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2.9. FEATURESEXTRACTION
221
Example363:image.jpg -luminance -stencil.
1 --area
fg 0
2.9.3 -at
line
Arguments:
• x0[%],y0[%],z0[%],x1[%],y1[%],z1[%]
Retrieve pixels of the selected images belonging to the specified line (x0,y0,z0)-(x1,y1,z1).
Example 364 : image.jpg --at
line 0,0,0,100%,100%,0
2.9.4 -barycenter
Compute the barycenter vector of pixel values.
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222
CHAPTER 2. LIST OF COMMANDS
Example 365 : 256,256 -ellipse 50%,50%,20%,20%,0,1,1 -deform 20 --barycenter --ellipse..
{@0,1},5,5,0,10
2.9.5 -detect
skin
Arguments:
• 0<=tolerance<=1,
skin
x,
skin
y,
skin
radius>=0
Detect skin in selected color images and output an appartenance probability map.
Detection is performed using CbCr chromaticity data of skin pixels.
If arguments ’skin
x’, ’skin
y’ and ’skin
radius’ are provided, skin pixels are learnt from the sample pixels
inside the circle located at (’skin
x’,’skin
y’) with radius ’skin
radius’.
Default value:
• ’tolerance=0.5’ and ’skin
x=skiny=radius=-1’.
2.9.6 -displacement (+)
Arguments:
• [source
image],
smoothness,
precision>=0,
nb
scales>=0,
iteration
max>=0,
is
backward={ 0
| 1
},
[guide]
Estimate displacement field between specified source and selected target images.
If ’smoothness>=0’, regularization type is set to isotropic, else to anisotropic.
If ’nbscales==0’, the number of scales used is estimated from the image size.
Default values:
• ’smoothness=0.1’, ’precision=5’, ’nb
scales=0’, ’iteration
max=10000’,
’is
backward=1’ and ’[guide]=(unused)’.
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2.9. FEATURES EXTRACTION
223
Example 366 : image.jpg --rotate 3,1,0,50%,50%,0.9 --displacement.
[-2] -quiver.
[-1],15,-20,1,1,{1.5
*
iM}
2.9.7 -distance (+)
Arguments:
• isovalue[%],
metric
• isovalue[%],[metric],
method
Compute the unsigned distance function to specified isovalue, opt. according to a custom metric.
’metric’ can be { 0=chebyshev | 1=manhattan | 2=euclidean | 3=squared-euclidean }.
’method’ can be { 0=fast-marching | 1=low-connectivity dijkstra | 2=high-connectivity dijkstra | 3=1+re-
turn path | 4=2+return path }.
Default value:
• ’metric=2’ and ’method=0’.
Example 367 : image.jpg -threshold 20% -distance 0 -pow 0.3
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224
CHAPTER 2. LIST OF COMMANDS
Example 368 : 400,400 -set 1,50%,50% --distance[0] 1,2 --distance[0] 1,1 -distance[0] 1,0
-mod 32 -threshold 16 -append c
Tutorial page:
http://gmic.eu/tutorial/_distance.shtml
2.9.8 -float2fft8
Convert selected float-valued images to 8bits fourier representations.
2.9.9 -fft82float
Convert selected 8bits fourier representations to float-valued images.
2.9.10 -fftpolar
Compute fourier transform of selected images, as centered magnitude/phase images.
Example 369 : image.jpg -fftpolar -ellipse 50%,50%,10,10,0,1,0 -ifftpolar
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2.9. FEATURES EXTRACTION
225
2.9.11 -histogram (+)
Arguments:
nb
levels>0[%],
value0[%],
value1[%]
Compute the histogram of selected images.
If valuerange is set, the histogram isestimated only for pixels in the specified valuerange. Argument ’value1’
must be specified if ’value0’ is set.
Default values:
• ’nb
levels=256’, ’value0=0%’ and ’value1=100%’.
Example 370 : image.jpg --histogram 64 -display
graph.
400,300,3
2.9.12 -histogram
nd
Arguments:
• nb
levels>0[%],
value0[%],
value1[%]
Compute the 1d,2d or 3d histogram of selected multi-channels images (having 1,2 or 3 channels).
If value range is set, the histogram is estimated only for pixels in the specified value range.
Default values:
• ’value0=0%’ and ’value1=100%’.
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226
CHAPTER 2. LIST OF COMMANDS
Example 371 : image.jpg -channels 0,1 --histogram
nd 256
2.9.13 -histogram
cumul
Arguments:
nb
levels>0,
is
normalized={ 0
| 1 },
val0[%],
val1[%]
Compute cumulative histogram of selected images.
Default values:
• ’nb
levels=256’, ’is
normalized=0’ and ’val0=val1=0’.
Example 372 : image.jpg --histogram
cumul 256 -histogram[0] 256 -display
graph 400,300,3
2.9.14 -histogram
pointwise
Arguments:
• nb
levels>0[%],
value0[%],
value1[%]
2.9. FEATURES EXTRACTION
227
Compute the histogram of each vector-valued point of selected images.
If value range is set, the histogram is estimated only for values in the specified value range.
Default values:
• ’value0=0%’ and ’value1=100%’.
2.9.15 -hough
Arguments:
width>0,
height>0,gradient
norm
voting={ 0
| 1 }
Compute hough transform (theta,rho) of selected images.
Default values:
• ’width=512’, ’height=width’ and ’gradient
norm
voting=1’.
Example 373 : image.jpg --blur.
1.5 -hough.
400,400 -blur.
0.5 -+.
1 -log.
2.9.16 -ifftpolar
Compute inverse fourier transform of selected images, from centered magnitude/phase images.
2.9.17 -isophotes
Arguments:
nb
levels>0
Render isophotes of selected images on a transparent background.
Default value:
• ’nb
levels=64’
228
CHAPTER 2. LIST OF COMMANDS
Example 374 : image.jpg -blur 2 -isophotes 6 -dilate
circ 5 -display
rgba
2.9.18 -label (+)
Arguments:
tolerance>=0,is
high
connectivity={ 0
| 1 }
Label connected components in selected images.
Default values:
• ’tolerance=0’ and ’is
high
connectivity=0’.
Example 375 : image.jpg -luminance -threshold 60% -label -normalize 0,255 -map 0
2.9. FEATURES EXTRACTION
229
Example 376 : 400,400 -set 1,50%,50% -distance 1 -mod 16 -threshold 8 -label -mod 255 -map 2
Tutorial page:
http://gmic.eu/tutorial/_label.shtml
2.9.19 -label
fg
Arguments:
• tolerance>=0,is
high
connectivity={ 0
| 1 }
Label connected components for non-zero values (foreground) in selected images.
Similar to ’-label’ except that 0-valued pixels are not labeled.
Default value:
• ’is
high
connectivity=0’.
2.9.20 -max
patch
Arguments:
patch
size>=1
Return locations of maximal values in local patch-based neighborhood of given size for selected images.
Default value:
• ’patch
size=16’.
230
CHAPTER 2. LIST OF COMMANDS
Example 377 : image.jpg -norm --max
patch 16
2.9.21 -min
patch
Arguments:
patch
size>=1
Return locations of minimal values in local patch-based neighborhood of given size for selected images.
Default value:
• ’patch
size=16’.
Example 378 : image.jpg -norm --min
patch 16
2.9.22 -minimal
path
Arguments:
• x0[%]>=0,y0[%]>=0,z0[%]>=0,x1[%]>=0,y1[%]>=0,z1[%]>=0,
is
hi-
gh
connectivity={ 0
| 1
}
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