opendr-eu · tsampazk · Apr 20, 2023 · Mar 14, 2023 · Mar 14, 2023 · Mar 14, 2023
@@ -69,18 +69,19 @@ Currently, apart from tools, opendr_ws contains the following ROS nodes (categor
 1. [Pose Estimation](src/opendr_perception/README.md#pose-estimation-ros-node)
 2. [High Resolution Pose Estimation](src/opendr_perception/README.md#high-resolution-pose-estimation-ros-node)
 3. [Fall Detection](src/opendr_perception/README.md#fall-detection-ros-node)
-4. [Face Detection](src/opendr_perception/README.md#face-detection-ros-node)
-5. [Face Recognition](src/opendr_perception/README.md#face-recognition-ros-node)
-6. [2D Object Detection](src/opendr_perception/README.md#2d-object-detection-ros-nodes)
-7. [2D Single Object Tracking](src/opendr_perception/README.md#2d-single-object-tracking-ros-node)
-8. [2D Object Tracking](src/opendr_perception/README.md#2d-object-tracking-ros-nodes)
-9. [Vision Based Panoptic Segmentation](src/opendr_perception/README.md#vision-based-panoptic-segmentation-ros-node)
-10. [Semantic Segmentation](src/opendr_perception/README.md#semantic-segmentation-ros-node)
-11. [Binary High Resolution](src/opendr_perception/README.md#binary-high-resolution-ros-node)
-12. [Image-based Facial Emotion Estimation](src/opendr_perception/README.md#image-based-facial-emotion-estimation-ros-node)
-13. [Landmark-based Facial Expression Recognition](src/opendr_perception/README.md#landmark-based-facial-expression-recognition-ros-node)
-14. [Skeleton-based Human Action Recognition](src/opendr_perception/README.md#skeleton-based-human-action-recognition-ros-node)
-15. [Video Human Activity Recognition](src/opendr_perception/README.md#video-human-activity-recognition-ros-node)
+4. [Wave Detection](src/opendr_perception/README.md#wave-detection-ros-node)
+5. [Face Detection](src/opendr_perception/README.md#face-detection-ros-node)
+6. [Face Recognition](src/opendr_perception/README.md#face-recognition-ros-node)
+7. [2D Object Detection](src/opendr_perception/README.md#2d-object-detection-ros-nodes)
+8. [2D Single Object Tracking](src/opendr_perception/README.md#2d-single-object-tracking-ros-node)
+9. [2D Object Tracking](src/opendr_perception/README.md#2d-object-tracking-ros-nodes)
+10. [Vision Based Panoptic Segmentation](src/opendr_perception/README.md#vision-based-panoptic-segmentation-ros-node)
+11. [Semantic Segmentation](src/opendr_perception/README.md#semantic-segmentation-ros-node)
+12. [Binary High Resolution](src/opendr_perception/README.md#binary-high-resolution-ros-node)
+13. [Image-based Facial Emotion Estimation](src/opendr_perception/README.md#image-based-facial-emotion-estimation-ros-node)
+14. [Landmark-based Facial Expression Recognition](src/opendr_perception/README.md#landmark-based-facial-expression-recognition-ros-node)
+15. [Skeleton-based Human Action Recognition](src/opendr_perception/README.md#skeleton-based-human-action-recognition-ros-nodes)
+16. [Video Human Activity Recognition](src/opendr_perception/README.md#video-human-activity-recognition-ros-node)
 
 ## RGB + Infrared input
 1. [End-to-End Multi-Modal Object Detection (GEM)](src/opendr_perception/README.md#2d-object-detection-gem-ros-node)

@@ -196,6 +196,28 @@ def from_ros_face(self, ros_hypothesis):
                             confidence=ros_hypothesis.score)
         return category
 
+    def to_ros_box(self, box):
+        """
+        Converts an OpenDR BoundingBox into a Detection2D that can carry the same information.
+        The bounding box is represented by its center coordinates as well as its width/height dimensions.
+        :param box: OpenDR bounding box to be converted
+        :type box: engine.target.BoundingBox
+        :return: ROS message with the Detection2D including the bounding box
+        :rtype: vision_msgs.msg.Detection2D
+        """
+        ros_box = Detection2D()
+        ros_box.bbox = BoundingBox2D()
+        ros_box.results.append(ObjectHypothesisWithPose())
+        ros_box.bbox.center = Pose2D()
+        ros_box.bbox.center.x = box.left + box.width / 2.
+        ros_box.bbox.center.y = box.top + box.height / 2.
+        ros_box.bbox.size_x = box.width
+        ros_box.bbox.size_y = box.height
+        ros_box.results[0].id = int(box.name)
+        if box.confidence:
+            ros_box.results[0].score = box.confidence
+        return ros_box
+
     def to_ros_boxes(self, box_list):
         """
         Converts an OpenDR BoundingBoxList into a Detection2DArray msg that can carry the same information.

