find_data_code_2d — Detect and read 2D data code symbols in an image or
train the 2D data code model.
find_data_code_2d(Image : SymbolXLDs : DataCodeHandle, GenParamName, GenParamValue : ResultHandles, DecodedDataStrings)
The operator find_data_code_2d detects 2D data code symbols
in the input image (Image) and reads the data that is encoded
in the symbol.
Before calling find_data_code_2d, a model of a class of 2D
data codes that matches the symbols in the images must be
created with create_data_code_2d_model or
read_data_code_2d_model.
The handle returned by these operators is passed to
find_data_code_2d in DataCodeHandle.
To look for more than one symbol in an image, the generic parameter
'stop_after_result_num' can be passed to
GenParamName together with the number of requested
symbols as GenParamValue.
As a result the operator returns for every successfully decoded
symbol the surrounding XLD contour (SymbolXLDs), a
result handle, which refers to a candidate structure that stores
additional information about the symbol as well as the search and
decoding process (ResultHandles), and the string that is
encoded in the symbol (DecodedDataStrings).
Passing the candidate handle from ResultHandles together
with the generic parameter 'decoded_data',
get_data_code_2d_results returns a tuple with the ASCII code
of all characters of the string (see also
Tuple / String Operations).
For an explanation of the concept of the 2D data code reader see the introduction of chapter Identification / Data Code.
The symbol structure of GS1 DataMatrix, GS1 QR Code, GS1 Aztec, and GS1 DotCode
is identical to the structure of Data Matrix ECC 200, QR Code, Aztec Code,
and DotCode, respectively. Therefore, all parameters, settings, and rules applying to
Data Matrix ECC 200, QR Code, Aztec Code, or DotCode apply to their GS1 variants as
well. The GS1 symbologies enforce merely additional rules for the format of
the data carried by the codes. The data has to be organized in so called GS1
application element strings according to the GS1 General Specifications.
For example, if the data code model DataCodeHandle is created as
GS1 DataMatrix, then find_data_code_2d only returns a result if
the underlying symbol is a valid Data Matrix ECC 200 symbol, and only if its
data conforms to the GS1 application element strings rules.
For the GS1 application element strings it is only checked if the
application identifiers exist in the GS1 General Specifications.
In contrast,
find_data_code_2d will return no results for Data Matrix ECC 200
containing general data. The same is valid for GS1 QR Code, GS1 Aztec,
and GS1 DotCode Code.
Adjusting the model
If there is a symbol in the image that cannot be read, it should be verified, whether the properties of the symbol fit the model parameters. Special attention should be paid to
the correct polarity ('polarity', light-on-dark or dark-on-light),
the symbol size ('symbol_size' for Data Matrix ECC 200, Aztec Code, and DotCode),
'version' for QR Code,
'format' for Aztec Code,
the module size ('module_size' for Data Matrix ECC 200, QR Code, Micro QR Code, Aztec Code, and DotCode, 'module_width' and 'module_aspect' for PDF417),
the possibility of a mirroring of the symbol ('mirrored'),
and the specified minimum contrast ('contrast_min' for Aztec Code, QR Code, and PDF417).
Further relevant parameters are the gap between neighboring
foreground modules and, for Data Matrix ECC 200, the maximum slant of the
L-shaped finder pattern ('slant_max').
The current settings for these parameters are returned by the
operator get_data_code_2d_param.
If necessary, the corresponding model parameters can be adjusted with
set_data_code_2d_param.
It is recommended to adjust the model as well as possible to the
symbols in the images also for runtime reasons.
In general, the runtime of find_data_code_2d is higher for
a more general model than for a more specific model.
One should take into account that a general model leads to a high
runtime especially if no valid data code can be found.
Train the model
Besides setting the model parameters manually with
set_data_code_2d_param, the model can also be trained with
find_data_code_2d based on one or several sample images.
For this, the generic parameter 'train' must be passed in
GenParamName.
The corresponding value passed in GenParamValue determines
the model parameters that should be learned.
The following values are possible:
All data code types:
All model parameters that can be trained.
Size of the data code symbol.
The model parameters trained for Data Matrix ECC200, PDF417, and DotCode are 'symbol_cols_min', 'symbol_cols_max', 'symbol_rows_min', and 'symbol_rows_max'. Additionally, the model parameter 'symbol_shape' (square or rectangular) is trained for Data Matrix ECC 200 only.
For QR Code and Micro QR Code it is also possible to pass 'version'. The model parameters trained hereby are 'symbol_size_min', 'symbol_size_max', 'version_min', and 'version_max'.
The model parameters trained for Aztec Code are 'symbol_size_min' and 'symbol_size_max'.
Size of the modules. The model parameters trained are 'module_size_min' and 'module_size_max'.
