apply_texture_inspection_model — Inspection of the texture within an image.
apply_texture_inspection_model(Image : NoveltyRegion : TextureInspectionModel : TextureInspectionResultID)
The operator apply_texture_inspection_model compares
Image with the trained texture inspection model
TextureInspectionModel. Image can either be a single
gray-value image or a tuple of gray-value images.
Pixels that do not fit to the texture inspection model are returned
in NoveltyRegion. Furthermore, if 'gen_result_handle' has
been set to 'true' with set_texture_inspection_model_param,
the operator also returns the result handle
TextureInspectionResultID with more detailed information on
the texture classification. If 'gen_result_handle' is
set to 'false', TextureInspectionResultID is empty.
For an explanation of the concept of the texture inspection see the introduction of chapter Inspection / Texture Inspection.
For each pyramid level texture features are extracted and classified
with the corresponding GMM classifier. The resulting novelty score is
then compared to
the novelty threshold of the current pyramid level and classified as
defective or not. The defective pixels are collected in a novelty region for
each pyramid level. These novelty regions are then combined to the
final novelty region returned in NoveltyRegion. The novelty
regions of adjacent levels in the image pyramid are intersected with each
other. This step helps to improve the robustness towards noise within the
single pyramid level responses. The intersected novelty regions are then
added to the returned 'novelty_region'. If a pyramid level has no
adjacent pyramid level the region itself is added to the
'novelty_region'. If, for example, 'num_levels' is set to 1,
no adjacent pyramid level exists and the 'novelty_region' is the
novelty region of the first pyramid level.
If 'gen_result_handle' is set to 'true', the result handle
TextureInspectionResultID contains
novelty score images and the resulting novelty regions for each pyramid level.
This information is useful for debugging and fine tuning of the model
parameters (e.g., the novelty thresholds) and can be accessed with
get_texture_inspection_result_object.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
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.
Image (input_object) singlechannelimage-array → object (byte / uint2)
Image of the texture to be inspected.
NoveltyRegion (output_object) region-array → object
Novelty regions.
TextureInspectionModel (input_control) texture_inspection_model → (handle)
Handle of the texture inspection model.
TextureInspectionResultID (output_control, state is modified) texture_inspection_result → (handle)
Handle of the inspection results.
* Create texture inspection model
create_texture_inspection_model ('basic', TextureInspectionModel)
* Set parameters
set_texture_inspection_model_param (TextureInspectionModel, \
'gen_result_handle', 'true')
* Make this short example fast:
set_texture_inspection_model_param (TextureInspectionModel, \
'gmm_em_max_iter', 1)
* Read and add training images
read_image (TrainImage, 'carpet/carpet_01')
add_texture_inspection_model_image (TrainImage, TextureInspectionModel, \
Indices)
* Train the model
train_texture_inspection_model (TextureInspectionModel)
* Read and apply a test image
read_image (TestImage, 'carpet/carpet_02')
apply_texture_inspection_model (TestImage, DefectCandidates, \
TextureInspectionModel, \
TextureInspectionResultID)
The operator apply_texture_inspection_model returns the value 2 (H_MSG_TRUE)
if the given parameters are valid and within acceptable range.
Otherwise, an exception will be raised.
train_texture_inspection_model
get_texture_inspection_result_object,
get_texture_inspection_model_param,
clear_texture_inspection_result,
clear_texture_inspection_model
Matching