@@ -31,6 +31,7 @@ catkin_install_python(PROGRAMS
    scripts/pose_estimation_node.py
    scripts/hr_pose_estimation_node.py
    scripts/fall_detection_node.py
+   scripts/wave_detection_node.py
    scripts/object_detection_2d_nanodet_node.py
    scripts/object_detection_2d_yolov5_node.py
    scripts/object_detection_2d_detr_node.py

@@ -120,32 +120,119 @@ The node publishes the detected poses in [OpenDR's 2D pose message format](../op
 
 You can find the fall detection ROS node python script [here](./scripts/fall_detection_node.py) to inspect the code and modify it as you wish to fit your needs.
 The node makes use of the toolkit's [fall detection tool](../../../../src/opendr/perception/fall_detection/fall_detector_learner.py) whose documentation can be found [here](../../../../docs/reference/fall-detection.md).
-Fall detection uses the toolkit's pose estimation tool internally.
+Fall detection is rule-based and works on top of pose estimation.
 
-<!-- TODO Should add information about taking advantage of the pose estimation ros node when running fall detection, see issue https://github.com/opendr-eu/opendr/issues/282 -->
+This node normally runs on `detection mode` where it subscribes to a topic of OpenDR poses and detects whether the poses are fallen persons or not.
+By providing an image topic the node runs on `visualization mode`. It also gets images, performs pose estimation internally and visualizes the output on an output image topic.
+Note that when providing an image topic the node has significantly worse performance in terms of speed, due to running pose estimation internally.
 
-#### Instructions for basic usage:
+- #### Instructions for basic usage in `detection mode`:
 
-1. Start the node responsible for publishing images. If you have a USB camera, then you can use the `usb_cam_node` as explained in the [prerequisites above](#prerequisites).
+1. Start the node responsible for publishing poses. Refer to the [pose estimation node above](#pose-estimation-ros-node).
 
 2. You are then ready to start the fall detection node:
 
     ```shell
     rosrun opendr_perception fall_detection_node.py
     ```
-    The following optional arguments are available:
+    The following optional arguments are available and relevant for running fall detection on pose messages only:
    - `-h or --help`: show a help message and exit
-   - `-i or --input_rgb_image_topic INPUT_RGB_IMAGE_TOPIC`: topic name for input RGB image (default=`/usb_cam/image_raw`)
-   - `-o or --output_rgb_image_topic OUTPUT_RGB_IMAGE_TOPIC`: topic name for output annotated RGB image, `None` to stop the node from publishing on this topic (default=`/opendr/image_fallen_annotated`)
-   - `-d or --detections_topic DETECTIONS_TOPIC`: topic name for detection messages, `None` to stop the node from publishing on this topic (default=`/opendr/fallen`)
+   - `-ip or --input_pose_topic INPUT_POSE_TOPIC`: topic name for input pose, `None` to stop the node from running detections on pose messages (default=`/opendr/poses`)
+   - `-d or --detections_topic DETECTIONS_TOPIC`: topic name for detection messages (default=`/opendr/fallen`)
+
+3. Detections are published on the `detections_topic`
+
+- #### Instructions for `visualization mode`:
+
+1. Start the node responsible for publishing images. If you have a USB camera, then you can use the `usb_cam_node` as explained in the [prerequisites above](#prerequisites).
+
+2. You are then ready to start the fall detection node in `visualization mode`, which needs an input image topic to be provided:
+
+    ```shell
+    rosrun opendr_perception fall_detection_node.py -ii /usb_cam/image_raw
+    ```
+    The following optional arguments are available and relevant for running fall detection on images. Note that the
+`input_rgb_image_topic` is required for running in `visualization mode`:
+   - `-h or --help`: show a help message and exit
+   - `-ii or --input_rgb_image_topic INPUT_RGB_IMAGE_TOPIC`: topic name for input RGB image (default=`None`)
+   - `-o or --output_rgb_image_topic OUTPUT_RGB_IMAGE_TOPIC`: topic name for output annotated RGB image (default=`/opendr/image_fallen_annotated`)
+   - `-d or --detections_topic DETECTIONS_TOPIC`: topic name for detection messages (default=`/opendr/fallen`)
    - `--device DEVICE`: device to use, either `cpu` or `cuda`, falls back to `cpu` if GPU or CUDA is not found (default=`cuda`)
    - `--accelerate`: acceleration flag that causes pose estimation that runs internally to run faster but with less accuracy
 