For PDF417 this includes the module width and the module aspect ratio, so thereby the respective model parameters are 'module_width_min', 'module_width_max', 'module_aspect_min', and 'module_aspect_max'.
Polarity of the symbols, i.e. they may appear dark on a light background or light on a dark background. The model parameter trained is 'polarity'.
Whether the symbols in the image are mirrored or not. The model parameter trained is 'mirrored'.
Data Matrix ECC 200, Aztec Code, PDF417, QR Code, and Micro QR Code only:
Robustness of the decoding of data codes with very small module sizes. This parameter is not trained for DotCode. The model parameter trained is 'small_modules_robustness'.
Aztec Code, PDF417, QR Code, and Micro QR Code only:
Minimum contrast for detecting the symbols. The model parameter trained is 'contrast_min'.
All data code types except PDF417::
Whether there is a gap between neighboring foreground modules or whether they are connected. The model parameters trained are 'module_gap_min' and 'module_gap_max'.
Data Matrix ECC 200 and Aztec Code only:
The allowed tolerance of the symbol search with respect to a defect or partially occluded finder pattern. The model parameter trained is 'finder_pattern_tolerance'.
Data Matrix ECC 200 and DotCode only:
Selection of candidate regions to be processed. The model parameter trained is 'candidate_selection'.
Data Matrix ECC 200 only:
The tolerance of the symbol search with respect to strong local contrast variations. The model parameter trained is 'contrast_tolerance'.
Algorithm for calculating the module positions (fixed or variable grid). The model parameter trained is 'module_grid'.
Adjusting different internal image processing parameters. Until now, only the maximum slant of the L-shaped finder pattern of the Data Matrix ECC 200 symbols is set (more parameters may follow in future). The model parameter trained is 'slant_max'.
QR Code only:
Whether the QR Code symbols follow the Model 1 or Model 2 specification. The model parameter trained is 'model_type'.
Aztec Code only
The number of additional pyramid levels. The model parameter trained is 'additional_levels'.
It is possible to train several of these parameters in one
call of find_data_code_2d by passing the generic parameter
'train' in a tuple more than once in conjunction with the
corresponding parameters: e.g.,
GenParamName = ['train','train'] and
GenParamValue = ['polarity','module_size'].
Furthermore, in conjunction with 'train' = 'all'
it is possible to exclude single parameters from training
explicitly again by passing 'train' more than once.
The names of the parameters to exclude, however, must be prefixed
by '~':
GenParamName = ['train','train'] and
GenParamValue =
['all','~contrast'],
e.g., trains all parameters except the minimum contrast.
For training the model, the following aspects should be considered:
To use several images for the training, the operator
find_data_code_2d must be called with the parameter
'train' once for every sample image.
It is also possible to train the model with several symbols in
one image.
Here, the generic parameter 'stop_after_result_num'
must be passed as a tuple to GenParamName together with
'train'.
The number of symbols in the image is passed in
GenParamValue together with the training parameters.
If the training image contains more symbols than the one that
shall be used for the training the domain of the image should
be reduced to the symbol of interest with reduce_domain.
In an application with very similar images, one image for training may be sufficient if the following assumptions are true: The symbol size (in modules) is the same for all symbols used in the application, foreground and background modules are of the same size and there is no gap between neighboring foreground modules, the background has no distinct texture; and the contrast of all images is almost the same. Otherwise, several images should be used for training.
In applications where the symbol size (in modules) is not fixed, the smallest as well as the biggest symbols should be used for the training. If this can not be guaranteed, the limits for the symbol size should be adapted manually after the training, or the symbol size should entirely be excluded from the training.
During the first call of find_data_code_2d in the
training mode, the trained model parameters are restricted to
the properties of the detected symbol.
Any successive training call will, where necessary, extend the
parameter range to cover the already trained symbols as well as
the new symbols.
Resetting the model with set_data_code_2d_param to one of
its default settings ('default_parameters' =
'standard_recognition', 'enhanced_recognition', or
'maximum_recognition') will also reset the training
state of the model.
With set_data_code_2d_param and 'trained' the training
state of parameters can be set to trained. Subsequent training of this
parameter will not reset its value, but extend it, so symbols readable
by the previous value can still be read after training.
If find_data_code_2d is not able to read the
symbol in the training image, this will produce no error or
exception handling.
This can simply be tested in the program by checking the
results of find_data_code_2d: SymbolXLDs,
ResultHandles, DecodedDataStrings.
These tuples will be empty, and the model will not be modified.
Note that during training, a possibly set timeout is ignored (see
set_data_code_2d_param).
Functionality of the symbol search
All data code types:
Depending on the current settings of the 2D data code model (see
set_data_code_2d_param), the operator
find_data_code_2d performs several passes for searching
the data code symbols.
