prepare_variation_modelT_prepare_variation_modelPrepareVariationModelPrepareVariationModelprepare_variation_model (算子)
名称
prepare_variation_modelT_prepare_variation_modelPrepareVariationModelPrepareVariationModelprepare_variation_model — 准备一个变体模型，以便与图像进行比较。
签名
描述
prepare_variation_modelprepare_variation_modelPrepareVariationModelPrepareVariationModelPrepareVariationModelprepare_variation_model prepares a variation model for the
image comparison with compare_variation_modelcompare_variation_modelCompareVariationModelCompareVariationModelCompareVariationModelcompare_variation_model or
compare_ext_variation_modelcompare_ext_variation_modelCompareExtVariationModelCompareExtVariationModelCompareExtVariationModelcompare_ext_variation_model。This is done by converting the
ideal image and the variation image that have been trained with
train_variation_modeltrain_variation_modelTrainVariationModelTrainVariationModelTrainVariationModeltrain_variation_model into two threshold images and storing
them in the variation model. These threshold images are used in
compare_variation_modelcompare_variation_modelCompareVariationModelCompareVariationModelCompareVariationModelcompare_variation_model or
compare_ext_variation_modelcompare_ext_variation_modelCompareExtVariationModelCompareExtVariationModelCompareExtVariationModelcompare_ext_variation_model to speed up the comparison of
the current image to the variation model.
Two thresholds are used to compute the threshold images. The
parameter AbsThresholdAbsThresholdAbsThresholdAbsThresholdabsThresholdabs_threshold 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
VarThresholdVarThresholdVarThresholdVarThresholdvarThresholdvar_threshold determines a factor relative to the variation
image for the minimum difference of the current image and the ideal
image. AbsThresholdAbsThresholdAbsThresholdAbsThresholdabsThresholdabs_threshold and VarThresholdVarThresholdVarThresholdVarThresholdvarThresholdvar_threshold 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_modelcompare_variation_modelCompareVariationModelCompareVariationModelCompareVariationModelcompare_variation_model, the output
region contains all points that differ substantially from the model,
i.e., that fulfill the following condition:
In compare_ext_variation_modelcompare_ext_variation_modelCompareExtVariationModelCompareExtVariationModelCompareExtVariationModelcompare_ext_variation_model, 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_modelget_thresh_images_variation_modelGetThreshImagesVariationModelGetThreshImagesVariationModelGetThreshImagesVariationModelget_thresh_images_variation_model。Furthermore, the
training data can be deleted with
clear_train_data_variation_modelclear_train_data_variation_modelClearTrainDataVariationModelClearTrainDataVariationModelClearTrainDataVariationModelclear_train_data_variation_model to save memory.
执行信息
- 多线程类型：可重入（与非独占算子并行运行）。
- 多线程作用域：全局（可从任何线程调用）。
- 未采用并行化处理。
此算子修改后续输入参数的状态：
在执行此算子时，若该参数值需在多个线程间使用，则必须对其访问进行同步。
参数
ModelIDModelIDModelIDModelIDmodelIDmodel_id (输入控制，状态被修改) variation_model → HVariationModel, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
ID of the variation model.
AbsThresholdAbsThresholdAbsThresholdAbsThresholdabsThresholdabs_threshold (输入控制) number(-array) → HTupleMaybeSequence[Union[float, int]]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)
Absolute minimum threshold for the differences
between the image and the variation model.
默认值：
10
建议值：
0, 5, 10, 15, 20, 30, 40, 50
限制：
AbsThreshold >= 0
VarThresholdVarThresholdVarThresholdVarThresholdvarThresholdvar_threshold (输入控制) number(-array) → HTupleMaybeSequence[Union[float, int]]HTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)
Threshold for the differences based on the variation
of the variation model.
默认值：
2
建议值：
1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5
限制：
VarThreshold >= 0
结果
prepare_variation_modelprepare_variation_modelPrepareVariationModelPrepareVariationModelPrepareVariationModelprepare_variation_model 在所有参数正确时返回 2 ( H_MSG_TRUE )。
可能的前趋
train_variation_modeltrain_variation_modelTrainVariationModelTrainVariationModelTrainVariationModeltrain_variation_model
可能的后继
compare_variation_modelcompare_variation_modelCompareVariationModelCompareVariationModelCompareVariationModelcompare_variation_model,
compare_ext_variation_modelcompare_ext_variation_modelCompareExtVariationModelCompareExtVariationModelCompareExtVariationModelcompare_ext_variation_model,
get_thresh_images_variation_modelget_thresh_images_variation_modelGetThreshImagesVariationModelGetThreshImagesVariationModelGetThreshImagesVariationModelget_thresh_images_variation_model,
clear_train_data_variation_modelclear_train_data_variation_modelClearTrainDataVariationModelClearTrainDataVariationModelClearTrainDataVariationModelclear_train_data_variation_model,
write_variation_modelwrite_variation_modelWriteVariationModelWriteVariationModelWriteVariationModelwrite_variation_model
替代
prepare_direct_variation_modelprepare_direct_variation_modelPrepareDirectVariationModelPrepareDirectVariationModelPrepareDirectVariationModelprepare_direct_variation_model
另见
create_variation_modelcreate_variation_modelCreateVariationModelCreateVariationModelCreateVariationModelcreate_variation_model
模块
匹配