Give Hermes recognitions, not just photos: faces(), tracking ids, discovery

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

pi/calibration/ov5647__base_soc_i2c0mux_i2c_1_ov5647_36_1296x972.yaml

@@ -0,0 +1,20 @@
+image_width: 1296
+image_height: 972
+camera_name: ov5647__base_soc_i2c0mux_i2c_1_ov5647_36_1296x972
+camera_matrix:
+  rows: 3
+  cols: 3
+  data: [1152.96331, 0, 652.952458, 0, 1151.52195, 463.351689, 0, 0, 1]
+distortion_model: plumb_bob
+distortion_coefficients:
+  rows: 1
+  cols: 5
+  data: [0.0727701258, -0.232756953, 0.00468014288, -0.00488550855, 0]
+rectification_matrix:
+  rows: 3
+  cols: 3
+  data: [1, 0, 0, 0, 1, 0, 0, 0, 1]
+projection_matrix:
+  rows: 3
+  cols: 4
+  data: [1152.96331, 0, 652.952458, 0, 0, 1151.52195, 463.351689, 0, 0, 0, 1, 0]