add camera_info input for the pinhole model

Nothing in a dataflow carries fx, fy, cx and cy except the camera, and every
pixel of a depth map needs them to become a 3D point. The node now accepts
them so whatever computes that next has them to hand.

They are read from two places because dora-zed publishes them twice: the
camera_info message, and the metadata of every frame. The per-frame copy is
not redundant -- camera_info goes out once per camera session, so a viewer
started after the camera would otherwise never see it, and reading frame
metadata means wiring the input is optional even for a camera that publishes
it.

A nine-element row-major K is accepted alongside the compact [fx, fy, cx, cy],
since that is how ROS spells the same model in sensor_msgs/CameraInfo.

The model is drawn under the caption rather than only stored. A depth map read
through the wrong intrinsics looks entirely plausible, and this is where that
becomes visible.

as_float32 moves to a payload module: camera_info arrives as a raw byte slice
for the same reason the depth map does, and the reinterpretation is now shared
rather than written twice.

Verified with dora-cli 1.0.1: validate passes with four contracts injected on
each side, the dataflow runs to a clean exit, and a composed frame was
rendered and inspected with the model drawn on it.

dora_image_view/depth.py

@@ -11,6 +11,8 @@ import os
 import cv2
 import numpy as np
 
+from dora_image_view.payload import as_float32
+
 # Perceptually uniform and readable in both directions, unlike JET.
 COLORMAP = cv2.COLORMAP_TURBO
 
@@ -85,21 +87,6 @@ def resolve_range(depth_mm: np.ndarray) -> tuple[float, float]:
     return float(low), float(high)
 
 
-def as_float32(storage) -> np.ndarray:
-    """Read the payload as float32, whether it arrived as floats or as bytes.
-
-    A node that sends the map as a raw byte slice -- dora-zed does -- produces a
-    UInt8 arrow array even though its manifest declares Float32, so the buffer
-    has to be reinterpreted rather than converted.
-    """
-    buffer = storage.to_numpy()
-
-    if buffer.dtype == np.uint8:
-        return buffer.view(np.float32)
-
-    return buffer.astype(np.float32, copy=False)
-
-
 def _hw(metadata: dict, size: int) -> tuple[int, int]:
     shape = metadata.get("shape")