Name
prepare_variation_model prepare_variation_model PrepareVariationModel prepare_variation_model PrepareVariationModel PrepareVariationModel — Prepare a variation model for comparison with an image.
prepare_variation_model prepare_variation_model PrepareVariationModel prepare_variation_model PrepareVariationModel PrepareVariationModel prepares a variation model for the
image comparison with compare_variation_model compare_variation_model CompareVariationModel compare_variation_model CompareVariationModel CompareVariationModel or
compare_ext_variation_model compare_ext_variation_model CompareExtVariationModel compare_ext_variation_model CompareExtVariationModel CompareExtVariationModel . This is done by converting the
ideal image and the variation image that have been trained with
train_variation_model train_variation_model TrainVariationModel train_variation_model TrainVariationModel TrainVariationModel into two threshold images and storing
them in the variation model. These threshold images are used in
compare_variation_model compare_variation_model CompareVariationModel compare_variation_model CompareVariationModel CompareVariationModel or
compare_ext_variation_model compare_ext_variation_model CompareExtVariationModel compare_ext_variation_model CompareExtVariationModel CompareExtVariationModel to speed up the comparison of
the current image to the variation model.
Two thresholds are used to compute the threshold images. The
parameter AbsThreshold AbsThreshold AbsThreshold AbsThreshold AbsThreshold absThreshold determines the minimum amount of
gray levels by which the image of the current object must differ
from the image of the ideal object. The parameter
VarThreshold VarThreshold VarThreshold VarThreshold VarThreshold varThreshold determines a factor relative to the variation
image for the minimum difference of the current image and the ideal
image. AbsThreshold AbsThreshold AbsThreshold AbsThreshold AbsThreshold absThreshold and VarThreshold VarThreshold VarThreshold VarThreshold VarThreshold varThreshold each can
contain one or two values. If two values are specified, different
thresholds can be determined for too bright and too dark pixels. In
this mode, the first value refers to too bright pixels, while the
second value refers to too dark pixels. If one value is specified,
this value refers to both the too bright and too dark pixels. Let
i(x,y) be the ideal image, v(x,y) the
variation image,
,
,
,
and
(or
,
,
, and
,
respectively). Then the two threshold images
are computed as follows:
If the current image c(x,y) is compared to the
variation model using compare_variation_model compare_variation_model CompareVariationModel compare_variation_model CompareVariationModel CompareVariationModel , the output
region contains all points that differ substantially from the model,
i.e., that fulfill the following condition:
In compare_ext_variation_model compare_ext_variation_model CompareExtVariationModel compare_ext_variation_model CompareExtVariationModel CompareExtVariationModel , extended comparison modes
are available, which return only too bright errors, only too dark
errors, or bright and dark errors as separate regions.
After the threshold images have been created they can be read out
with get_thresh_images_variation_model get_thresh_images_variation_model GetThreshImagesVariationModel get_thresh_images_variation_model GetThreshImagesVariationModel GetThreshImagesVariationModel . Furthermore, the
training data can be deleted with
clear_train_data_variation_model clear_train_data_variation_model ClearTrainDataVariationModel clear_train_data_variation_model ClearTrainDataVariationModel ClearTrainDataVariationModel to save memory.
Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Processed without parallelization.
This operator modifies the state of the following input parameter:
The value of this parameter may not be shared across multiple threads without external synchronization.
ID of the variation model.
Absolute minimum threshold for the differences
between the image and the variation model.
Default value: 10
Suggested values: 0, 5, 10, 15, 20, 30, 40, 50
Restriction: AbsThreshold >= 0
Threshold for the differences based on the variation
of the variation model.
Default value: 2
Suggested values: 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5
Restriction: VarThreshold >= 0
prepare_variation_model prepare_variation_model PrepareVariationModel prepare_variation_model PrepareVariationModel PrepareVariationModel returns 2 (H_MSG_TRUE) if all parameters are
correct.
train_variation_model train_variation_model TrainVariationModel train_variation_model TrainVariationModel TrainVariationModel
compare_variation_model compare_variation_model CompareVariationModel compare_variation_model CompareVariationModel CompareVariationModel ,
compare_ext_variation_model compare_ext_variation_model CompareExtVariationModel compare_ext_variation_model CompareExtVariationModel CompareExtVariationModel ,
get_thresh_images_variation_model get_thresh_images_variation_model GetThreshImagesVariationModel get_thresh_images_variation_model GetThreshImagesVariationModel GetThreshImagesVariationModel ,
clear_train_data_variation_model clear_train_data_variation_model ClearTrainDataVariationModel clear_train_data_variation_model ClearTrainDataVariationModel ClearTrainDataVariationModel ,
write_variation_model write_variation_model WriteVariationModel write_variation_model WriteVariationModel WriteVariationModel
prepare_direct_variation_model prepare_direct_variation_model PrepareDirectVariationModel prepare_direct_variation_model PrepareDirectVariationModel PrepareDirectVariationModel
create_variation_model create_variation_model CreateVariationModel create_variation_model CreateVariationModel CreateVariationModel
Matching