Merge pull request #1809 from tier4/sync-awf-latest

chore: sync awf-latest

perception/autoware_image_projection_based_fusion/src/pointpainting_fusion/node.cpp

@@ -234,8 +234,7 @@ void PointPaintingFusionNode::preprocess(PointCloudMsgType & painted_pointcloud_
   sensor_msgs::PointCloud2Iterator<float> iter_painted_z(painted_pointcloud_msg, "z");
   for (sensor_msgs::PointCloud2ConstIterator<float> iter_x(tmp, "x"), iter_y(tmp, "y"),
        iter_z(tmp, "z");
-       iter_x != iter_x.end();
-       ++iter_x, ++iter_y, ++iter_z, ++iter_painted_x, ++iter_painted_y, ++iter_painted_z) {
+       iter_x != iter_x.end(); ++iter_x, ++iter_y, ++iter_z) {
     if (
       *iter_x <= pointcloud_range.at(0) || *iter_x >= pointcloud_range.at(3) ||
       *iter_y <= pointcloud_range.at(1) || *iter_y >= pointcloud_range.at(4)) {
@@ -245,6 +244,9 @@ void PointPaintingFusionNode::preprocess(PointCloudMsgType & painted_pointcloud_
       *iter_painted_y = *iter_y;
       *iter_painted_z = *iter_z;
       j += painted_point_step;
+      ++iter_painted_x;
+      ++iter_painted_y;
+      ++iter_painted_z;
     }
   }
 

perception/autoware_traffic_light_fine_detector/README.md

@@ -43,23 +43,7 @@ ROIs detected from YOLOX will be selected by a combination of `expect/rois`. At
 
 ## Parameters
 
-### Core Parameters
-
-| Name                         | Type   | Default Value | Description                                                            |
-| ---------------------------- | ------ | ------------- | ---------------------------------------------------------------------- |
-| `fine_detector_score_thresh` | double | 0.3           | If the objectness score is less than this value, the object is ignored |
-| `fine_detector_nms_thresh`   | double | 0.65          | IoU threshold to perform Non-Maximum Suppression                       |
-
-### Node Parameters
-
-| Name                       | Type    | Default Value               | Description                                                        |
-| -------------------------- | ------- | --------------------------- | ------------------------------------------------------------------ |
-| `data_path`                | string  | "$(env HOME)/autoware_data" | packages data and artifacts directory path                         |
-| `fine_detector_model_path` | string  | ""                          | The onnx file name for yolo model                                  |
-| `fine_detector_label_path` | string  | ""                          | The label file with label names for detected objects written on it |
-| `fine_detector_precision`  | string  | "fp16"                      | The inference mode: "fp32", "fp16"                                 |
-| `approximate_sync`         | bool    | false                       | Flag for whether to ues approximate sync policy                    |
-| `gpu_id`                   | integer | 0                           | ID for the selecting CUDA GPU device                               |
+{{ json_to_markdown("perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json") }} |
 
 ## Assumptions / Known limits
 

perception/autoware_traffic_light_fine_detector/launch/traffic_light_fine_detector.launch.xml

@@ -3,11 +3,15 @@
   <arg name="input/rois" default="/traffic_light_map_based_detector/output/rois"/>
   <arg name="expect/rois" default="~/expect/rois"/>
   <arg name="output/rois" default="~/output/rois"/>
+  <arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
   <arg
     name="traffic_light_fine_detector_param_path"
     default="$(find-pkg-share autoware_traffic_light_fine_detector)/config/traffic_light_fine_detector.param.yaml"
     description="fine detector param path"
   />
+  <arg name="traffic_light_fine_detector_model_path" default="$(var data_path)/traffic_light_fine_detector" description="path to fine detector onnx model and label"/>
+  <arg name="traffic_light_fine_detector_label_name" default="tlr_labels.txt" description="fine detector label name"/>
+  <arg name="traffic_light_fine_detector_model_name" default="tlr_car_ped_yolox_s_batch_6" description="fine detector onnx model name"/>
   <arg name="build_only" default="false" description="exit after trt engine is built"/>
 
   <node pkg="autoware_traffic_light_fine_detector" exec="traffic_light_fine_detector_node" name="traffic_light_fine_detector" output="screen">

perception/autoware_traffic_light_fine_detector/schema/traffic_light_fine_detector.schema.json

