPU type | number of steps | elapsed time | specials |
---|---|---|---|---|

Tcl | 2,6 GHz Intel Core i7 | 24 | 22.67 sec | Tcl with VecTcl commands |

C# | 2,6 GHz Intel Core i7 | 360 | 0.894 sec | Running with mono on my MAC |

C | 2,6 GHz Intel Core i7 | 360 | 1.036 sec | Pure C - Single Processor |

C | 2,6 GHz Intel Core i7 | 360 | 0.454 sec | Pure C - OpenMP with 8 threads |

CUDA | GeForce GTX 260 | 360 | 0.074 sec | GPU-Processing with 512 threads |

[auriocus] Nice! Apparently, lots of time is spent to transfer the photo image into a VecTcl array. There is a secondary package vectcl::tk which does the conversion in C (numarray::fromPhoto). It is fast enough for interactive frame rates, see https://www.youtube.com/watch?v=88J0tVFE_ic

[DEC]: Is the Tcl time without the puts's?

JMeh: There are only 24 puts in total (inside the loop) - that doesn't matter

[DEC] Do all the other code examples use a GUI? Just trying to understand where the time is used in the Tcl/Tk version and can it be improved.

JMeh: No, this one here is the only one with a GUI, but that's not the reason. Look at the source, I think, it's well documented. You will see several neted loops. The outer one "while alpha < fullCircle { ..." loops over each angle, it is followed by a loop over the radius "for ri = 0:m { ..." to determine the brightness value of the edge of the imprint, followed by a double loop over the coordinates of the hough volume "for y = 0:C_HEIGHT-1 { for x = 0:C_WIDTH-1 { ..." to accumulate the brightness in the hough volume. The whole is followed by the last triple loop over the whole hough volume to get the maximum, but it's supported by the max-vectcl function, which will speed it up dramatically, because on the average only every seventh pass requires a further search.

Let us calculate the effort (assuming a radius of 353 pixel):

Step | Calculation | Number of passes | Sum |
---|---|---|---|

1 | Determine brightness | 25 * 353 | = 8825 |

2 | Accumulate hough space | 25 * 250 * 250 | = 1562500 |

3 | Search maximum | 250 * 250 / 7 | = 9000 |

And here is the application:

