Release Notes for Python Edition - 2.x
2.4.2100 (11/07/2024)
New
- Added support for
numpy.ndarray
image type in thecapture
interface.
Improved
- Optimized the usage of list type member variables, allowing modification of individual objects within the list without needing to reassign the entire list.
- Improved the usage of
EnumPresetTemplate
andEnumBarcodeFormat
, no longer requiring.value
for accessing enum members.
Fixed
- Fixed a bug where initializing
FileImageTag
would raise aTypeError
exception.
2.4.2000 (10/10/2024)
Highlights
This is the first release of the Python Edition of DynamsoftCaptureVision (DCV)
architecture, which is newly established to aggregate the features of functional products powered by Dynamsoft. The features are designed to be pluggable, customizable and interactable. In addition, the functional products share the computation so that their processing speed is much higher than working individually.
DynamsoftCaptureVision
architecture consists of:ImageSourceAdapter(ISA)
, the standard input interface for you to convert image data from different sources into the standard input image data. In addition,ISA
incorporates an image buffer management system that allows instant access to the buffered image data.CaptureVisionRouter (CVR)
, an engine for you to update templates, retrieve images fromISA
, coordinate corresponding functional products and dispatch the results to the receivers.- Functional products that perform image processing, content understanding and semantic processing. The functional products are pluggable and passively called by CVR when they are required.
- Result receiver interfaces. You can implement
CapturedResultReceiver (CRR)
to receive theCapturedResults
that output when the processing on an image is finalized.
- The parameter template system has been comprehensively upgraded.
- Multiple algorithm task settings are available. You can define barcode decoding, label recognizing, document scanning and semantic processing tasks in one template file.
- Extended the feature of the ROI system. By configuring the
target ROI
parameters, you can not only specify anROI
on the original image but also define the dependencies of the algorithm tasks. This feature enables you to customize the workflow when processing complex scenarios. - The image processing parameters are separated from the task parameters so that the template settings become more clear and concise.
- The
intermediate result
system has been improved.- Achieved the
intermediate result
sharing between different functional products. The results that have the same image source and processing parameters are directly reused, which speeds up the image processing workflow. You don’t need to add any additional code to enable theintermediate result
sharing. The library can recognize all the reusable results automatically based on the template file you uploaded.
- Achieved the