parse_depth_mode returned NEURAL for any unrecognised string, so a typo
silently changed depth quality, and NEURAL_PLUS and NEURAL_LIGHT could not be
selected at all. All six modes are now reachable and an unknown name exits
non-zero listing the accepted values.
Parsing moves into Config::from_env() and main reads the configuration before
init_dora_node(), so a bad value fails with its own message instead of after
the node has registered with the dataflow.
Add ZED_CONFIDENCE_THRESHOLD (60) and ZED_TEXTURE_CONFIDENCE_THRESHOLD (100),
which were never set, and change the defaults to 1080 / 200 mm / 600 mm to
match the reference driver perception was tuned against. The resolved settings
are logged at startup.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y7g2cBJ5e3r15ZbJ9WZn8c
Publish sl::MEASURE::DEPTH as float32 [H, W] millimetres on every frame that
carries image_bgr, with the intrinsics, frame_id and timestamps the other
outputs carry.
The map is copied out rather than published in place: sl::Mat rows can be
padded, so the buffer is not necessarily H * W floats. inf and nan are cleared
to 0 in the same pass, matching the reference driver — a hole is meaningful and
a consumer cannot back-project a non-finite value.
Consumers that need 3D can now back-project depth with the published
intrinsics instead of receiving a point cloud.
Also drops the stale objects entry from the dataflow outputs, missed when
detection was removed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y7g2cBJ5e3r15ZbJ9WZn8c
steady_clock's epoch is unspecified and typically counts from process start or
boot, so the published timestamp_ns could not be compared against another
node's timestamps, wall clock or a recording — and it recorded when the node
processed the frame rather than when the camera captured it.
Publish two keys on every output instead: device_timestamp_ns from
sl::Camera::getTimestamp(TIME_REFERENCE::IMAGE), and host_timestamp_ns from
system_clock. Both are Unix-epoch nanoseconds, set as dora timestamp
parameters. The SDK clock source is pinned to SYSTEM_CLOCK at construction,
since it is process-wide and decides the epoch of the device value.
Both are kept because the wall clock can step when NTP synchronises after boot
on a Jetson, while the device timestamp is what two nodes reading the same
frame can agree on.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y7g2cBJ5e3r15ZbJ9WZn8c
Add engines::ZedNative class wrapping ZED SDK's TensorRT-optimized
object detection. On every frame grab, detected objects (label,
confidence, 3D position, 2D/3D bounding boxes) are serialized as a
flat float32 array and sent as a new "objects" dora output with
frame_id correlation to image_bgr.
Extract shared env-var helpers into src/util/env.h and expose
sl::Camera& via Camera::handle() for engine access.
Closes#3
Extract camera lifecycle (open, warmup, grab, close) and
environment-based configuration into src/zed/camera.{h,cc}.
Thin main.cc now only handles the dora event loop.
Closes#2