The search starts at the highest pyramid level, where -
according to the maximum module size defined in the data code model
- the modules can be separated.
In addition, in every pyramid level the preprocessing can vary
depending on the presets for the module gap.
If the data code model enables dark symbols on a light background as
well as light symbols on a dark background, within the current pyramid
level, the dark symbols are searched first.
Then the passes for searching light symbols follow.
A pass consists of two phases: The search phase is used to
look for the finder patterns and to generate a symbol candidate for
every detected finder pattern, and the evaluation phase, where in a
lower pyramid level all candidates are investigated and - if
possible - read.
The operator call is either terminated after successfully decoding
the requested number of symbols, after processing all search passes,
or due to a timeout (see set_data_code_2d_param). The
number of requested symbols can be specified via the generic
parameter GenParamName = 'stop_after_result_num'.
Without specifying this number the search stops as soon as one
symbol could be decoded.
Data Matrix ECC 200:
The complexity of the symbol search can be controlled with the
generic parameter GenParamName = 'symbol_search'.
Per default, 'symbol_search' is set to
GenParamValue = 'default'.
For simple images, i.e. images that:
only contain symbols with high contrast and a large quiet zone and
show a homogeneous background
a less complex and (in simple cases) faster symbol search method can be used by setting 'symbol_search' to 'rudimental'. Please note that in this case the following parameters do not have any effect: 'module_gap_min', 'module_gap_max', 'module_gap', 'finder_pattern_tolerance','contrast_tolerance', and 'candidate_selection''. Further, no parameters can be trained with this method.
For large images with many codes and a highly structured background, the number of code candidates can be very high for certain parameter settings, e.g. for 'maximum_recognition'. For performance reasons, the ECC 200 reader stops by default after 10000 processed candidates.
For cases, where more candidates need to be considered in order to
decode all codes in the image the 'symbol_search' can be set
to 'exhaustive'. This will stop the search only when
'stop_after_result_num' codes have been decoded or all
candidates have been processed. Please note that this may increase
the runtime accordingly. The actual number of candidates can be
queried with get_data_code_2d_results.
Query results of the symbol search
With the result handles and the operators
get_data_code_2d_results and
get_data_code_2d_objects, additional data can be requested
about the search process, e.g., the number of internal search passes
or the number of investigated candidates, and - together with
the ResultHandles - about the symbols, like the symbol
and module size, the contrast, or the raw data coded in the symbol.
In addition, these operators provide information about all
investigated candidates that could not be read.
In particular, this helps to determine if a candidate was actually
generated at the symbol's position during the preprocessing and
- by the value of a status variable - why the search or
reading was aborted.
Further information about the parameters can be found with the
operators get_data_code_2d_results and
get_data_code_2d_objects.
Timeout and Abort
The operator find_data_code_2d can be aborted by a timeout and
dynamically. With the operator set_data_code_2d_param you can
specify a timeout. If find_data_code_2d reaches this timeout, it
returns all codes decoded so far. Alternatively, you can call
set_data_code_2d_param with 'abort' from another thread to
abort find_data_code_2d dynamically.
The information whether the operator was aborted or not can be queried by
calling get_data_code_2d_results with the parameter 'aborted'.
Furthermore, the operator find_data_code_2d can be canceled, which
means no result is returned but instead an error is returned. This can be done
with the operator set_operator_timeout or interrupt_operator.
If find_data_code_2d is canceled by set_operator_timeout,
H_ERR_TIMEOUT (9400) is returned. If find_data_code_2d is
canceled by interrupt_operator, H_ERR_CANCEL (22) is returned.
Note: Both mentioned operators are only supported in cancel mode.
Special parallelization for Data Matrix ECC 200
The operator find_data_code_2d supports special parallelization
features for Data Matrix ECC 200. This additional internal parallelization
can lead to a significantly shorter execution time under certain
circumstances. In general, faster calculation times can be expected if,
e.g., set_data_code_2d_param is used to set the value 'any'
for one or more of the parameters 'polarity', 'contrast_tolerance'
and 'finder_pattern_tolerance', or if the set values for
'module_gap_min' and 'module_gap_max' differ. In all these cases,
a series of parameter values must be taken into account. This applies in particular
if 'default_parameters' has been set to 'enhanced_recognition'
or 'maximum_recognition'.
Please note, that the memory consumption increases with the number of parallel
running threads. To reduce the memory consumption set_system can be used
to either set the number of threads with the parameter 'thread_num', or
to switch off the temporary memory cache with the parameter 'temporary_mem_cache'.