# # Hough Transformation Example # package require Tk package require vectcl namespace import vectcl::vexpr set C_RANGE 125 ;# the radius in the hough volume set C_WIDTH [vexpr {2 * C_RANGE}] ;# width of hough volume set C_HEIGHT [vexpr {2 * C_RANGE}] ;# height of hough volume set R_MIN 100 ;# minimum of valid radius set R_MAX 550 ;# maximum of valid radius set R_WIDTH [vexpr {R_MAX - R_MIN}] set DEG_STEP 15 ;# search for edges every n degrees set hotspotX 739 ;# X coordinate in the white spot set hotspotY 605 ;# Y coordinate in the white spot set scrnCenterY 675 ;# Y coordinate of the center of the screen set scrnLeft 0 ;# X coordinate of the left bound of the screen set scrnRight 0 ;# X coordinate of the left bound of the screen set scrnWidth 0 ;# width of screen bounds set scrnBrMin 250 ;# minimum screen brightness = 25 % (per mill) set threshold 100 ;# minimum brightness difference = 10 % (per mill) proc MarkCenter {xc yc} { vexpr { x1 = xc -25 x2 = xc +25 y1 = yc -25 y2 = yc +25 } .c create line $x1 $yc $x2 $yc -width 1 -fill #ff00ff .c create line $xc $y1 $xc $y2 -width 1 -fill #ff00ff update } proc MarkScreenBounds {} { global scrnCenterY scrnLeft scrnRight scrnWidth puts "screen bounds: $scrnLeft..$scrnRight = $scrnWidth pixel" vexpr { y = scrnCenterY y1 = y -50 y2 = y +50 x1 = scrnLeft -30 x2 = scrnLeft x3 = scrnRight x4 = scrnRight +30 } .c create line $x1 $y $x2 $y -width 2 -fill cyan -arrow last .c create line $x3 $y $x4 $y -width 2 -fill cyan -arrow first .c create line $x2 $y1 $x2 $y2 -width 1 -fill cyan .c create line $x3 $y1 $x3 $y2 -width 1 -fill cyan } proc MarkPoint {alpha r xp yp br} { puts [format " alpha=%5.3f r=%3d P=(%4d,%4d) br=%d" $alpha $r $xp $yp $br] vexpr { x1 = xp - 7 x2 = xp + 7 y1 = yp - 7 y2 = yp + 7 } .c create oval $x1 $y1 $x2 $y2 -outline #ff0000 -fill {} -width 2 update } proc MarkImprint {h_max m_x m_y m_r} { puts [format "h_max=%.0f @ P=(%u,%u) R=%u" $h_max $m_x $m_y $m_r] vexpr { x1 = m_x - m_r x2 = m_x + m_r y1 = m_y - m_r y2 = m_y + m_r } .c create oval $x1 $y1 $x2 $y2 -outline #00ff00 -fill {} -width 3 vexpr { x1 = m_x - 5 x2 = m_x + 5 y1 = m_y - 5 y2 = m_y + 5 } .c create oval $x1 $y1 $x2 $y2 -fill #00ff00 update } proc ShowHB {hbval} { .c create text 50 50 -anchor nw -font {Arial -24 bold} -fill white \ -text [format "HB=%.1f" $hbval] } namespace import tcl::mathfunc::int namespace import tcl::mathfunc::round namespace import tcl::mathfunc::hypot proc CalcHB {rPixel} { global scrnWidth vexpr { screenDiameter = 135.1 # diameter of the original screen in mm enlargement = 20 # optical factor of the HB gauge force = 29419.95 # pressure force in N ballDiameter = 10 # ball diameter in mm PI = 2 * asin(1.0) # calculate the diameter of the imprint in mm imprintDiameter = screenDiameter * 2 * rPixel / scrnWidth / enlargement imprintDepth = 0.0 # the depth of the imprint hbValue = 0.0 # the brinell hardness value if imprintDiameter < ballDiameter { imprintDepth = abs(ballDiameter - hypot(ballDiameter, imprintDiameter)) / 2 if imprintDepth > 0.0 { hbValue = force / (9.80665 * PI * ballDiameter * imprintDepth) } } } puts [format "D=%.3f mm, d=%.3f mm, h=%.6f mm, HB=%.1f" \ $ballDiameter $imprintDiameter $imprintDepth $hbValue] ShowHB $hbValue return $hbValue } proc houghtrans {} { global C_WIDTH C_HEIGHT R_WIDTH C_RANGE R_MIN R_MAX DEG_STEP \ scrnCenterY scrnLeft scrnRight scrnWidth scrnBrMin \ hotspotX hotspotY threshold set t_start [clock milliseconds] puts "Houghtrans - Tcl edition" puts "reading picture data ..." image create photo hbcam -file hbcam.png set w [image width hbcam] set h [image height hbcam] pack [canvas .c -width $w -height $h] -expand yes -fill both .c create image 0 0 -image hbcam -anchor nw update # copy image into pixel (a vectcl 2D vector) .c create line 0 0 $w 0 -width 3 -fill yellow -tags act_row vexpr {pixel = constfill(0,h,w)} ;# create 2D pixel array for {set y 0} {$y < $h} {incr y} { ;# for each row if {$y % 5 == 0} { .c coords act_row 0 $y $w $y ;# show where we are (sometimes) update } for {set x 0} {$x < $w} {incr x} { ;# for each column set red [lindex [hbcam get $x $y] 0] vexpr {pixel[y,x] = red / 0.255} ;# set gray value (brightness) in per mill } } .c delete act_row MarkCenter $hotspotX $hotspotY puts "searching edges ..." vexpr { # allocate 3D hough volume houghdata = constfill(0,C_HEIGHT,C_WIDTH,R_WIDTH) # search for left screen bound searchLimit = w/4 # search up to 1/4 of the picture scrnLeft = 0 # init left screen bound x = 0 # start from left side while x < searchLimit { # search from left to right if pixel[scrnCenterY,x] > scrnBrMin { # search for more than x% brightness scrnLeft = x # found left bound x = searchLimit # finish loop } else { x += 1 } } # search for right screen bound scrnRight = 0 searchLimit = 3*w/4 # search up to 3/4 of the picture x = w-1 # start from right side while x > searchLimit { # search from right to left if pixel[scrnCenterY,x] > scrnBrMin { # search for more than x% brightness scrnRight = x # found right bound x = searchLimit # finish loop } else { x -= 1 } } scrnWidth = scrnRight - scrnLeft # this is the reference width MarkScreenBounds() # show arrows for screen bounds # search for edge of impression ... fullCircle = 4 * asin(1.0) # 2 pi delta = fullCircle / (360.0 / DEG_STEP) # angle step size alpha = 0.0 # start at zero degrees while alpha < fullCircle { # for each angle ... br = 0 # init brightness xp = 0 # init x coordinate of resulting point yp = 0 # init y coordinate of resulting point fx = cos(alpha) # vector x-scaling fy = sin(alpha) # vector y-scaling br_list = constfill(-1.0, 30) # make a vector for 30 brightness values l = h / 2 # not more than half height of picture m = l -1 # upper radius limit r = 0 # resulting radius for ri = 0:m { x = hotspotX + round(fx * double(ri)) # calculate x coordinate y = hotspotY + round(fy * double(ri)) # calculate y coordinate if x > 0 && x < w && y > 0 && y < h { # is this point in my area? db = 0 # reset brightness difference br_list[29] = pixel[y,x] # save brightness of point in per mill if br_list[0] != -1.0 { # if we have enough samples, calculate the jump b1 = mean(br_list[0:24]) # inner brightness b2 = mean(br_list[25:29]) # outer brightness db = int(b2 - b1) # get brightness difference } br_list[0:28] = br_list[1:29] # shift the brightness buffer if db > threshold { # minimum brightness reached xp = x # set resulting x coordinate yp = y # set resulting y coordinate br = db # set resulting brightness r = ri # set resulting radius ri = m # finish, leave the loop } } } if r > 0 && r < l { # Found edge! MarkPoint(alpha,r,xp,yp,br) # show where we are for y = 0:C_HEIGHT-1 { # for each row in the hough volume ... for x = 0:C_WIDTH-1 { # for each column in the hough volume ... dx = xp - (hotspotX + x - C_RANGE) # x-distance from hotspot point dy = (hotspotY + y - C_RANGE) - yp # y-distance from hotspot point ri = int(hypot(dx, dy)) # calculate the radius of this point if ri >= R_MIN && ri < R_MAX { i = ri - R_MIN # radius index goes from R_MIN to R_MAX houghdata[y,x,i] += br # add brightness value for this point } } } } alpha += delta # next angle } # searching the hough volume for it's maximum value ... h_max = -1 m_x = -1 m_y = -1 m_r = -1 for y = 0:C_HEIGHT-1 { # for each row in the hough volume ... for x = 0:C_WIDTH-1 { # for each column in the hough volume ... h_val = max(houghdata[y,x,:]) # search new maximum with vexpr max vector function if h_val > h_max { for r = 0:R_WIDTH-1 { # for each radius row in the hough volume ... h_val = houghdata[y,x,r] if h_val > h_max { # look for the greatest added brightness h_max = h_val # my maximum value (until now) m_x = hotspotX + x - C_RANGE # copy x coordinate m_y = hotspotY + y - C_RANGE # copy y coordinate m_r = r + R_MIN # copy radius } } } } } MarkImprint(h_max,m_x,m_y,m_r) # show the resulting circle and midpoint CalcHB(m_r) # calc brinell hardness with the determined radius } set t_end [clock milliseconds] puts [format "time=%.3f sec" [expr ($t_end - $t_start) / 1000.0]] } houghtrans