find_data_code_2d — Detect and read 2D data code symbols in an image or train the 2D data code model.
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 GenParamNames together with the number of requested symbols as GenParamValues.
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.
The symbol structure of GS1 DataMatrix, GS1 QR Code, and GS1 Aztec is identical to the structure of ECC 200, QR Code, and Aztec Code, respectively. Therefore, all parameters, settings, and rules applying to ECC 200, QR Code, or Aztec Code 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 ECC 200 symbol, and only if its data conforms to the GS1 application element strings rules. In contrast, find_data_code_2d will return no results for ECC 200 containing general data. The same is valid for GS1 QR Code and GS1 Aztec 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 ECC 200 and Aztec Code),
'version' for QR Code,
'format' for Aztec Code,
the module size ('module_size' for ECC 200, QR Code, Micro QR Code, and Aztec Code, 'module_width' and 'module_aspect' for PDF417),
the possibility of a mirroring of the symbol ('mirrored'),
and the specified minimum contrast ('contrast_min').
Further relevant parameters are the gap between neighboring foreground modules and, for 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 GenParamNames. The corresponding value passed in GenParamValues determines the model parameters that should be learned. The following values are possible:
All data code types:
All model parameters that can be trained,
Symbol size and for ECC 200 also the symbol shape (rectangle or square); for QR Code and Micro QR Code it is also possible to pass 'version'.
Size of the modules; for PDF417 this includes the module width and the module aspect ratio.
Robustness of the decoding of data codes with very small module sizes.
Polarity of the symbols: they may appear dark on a light background or light on a dark background.
Whether the symbols in the image are mirrored or not.
Minimum contrast for detecting the symbols.
ECC 200, Aztec Code, QR Code, and Micro QR Code only:
Shape of the modules, especially whether there is a gap between neighboring foreground modules or whether they are connected.
ECC 200 and Aztec Code only:
The allowed tolerance of the symbol search with respect to a defect or partially occluded finder pattern.
ECC 200 only:
The tolerance of the symbol search with respect to strong local contrast variations.
Algorithm for calculating the module positions (fixed or variable grid).
Adjusting different internal image processing parameters; until now, only the maximum slant of the L-shaped finder pattern of the ECC 200 symbols is set; more parameters may follow in future.
QR Code only:
Whether the QR Code symbols follow the Model 1 or Model 2 specification.
Aztec Code only
The number of additional pyramid 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., GenParamNames = ['train','train'] and GenParamValues = ['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 '~': GenParamNames = ['train','train'] and GenParamValues = ['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 GenParamNames together with 'train'. The number of symbols in the image is passed in GenParamValues 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.
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
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 GenParamNames = 'stop_after_result_num'. Without specifying this number the search stops as soon as one symbol could be decoded.
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.
With the operator set_data_code_2d_param you can specify a timeout for find_data_code_2d. If find_data_code_2d reaches this timeout, it returns all intermediate results. Whether a timeout occurred or not can be queried by calling get_data_code_2d_results with the parameter 'timeout_occurred'.
XLD contours that surround the successfully decoded data code symbols.
Handle of the 2D data code model.
Names of (optional) parameters for controlling the behavior of the operator.
Default value: 
List of values: 'stop_after_result_num', 'train'
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', 'module_grid', 'finder_pattern_tolerance', 'contrast_tolerance', 'image_proc', 1, 2, 3
Handles of all successfully decoded 2D data code symbols.
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_008') * 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 + 'x' + \ ModuleSize) 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