The Data Matrix ECC 200 specific parallelization can also be easily disabled
by setting the generic parameter GenParamName = 'specific_parallelization'
to 'disable'. Note that this will only switch off the additional internal
parallelization for Data Matrix ECC 200. Certain sub-tasks might be still
processed in parallel. For more information on how to disable the entire
parallelization, see the operator reference of set_system with its
parameters 'thread_num' and 'parallelize_operators'. In general,
however, it should not be necessary to switch off the specific parallelization.
Per default, 'specific_parallelization' is set to 'enable'.
Chinese characters
If a QR Code contains Chinese characters encoded according to the
Chinese national standard GBT 18284-2000, find_data_code_2d
returns these characters UTF-8 encoded in DecodedDataStrings, if
the system parameter 'filename_encoding' is set to 'utf8'.
The contents of 'decoded_data', which can be retrieved with
get_data_code_2d_results, are never converted to UTF-8.
This operator supports canceling timeouts and interrupts.
This operator modifies the state of the following input parameter:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
Image (input_object) singlechannelimage → object (byte)
Input image. If the image has a reduced domain, the data code search is reduced to that domain. This usually reduces the runtime of the operator. However, if the datacode is not fully inside the domain, the datacode might not be found correctly. In rare cases, data codes may be found outside the domain. If these results are undesirable, they have to be subsequently eliminated.
SymbolXLDs (output_object) xld_cont(-array) → object
XLD contours that surround the successfully decoded data
code symbols. The order of the contour points reflects the
orientation of the detected symbols. The contours begin in
the top left corner (see 'orientation' in the
reference of get_data_code_2d_results) and
continue clockwise.
SymbolXLDs
DataCodeHandle (input_control, state is modified) datacode_2d → (handle)
Handle of the 2D data code model.
GenParamName (input_control) attribute.name(-array) → (string)
Names of (optional) parameters for controlling the behavior of the operator.
Default value: []
List of values: 'specific_parallelization', 'stop_after_result_num', 'symbol_search', 'train'
GenParamValue (input_control) attribute.value(-array) → (integer / real / string)
Values of the optional generic parameters.
Default value: []
Suggested values: 'all', 'model_type', 'symbol_size', 'version', 'module_size', 'small_modules_robustness', 'module_shape', 'polarity', 'mirrored', 'contrast', 'candidate_selection', 'module_grid', 'finder_pattern_tolerance', 'contrast_tolerance', 'additional_levels', 'image_proc', 'rudimental', 'default', 'exhaustive', 1, 2, 3, 'enable', 'disable'
ResultHandles (output_control) integer(-array) → (integer)
Handles of all successfully decoded 2D data code symbols.
DecodedDataStrings (output_control) string(-array) → (string)
Decoded data strings of all detected 2D data code symbols in the image.
* Examples showing the use of find_data_code_2d.
* First, the operator is used to train the model, afterwards it is used to
* read the symbol in another image.
* Create a model for reading Data matrix ECC 200 codes
create_data_code_2d_model ('Data Matrix ECC 200', [], [], DataCodeHandle)
* Read a training image
read_image (Image, 'datacode/ecc200/ecc200_cpu_007')
* Train the model with the symbol in the image
find_data_code_2d (Image, SymbolXLDs, DataCodeHandle, 'train', 'all', \
ResultHandles, DecodedDataStrings)
*
* End of training / begin of normal application
*
* Read an image
read_image (Image, 'datacode/ecc200/ecc200_cpu_010')
* Read the symbol in the image
find_data_code_2d (Image, SymbolXLDs, DataCodeHandle, [], [], \
ResultHandles, DecodedDataStrings)
* Display all symbols, the strings encoded in them, and the module size
dev_set_color ('green')
for i := 0 to |ResultHandles| - 1 by 1
select_obj (SymbolXLDs, SymbolXLD, i+1)
dev_display (SymbolXLD)
get_contour_xld (SymbolXLD, Row, Col)
set_tposition (WindowHandle, max(Row), min(Col))
write_string (WindowHandle, DecodedDataStrings[i])
get_data_code_2d_results (DataCodeHandle, ResultHandles[i], \
['module_height','module_width'], ModuleSize)
new_line (WindowHandle)
write_string (WindowHandle, 'module size = ' + ModuleSize[0] + 'x' + \
ModuleSize[1])
endfor
* Clear the model
clear_data_code_2d_model (DataCodeHandle)
The operator find_data_code_2d returns the value 2 (H_MSG_TRUE)
if the given parameters are correct.
Otherwise, an exception is raised.
create_data_code_2d_model,
read_data_code_2d_model,
set_data_code_2d_param
get_data_code_2d_results,
get_data_code_2d_objects,
write_data_code_2d_model
create_data_code_2d_model,
set_data_code_2d_param,
get_data_code_2d_results,
get_data_code_2d_objects
GS1 General Specifications; Version 12; Issue 1, Jan-2012; GS1.
Data Code