add docs, define API

src/rai/rai/tools/ros/utils.py

@@ -35,8 +35,11 @@ def import_message_from_str(msg_type: str) -> Type[object]:
     return import_message_from_namespaced_type(msg_namespaced_type)
 
 
-def convert_ros_img_to_ndarray(msg: sensor_msgs.msg.Image) -> np.ndarray:
-    encoding = msg.encoding.lower()
+def convert_ros_img_to_ndarray(
+    msg: sensor_msgs.msg.Image, encoding: str = ""
+) -> np.ndarray:
+    if encoding == "":
+        encoding = msg.encoding.lower()
 
     if encoding == "rgb8":
         image_data = np.frombuffer(msg.data, np.uint8)
@@ -52,7 +55,7 @@ def convert_ros_img_to_ndarray(msg: sensor_msgs.msg.Image) -> np.ndarray:
         image_data = np.frombuffer(msg.data, np.uint16)
         image = image_data.reshape((msg.height, msg.width))
     else:
-        raise ValueError(f"Unsupported encoding: {msg.encoding}")
+        raise ValueError(f"Unsupported encoding: {encoding}")
 
     return image
 

src/rai_extensions/rai_grounding_dino/README.md

@@ -58,6 +58,49 @@ ros2 launch rai_grounding_dino gdino_launch.xml [weights_path:=PATH/TO/WEIGHTS]
 > By default the weights will be downloaded to `$(ros2 pkg prefix rai_grounding_dino)/share/weights/`.
 > You can change this path if you downloaded the weights manually or moved them.
 
+### RAI Tools
+
+This package provides the following tools:
+
+- `GetDetectionTool`
+  This tool calls the grounding dino service to use the model to see if the message from the provided camera topic contains objects from a comma separated prompt.
+
+  **Example call**
+
+  ```
+  x = GetDetectionTool(node=RaiBaseNode(node_name="test_node"))._run(
+      camera_topic="/camera/camera/color/image_raw",
+      object_names=["chair", "human", "plushie", "box", "ball"],
+  )
+
+  ```
+
+  **Example output**
+
+  ```
+  I have detected the following items in the picture - chair, human
+  ```
+
+- `GetDistanceToObjectsTool`
+  This tool calls the grounding dino service to use the model to see if the message from the provided camera topic contains objects from a comma separated prompt. Then it utilises messages from depth camera to create an estimation of distance to a detected object.
+
+  **Example call**
+
+  ```
+  x = GetDistanceToObjectsTool(node=RaiBaseNode(node_name="test_node"))._run(
+      camera_topic="/camera/camera/color/image_raw",
+      depth_topic="/camera/camera/depth/image_rect_raw",
+      object_names=["chair", "human", "plushie", "box", "ball"],
+  )
+
+  ```
+
+  **Example output**
+
+  ```
+  I have detected the following items in the picture human: 1.68 m away, chair: 2.20 m away
+  ```
+
 ### Example
 
 An example client is provided with the package as `rai_grounding_dino/talker.py`

src/rai_extensions/rai_grounding_dino/rai_grounding_dino/__init__.py

@@ -12,3 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+
+from .grounding_dino import GDINO_NODE_NAME, GDINO_SERVICE_NAME
+from .tools import GetDetectionTool, GetDistanceToObjectsTool
+
+__all__ = [
+    "GetDistanceToObjectsTool",
+    "GetDetectionTool",
+    "GDINO_NODE_NAME",
+    "GDINO_SERVICE_NAME",
+]

src/rai_extensions/rai_grounding_dino/rai_grounding_dino/grounding_dino.py

@@ -34,11 +34,15 @@ class GDRequest(TypedDict):
     source_img: Image
 
 
+GDINO_NODE_NAME = "grounding_dino"
+GDINO_SERVICE_NAME = "grounding_dino_classify"
+
+
 class GDinoService(Node):
     def __init__(self):
-        super().__init__(node_name="grounding_dino", parameter_overrides=[])
+        super().__init__(node_name=GDINO_NODE_NAME, parameter_overrides=[])
         self.srv = self.create_service(
-            RAIGroundingDino, "grounding_dino_classify", self.classify_callback
+            RAIGroundingDino, GDINO_SERVICE_NAME, self.classify_callback
         )
         self.declare_parameter("weights_path", "")
         try:

src/rai_extensions/rai_grounding_dino/rai_grounding_dino/tools.py

@@ -19,6 +19,7 @@
 import sensor_msgs.msg
 from langchain_core.pydantic_v1 import Field
 from pydantic import BaseModel
+from rai_grounding_dino import GDINO_SERVICE_NAME
 from rclpy import Future
 from rclpy.exceptions import (
     ParameterNotDeclaredException,
@@ -98,7 +99,7 @@ def _spin(self, future: Future) -> Optional[RAIGroundingDino.Response]:
     def _call_gdino_node(
         self, camera_img_message: sensor_msgs.msg.Image, object_names: list[str]
     ) -> Future:
-        cli = self.node.create_client(RAIGroundingDino, "grounding_dino_classify")
+        cli = self.node.create_client(RAIGroundingDino, GDINO_SERVICE_NAME)
         while not cli.wait_for_service(timeout_sec=1.0):
             self.node.get_logger().info("service not available, waiting again...")
         req = RAIGroundingDino.Request()
@@ -156,7 +157,7 @@ def _run(
             resolved = self._spin(future)
             if resolved is not None:
                 detected = self._parse_detection_array(resolved)
-                names = [det.class_name for det in detected]
+                names = ", ".join([det.class_name for det in detected])
                 return f"I have detected the following items in the picture {names}"
 
         return "Failed to get detection"
@@ -241,7 +242,7 @@ def _run(
                 measurements = self._get_distance_from_detections(
                     depth_img_msg, detected, threshold, conversion_ratio
                 )
-                measurement_string = " ".join(
+                measurement_string = ", ".join(
                     [
                         f"{measurement[0]}: {measurement[1]:.2f}m away"
                         for measurement in measurements