Operator Reference
compare_ext_variation_model (Operator)
compare_ext_variation_model — Compare an image to a variation model.
Signature
Description
compare_ext_variation_model compares the input image
Image to the variation model given by ModelID.
compare_ext_variation_model is an extension of
compare_variation_model that provides more modes for the
image comparison. Before compare_ext_variation_model can be
called, the two internal threshold images of the variation model
must have been created with prepare_variation_model or
prepare_direct_variation_model. Let c(x,y)
denote the input image Image and
denote the two threshold images (see prepare_variation_model
or prepare_direct_variation_model). Then, for Mode
= 'absolute' the output region Region contains
all points that differ substantially from the model, i.e., the
points that fulfill the following condition:
This mode is identical to compare_variation_model. For
Mode = 'light', Region contains all
points that are too bright:
For Mode = 'dark', Region contains all
points that are too dark:
Finally, for Mode = 'light_dark' two regions are
returned in Region. The first region contains the result
of Mode = 'light', while the second region
contains the result of Mode = 'dark'. The
respective regions can be selected with select_obj.
Execution Information
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Automatically parallelized on tuple level.
- Automatically parallelized on domain level.
Parameters
Image (input_object) singlechannelimage(-array) → object (byte / int2 / uint2)
Image of the object to be compared.
Region (output_object) region(-array) → object
Region containing the points that differ substantially from the model.
ModelID (input_control) variation_model → (handle)
ID of the variation model.
Mode (input_control) string → (string)
Method used for comparing the variation model.
Default: 'absolute'
Suggested values: 'absolute', 'light', 'dark', 'light_dark'
Example (HDevelop)
read_shape_model ('model.shm', TemplateID)
read_variation_model ('model.var', ModelID)
for K := 1 to 10 by 1
read_image (Image, 'pen-' + K$'02')
find_generic_shape_model (Image, TemplateID, MatchResultID, \
NumMatchResult)
get_generic_shape_model_result (MatchResultID, 'all', 'hom_mat_2d', \
HomMat2D)
dev_display (Image)
if (NumMatchResult == 1)
affine_trans_image (Image, ImageTrans, HomMat2D, 'constant', \
'false')
compare_ext_variation_model (ImageTrans, RegionDiff, ModelID, \
'absolute')
dev_display (RegionDiff)
endif
endfor
Result
compare_ext_variation_model returns 2 (
H_MSG_TRUE)
if all parameters
are correct and if the internal threshold images have been generated
with prepare_variation_model or
prepare_direct_variation_model.
Possible Predecessors
prepare_variation_model,
prepare_direct_variation_model
Possible Successors
Alternatives
compare_variation_model,
dyn_threshold
See also
get_thresh_images_variation_model
Module
Matching