Populate classId and confidence level for object detection in Java simulation (#2372)

## Description

compute confidence level based on target area in total image size and
populate classId and confidence level in Java (while building the
PhotonTrackedTarget)

## Changes
- Add new VisionTargetSim constructor for object detection
- If class ID specified but confidence = -1, estimate based on total
area

---------

Co-authored-by: Matt Morley <matthew.morley.ca@gmail.com>

photon-lib/py/photonlibpy/simulation/visionTargetSim.py

@@ -1,4 +1,5 @@
 import math
+from typing import overload
 
 from wpimath.geometry import Pose3d, Translation3d
 
@@ -8,19 +9,78 @@ from ..estimation.targetModel import TargetModel
 class VisionTargetSim:
     """Describes a vision target located somewhere on the field that your vision system can detect."""
 
-    def __init__(self, pose: Pose3d, model: TargetModel, id: int = -1):
-        """Describes a fiducial tag located somewhere on the field that your vision system can detect.
+    @overload
+    def __init__(self, pose: Pose3d, model: TargetModel) -> None:
+        """
+        Describes a retro-reflective/colored shape vision target located somewhere on the field that
+        your vision system can detect.
+
+        :param pose:  Pose3d of the target in field-relative coordinates
+        :param model: TargetModel which describes the shape of the target
+        """
+        ...
+
+    @overload
+    def __init__(self, pose: Pose3d, model: TargetModel, id: int) -> None:
+        """
+        Describes a fiducial tag located somewhere on the field that your vision system can detect.
 
         :param pose:  Pose3d of the tag in field-relative coordinates
-        :param model: TargetModel which describes the shape of the target(tag)
+        :param model: TargetModel which describes the geometry of the target (tag)
         :param id:    The ID of this fiducial tag
         """
+        ...
+
+    @overload
+    def __init__(
+        self, pose: Pose3d, model: TargetModel, objDetClassId: int, objDetConf: float
+    ) -> None:
+        """
+        Describes an object-detection vision target located somewhere on the field that your vision
+        system can detect.
+
+        :param pose:          Pose3d of the target in field-relative coordinates
+        :param model:         TargetModel which describes the shape of the target
+        :param objDetClassId: The object detection class ID, or -1 to exclude from object detection
+        :param objDetConf:    The object detection confidence, or -1.0 to compute from target area
+                              in the camera's field of view
+        """
+        ...
+
+    def __init__(
+        self,
+        pose: Pose3d,
+        model: TargetModel,
+        *args,
+        **kwargs,
+    ):
+        if kwargs:
+            raise TypeError(
+                f"VisionTargetSim does not accept keyword arguments: {list(kwargs.keys())}"
+            )
 
         self.pose: Pose3d = pose
         self.model: TargetModel = model
-        self.fiducialId: int = id
-        self.objDetClassId: int = -1
-        self.objDetConf: float = -1.0
+
+        if len(args) == 0:
+            # VisionTargetSim(pose, model)
+            self.fiducialId: int = -1
+            self.objDetClassId: int = -1
+            self.objDetConf: float = -1.0
+        elif len(args) == 1:
+            # VisionTargetSim(pose, model, id)
+            self.fiducialId = args[0]
+            self.objDetClassId = -1
+            self.objDetConf = -1.0
+        elif len(args) == 2:
+            # VisionTargetSim(pose, model, objDetClassId, objDetConf)
+            self.fiducialId = -1
+            self.objDetClassId = args[0]
+            self.objDetConf = args[1]
+        else:
+            raise ValueError(
+                f"VisionTargetSim takes 2-4 arguments, got {2 + len(args)}"
+            )
 
     def __lt__(self, right) -> bool:
         return self.pose.translation().norm() < right.pose.translation().norm()