> For the complete documentation index, see [llms.txt](https://docs.degirum.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.degirum.com/degirum-tools/release-notes.md). # Release Notes ### Version 1.4.0 (5/15/2026) **New Features and Modifications** 1. New `gst` subpackage providing a complete GStreamer-based video pipeline toolkit: * `setup_gst_environment(*plugin_dirs)` — initializes GStreamer and optionally registers custom plugin directories. Must be called before any gi import. * `GstPipelineHandler` — manages the full lifecycle of a GStreamer pipeline, with optional named appsink queues for Python frame consumption and throughput probing. * `GstElementBase` — mixin base class for implementing custom GStreamer Python elements with minimal boilerplate. Pad templates, worker threads, and state management are handled automatically. * `GstAiElement` — ready-to-use GStreamer filter element that accepts BGR/RGB video frames, runs AI inference via a DeGirum model, and pushes annotated frames and/or serialized JSON inference results downstream. Supports an optional secondary full-resolution input pad (sink\_full) for high-quality overlay rendering at a different resolution than the model input. * `map_gst_buffer(buf_or_sample, readonly)` — context manager for safe `Gst.Buffer` memory mapping. * `build_gst_pipeline(source)` — builds a gst-launch-style pipeline string for camera devices (including Windows via mfvideosrc/ksvideosrc), RTSP streams, and video files. * `VideoCaptureGst` — `cv2.VideoCapture`-compatible class backed by a GStreamer pipeline. 2. `ObjectTracker` analyzer now extends object trails with predicted positions during tracking timeouts, using the tracker's internal motion estimate for lost tracks. This preserves trail continuity when objects temporarily leave the frame. 3. `VideoSourceGizmo` now accepts a pre-created `VideoCaptureProtocol` object (e.g., `cv2.VideoCapture` or `VideoCaptureGst`) as the video\_source argument. Additional keyword arguments `(**kwargs)` are forwarded to the `cv2.VideoCapture` constructor when opening from a path or device index. 4. `VideoStreamerGizmo` and `VideoStreamer` gain a `vcodec` parameter to configure the video codec (defaults to `libx264`). 5. `Stream.close()` gains a `force=True` option that atomically clears the queue and inserts the poison pill without blocking, preventing deadlocks when a producer has filled the queue. **Bug Fixes** 1. `VideoStreamerGizmo` frame scheduling: duplicate frames were injected using an incorrect rate estimate that could cause a spin-loop. The logic is now based on an absolute deadline (next\_frame\_due\_s) and stops duplicating when the encoder write time exceeds the target frame interval. 2. `VideoStreamer`: `pix_fmt` was always passed to FFmpeg as bgr24 regardless of the configured pixel format. It now correctly uses the `pix_fmt` constructor argument. 3. `video_source()`: `CAP_PROP_FRAME_COUNT` can return `None` for certain capture backends (e.g., `VideoCaptureGst`), causing a `TypeError`. The comparison now handles `None` correctly. 4. `MediaServer` subprocess is now started with `start_new_session=True`, preventing signal propagation from the parent process to the media server process. *** ### Version 1.3.1 (4/21/2026) **Bug Fixes** `VideoStreamer` class which uses ffmpeg for video streaming, now uses TCP protocol for RTSP streaming. Previous versions used UDP protocol, which had lower latency, but may result in corrupted video streams on certain systems. *** ### Version 1.3.0 (4/3/2026) **New Features and Modifications** 1. Performance of `ObjectTracker` analyzer is greatly improved. 2. `frame_size` parameter is added to `ZoneCounter` analyzer constructor. It specifies the frame size (width, height) when inference results do not have `.image` attribute. 3. Added new `ipc` subpackage providing transparent inter-process communication: use `ipc.spawn(MyClass, ...)` to run any class instance in an isolated child process with all public methods automatically available as ZeroMQ RPC endpoints, with MsgPack serialization and InOut/Out wrappers for mutable argument writeback. *** ### Version 1.2.2 (3/17/2026) **Bug Fixes** 1. EventDetector analyzer raises exception when `result` argument of `analyze` method has `events_detected` attribute of `dict` type. 2. EventDetector analyzer incorrectly generates events when the event history is not fully accummulated yet. *** ### Version 1.2.1 (3/13/2026) **New Features and Modifications** 1. `ResultAnalyzerBase` base class `analyze` and `annotate` methods are modified to ignore `None` results. This allows using analyzers with models in `non_blocking_batch_predict` mode. **Bug Fixes** 1. `NoneType object has no attribute encoding` exception is raised during `import degirum_tools` if the process has no stdout attached. *** ### Version 1.2.0 (2/23/2026) **New Features and Modifications** 1. `SceneCutDetector` analyzer is implemented. `SceneCutDetector` analyzer detects scene cuts in video streams by comparing frame-to-frame differences using an adaptive thresholding approach. Key Features: * **Adaptive Thresholding**: Uses rolling average of previous frames to adapt to gradual changes * **HSV Color Space**: Analyzes differences in hue, saturation, and luminance channels * **Configurable Parameters**: Adjustable sensitivity, minimum scene length, and window size * **Real-time Detection**: Causal approach using only past frames for zero latency * **Scene Cut Flag**: Adds `scene_cut` boolean attribute to inference results Typical Usage: 1. Create a `SceneCutDetector` instance with desired parameters 2. Attach it to a model or inference pipeline 3. Process video frames through the analyzer 4. Check `result.scene_cut` flag to detect scene transitions 5. Use scene cut information for downstream processing or triggering actions Put it before `ObjectTracker` in the analyzer pipeline to ensure cuts are detected before tracking is applied, allowing you to reset object tracker on scene changes. Integration Notes: * Works with any inference results that contain image data * Can be combined with other analyzers in a pipeline * Useful for video segmentation, activity detection, and content analysis * Maintains internal state to track frame history Configuration Options: * `adaptive_threshold`: Sensitivity ratio for detecting cuts (higher = less sensitive) * `min_scene_len`: Minimum frames between detected cuts to avoid false positives * `window_width`: Number of previous frames for rolling average calculation * `min_content_val`: Minimum absolute change threshold for scene cuts * `luma_only`: Use only brightness changes for faster processing 2. `reset_at_scene_cut` parameter is added to the `ObjectTracker` analyzer. When `True`, all tracks are cleared when a scene cut is detected. Requires the result to have a `scene_cut` attribute (set by `SceneCutDetector`, see above). Use this feature to avoid tracking objects across scene transitions in videos with cuts or edits. *** ### Version 1.1.0 (2/11/2026) **New Features and Modifications** 1. `IteratorSourceGizmo`: `fps` constructor parameter is added. This is optional parameter with default value 0.0. It specifies the FPS value to be included in the metadata. **Bug Fixes** 1. `VideoStreamerGizmo`: check for zero FPS is added to prevent division by zero in case of zero-FPS sources. *** ### Version 1.0.0 (1/27/2026) First official release of degirum-tools. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.degirum.com/degirum-tools/release-notes.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.