Hermes asked for structured detections instead of a bare JPEG. faces() returns the
faces in view with no photo: a persistent track id, bbox, detection confidence, how
long the person has been in view, and identity {name, similarity} or null, never a
made-up name. look() returns that same JSON as text plus the photo (send_image can
skip it); list_people() gives names and counts; robot://status and two prompts make
the server discoverable, where list_resources and list_prompts were empty. The
detector now tracks faces across frames (FaceTracker) and publishes the track id and
the identity as a second hypothesis.
Deliberately not built: embeddings in responses, age/gender attributes, auto-updating
face data, and a remote enroll tool (a prompt could make the robot call anyone "Kyle").
Checked end to end: a real Hermes turn on the local ornith model called faces() (0.03 s,
no image) and answered that Kyle was there at 0.78 similarity. Also: the camera is
calibrated (18 of 24 views, 0.296 px, outlier rejection added) and the calibration is
kept in pi/calibration and restored by the installer.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AFN8nRRCiDiMN7ChBpHJhs
hermes_vision.calibrate collects 20 varied checkerboard views from the camera topic
(rejecting repeats of a pose, which constrain nothing), solves with OpenCV and writes
the camera_info YAML camera_ros loads, named with the resolution as it expects.
Tested: the solver recovers a known synthetic camera (focal length within 1%), the
YAML round-trips, and real board detection works on a rendered board. Not yet run on
the real camera: it needs someone holding a board in front of it, with the focus set.
docs/checkerboard-9x6.png is a 10x7-square board that can be shown on a screen.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AFN8nRRCiDiMN7ChBpHJhs
kyleandClaude Sonnet 5.5