Skip to main content
Version: 0.8.x [Latest Beta]

Changelog

0.8.0​

See the Migration guide for an action-oriented checklist.

  • General changes:

    • New OCR Version: Added support for a new OCR version that uses a different decoding method.
    • Faster Image Conversion: Image conversion into the internal format should be much faster than before, but uses a different call structure (see below).
    • New BoundingBoxClassFilter node: Filter detection results down to a configurable allow-list of class indices.
      • C: denkflow_pipeline_add_bounding_box_class_filter_node
      • Python: Pipeline.add_bounding_box_class_filter_node(...)
    • Override the device of any node before initialization: Both exported .denkflow graphs and custom pipelines now support set_node_device / denkflow_pipeline_set_node_device to redirect a single node to a different execution provider before initialize() is called.

  • Breaking Changes:

    • Internal file format: Changes to *.denkmodel and *.denkflow files. Old files need to be re-exported.

    • C-API symbol prefix: All C-API functions, types, and global constants are now consistently namespaced to avoid collisions when integrating DENKflow alongside other shared libraries.

      • Functions are prefixed with denkflow_. For example, pipeline_new is now denkflow_pipeline_new, image_tensor_from_file is now denkflow_image_tensor_from_file, and so on.
      • Types are prefixed with Denkflow. For example, Pipeline is now DenkflowPipeline, ImageTensor is now DenkflowImageTensor, Point is now DenkflowPoint, Rect is now DenkflowRect, Receiver_Tensor is now DenkflowReceiverTensor, etc. Enum variants follow their type, e.g. DenkflowResizeMode_Stretch. DenkflowResult was already prefixed and is unchanged.
      • Global constants: ERROR_BUFFER_SIZE is now DENKFLOW_ERROR_BUFFER_SIZE; NULL_BYTE has been removed.

    • Optional C-API string arguments now accept NULL / nullptr: Functions that take an optional string (such as denkflow_hub_license_source_from_pat's endpoint) now follow the universal C convention and treat a NULL pointer as "argument absent". The previous workaround of passing a pointer to a single zero byte (NULL_BYTE) has been removed.

    • Unified resize mode for image resize and image patches: A single resize-mode enum (with variants CenterPadBlack, Stretch, CenterPadWhite) replaces the per-node enums in every binding. The new variant CenterPadWhite was added in this release.

      • C: DenkflowResizeMode is shared between denkflow_pipeline_add_image_resize_node and denkflow_pipeline_add_image_patches_node.
      • Python: Pipeline.add_image_resize_node, Pipeline.add_image_patches_node, and Pipeline.change_image_patches_node all take resize_mode as a string — one of "CenterPadBlack" (default), "Stretch", or "CenterPadWhite".

    • denkflow_pipeline_add_image_resize_node signature change: The bool keep_aspect_ratio parameter was replaced by DenkflowResizeMode resize_mode. The previous keep_aspect_ratio = true corresponds to DenkflowResizeMode_CenterPadBlack; keep_aspect_ratio = false corresponds to DenkflowResizeMode_Stretch.

    • Pipeline.add_image_resize_node signature change (Python): The keep_aspect_ratio: bool keyword argument was removed and replaced by resize_mode: str = "CenterPadBlack". Migrate keep_aspect_ratio=True → resize_mode="CenterPadBlack" and keep_aspect_ratio=False → resize_mode="Stretch".

    • Python device= keyword renamed to execution_provider=: All Python pipeline methods that previously accepted a device: str keyword (add_image_resize_node, add_object_detection_node, add_image_classification_node, add_bounding_box_filter_node, add_bounding_box_class_filter_node, add_image_patches_node, add_ocr_node, add_image_segmentation_node, add_image_instance_segmentation_node, add_image_anomaly_detection_node) now accept execution_provider: str instead, matching the C-API. The same applies to Pipeline.set_node_device(node_name, execution_provider, device_id). The device_id parameter is unchanged. Migrate device="cuda" → execution_provider="cuda".

    • denkflow_pipeline_add_image_patches_node signature change: Two parameters were added between target_size_topic and execution_provider:

      • DenkflowResizeMode resize_mode (default in higher-level wrappers: CenterPadBlack)
      • DenkflowImagePatchesNodeResizeMethod resize_method (default: Bilinear; values Nearest, Bilinear, Bicubic, Area)

      In Python, Pipeline.add_image_patches_node and Pipeline.change_image_patches_node take resize_mode: str = "CenterPadBlack" (values "CenterPadBlack", "Stretch", "CenterPadWhite") and resize_method: str = "Bilinear" (values "Nearest", "Bilinear", "Bicubic", "Area"). The Python wrapper additionally exposes bounding_box_extend_ratio: float = 0.0.

    • New constructors for in-memory image tensors: The single old image_tensor_from_image_data / from_numpy family was replaced by a triple of constructors that make the data-conversion contract explicit. See Creating ImageTensors for the details.

      • C:
        • denkflow_image_tensor_from_buffer — generic constructor that accepts arbitrary data type, memory layout, and channel layout, and converts to the canonical inference format internally.
        • denkflow_image_tensor_from_buffer_raw — expects a buffer that is already BGR / BCHW / float32, performs no conversion but copies the buffer internally.
        • denkflow_image_tensor_from_buffer_unsafe — same input requirements as _raw, but does not copy the buffer; the caller must keep it alive (and untouched) until the pipeline run that consumes the tensor has returned.
        • The previous denkflow_image_tensor_from_image_data no longer exists.
      • Python:
        • ImageTensor.from_numpy — generic constructor that accepts arbitrary data type, memory layout, and channel layout, and converts to the canonical inference format internally.
        • ImageTensor.from_numpy_raw — expects a buffer that is already BGR / BCHW / float32, performs no conversion but copies the buffer internally.
        • ImageTensor.from_numpy_unsafe — same input requirements as _raw, but does not copy the buffer; the caller must keep it alive (and untouched) until the pipeline run that consumes the tensor has returned.
        • The previous ImageTensor.from_numpy_opencv(...) has been removed. Use from_numpy(..., memory_layout="HWC", channel_layout="BGR") instead.

0.7.0​

  • General changes:
    • Super Sessions: Nodes are now automatically clustered by execution device in the background. This can improve performance in certain scenarios.
    • Customizable Channel Size: This allows for the creation of input data queues by publishing multiple times to the same topic.
    • Parallel Pipeline Runs: Added an alternative method to run a pipeline called start. This allows running the pipeline and publishing data in separate threads.
    • Add Nodes via C-API: The C-API now fully supports the addition of nodes to a pipeline.
  • Breaking Changes:

    • Subscribe / receive split: Previous versions had different subscribe_* methods for each tensor type but a single generic receive method. This was inverted: there is now a single subscribe(topic) method that returns a generic receiver, and the receiver exposes one typed receive_*_tensor() method per tensor type:

      • Python: receiver = pipeline.subscribe(topic) → receiver.receive_bounding_box_tensor(), receiver.receive_image_tensor(), receiver.receive_scalar_tensor(), receiver.receive_ocr_tensor(), receiver.receive_segmentation_mask_tensor(), receiver.receive_instance_segmentation_mask_tensor().
      • C: denkflow_initialized_pipeline_subscribe(&receiver, initialized, topic) → denkflow_receiver_receive_bounding_box_tensor(&tensor, receiver), etc.

      Calls to the old subscribe_bounding_box_tensor / subscribe_image_tensor / etc. methods no longer compile.

    • The C-API function denkflow_pipeline_from_denkflow now takes a single pointer to the license source instead of a double pointer.