-3. Default output topics:
-   - Output images: `/opendr/image_fallen_annotated`
-   - Detection messages: `/opendr/fallen`
+- Default output topics:
+  - Detection messages: `/opendr/fallen`
+  - Output images: `/opendr/image_fallen_annotated`
 
-   For viewing the output, refer to the [notes above.](#notes)
+  For viewing the output, refer to the [notes above.](#notes)
+
+**Notes**
+
+Note that when the node runs on the default `detection mode` it is significantly faster than when it is provided with an 
+input image topic. However, pose estimation needs to be performed externally on another node which publishes poses.
+When an input image topic is provided and the node runs in `visualization mode`, it runs pose estimation internally, and 
+consequently it is recommended to only use it for testing purposes and not run other pose estimation nodes in parallel.
+The node can run in both modes in parallel or only on one of the two. To run the node only on `visualization mode` provide
+the argument `-ip None` to disable the `detection mode`. Detection messages on `detections_topic` are published in both modes.
+
+### Wave Detection ROS Node
+
+You can find the wave detection ROS node python script [here](./scripts/wave_detection_node.py) to inspect the code and modify it as you wish to fit your needs.
+The node is based on a [wave detection demo of the Lightweight OpenPose tool](../../../../projects/python/perception/pose_estimation/lightweight_open_pose/demos/wave_detection_demo.py).
+Wave detection is rule-based and works on top of pose estimation.
+
+This node normally runs on `detection mode` where it subscribes to a topic of OpenDR poses and detects whether the poses are waving or not.
+By providing an image topic the node runs on `visualization mode`. It also gets images, performs pose estimation internally and visualizes the output on an output image topic.
+Note that when providing an image topic the node has significantly worse performance in terms of speed, due to running pose estimation internally.
+
+- #### Instructions for basic usage in `detection mode`:
+
+1. Start the node responsible for publishing poses. Refer to the [pose estimation node above](#pose-estimation-ros-node).
+
+2. You are then ready to start the wave detection node:
+
+    ```shell
+    rosrun opendr_perception wave_detection_node.py
+    ```
+    The following optional arguments are available and relevant for running fall detection on pose messages only:
+   - `-h or --help`: show a help message and exit
+   - `-ip or --input_pose_topic INPUT_POSE_TOPIC`: topic name for input pose, `None` to stop the node from running detections on pose messages (default=`/opendr/poses`)
+   - `-d or --detections_topic DETECTIONS_TOPIC`: topic name for detection messages (default=`/opendr/wave`)
+
+3. Detections are published on the `detections_topic`
+
+- #### Instructions for `visualization mode`:
+
+1. Start the node responsible for publishing images. If you have a USB camera, then you can use the `usb_cam_node` as explained in the [prerequisites above](#prerequisites).
+
+2. You are then ready to start the wave detection node in `visualization mode`, which needs an input image topic to be provided:
+
+    ```shell
+    rosrun opendr_perception wave_detection_node.py -ii /usb_cam/image_raw
+    ```
+    The following optional arguments are available and relevant for running wave detection on images. Note that the
+`input_rgb_image_topic` is required for running in `visualization mode`:
+   - `-h or --help`: show a help message and exit
+   - `-ii or --input_rgb_image_topic INPUT_RGB_IMAGE_TOPIC`: topic name for input RGB image (default=`None`)
+   - `-o or --output_rgb_image_topic OUTPUT_RGB_IMAGE_TOPIC`: topic name for output annotated RGB image (default=`/opendr/image_wave_annotated`)
+   - `-d or --detections_topic DETECTIONS_TOPIC`: topic name for detection messages (default=`/opendr/wave`)
+   - `--device DEVICE`: device to use, either `cpu` or `cuda`, falls back to `cpu` if GPU or CUDA is not found (default=`cuda`)
+   - `--accelerate`: acceleration flag that causes pose estimation that runs internally to run faster but with less accuracy
+
+- Default output topics:
+  - Detection messages: `/opendr/wave`
+  - Output images: `/opendr/image_wave_annotated`
+
+  For viewing the output, refer to the [notes above.](#notes)
+
+**Notes**
+
+Note that when the node runs on the default `detection mode` it is significantly faster than when it is provided with an 
+input image topic. However, pose estimation needs to be performed externally on another node which publishes poses.
+When an input image topic is provided and the node runs in `visualization mode`, it runs pose estimation internally, and 
+consequently it is recommended to only use it for testing purposes and not run other pose estimation nodes in parallel.
+The node can run in both modes in parallel or only on one of the two. To run the node only on `visualization mode` provide
+the argument `-ip None` to disable the `detection mode`. Detection messages on `detections_topic` are published in both modes.
 
 ### Face Detection ROS Node