@@ -0,0 +1,71 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "autoware_traffic_light_fine_detector parameter",
+  "type": "object",
+  "definitions": {
+    "traffic_light_fine_detector": {
+      "type": "object",
+      "properties": {
+        "fine_detector_label_path": {
+          "type": "string",
+          "description": "The label file with label names for detected objects written on it.",
+          "default": "$(var traffic_light_fine_detector_model_path)/$(var traffic_light_fine_detector_label_name)"
+        },
+        "fine_detector_model_path": {
+          "type": "string",
+          "description": "The ONNX file name for the YOLO model.",
+          "default": "$(var traffic_light_fine_detector_model_path)/$(var traffic_light_fine_detector_model_name).onnx"
+        },
+        "fine_detector_precision": {
+          "type": "string",
+          "description": "Precision used for traffic light fine detector inference. Valid values: [fp32, fp16].",
+          "default": "fp16"
+        },
+        "fine_detector_score_thresh": {
+          "type": "number",
+          "description": "If the objectness score is less than this value, the object is ignored.",
+          "default": 0.3
+        },
+        "fine_detector_nms_thresh": {
+          "type": "number",
+          "description": "IoU threshold to perform Non-Maximum Suppression (NMS).",
+          "default": 0.65
+        },
+        "approximate_sync": {
+          "type": "boolean",
+          "description": "Flag for whether to use approximate sync policy.",
+          "default": false
+        },
+        "gpu_id": {
+          "type": "integer",
+          "description": "ID for selecting the CUDA GPU device.",
+          "default": 0
+        }
+      },
+      "required": [
+        "fine_detector_label_path",
+        "fine_detector_model_path",
+        "fine_detector_precision",
+        "fine_detector_score_thresh",
+        "fine_detector_nms_thresh",
+        "approximate_sync",
+        "gpu_id"
+      ],
+      "additionalProperties": false
+    }
+  },
+  "properties": {
+    "/**": {
+      "type": "object",
+      "properties": {
+        "ros__parameters": {
+          "$ref": "#/definitions/traffic_light_fine_detector"
+        }
+      },
+      "required": ["ros__parameters"],
+      "additionalProperties": false
+    }
+  },
+  "required": ["/**"],
+  "additionalProperties": false
+}

perception/autoware_traffic_light_fine_detector/src/traffic_light_fine_detector_node.cpp

@@ -59,17 +59,18 @@ TrafficLightFineDetectorNode::TrafficLightFineDetectorNode(const rclcpp::NodeOpt
   using std::placeholders::_2;
   using std::placeholders::_3;
 
-  std::string model_path = declare_parameter("fine_detector_model_path", "");
-  std::string label_path = declare_parameter("fine_detector_label_path", "");
-  std::string precision = declare_parameter("fine_detector_precision", "fp16");
-  const uint8_t gpu_id = declare_parameter("gpu_id", 0);
+  std::string model_path = this->declare_parameter<std::string>("fine_detector_model_path");
+  std::string label_path = this->declare_parameter<std::string>("fine_detector_label_path");
+  std::string precision = this->declare_parameter<std::string>("fine_detector_precision");
+  const uint8_t gpu_id = this->declare_parameter<uint8_t>("gpu_id");
   // Objects with a score lower than this value will be ignored.
   // This threshold will be ignored if specified model contains EfficientNMS_TRT module in it
-  score_thresh_ = declare_parameter("fine_detector_score_thresh", 0.3);
+  score_thresh_ = this->declare_parameter<double>("fine_detector_score_thresh");
   // Detection results will be ignored if IoU over this value.
   // This threshold will be ignored if specified model contains EfficientNMS_TRT module in it
-  float nms_threshold = declare_parameter("fine_detector_nms_thresh", 0.65);
-  is_approximate_sync_ = this->declare_parameter<bool>("approximate_sync", false);
+  float nms_threshold =
+    static_cast<float>(this->declare_parameter<double>("fine_detector_nms_thresh"));
+  is_approximate_sync_ = this->declare_parameter<bool>("approximate_sync");
 
   if (!readLabelFile(label_path, tlr_label_id_, num_class)) {
     RCLCPP_ERROR(this->get_logger(), "Could not find tlr id");
@@ -113,7 +114,7 @@ TrafficLightFineDetectorNode::TrafficLightFineDetectorNode(const rclcpp::NodeOpt
     sync_->registerCallback(std::bind(&TrafficLightFineDetectorNode::callback, this, _1, _2, _3));
   }
 
-  if (declare_parameter("build_only", false)) {
+  if (this->declare_parameter<bool>("build_only")) {
     RCLCPP_INFO(get_logger(), "TensorRT engine is built and shutdown node.");
     rclcpp::shutdown();
   }