The ROI is managed by the buttons

• Paste - paste the ROI from the image to the scorpion clipboard
• Copy - copy the ROI to the image from the scorpion clipboard

Point & Click Clipboard Support

The rectangular ROI is defined by four points.

 One point will change the center point.

Colors

• Show best reference - when active the best reference is selected for each inspection
• Current Image - Image is updated when an inspection is performed - the image region is transferred from the captured image
• Selected Reference - displays the image of the best or selected reference
• Status bar
  • Best - Name of color
  • Dist  - Distance of best match
  • Sep  - Logarithmic separation between best color and second best color

• - Adds a color with a given name
• - Removes a color with it's reference images
• - Adds the current image as a reference to the selected reference
• - Removes the selected reference

Reference Mouse Menu

The following items are available in the reference list mouse menu:

• Add Reference

• Delete Reference

• Rename Reference

• Delete All References

Classification

Prefilter

Specifies the maximum deviation of the HSI from a reference image - these filters are normally off. Can be used to tell the tool that there is no or little intensity variations.

• H - max deviation

• S - max deviation

• I - max deviation

• H std - max deviation

• S std - max deviation

• I std - max deviation

Feature distance

• Activates the color planes to be part of the distance calculation

  • H - activates Hue

  • S - activates Saturation

  • I - activates Intensity

• Weighting factors - coeffisients for the feature distance formula

  • H - Mean Hue - default 4 - increases the importance of the Hue value

  • S - Mean Saturation - default 1

  • I - Mean Intensity - default 1

  • H std - Standard Deviation Hue

  • S std - Standard Deviation Saturation

  • I std - Standard Deviation Intensity

• Weighing of H by

  • S - Saturation - default on

  • I - Intensity - default  on

• Feature Distance Formula

  • Original measure H,S and I equivalent - d = sqrt(H*H + S*S + I*I)

  • H linearly weighted by normalized S and I as selected

  • H weighted by square  root of normalized S and I as selected - recommended Feature Distance

Constraints

• Maximum distance - the maximum distance between the current image for a reference image to be accepted

• Minimum Separation - the minimum separation between the best and the second best color for the color to be accepted

Note : when a color isn't accepted the match result is 0 or FALSE

Features

The feature list display a sorted, by distance, list of reference images. The accepted reference are checked with a green arrow.

Results

Visualisation

ExecuteCmd support (see also executeCmd)

Example 1:

features = eval(GetValue('ColorMatcher.Feature[]'))  # a python dictionary is generated
print features

#provides the following console output

{'blue': {'1': (137.83000000000001, 232.78, 52.390000000000001), '0': (118.36, 115.31999999999999, 81.519999999999996), '3': (121.0, 131.84, 71.079999999999998), '2': (146.91999999999999, 255.0, 71.219999999999999), '5': (118.68000000000001, 140.12, 71.239999999999995), '4': (126.0, 165.19999999999999, 71.359999999999999), '7': (102.04000000000001, 82.680000000000007, 81.680000000000007), '6': (60.799999999999997, 71.439999999999998, 95.560000000000002),
'green': {'1': (86.920000000000002, 43.219999999999999, 102.61), '0': (51.399999999999999, 97.439999999999998, 94.200000000000003), '3': (51.159999999999997, 85.319999999999993, 99.319999999999993), '2': (53.880000000000003, 109.40000000000001,85.439999999999998),
 'black': {'1': (143.0, 22.640000000000001, 51.969999999999999), '0': (43.920000000000002, 68.879999999999995, 35.399999999999999),
 'tree': {'1': (32.479999999999997, 63.159999999999997, 131.63999
999999999), '0': (33.079999999999998, 85.879999999999995, 119.88), '3': (31.84, 98.519999999999996, 118.48), '2': (31.48, 76.239999999999995, 126.04000000000001),
'red': {'1': (9.4000000000000004, 114.16, 100.08), '0': (9.5999999999999996, 213.56, 74.090000000000003), '3': (10.56, 101.52, 106.40000000000001), '2': (14.68, 75.120000000000005, 119.72)}}

Note  : this result is provided to enable creation of  custom feature distance in python

Example 2:

colors = eval(GetValue('ColorMatcher.Color[]'))  # generates a python list
print colors

#provides the following console output

('tree', 'black', 'blue', 'green', 'red')

Example 3:

distances = eval(GetValue('ColorMatcher.Distance[]'))  # generates a python list
print distances

# the features distance corresponds to the color of the corresponding element in the Color[] result

#provides the following console output

(46.299623839928799, 50.171249162203203, 54.0035532623723, 61.197111299248, 62.209603052612501)

Example 4:

It is possible to define classification logic using a python script. The following script example shows how to classify based on hsi values from a Color Matcher:

h = GetValue('cm.H')
s = GetValue('cm.S')
i = GetValue('cm.I')

print 'hsi - ',h,s,i

red = ( 158 < h < 168 ) and ( s > 200 ) and ( 50 < i < 100 )
SetValue('Red.Value',red)

blue = ( 0 < h < 15 ) and ( 75 < s < 225 ) and ( 80 < i < 200 )
SetValue('Blue.Value',blue)

darkgrey = ( 118 < h < 150 ) and ( 0 < s < 75 ) and ( 60 < i < 120 )
SetValue('DarkGrey.Value',darkgrey)

Black = ( 0 < s < 40) and ( 0 < i < 60 )
SetValue('Black.Value',Black)

WhiteYellow = ( 127 < h < 137 ) and ( 100 < s < 200 ) and ( 120 < i < 180 )
SetValue('White_Yellow.Value',WhiteYellow)

brown = ( 138 < h < 154 ) and ( 50 < s < 150 ) and ( 80 < i < 120 )
SetValue('Brown.Value',brown)